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Global information
- Generated on Tue Feb 17 05:59:34 2026
- Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-02-17_070000.log
- Parsed 1,302,440 log entries in 33s
- Log start from 2026-02-17 07:00:00 to 2026-02-17 07:59:33
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Overview
Global Stats
- 231 Number of unique normalized queries
- 150,817 Number of queries
- 2h11m9s Total query duration
- 2026-02-17 07:00:00 First query
- 2026-02-17 07:59:33 Last query
- 2,561 queries/s at 2026-02-17 07:45:04 Query peak
- 2h11m9s Total query duration
- 7s16ms Prepare/parse total duration
- 46s717ms Bind total duration
- 2h10m15s Execute total duration
- 1 Number of events
- 1 Number of unique normalized events
- 1 Max number of times the same event was reported
- 0 Number of cancellation
- 38 Total number of automatic vacuums
- 56 Total number of automatic analyzes
- 699 Number temporary file
- 159.41 MiB Max size of temporary file
- 8.22 MiB Average size of temporary file
- 2,795 Total number of sessions
- 13 sessions at 2026-02-17 07:58:48 Session peak
- 2d1h9m8s Total duration of sessions
- 1m3s Average duration of sessions
- 53 Average queries per session
- 2s815ms Average queries duration per session
- 1m Average idle time per session
- 2,786 Total number of connections
- 27 connections/s at 2026-02-17 07:18:48 Connection peak
- 3 Total number of databases
SQL Traffic
Key values
- 2,561 queries/s Query Peak
- 2026-02-17 07:45:04 Date
SELECT Traffic
Key values
- 1,276 queries/s Query Peak
- 2026-02-17 07:45:04 Date
INSERT/UPDATE/DELETE Traffic
Key values
- 158 queries/s Query Peak
- 2026-02-17 07:00:53 Date
Queries duration
Key values
- 2h11m9s Total query duration
Prepared queries ratio
Key values
- 0.00 Ratio of bind vs prepare
- 0.00 % Ratio between prepared and "usual" statements
General Activity
↑ Back to the top of the General Activity tableDay Hour Count Min duration Max duration Avg duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Feb 17 07 150,817 0ms 39s711ms 51ms 4m51s 5m50s 6m26s Day Hour SELECT COPY TO Average Duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Feb 17 07 40,314 26 0ms 0ms 0ms 0ms Day Hour INSERT UPDATE DELETE COPY FROM Average Duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Feb 17 07 23,332 2,653 16 96 0ms 0ms 0ms 0ms Day Hour Prepare Bind Bind/Prepare Percentage of prepare Feb 17 07 18,735 51,924 2.77 26.31% Day Hour Count Average / Second Feb 17 07 2,786 0.77/s Day Hour Count Average Duration Average idle time Feb 17 07 2,795 1m3s 1m -
Connections
Established Connections
Key values
- 27 connections Connection Peak
- 2026-02-17 07:18:48 Date
Connections per database
Key values
- acaweb_fx Main Database
- 2,786 connections Total
Connections per user
Key values
- postgres Main User
- 2,786 connections Total
Connections per host
Key values
- 192.168.4.142 Main host with 1150 connections
- 2,786 Total connections
Host Count 127.0.0.1 112 192.168.0.114 1 192.168.0.216 103 192.168.0.74 152 192.168.1.145 118 192.168.1.15 169 192.168.1.20 145 192.168.1.231 5 192.168.1.239 14 192.168.1.90 51 192.168.2.126 39 192.168.2.182 12 192.168.3.199 36 192.168.4.103 4 192.168.4.142 1,150 192.168.4.150 10 192.168.4.17 1 192.168.4.238 16 192.168.4.33 70 192.168.4.39 4 192.168.4.98 330 [local] 244 -
Sessions
Simultaneous sessions
Key values
- 13 sessions Session Peak
- 2026-02-17 07:58:48 Date
Histogram of session times
Key values
- 2,238 0-500ms duration
Sessions per database
Key values
- acaweb_fx Main Database
- 2,795 sessions Total
Sessions per user
Key values
- postgres Main User
- 2,795 sessions Total
Sessions per host
Key values
- 192.168.4.142 Main Host
- 2,795 sessions Total
Host Count Total Duration Average Duration 127.0.0.1 112 7s668ms 68ms 192.168.0.114 1 5m 5m 192.168.0.216 103 1m43s 1s2ms 192.168.0.74 151 3h2m41s 1m12s 192.168.1.145 118 2h10m59s 1m6s 192.168.1.15 169 8h27m5s 3m 192.168.1.20 145 11h51m37s 4m54s 192.168.1.231 15 3h5m41s 12m22s 192.168.1.239 14 94ms 6ms 192.168.1.90 51 34s780ms 681ms 192.168.2.126 39 5s578ms 143ms 192.168.2.182 12 782ms 65ms 192.168.3.199 36 1s384ms 38ms 192.168.4.103 4 26s178ms 6s544ms 192.168.4.142 1,150 8m41s 453ms 192.168.4.150 10 20h7m25s 2h44s 192.168.4.17 1 240ms 240ms 192.168.4.238 16 20s753ms 1s297ms 192.168.4.33 70 2m31s 2s161ms 192.168.4.39 4 48ms 12ms 192.168.4.98 330 16s83ms 48ms [local] 244 3m47s 932ms -
Checkpoints / Restartpoints
Checkpoints Buffers
Key values
- 12,176 buffers Checkpoint Peak
- 2026-02-17 07:07:02 Date
- 210.000 seconds Highest write time
- 0.004 seconds Sync time
Checkpoints Wal files
Key values
- 5 files Wal files usage Peak
- 2026-02-17 07:07:02 Date
Checkpoints distance
Key values
- 185.58 Mo Distance Peak
- 2026-02-17 07:07:02 Date
Checkpoints Activity
↑ Back to the top of the Checkpoint Activity tableDay Hour Written buffers Write time Sync time Total time Feb 17 07 41,898 1,826.481s 0.025s 1,826.818s Day Hour Added Removed Recycled Synced files Longest sync Average sync Feb 17 07 0 0 22 1,838 0.003s 0s Day Hour Count Avg time (sec) Feb 17 07 0 0s Day Hour Mean distance Mean estimate Feb 17 07 30,584.17 kB 67,198.08 kB -
Temporary Files
Size of temporary files
Key values
- 183.98 MiB Temp Files size Peak
- 2026-02-17 07:20:08 Date
Number of temporary files
Key values
- 30 per second Temp Files Peak
- 2026-02-17 07:17:13 Date
Temporary Files Activity
↑ Back to the top of the Temporary Files Activity tableDay Hour Count Total size Average size Feb 17 07 699 5.61 GiB 8.22 MiB Queries generating the most temporary files (N)
Rank Count Total size Min size Max size Avg size Query 1 57 235.34 MiB 4.11 MiB 4.17 MiB 4.13 MiB select resultuid from relevance_fibonacci_results order by resultuid desc limit ?), fr as ( select a.*, rr.age, rr.relevant from fibonacci_results a left outer join relevance_fibonacci_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) end), all_results as ( select fr.resultuid as resultuid, fr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, fr.pattern as pattern_name, fr.timed as timed, fr.patternendtime as identified, dtt.timezone as timezone, fr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when fr.age is not null then fr.age when fr.resultuid <= rm.resultuid then ? else ? end as age, case when fr.relevant is not null then fr.relevant when fr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from fr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = fr.symbolid inner join symbols s on fr.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on fr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join downloadersymbolsettings dss on fr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left join lateral calc_fib_signal_filter (fr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where fr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (fr.simulation = ? or fr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or fr.pattern in (...)) and (? = ? or fr.patternlengthbars <= ?) and (? = ? or (? = ? and fr.timed > cast(? as timestamp)) or (? = ? and fr.timed < cast(? as timestamp)))), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;-
SELECT resultuid FROM relevance_fibonacci_results ORDER BY resultuid DESC LIMIT 1), fr AS ( SELECT a.*, rr.age, rr.relevant from fibonacci_results a LEFT OUTER JOIN relevance_fibonacci_results rr on a.resultuid = rr.resultuid WHERE CASE WHEN FALSE = $1 THEN true ELSE a.resultuid > ( select min(resultuid) from relevance_fibonacci_results) END), all_results AS ( SELECT fr.resultuid AS resultuid, fr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, fr.pattern AS pattern_name, fr.timed AS timed, fr.patternendtime AS identified, dtt.timezone AS timezone, fr.patternlengthbars AS length, g.basegroupname, newLevels.filtered, CASE WHEN fr.age IS NOT NULL THEN fr.age WHEN fr.resultuid <= rm.resultuid THEN 11 ELSE 0 END as age, CASE WHEN fr.relevant IS NOT NULL THEN fr.relevant WHEN fr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM fr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $2 AND bsl.symbolid = fr.symbolid INNER JOIN symbols s ON fr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN symbolgroup sg on fr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN downloadersymbolsettings dss ON fr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN rar_max rm ON 1 = 1 LEFT JOIN LATERAL calc_fib_signal_filter (fr.resultuid) newLevels on true LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE fr.gmttimefound > now() - INTERVAL '7 DAYS' AND dss.enabled = 1 AND s.deleted = 0 AND (fr.simulation = 0 OR fr.simulation IS NULL) AND ($3 = 0 OR s.timegranularity in ($4, $5, $6, $7, $8, $9, $10)) AND ($11 = 0 OR s.exchange in ($12)) AND ($13 = 0 OR coalesce(bim.code, s.symbol) in ($14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226, $227, $228, $229, $230, $231, $232, $233, $234, $235, $236, $237, $238, $239, $240, $241, $242, $243, $244, $245, $246, $247, $248, $249, $250, $251, $252, $253, $254, $255, $256, $257, $258, $259, $260, $261, $262, $263, $264, $265, $266, $267, $268, $269, $270, $271, $272, $273, $274, $275, $276, $277, $278, $279, $280, $281, $282, $283, $284, $285, $286, $287, $288, $289, $290, $291, $292, $293, $294, $295, $296, $297, $298, $299, $300, $301, $302, $303, $304, $305, $306, $307, $308, $309, $310, $311, $312, $313, $314, $315, $316, $317, $318, $319, $320, $321, $322, $323)) AND ($324 = 0 OR fr.pattern in ($325)) AND ($326 = 0 OR fr.patternlengthbars <= $327) AND ($328 = 0 OR ($329 = 1 AND fr.timed > cast('1970-01-01' as timestamp)) OR ($330 = 2 AND fr.timed < cast('1970-01-01' as timestamp)))), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $331 OR relevant = 1) AND ($332 = 0 OR age <= $333) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC;
Date: 2026-02-17 07:00:46 Duration: 0ms
2 34 185.56 MiB 3.69 MiB 9.68 MiB 5.46 MiB jr.resultuid as resultuid, jr.direction as direction, jr.patternendtime as identified, jr.patternlengthbars as length, jr.patternstarttime as patternstarttime, case when jr.trendchangeid = ? then ? else ? end as trendchange, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, jp.patternname as pattern_name, dtt.timezone as timezone, ? as age, cps.pip, g.basegroupname from japsticks_results jr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = jr.symbolid inner join relevance_japsticks_results rar on rar.resultuid = jr.resultuid inner join symbols s on jr.symbolid = s.symbolid and s.nonliquid = ? inner join japsticks_patterns jp on jr.patternid = jp.id inner join downloadersymbolsettings dss on jr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where jr.gmttimefound > now() - interval ? and s.deleted = ? and (jr.simulation = ? or jr.simulation is null) and (rar.relevant = ?) --and (semicolon_age = ? or rar.age <= semicolon_age) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or jp.patternname in (...)) and (? = ? or jr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results order by symbolid, resultuid ) select * from results order by identified desc, length desc ;-
jr.resultuid AS resultuid, jr.direction AS direction, jr.patternendtime AS identified, jr.patternlengthbars AS length, jr.patternstarttime AS patternstarttime, case when jr.trendchangeid = 1 then 'Continuation' else 'Reversal' end AS trendchange, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, jp.patternname AS pattern_name, dtt.timezone AS timezone, 0 AS age, cps.pip, g.basegroupname FROM japsticks_results jr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = jr.symbolid INNER JOIN relevance_japsticks_results rar ON rar.resultuid = jr.resultuid INNER JOIN symbols s ON jr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN japsticks_patterns jp ON jr.patternid = jp.id INNER JOIN downloadersymbolsettings dss ON jr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on s.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' WHERE jr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (jr.simulation = 0 OR jr.simulation IS NULL) AND (rar.relevant = 1) --AND (semicolon_age = 0 OR rar.age <= semicolon_age) AND ($2 = 0 OR s.timegranularity in ($3)) AND ($4 = 0 OR s.exchange in ($5)) AND ($6 = 0 OR coalesce(bim.code, s.symbol) in ($7)) AND ($8 = 0 OR jp.patternname in ($9)) AND ($10 = 0 OR jr.patternlengthbars <= $11)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC;
Date: 2026-02-17 07:01:14 Duration: 0ms
3 30 1.65 GiB 4.52 MiB 159.41 MiB 56.42 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = ? ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = ? ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = ?) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, ?::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> ? ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = ?) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = ? where (ok.r is null or ok.r = ?) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = ?) and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > ? * ? and last.eventtimestamp > current_timestamp - interval ? and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval ?) and last.eventtimestamp > current_timestamp - interval ? and broker.r = ?;-
with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;
Date: 2026-02-17 07:00:06 Duration: 0ms
4 27 87.54 MiB 3.15 MiB 3.31 MiB 3.24 MiB select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ?), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?)), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;-
SELECT resultuid FROM relevance_consecutivecandles_results ORDER BY resultuid DESC LIMIT 1), all_results AS ( SELECT ccr.resultuid AS resultuid, ccr.direction AS direction, s.exchange AS exchange, s.symbolid AS symbolid, coalesce(bim.code, s.symbol) AS symbol_code, s.longname AS symbol_name, s.timegranularity AS interval, ccr.patternendtime AS identified, dtt.timezone AS timezone, ccr.qtyconsecutivecandles AS length, g.basegroupname, CASE WHEN rcr.age IS NOT NULL THEN rcr.age WHEN ccr.resultuid <= rm.resultuid THEN 1 ELSE 0 END as age, CASE WHEN rcr.relevant IS NOT NULL THEN rcr.relevant WHEN ccr.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip, newLevels.filtered FROM consecutivecandles_results ccr INNER JOIN brokersymbollist bsl ON bsl.brokerid = $1 AND bsl.symbolid = ccr.symbolid INNER JOIN symbols s ON ccr.symbolid = s.symbolid AND s.nonliquid = 0 INNER JOIN downloadersymbolsettings dss ON ccr.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname AND dtt.dayofweek = 3 INNER JOIN symbolgroup sg on ccr.symbolid = sg.symbolid INNER JOIN groups g ON sg.groupid = g.groupid INNER JOIN brokergroups bg on g.groupid = bg.groupid AND bsl.brokerid = bg.brokerid INNER JOIN rar_max rm ON 1 = 1 LEFT OUTER JOIN relevance_consecutivecandles_results rcr ON rcr.resultuid = ccr.resultuid LEFT JOIN currencypips cps on cps.symbol = s.symbol LEFT OUTER JOIN brokerinstrumentmap bim ON dss.datafeedinstrumentid = bim.datafeedinstrumentid AND bim.brokerid = bsl.brokerid AND bim.TYPE = 'OUTBOUND' LEFT JOIN LATERAL calc_cc_signal_filter (ccr.resultuid) newLevels on true WHERE ccr.gmttimefound > now() - INTERVAL '7 DAYS' AND s.deleted = 0 AND (ccr.simulation = 0 OR ccr.simulation IS NULL) AND ($2 = 0 OR s.timegranularity in ($3, $4, $5, $6, $7, $8, $9)) AND ($10 = 0 OR s.exchange in ($11)) AND ($12 = 0 OR coalesce(bim.code, s.symbol) in ($13, $14, $15, $16, $17, $18, $19, $20, $21, $22, $23, $24, $25, $26, $27, $28, $29, $30, $31, $32, $33, $34, $35, $36, $37, $38, $39, $40, $41, $42, $43, $44, $45, $46, $47, $48, $49, $50, $51, $52, $53, $54, $55, $56, $57, $58, $59, $60, $61, $62, $63, $64, $65, $66, $67, $68, $69, $70, $71, $72, $73, $74, $75, $76, $77, $78, $79, $80, $81, $82, $83, $84, $85, $86, $87, $88, $89, $90, $91, $92, $93, $94, $95, $96, $97, $98, $99, $100, $101, $102, $103, $104, $105, $106, $107, $108, $109, $110, $111, $112, $113, $114, $115, $116, $117, $118, $119, $120, $121, $122, $123, $124, $125, $126, $127, $128, $129, $130, $131, $132, $133, $134, $135, $136, $137, $138, $139, $140, $141, $142, $143, $144, $145, $146, $147, $148, $149, $150, $151, $152, $153, $154, $155, $156, $157, $158, $159, $160, $161, $162, $163, $164, $165, $166, $167, $168, $169, $170, $171, $172, $173, $174, $175, $176, $177, $178, $179, $180, $181, $182, $183, $184, $185, $186, $187, $188, $189, $190, $191, $192, $193, $194, $195, $196, $197, $198, $199, $200, $201, $202, $203, $204, $205, $206, $207, $208, $209, $210, $211, $212, $213, $214, $215, $216, $217, $218, $219, $220, $221, $222, $223, $224, $225, $226, $227, $228, $229, $230, $231, $232, $233, $234, $235, $236, $237, $238, $239, $240, $241, $242, $243, $244, $245, $246, $247, $248, $249, $250, $251, $252, $253, $254, $255, $256, $257, $258, $259, $260, $261, $262, $263, $264, $265, $266, $267, $268, $269, $270, $271, $272, $273, $274, $275, $276, $277, $278, $279, $280, $281, $282, $283, $284, $285, $286, $287, $288, $289, $290, $291, $292, $293, $294, $295, $296, $297, $298, $299, $300, $301, $302, $303, $304, $305, $306, $307, $308, $309, $310, $311, $312, $313, $314, $315, $316, $317, $318, $319, $320, $321, $322)) AND ($323 = 0 OR ccr.patternlengthbars <= $324)), results AS ( SELECT DISTINCT ON (symbolid) * FROM all_results WHERE (FALSE = $325 OR relevant = 1) AND ($326 = 0 OR age <= $327) ORDER BY symbolid, resultuid ) SELECT * from results ORDER BY identified DESC, length DESC;
Date: 2026-02-17 07:01:20 Duration: 0ms
5 16 738.25 MiB 46.14 MiB 46.14 MiB 46.14 MiB update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type = ?) sub where result_uid = sub.resultuid;-
UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type = 'cp') sub WHERE result_uid = sub.resultuid;
Date: 2026-02-17 07:01:14 Duration: 0ms
6 16 1.22 GiB 78.33 MiB 78.34 MiB 78.33 MiB with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ?) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then ? else ? end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then ? else ? end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike ? inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike ?) sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike ?; update solr_relevance_old set newrelevant = ? where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null); update solr_relevance_old set new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total from ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total from whatshot_probability where type in (...)) sub where result_uid = sub.resultuid;-
with max_ra as ( select resultuid from relevance_keylevels_results order by resultuid desc limit 1) update solr_relevance_old set newrelevant = sub.relevant, newage = sub.age from ( select so.uuid, case when ra.relevant is not null then ra.relevant when so.result_uid < max_ra.resultuid then 0 else 1 end as relevant, case when ra.age is not null then ra.age when so.result_uid < max_ra.resultuid then 11 else 0 end as age, so.result_uid from max_ra, solr_relevance_old so inner join keylevels_results k on so.result_uid = k.resultuid and so.uuid ilike 'kl_%' inner join downloadersymbolsettings dss on k.symbolid = dss.symbolid left outer join relevance_keylevels_results ra on so.result_uid = ra.resultuid and so.uuid ilike 'kl_%') sub where solr_relevance_old.result_uid = sub.result_uid and solr_relevance_old.uuid ilike 'kl_%'; update solr_relevance_old set newrelevant = 0 where result_uid in ( select result_uid from solr_relevance_old s left outer join keylevels_results a on a.resultuid = s.result_uid where s.uuid ilike 'kl_%' and a.resultuid is null); UPDATE solr_relevance_old SET new_hod_correct = sub.hod_correct, new_hod_percent = sub.hod_percent, new_hod_total = sub.hod_total, new_pattern_correct = sub.pattern_correct, new_pattern_percent = sub.pattern_percent, new_pattern_total = sub.pattern_total, new_percent = sub.percent, new_symbol_correct = sub.symbol_correct, new_symbol_percent = sub.symbol_percent, new_symbol_total = sub.symbol_total FROM ( select distinct resultuid, hod_correct, hod_percent, hod_total, hod, pattern_correct, pattern_percent, pattern_total, percent, symbol_correct, symbol_percent, symbol_total FROM whatshot_probability WHERE type in ('kl', 'ekl')) sub WHERE result_uid = sub.resultuid;
Date: 2026-02-17 07:01:18 Duration: 0ms
7 8 1.06 GiB 135.05 MiB 135.10 MiB 135.08 MiB select updateresultsmaterializedview ();-
select updateresultsmaterializedview ();
Date: 2026-02-17 07:02:19 Duration: 0ms
8 4 330.36 MiB 82.52 MiB 82.68 MiB 82.59 MiB select updateageforrelevantresults ();-
select updateageforrelevantresults ();
Date: 2026-02-17 07:02:05 Duration: 0ms
9 1 9.68 MiB 9.68 MiB 9.68 MiB 9.68 MiB select resultuid from relevance_keylevels_results order by resultuid desc limit ? ) select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as ruid, s.symbolid as sid, s.symbol as sym, longname, shortname, exchange as e, timegranularity as tg, a.patternid as pid, a.direction as d, a.patternprice as pp, atbaridentified as pet, case when (x9 != ?) then x9 when (x8 != ?) then x8 when (x7 != ?) then x7 when (x6 != ?) then x6 when (x5 != ?) then x5 when (x4 != ?) then x4 when (x3 != ?) then x3 when (x2 != ?) then x2 end as pst, patternprice as patp, x0, x1, x2, case when (x3 != ?) then x3 else ? end as x3, case when (x4 != ?) then x4 else ? end as x4, case when (x5 != ?) then x5 else ? end as x5, case when (x6 != ?) then x6 else ? end as x6, case when (x7 != ?) then x7 else ? end as x7, case when (x8 != ?) then x8 else ? end as x8, errormargin as erm, breakoutprice as pe, breakoutbars as be, breakout, atbaridentified as atbar, atpriceidentified as atprice, patternlengthbars as l, bandwidth as bw, qtytp as qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzos, dtt.timezone as timezone, approachingtimestamp as apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb, furthestprice as fp, newlevels.filtered, a.uniquepointsvalue as upv, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from keylevels_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join hrspatterns p on a.patternid = p.patternid inner join rar_max rm on ? = ? left outer join relevance_keylevels_results rar on a.resultuid = rar.resultuid left join lateral calc_kl_signal_filter (a.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;-
SELECT resultuid FROM relevance_keylevels_results ORDER BY resultuid DESC LIMIT 1 ) SELECT CASE WHEN a.old_resultuid = $1 THEN a.old_resultuid ELSE a.resultuid END AS ruid, s.symbolid AS sid, s.symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice AS pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '0' END AS x3, CASE WHEN (x4 != '') THEN x4 ELSE '0' END AS x4, CASE WHEN (x5 != '') THEN x5 ELSE '0' END AS x5, CASE WHEN (x6 != '') THEN x6 ELSE '0' END AS x6, CASE WHEN (x7 != '') THEN x7 ELSE '0' END AS x7, CASE WHEN (x8 != '') THEN x8 ELSE '0' END AS x8, errorMargin AS erm, breakoutprice AS pE, breakoutbars AS be, breakout, atbaridentified AS atBar, atpriceidentified AS atPrice, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname AS patternname, dtt.absolutetimezoneoffset AS tzOs, dtt.timezone AS timezone, approachingtimestamp AS apt, approachingregion AS apr, predictionpricefrom AS ppf, predictionpriceto AS ppt, predictiontimefrom AS ptf, predictiontimebars AS ptb, furthestprice AS fp, newLevels.filtered, a.uniquepointsvalue AS upv, CASE WHEN rar.age IS NOT NULL THEN rar.age WHEN a.resultuid <= rm.resultuid THEN 11 ELSE 0 END as age, CASE WHEN rar.relevant IS NOT NULL THEN rar.relevant WHEN a.resultuid <= rm.resultuid THEN 0 ELSE 1 END as relevant, cps.pip FROM keylevels_results a INNER JOIN downloadersymbolsettings dss ON a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname INNER JOIN symbols s ON a.symbolid = s.symbolid INNER JOIN hrspatterns p ON a.patternid = p.patternid INNER JOIN rar_max rm ON 1 = 1 LEFT OUTER JOIN relevance_keylevels_results rar ON a.resultuid = rar.resultuid LEFT JOIN LATERAL calc_kl_signal_filter (a.resultuid) newLevels on true LEFT JOIN currencypips cps on cps.symbol = s.symbol WHERE (a.old_resultuid = $2 OR a.resultuid = $3) AND dtt.dayofweek = 3;
Date: 2026-02-17 07:28:18 Duration: 0ms
Queries generating the largest temporary files
Rank Size Query 1 159.41 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-17 07:30:05 ]
2 156.41 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-17 07:50:08 ]
3 135.10 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 07:47:20 ]
4 135.09 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 07:32:14 ]
5 135.09 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 07:50:32 ]
6 135.08 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 07:02:19 ]
7 135.08 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 07:35:32 ]
8 135.06 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 07:17:21 ]
9 135.06 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 07:20:32 ]
10 135.05 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 07:05:32 ]
11 105.06 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-17 07:00:03 ]
12 97.86 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-17 07:40:04 ]
13 96.49 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-17 07:10:04 ]
14 86.43 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-17 07:20:08 ]
15 85.27 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-17 07:40:07 ]
16 82.68 MiB select updateageforrelevantresults ();[ Date: 2026-02-17 07:02:05 ]
17 82.59 MiB select updateageforrelevantresults ();[ Date: 2026-02-17 07:32:05 ]
18 82.57 MiB select updateageforrelevantresults ();[ Date: 2026-02-17 07:47:06 ]
19 82.52 MiB select updateageforrelevantresults ();[ Date: 2026-02-17 07:17:06 ]
20 81.36 MiB with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;[ Date: 2026-02-17 07:20:06 ]
-
Vacuums
Vacuums / Analyzes Distribution
Key values
- 0 sec Highest CPU-cost vacuum
Table
Database - Date
- 0 sec Highest CPU-cost analyze
Table
Database - Date
Analyzes per table
Key values
- public.solr_relevance_old (16) Main table analyzed (database acaweb_fx)
- 56 analyzes Total
Table Number of analyzes acaweb_fx.public.solr_relevance_old 16 acaweb_fx.pg_catalog.pg_attribute 8 acaweb_fx.public.relevance_keylevels_results 4 acaweb_fx.pg_catalog.pg_class 4 acaweb_fx.pg_catalog.pg_type 4 acaweb_fx.public.datafeeds_latestrun 3 acaweb_fx.public.relevance_fibonacci_results 3 acaweb_fx.public.relevance_autochartist_results 3 acaweb_fx.pg_catalog.pg_index 2 acaweb_fx.public.latest_t15_candle_view 2 acaweb_fx.pg_catalog.pg_depend 2 acaweb_fx.public.autochartist_symbolupdates 1 acaweb_fx.public.solr_imports 1 acaweb_fx.public.latest_candle_datetime_per_receng 1 acaweb_fx.public.relevance_consecutivecandles_results 1 acaweb_fx.public.symbollatestupdatetime 1 Total 56 Vacuums per table
Key values
- public.solr_relevance_old (16) Main table vacuumed on database acaweb_fx
- 38 vacuums Total
Index Buffer usage Skipped WAL usage Table Vacuums scans hits misses dirtied pins frozen records full page bytes acaweb_fx.public.solr_relevance_old 16 16 13,628 0 70 0 0 9,915 1,222 5,852,777 acaweb_fx.pg_catalog.pg_attribute 4 4 3,284 0 566 0 268 1,358 522 2,911,560 acaweb_fx.public.datafeeds_latestrun 3 0 350 0 6 0 0 39 6 40,622 acaweb_fx.pg_toast.pg_toast_2619 2 2 276 0 55 0 0 207 53 196,668 acaweb_fx.pg_catalog.pg_type 2 2 338 0 47 0 0 143 39 218,923 acaweb_fx.public.relevance_keylevels_results 2 2 7,425 0 332 2 140 1,950 314 1,071,623 acaweb_fx.pg_catalog.pg_class 2 2 901 0 73 0 0 248 67 376,421 acaweb_fx.pg_catalog.pg_index 1 1 111 0 19 0 0 31 16 92,282 acaweb_fx.public.autochartist_symbolupdates 1 1 20,935 0 3,085 4 38,539 5,120 3,015 1,631,576 acaweb_fx.pg_catalog.pg_statistic 1 1 1,032 0 196 0 582 497 184 692,227 acaweb_fx.public.latest_t15_candle_view 1 1 66 0 1 0 0 6 1 9,037 acaweb_fx.pg_catalog.pg_depend 1 1 398 0 81 0 59 186 68 344,600 acaweb_fx.public.relevance_autochartist_results 1 1 3,194 0 268 0 237 531 218 665,933 acaweb_fx.public.relevance_fibonacci_results 1 1 1,169 0 78 0 43 171 59 214,183 Total 38 35 53,107 44,849 4,877 6 39,868 20,402 5,784 14,318,432 Tuples removed per table
Key values
- public.solr_relevance_old (69640) Main table with removed tuples on database acaweb_fx
- 85675 tuples Total removed
Index Tuples Pages Table Vacuums scans removed remain not yet removable removed remain acaweb_fx.public.solr_relevance_old 16 16 69,640 98,123 0 0 3,395 acaweb_fx.pg_catalog.pg_attribute 4 4 6,610 42,844 0 0 1,044 acaweb_fx.public.autochartist_symbolupdates 1 1 4,972 48,688 106 0 40,691 acaweb_fx.public.relevance_keylevels_results 2 2 1,610 24,767 0 0 558 acaweb_fx.pg_catalog.pg_depend 1 1 808 14,429 0 0 142 acaweb_fx.pg_catalog.pg_type 2 2 636 2,932 36 0 86 acaweb_fx.pg_catalog.pg_statistic 1 1 582 3,727 0 0 1,194 acaweb_fx.pg_catalog.pg_class 2 2 244 3,354 54 0 300 acaweb_fx.public.datafeeds_latestrun 3 0 163 42 0 0 48 acaweb_fx.pg_toast.pg_toast_2619 2 2 154 339 5 0 103 acaweb_fx.public.relevance_autochartist_results 1 1 141 8,845 233 0 380 acaweb_fx.public.latest_t15_candle_view 1 1 56 14 0 0 1 acaweb_fx.public.relevance_fibonacci_results 1 1 45 1,579 47 0 102 acaweb_fx.pg_catalog.pg_index 1 1 14 813 0 1 22 Total 38 35 85,675 250,496 481 1 48,066 Pages removed per table
Key values
- pg_catalog.pg_index (1) Main table with removed pages on database acaweb_fx
- 1 pages Total removed
Table Number of vacuums Index scans Tuples removed Pages removed acaweb_fx.pg_catalog.pg_index 1 1 14 1 acaweb_fx.pg_toast.pg_toast_2619 2 2 154 0 acaweb_fx.pg_catalog.pg_type 2 2 636 0 acaweb_fx.public.datafeeds_latestrun 3 0 163 0 acaweb_fx.public.autochartist_symbolupdates 1 1 4972 0 acaweb_fx.pg_catalog.pg_statistic 1 1 582 0 acaweb_fx.pg_catalog.pg_attribute 4 4 6610 0 acaweb_fx.public.latest_t15_candle_view 1 1 56 0 acaweb_fx.pg_catalog.pg_depend 1 1 808 0 acaweb_fx.public.relevance_keylevels_results 2 2 1610 0 acaweb_fx.public.relevance_autochartist_results 1 1 141 0 acaweb_fx.public.solr_relevance_old 16 16 69640 0 acaweb_fx.pg_catalog.pg_class 2 2 244 0 acaweb_fx.public.relevance_fibonacci_results 1 1 45 0 Total 38 35 85,675 1 Autovacuum Activity
↑ Back to the top of the Autovacuum Activity tableDay Hour VACUUMs ANALYZEs Feb 17 07 38 56 - 0 sec Highest CPU-cost vacuum
-
Locks
Locks by types
Key values
- unknown Main Lock Type
- 0 locks Total
Most frequent waiting queries (N)
Rank Count Total time Min time Max time Avg duration Query NO DATASET
Queries that waited the most
Rank Wait time Query NO DATASET
-
Queries
Queries by type
Key values
- 40,314 Total read queries
- 30,891 Total write queries
Queries by database
Key values
- unknown Main database
- 149,895 Requests
- 2h10m15s (unknown)
- Main time consuming database
Database Request type Count Duration acaweb_fx Total 852 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 169 0ms select 72 0ms tcl 331 0ms update 38 0ms socialmedia Total 70 0ms select 70 0ms unknown Total 149,895 2h10m15s copy from 16 0ms cte 3,986 0ms insert 23,332 0ms others 4,034 0ms select 40,172 0ms tcl 331 0ms update 2,615 0ms Queries by user
Key values
- unknown Main user
- 149,895 Requests
User Request type Count Duration postgres Total 922 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 169 0ms select 142 0ms tcl 331 0ms update 38 0ms unknown Total 149,895 2h10m15s copy from 16 0ms cte 3,986 0ms insert 23,332 0ms others 4,034 0ms select 40,172 0ms tcl 331 0ms update 2,615 0ms Duration by user
Key values
- 2h10m15s (unknown) Main time consuming user
User Request type Count Duration postgres Total 922 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 169 0ms select 142 0ms tcl 331 0ms update 38 0ms unknown Total 149,895 2h10m15s copy from 16 0ms cte 3,986 0ms insert 23,332 0ms others 4,034 0ms select 40,172 0ms tcl 331 0ms update 2,615 0ms Queries by host
Key values
- unknown Main host
- 150,817 Requests
- 2h10m15s (unknown)
- Main time consuming host
Queries by application
Key values
- unknown Main application
- 150,461 Requests
- 2h10m15s (unknown)
- Main time consuming application
Number of cancelled queries
Key values
- 0 per second Cancelled query Peak
- 2026-02-17 07:51:48 Date
Number of cancelled queries (5 minutes period)
NO DATASET
-
Top Queries
Histogram of query times
Key values
- 50,305 0-1ms duration
Slowest individual queries
Rank Duration Query NO DATASET
Time consuming queries
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 0ms 20 0ms 0ms 0ms select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Feb 17 07 20 0ms 0ms 2 0ms 232 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ? ), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;Times Reported Time consuming queries #2
Day Hour Count Duration Avg duration Feb 17 07 232 0ms 0ms 3 0ms 5 0ms 0ms 0ms select trumpetsymbolid as sid, trumpettimegranularity as tg from brokersymbollist bsl left join powerstats_symboldata psd on bsl.symbolid = psd.symbolid left join downloadersymbolsettings dss on psd.symbolid = dss.symbolid left join symbols s on dss.symbolid = s.symbolid left outer join brokerinstrumentmap bdfi on code = ? and bdfi.brokerid = ? and dss.datafeedinstrumentid = bdfi.datafeedinstrumentid where (code = ? or s.symbol = ?) and bsl.brokerid = ? and dss.classname <> ? group by trumpetsymbolid, trumpettimegranularity;Times Reported Time consuming queries #3
Day Hour Count Duration Avg duration Feb 17 07 5 0ms 0ms 4 0ms 36 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.symbolid, patternstarttime, patternendtime, timegranularity, ? as direction, case when bmr.old_resultuid = ? then bmr.old_resultuid else bmr.resultuid end as uid, s.exchange, s.symbol, s.longname, s.shortname, dtt.timezone, bmr.patternmovement, bmr.statisticalmovement, bmr.fromprice, bmr.toprice, bmr.percentile, bmr.patternlengthbars, case when rbr.age is not null then rbr.age when bmr.resultuid <= rm.resultuid then ? else ? end as age, case when rbr.relevant is not null then rbr.relevant when bmr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from bigmovement_results bmr inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on bmr.symbolid = s.symbolid inner join rar_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid left join currencypips cps on cps.symbol = s.symbol where (bmr.old_resultuid = ? or bmr.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Feb 17 07 36 0ms 0ms 5 0ms 2,224 0ms 0ms 0ms insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;Times Reported Time consuming queries #5
Day Hour Count Duration Avg duration Feb 17 07 2,224 0ms 0ms 6 0ms 4 0ms 0ms 0ms select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Feb 17 07 4 0ms 0ms 7 0ms 208 0ms 0ms 0ms select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, timegranularity as interval, direction as direction, patternendtime as patternendtime, patternstartprice as psp, patternendprice as pep, target03 as t03, target16 as t16, patternlengthbars as length, p.patternname as patternname, dtt.timezone, cps.pip from fibonacci_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join fibonaccipatterns p on a.pattern = p.patternname left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Feb 17 07 208 0ms 0ms 8 0ms 4 0ms 0ms 0ms select updaterelevantforrelevantresults ();Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Feb 17 07 4 0ms 0ms 9 0ms 2,167 0ms 0ms 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Feb 17 07 2,167 0ms 0ms 10 0ms 8 0ms 0ms 0ms select t.pricedatetime as pricedatetime, t.open as open, t.high as high, t.low as low, t.close "..." close, t.volume as volume, t.bsf as bsf from t1440 t where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?;Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Feb 17 07 8 0ms 0ms 11 0ms 1 0ms 0ms 0ms insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ?united kingdom unemployment rate\?s biggest movers\": \"maiores movimenta\\\\u00e7\\\\u00f5es de hoje\", \"dictionary_zeromarkets ?_silver\": \"xagusd\", \"dictionary_upcomingearnings_title\": \"an\\\\u00fancios de lucros entre {fromdate} e {todate}.\", \"params_creatomate_snapshots_value\": null, \"data_affectedtickers_1_description\": \"a amplitude de movimento esperada durante o evento est\\\\u00e1 entre ?.? e ?.?\", \"data_affectedtickers_1_normal_from\": ?.?, \"data_affectedtickers_2_description\": \"a amplitude de movimento esperada durante o evento est\\\\u00e1 entre ?.? e ?.?\", \"data_affectedtickers_2_normal_from\": ?.?, \"dictionary_brent crude oil futures\": \"brent crude oil futures\", \"dictionary_the biggest losers are:\": \"os maiores perdedores s\\\\u00e3o:\", \"dictionary_thisweekseconomicevents\": \"eventos econ\\\\u00f4micos desta semana\", \"dictionary_us dollar index futures\": \"us dollar index futures\", \"dictionary_zeromarkets ?_aud/usd\": \"audusd\", \"dictionary_zeromarkets ?_eur/usd\": \"eurusd\", \"dictionary_zeromarkets ?_gbp/usd\": \"gbpusd\", \"dictionary_zeromarkets ?_nzd/usd\": \"nzdusd\", \"dictionary_zeromarkets ?_usd/cad\": \"usdcad\", \"dictionary_zeromarkets ?_usd/chf\": \"usdchf\", \"dictionary_zeromarkets ?_usd/jpy\": \"usdjpy\", \"dictionary_volatilitywarning_title\": \"volatilidade esperada como resultado de \? em {country_name} nas pr\\\\u00f3ximas {hours_ahead} horas.\", \"params_creatomate_templateid_value\": \"a53e03b0-3d70-4faa-b766-d634ad553fe6,b56bf30a-04c6-4a1d-a7ec-88bf873f10cd\\\\r\\\\n\\\\r\\\\n\", \"params_title_character_limit_value\": ?, \"data_affectedtickers_1_diff_percent\": ?.?, \"data_affectedtickers_2_diff_percent\": ?.?, \"data_affectedtickers_3_name.visible\": false, \"data_affectedtickers_4_name.visible\": false, \"data_affectedtickers_5_name.visible\": false, \"data_affectedtickers_6_name.visible\": false, \"data_affectedtickers_7_name.visible\": false, \"data_affectedtickers_8_name.visible\": false, \"data_affectedtickers_9_name.visible\": false, \"dictionary_the biggest winners are:\": \"os maiores vencedores s\\\\u00e3o:\", \"dictionary_zeromarkets ?_ftse ?\": \"uk100\", \"dictionary_highimpactevent_longtext\": \"{eventname} ser\\\\u00e1 divulgado em {countryname} nas pr\\\\u00f3ximas {hours_ahead} horas. o valor esperado para esta divulga\\\\u00e7\\\\u00e3o \\\\u00e9 {consensus}. {description}\", \"params_probability_of_posting_value\": ?, \"data_affectedtickers_10_name.visible\": false, \"data_affectedtickers_3_shape.visible\": false, \"data_affectedtickers_4_shape.visible\": false, \"data_affectedtickers_5_shape.visible\": false, \"data_affectedtickers_6_shape.visible\": false, \"data_affectedtickers_7_shape.visible\": false, \"data_affectedtickers_8_shape.visible\": false, \"data_affectedtickers_9_shape.visible\": false, \"dictionary_909_wti crude oil futures\": \"wti\", \"dictionary_e-mini nasdaq-100 futures\": \"e-mini nasdaq-100 futures\", \"dictionary_nikkei ? dollar futures\": \"nikkei ? dollar futures\", \"dictionary_thisweeksearningsreleases\": \"divulga\\\\u00e7\\\\u00f5es de altas desta semana\", \"dictionary_zeromarkets ?_crude oil\": \"xtiusd\", \"dictionary_zeromarkets ?_hang seng\": \"hk50\", \"dictionary_highimpactevent_shorttext\": \"{eventname} ser\\\\u00e1 divulgado em {countryname}. o valor esperado para esta divulga\\\\u00e7\\\\u00e3o \\\\u00e9 {consensus}.\", \"data_affectedtickers_10_shape.visible\": false, \"data_affectedtickers_3_ticker.visible\": false, \"data_affectedtickers_4_ticker.visible\": false, \"data_affectedtickers_5_ticker.visible\": false, \"data_affectedtickers_6_ticker.visible\": false, \"data_affectedtickers_7_ticker.visible\": false, \"data_affectedtickers_8_ticker.visible\": false, \"data_affectedtickers_9_ticker.visible\": false, \"dictionary_nohighimpacteconomicevents\": \"sem eventos econ\\\\u00f4micos de alta volatilidade\", \"dictionary_this week\?s biggest movers are:\": \"as maiores movimenta\\\\u00e7\\\\u00f5es desta semana s\\\\u00e3o:\", \"dictionary_upcominghighimpacteconomicevent\": \"pr\\\\u00f3ximo evento econ\\\\u00f4mico de alta volatilidade\", \"data_affectedtickers_10_description.visible\": false, \"data_affectedtickers_10_normal_from.visible\": false, \"data_affectedtickers_3_diff_percent.visible\": false, \"data_affectedtickers_4_diff_percent.visible\": false, \"data_affectedtickers_5_diff_percent.visible\": false, \"data_affectedtickers_6_diff_percent.visible\": false, \"data_affectedtickers_7_diff_percent.visible\": false, \"data_affectedtickers_8_diff_percent.visible\": false, \"data_affectedtickers_9_diff_percent.visible\": false, \"dictionary_zeromarkets ?_platinum futures\": \"xptusd\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany}) est\\\\u00e1 divulgando os lucros hoje. quando o lpa \\\\u00e9 {delta_sign} do esperado, {earningscompany} tem uma probabilidade de {probability} de uma varia\\\\u00e7\\\\u00e3o de {mean_mov_percent} no m\\\\u00eas seguinte a esta divulga\\\\u00e7\\\\u00e3o.\\\\\\\\nem divulga\\\\u00e7\\\\u00f5es anteriores de lucros onde o lpa foi {delta_sign} do esperado, as seguintes a\\\\u00e7\\\\u00f5es foram impactadas:\", \"data_affectedtickers_10_diff_percent.visible\": false, \"data_affectedtickers_1_details_symbol_ticker\": \"gbpusd\", \"data_affectedtickers_2_details_symbol_ticker\": \"eurusd\", \"dictionary_2-year u.s. treasury note futures\": \"?-year u.s. treasury note futures\", \"dictionary_e-mini russell ? index futures\": \"e-mini russell ? index futures\", \"dictionary_impactofearningsrelease_shorttext\": \"{earningscompany_name} ({earningscompany}) est\\\\u00e1 divulgando os lucros hoje e impactar\\\\u00e1 as seguintes a\\\\u00e7\\\\u00f5es:\", \"dictionary_upcomingearnings_title_noearnings\": \"nenhum an\\\\u00fancio de lucros programado entre {fromdate} e {todate}.\", \"dictionary_10-year u.s. treasury note futures\": \"?-year u.s. treasury note futures\", \"dictionary_calendarofhighimpacteconomicevents\": \"calend\\\\u00e1rio de eventos econ\\\\u00f4micos importantes\", \"data_affectedtickers_1_details_symbol_exchange\": \"forex\", \"data_affectedtickers_1_details_symbol_interval\": ?, \"data_affectedtickers_1_details_symbol_pip_size\": ?.?, \"data_affectedtickers_2_details_symbol_exchange\": \"forex\", \"data_affectedtickers_2_details_symbol_interval\": ?, \"data_affectedtickers_2_details_symbol_pip_size\": ?.?, \"dictionary_market moves for the last ? hours:\": \"movimenta\\\\u00e7\\\\u00f5es do mercado nas \\\\u00faltimas ? horas:\", \"data_affectedtickers_1_details_symbol_pip_value\": ?, \"data_affectedtickers_2_details_symbol_pip_value\": ?, \"data_affectedtickers_3_diff_percent_str.visible\": false, \"data_affectedtickers_4_diff_percent_str.visible\": false, \"data_affectedtickers_5_diff_percent_str.visible\": false, \"data_affectedtickers_6_diff_percent_str.visible\": false, \"data_affectedtickers_7_diff_percent_str.visible\": false, \"data_affectedtickers_8_diff_percent_str.visible\": false, \"data_affectedtickers_9_diff_percent_str.visible\": false, \"params_creatomate_templateselectionscheme_value\": \"random\", \"data_affectedtickers_10_diff_percent_str.visible\": false, \"data_affectedtickers_1_volatility_increase_label\": \"aumento de volatilidade\", \"data_affectedtickers_2_volatility_increase_label\": \"aumento de volatilidade\", \"dictionary_zeromarkets ?_wti crude oil futures\": \"wti\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): probabilidade de {probability} de uma varia\\\\u00e7\\\\u00e3o de {mean_mov_percent} no m\\\\u00eas seguinte a esta divulga\\\\u00e7\\\\u00e3o.\", \"dictionary_market movements in the last ? hours.\": \"movimenta\\\\u00e7\\\\u00f5es do mercado nas \\\\u00faltimas ? horas.\", \"data_affectedtickers_1_details_volatility_diff_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p75\": ?.?, \"dictionary_companies releasing earnings this week:\": \"empresas divulgando lucros nesta semana:\", \"dictionary_zeromarkets ?_brent crude oil futures\": \"xbrusd\", \"dictionary_zeromarkets ?_us dollar index futures\": \"usdx\", \"data_affectedtickers_1_details_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_2_details_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_1_details_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_symbol_value_currency\": \"usd\", \"data_affectedtickers_1_details_symbol_value_lot_size\": ?, \"data_affectedtickers_2_details_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_symbol_value_currency\": \"usd\", \"data_affectedtickers_2_details_symbol_value_lot_size\": ?, \"dictionary_zeromarkets ?_nikkei ? dollar futures\": \"jp225\", \"data_affectedtickers_1_details_symbol_value_date_time\": \"?-01-30t21:?:?.?z\", \"data_affectedtickers_1_details_volatility_diff_median\": ?.?, \"data_affectedtickers_2_details_symbol_value_date_time\": \"?-02-17t04:?:?.?z\", \"data_affectedtickers_2_details_volatility_diff_median\": ?.?, \"dictionary_high impact economic events for this week:\": \"eventos econ\\\\u00f4micos de alto impacto para esta semana:\", \"data_affectedtickers_1_details_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_1_details_volatility_diff_std_dev\": ?.?, \"data_affectedtickers_2_details_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_2_details_volatility_diff_std_dev\": ?.?e-06, \"dictionary_e-mini dow jones industrial average futures\": \"e-mini dow jones industrial average futures\", \"data_affectedtickers_1_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_2_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_1_details_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_value_max\": ?, \"data_affectedtickers_1_details_volatility_diff_value_min\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_value_max\": ?, \"data_affectedtickers_2_details_volatility_diff_value_min\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_3_volatility_increase_label.visible\": false, \"data_affectedtickers_4_volatility_increase_label.visible\": false, \"data_affectedtickers_5_volatility_increase_label.visible\": false, \"data_affectedtickers_6_volatility_increase_label.visible\": false, \"data_affectedtickers_7_volatility_increase_label.visible\": false, \"data_affectedtickers_8_volatility_increase_label.visible\": false, \"data_affectedtickers_9_volatility_increase_label.visible\": false, \"data_affectedtickers_10_volatility_increase_label.visible\": false, \"data_affectedtickers_1_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_event_volatility_value_p75\": ?, \"data_affectedtickers_1_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_mean\": ?, \"data_affectedtickers_2_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_mean\": ?, \"dictionary_no earnings announcements scheduled this week.\": \"nenhum an\\\\u00fancio de lucros programado para esta semana.\", \"dictionary_upcoming earnings announcements for this week:\": \"pr\\\\u00f3ximos an\\\\u00fancios de lucros para esta semana:\", \"data_affectedtickers_1_details_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_2_details_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_1_details_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_median\": ?, \"data_affectedtickers_2_details_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_median\": ?, \"data_affectedtickers_1_details_event_volatility_value_median\": ?, \"data_affectedtickers_1_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p75\": ?, \"dictionary_these are the market movements for the last week:\": \"estas s\\\\u00e3o as movimenta\\\\u00e7\\\\u00f5es do mercado na \\\\u00faltima semana:\", \"data_affectedtickers_1_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_median\": ?, \"dictionary_these are the market movements for the last ? hours:\": \"estas s\\\\u00e3o as movimenta\\\\u00e7\\\\u00f5es do mercado nas \\\\u00faltimas ? horas:\"}}?}", "trace": "Traceback (most recent call last):\n File \"/var/task/lambda_function.py\", line ?, in lambda_handler\n results[?creatomate_response?] = Webhook_Creatomate.render(processlog, templateid,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/var/task/webhook_creatomate.py\", line ?, in render\n Webhook_Creatomate.waitForRender(apikey, creatomate_response[?][?id?], timeout)\n File \"/var/task/webhook_creatomate.py\", line ?, in waitForRender\n raise Exception(status[?error_message?] +\". render details: \" + json.dumps(status))\nException: An HTTP ? status was received while trying to download the file: https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg (element data_CountryIcon). Render details: {\"id\": \"?a249b0d-2f5f-48e3-b32e-37ce15035571\", \"status\": \"failed\", \"error_message\": \"an http ? status was received while trying to download the file: https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg (element data_countryicon)\", \"url\": \"https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/?a249b0d-2f5f-48e3-b32e-37ce15035571.png\", \"template_id\": \"a53e03b0-3d70-4faa-b766-d634ad553fe6\", \"template_name\": \"volatility warning png ? en pt\", \"template_tags\": [], \"output_format\": \"png\", \"modifications\": {\"data_date\": \"? fev., ?am utc\", \"data_name\": \"united kingdom unemployment rate\", \"text_title\": \"volatilidade esperada como resultado de ?united kingdom unemployment rate? em great britain nas pr\\u00f3ximas ? horas.\", \"has_results\": true, \"data_heading\": \"aviso de volatilidade\", \"data_country\": \"gb\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"dictionary_dax\": \"dax\", \"dictionary_utc\": \"utc\", \"text_long_text\": \"united kingdom unemployment rate (great britain) resultar\\u00e1 em aumentos significativos na volatilidade. eurusd - ?.?\\ngbpusd - ?.?\\n\", \"dictionary_corn\": \"milho\", \"dictionary_gold\": \"ouro\", \"dictionary_hour\": \"hora\", \"dictionary_open\": \"abrir\", \"dictionary_time\": \"tempo\", \"text_short_text\": \"united kingdom unemployment rate (great britain) resultar\\u00e1 em aumentos significativos na volatilidade. eurusd - ?.?\\ngbpusd - ?.?\\n\", \"data_countryicon\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg\", \"data_description\": \"in the united kingdom, the unemployment rate measures the number of people actively looking for a job as a percentage of the labour force.\", \"data_released_at\": \"?-02-17t07:?\", \"dictionary_close\": \"fechar\", \"dictionary_daily\": \"diariamente\", \"dictionary_event\": \"evento\", 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{probability} de uma varia\\u00e7\\u00e3o de {mean_mov_percent} no m\\u00eas seguinte a esta divulga\\u00e7\\u00e3o.\", \"dictionary_market movements in the last ? hours.\": \"movimenta\\u00e7\\u00f5es do mercado nas \\u00faltimas ? horas.\", \"data_affectedtickers_1_details_volatility_diff_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p75\": ?.?, \"dictionary_companies releasing earnings this week:\": \"empresas divulgando lucros nesta semana:\", \"dictionary_zeromarkets ?_brent crude oil futures\": \"xbrusd\", \"dictionary_zeromarkets ?_us dollar index futures\": \"usdx\", \"data_affectedtickers_1_details_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_2_details_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_1_details_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_symbol_value_currency\": \"usd\", \"data_affectedtickers_1_details_symbol_value_lot_size\": ?, \"data_affectedtickers_2_details_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_symbol_value_currency\": \"usd\", \"data_affectedtickers_2_details_symbol_value_lot_size\": ?, \"dictionary_zeromarkets ?_nikkei ? dollar futures\": \"jp225\", \"data_affectedtickers_1_details_symbol_value_date_time\": \"?-01-30t21:?:?.?z\", \"data_affectedtickers_1_details_volatility_diff_median\": ?.?, \"data_affectedtickers_2_details_symbol_value_date_time\": \"?-02-17t04:?:?.?z\", \"data_affectedtickers_2_details_volatility_diff_median\": ?.?, \"dictionary_high impact economic events for this week:\": \"eventos econ\\u00f4micos de alto impacto para esta semana:\", \"data_affectedtickers_1_details_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_1_details_volatility_diff_std_dev\": ?.?, \"data_affectedtickers_2_details_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_2_details_volatility_diff_std_dev\": ?.?e-06, \"dictionary_e-mini dow jones industrial average futures\": \"e-mini dow jones industrial average futures\", \"data_affectedtickers_1_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_2_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_1_details_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_value_max\": ?, \"data_affectedtickers_1_details_volatility_diff_value_min\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_value_max\": ?, \"data_affectedtickers_2_details_volatility_diff_value_min\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_3_volatility_increase_label.visible\": false, \"data_affectedtickers_4_volatility_increase_label.visible\": false, \"data_affectedtickers_5_volatility_increase_label.visible\": false, \"data_affectedtickers_6_volatility_increase_label.visible\": false, \"data_affectedtickers_7_volatility_increase_label.visible\": false, \"data_affectedtickers_8_volatility_increase_label.visible\": false, \"data_affectedtickers_9_volatility_increase_label.visible\": false, \"data_affectedtickers_10_volatility_increase_label.visible\": false, \"data_affectedtickers_1_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_event_volatility_value_p75\": ?, \"data_affectedtickers_1_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_mean\": ?, \"data_affectedtickers_2_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_mean\": ?, \"dictionary_no earnings announcements scheduled this week.\": \"nenhum an\\u00fancio de lucros programado para esta semana.\", \"dictionary_upcoming earnings announcements for this week:\": \"pr\\u00f3ximos an\\u00fancios de lucros para esta semana:\", \"data_affectedtickers_1_details_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_2_details_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_1_details_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_median\": ?, \"data_affectedtickers_2_details_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_median\": ?, \"data_affectedtickers_1_details_event_volatility_value_median\": ?, \"data_affectedtickers_1_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p75\": ?, \"dictionary_these are the market movements for the last week:\": \"estas s\\u00e3o as movimenta\\u00e7\\u00f5es do mercado na \\u00faltima semana:\", \"data_affectedtickers_1_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_median\": ?, \"dictionary_these are the market movements for the last ? hours:\": \"estas s\\u00e3o as movimenta\\u00e7\\u00f5es do mercado nas \\u00faltimas ? horas:\"}}" }?json');Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Feb 17 07 1 0ms 0ms 12 0ms 18 0ms 0ms 0ms select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Feb 17 07 18 0ms 0ms 13 0ms 238 0ms 0ms 0ms select v.datname, c.relname, v.phase, v.heap_blks_total, v.heap_blks_scanned, v.heap_blks_vacuumed, v.index_vacuum_count, v.max_dead_tuples, v.num_dead_tuples from pg_stat_progress_vacuum as v join pg_class c on c.oid = v.relid;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Feb 17 07 238 0ms 0ms 14 0ms 1 0ms 0ms 0ms insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ?\\\\uc2e4\\\\uc5c5\\\\ub960\?s biggest movers\": \"\\\\uc624\\\\ub298 \\\\uac00\\\\uc7a5 \\\\ud070 \\\\uc6c0\\\\uc9c1\\\\uc784\", \"dictionary_Zeromarkets ?_Silver\": \"XAGUSD\", \"dictionary_upcomingearnings_title\": \"{fromdate}\\\\ubd80\\\\ud130 {todate}\\\\uae4c\\\\uc9c0\\\\uc758 \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c.\", \"params_creatomate_snapshots_value\": null, \"data_affectedtickers_1_description\": \"\\\\uc774\\\\ubca4\\\\ud2b8 \\\\uc911 \\\\uc608\\\\uc0c1 \\\\uc774\\\\ub3d9 \\\\ubc94\\\\uc704\\\\ub294 ?.?\\\\uc5d0\\\\uc11c ?.? \\\\uc0ac\\\\uc774\\\\uc785\\\\ub2c8\\\\ub2e4.\", \"data_affectedtickers_1_normal_from\": ?.?, \"data_affectedtickers_2_description\": \"\\\\uc774\\\\ubca4\\\\ud2b8 \\\\uc911 \\\\uc608\\\\uc0c1 \\\\uc774\\\\ub3d9 \\\\ubc94\\\\uc704\\\\ub294 ?.?\\\\uc5d0\\\\uc11c ?.? \\\\uc0ac\\\\uc774\\\\uc785\\\\ub2c8\\\\ub2e4.\", \"data_affectedtickers_2_normal_from\": ?.?, \"dictionary_Brent Crude Oil Futures\": \"Brent Crude Oil Futures\", \"dictionary_The biggest losers are:\": \"\\\\uac00\\\\uc7a5 \\\\ud070 \\\\ud328\\\\uc790\\\\ub294 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"dictionary_ThisWeeksEconomicEvents\": \"\\\\uae08\\\\uc8fc \\\\uacbd\\\\uc81c \\\\uc774\\\\ubca4\\\\ud2b8\", \"dictionary_US Dollar Index Futures\": \"US Dollar Index Futures\", \"dictionary_Zeromarkets ?_AUD/USD\": \"AUDUSD\", \"dictionary_Zeromarkets ?_EUR/USD\": \"EURUSD\", \"dictionary_Zeromarkets ?_GBP/USD\": \"GBPUSD\", \"dictionary_Zeromarkets ?_NZD/USD\": \"NZDUSD\", \"dictionary_Zeromarkets ?_USD/CAD\": \"USDCAD\", \"dictionary_Zeromarkets ?_USD/CHF\": \"USDCHF\", \"dictionary_Zeromarkets ?_USD/JPY\": \"USDJPY\", \"dictionary_volatilitywarning_title\": \"{hours_ahead}\\\\uc2dc\\\\uac04 \\\\ub0b4 {country_name}\\\\uc5d0\\\\uc11c \?\\\\uc73c\\\\ub85c \\\\uc778\\\\ud55c \\\\ubcc0\\\\ub3d9\\\\uc131\\\\uc774 \\\\uc608\\\\uc0c1\\\\ub429\\\\ub2c8\\\\ub2e4.\", \"params_creatomate_templateid_value\": \"?aa08409-8a69-4164-bd5d-c37b5949c5df,bcbd769a-c0a7-48bf-a9f3-9c7c6ffc975b\", \"params_title_character_limit_value\": ?, \"data_affectedtickers_1_diff_percent\": ?.?, \"data_affectedtickers_2_diff_percent\": ?.?, \"data_affectedtickers_3_name.visible\": false, \"data_affectedtickers_4_name.visible\": false, \"data_affectedtickers_5_name.visible\": false, \"data_affectedtickers_6_name.visible\": false, \"data_affectedtickers_7_name.visible\": false, \"data_affectedtickers_8_name.visible\": false, \"data_affectedtickers_9_name.visible\": false, \"dictionary_The biggest winners are:\": \"\\\\uac00\\\\uc7a5 \\\\ud070 \\\\uc2b9\\\\uc790\\\\ub294 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"dictionary_Zeromarkets ?_FTSE ?\": \"UK?\", \"dictionary_highimpactevent_longtext\": \"{eventname}\\\\uac00 {hours_ahead}\\\\uc2dc\\\\uac04 \\\\ub0b4 {countryname}\\\\uc5d0\\\\uc11c \\\\ubc1c\\\\ud45c\\\\ub429\\\\ub2c8\\\\ub2e4. \\\\uc608\\\\uc0c1\\\\uce58\\\\ub294 {consensus}\\\\uc785\\\\ub2c8\\\\ub2e4. {description}\", \"params_probability_of_posting_value\": ?, \"data_affectedtickers_10_name.visible\": false, \"data_affectedtickers_3_Shape.visible\": false, \"data_affectedtickers_4_Shape.visible\": false, \"data_affectedtickers_5_Shape.visible\": false, \"data_affectedtickers_6_Shape.visible\": false, \"data_affectedtickers_7_Shape.visible\": false, \"data_affectedtickers_8_Shape.visible\": false, \"data_affectedtickers_9_Shape.visible\": false, \"dictionary_909_WTI Crude Oil Futures\": \"WTI\", \"dictionary_E-mini NASDAQ? Futures\": \"E-mini NASDAQ? Futures\", \"dictionary_Nikkei ? Dollar Futures\": \"Nikkei ? Dollar Futures\", \"dictionary_ThisWeeksEarningsReleases\": \"\\\\uae08\\\\uc8fc \\\\uc218\\\\uc775 \\\\ubc1c\\\\ud45c\", \"dictionary_Zeromarkets ?_Crude oil\": \"XTIUSD\", \"dictionary_Zeromarkets ?_Hang Seng\": \"HK?\", \"dictionary_highimpactevent_shorttext\": \"{eventname}\\\\uac00 {countryname}\\\\uc5d0\\\\uc11c \\\\ubc1c\\\\ud45c\\\\ub429\\\\ub2c8\\\\ub2e4. \\\\uc608\\\\uc0c1\\\\uce58\\\\ub294 {consensus}\\\\uc785\\\\ub2c8\\\\ub2e4.\", \"data_affectedtickers_10_Shape.visible\": false, \"data_affectedtickers_3_ticker.visible\": false, \"data_affectedtickers_4_ticker.visible\": false, \"data_affectedtickers_5_ticker.visible\": false, \"data_affectedtickers_6_ticker.visible\": false, \"data_affectedtickers_7_ticker.visible\": false, \"data_affectedtickers_8_ticker.visible\": false, \"data_affectedtickers_9_ticker.visible\": false, \"dictionary_Nohighimpacteconomicevents\": \"\\\\ud070 \\\\uc601\\\\ud5a5\\\\uc744 \\\\ubbf8\\\\uce58\\\\ub294 \\\\uacbd\\\\uc81c \\\\uc0ac\\\\uac74 \\\\uc5c6\\\\uc74c\", \"dictionary_This week\?s biggest movers are:\": \"\\\\uc774\\\\ubc88 \\\\uc8fc \\\\uac00\\\\uc7a5 \\\\ud070 \\\\uc6c0\\\\uc9c1\\\\uc784\\\\uc740 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"dictionary_UpcomingHighImpactEconomicEvent\": \"\\\\ub2e4\\\\uac00\\\\uc624\\\\ub294 \\\\uc601\\\\ud5a5\\\\ub825 \\\\uc788\\\\ub294 \\\\uacbd\\\\uc81c \\\\uc774\\\\ubca4\\\\ud2b8\", \"data_affectedtickers_10_description.visible\": false, \"data_affectedtickers_10_normal_from.visible\": false, \"data_affectedtickers_3_diff_percent.visible\": false, \"data_affectedtickers_4_diff_percent.visible\": false, \"data_affectedtickers_5_diff_percent.visible\": false, \"data_affectedtickers_6_diff_percent.visible\": false, \"data_affectedtickers_7_diff_percent.visible\": false, \"data_affectedtickers_8_diff_percent.visible\": false, \"data_affectedtickers_9_diff_percent.visible\": false, \"dictionary_Zeromarkets ?_Platinum Futures\": \"XPTUSD\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany})\\\\uac00 \\\\uc624\\\\ub298 \\\\uc2e4\\\\uc801\\\\uc744 \\\\ubc1c\\\\ud45c\\\\ud569\\\\ub2c8\\\\ub2e4. EPS\\\\uac00 \\\\uc608\\\\uc0c1\\\\ubcf4\\\\ub2e4 {delta_sign}\\\\uc77c \\\\uacbd\\\\uc6b0, {earningscompany}\\\\ub294 \\\\ub2e4\\\\uc74c \\\\ub2ec \\\\ub3d9\\\\uc548 {mean_mov_percent} \\\\ubcc0\\\\ub3d9 \\\\uac00\\\\ub2a5\\\\uc131\\\\uc774 {probability}\\\\uc785\\\\ub2c8\\\\ub2e4.\\\\\\\\n\\\\uc774\\\\uc804 EPS\\\\uac00 \\\\uc608\\\\uc0c1\\\\ubcf4\\\\ub2e4 {delta_sign}\\\\uc77c \\\\ub54c \\\\uc601\\\\ud5a5\\\\uc744 \\\\ubc1b\\\\uc740 \\\\uc8fc\\\\uc2dd\\\\uc740 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"data_affectedtickers_10_diff_percent.visible\": false, \"data_affectedtickers_1_details_symbol_ticker\": \"GBPUSD\", \"data_affectedtickers_2_details_symbol_ticker\": \"EURUSD\", \"dictionary_2-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_E-mini Russell ? Index Futures\": \"E-mini Russell ? Index Futures\", \"dictionary_impactofearningsrelease_shorttext\": \"{earningscompany_name} ({earningscompany})\\\\uac00 \\\\uc624\\\\ub298 \\\\uc2e4\\\\uc801\\\\uc744 \\\\ubc1c\\\\ud45c\\\\ud558\\\\uba70 \\\\ub2e4\\\\uc74c \\\\uc8fc\\\\uc2dd\\\\uc5d0 \\\\uc601\\\\ud5a5\\\\uc744 \\\\ubbf8\\\\uce60 \\\\uac83\\\\uc785\\\\ub2c8\\\\ub2e4:\", \"dictionary_upcomingearnings_title_noearnings\": \"{fromdate}\\\\ubd80\\\\ud130 {todate}\\\\uae4c\\\\uc9c0\\\\ub294 \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c\\\\uac00 \\\\uc608\\\\uc815\\\\ub418\\\\uc5b4 \\\\uc788\\\\uc9c0 \\\\uc54a\\\\uc2b5\\\\ub2c8\\\\ub2e4.\", \"dictionary_10-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_CalendarofHighimpactEconomicEvents\": \"\\\\uc601\\\\ud5a5\\\\ub825\\\\uc774 \\\\ud070 \\\\uacbd\\\\uc81c \\\\uc774\\\\ubca4\\\\ud2b8 \\\\uc77c\\\\uc815\", \"data_affectedtickers_1_details_symbol_exchange\": \"FOREX\", \"data_affectedtickers_1_details_symbol_interval\": ?, \"data_affectedtickers_1_details_symbol_pip_size\": ?.?, \"data_affectedtickers_2_details_symbol_exchange\": \"FOREX\", \"data_affectedtickers_2_details_symbol_interval\": ?, \"data_affectedtickers_2_details_symbol_pip_size\": ?.?, \"dictionary_Market moves for the last ? hours:\": \"\\\\uc9c0\\\\ub09c ?\\\\uc2dc\\\\uac04 \\\\ub3d9\\\\uc548\\\\uc758 \\\\uc2dc\\\\uc7a5 \\\\ubcc0\\\\ub3d9:\", \"data_affectedtickers_1_details_symbol_pip_value\": ?, \"data_affectedtickers_2_details_symbol_pip_value\": ?, \"data_affectedtickers_3_diff_percent_str.visible\": false, \"data_affectedtickers_4_diff_percent_str.visible\": false, \"data_affectedtickers_5_diff_percent_str.visible\": false, \"data_affectedtickers_6_diff_percent_str.visible\": false, \"data_affectedtickers_7_diff_percent_str.visible\": false, \"data_affectedtickers_8_diff_percent_str.visible\": false, \"data_affectedtickers_9_diff_percent_str.visible\": false, \"params_creatomate_templateselectionscheme_value\": \"random\", \"data_affectedtickers_10_diff_percent_str.visible\": false, \"data_affectedtickers_1_volatility_increase_label\": \"\\\\ubcc0\\\\ub3d9\\\\uc131 \\\\uc99d\\\\uac00\", \"data_affectedtickers_2_volatility_increase_label\": \"\\\\ubcc0\\\\ub3d9\\\\uc131 \\\\uc99d\\\\uac00\", \"dictionary_Zeromarkets ?_WTI Crude Oil Futures\": \"WTI\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): {mean_mov_percent} \\\\ubcc0\\\\ub3d9 \\\\uac00\\\\ub2a5\\\\uc131\\\\uc774 {probability}\\\\uc785\\\\ub2c8\\\\ub2e4.\", \"dictionary_Market movements in the last ? hours.\": \"\\\\uc9c0\\\\ub09c ?\\\\uc2dc\\\\uac04 \\\\ub3d9\\\\uc548\\\\uc758 \\\\uc2dc\\\\uc7a5 \\\\uc6c0\\\\uc9c1\\\\uc784.\", \"data_affectedtickers_1_details_volatility_diff_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p75\": ?.?, \"dictionary_Companies releasing earnings this week:\": \"\\\\uc774\\\\ubc88 \\\\uc8fc \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c \\\\uae30\\\\uc5c5:\", \"dictionary_Zeromarkets ?_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_Zeromarkets ?_US Dollar Index Futures\": \"USDX\", \"data_affectedtickers_1_details_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_2_details_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_1_details_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_symbol_value_currency\": \"USD\", \"data_affectedtickers_1_details_symbol_value_lot_size\": ?, \"data_affectedtickers_2_details_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_symbol_value_currency\": \"USD\", \"data_affectedtickers_2_details_symbol_value_lot_size\": ?, \"dictionary_Zeromarkets ?_Nikkei ? Dollar Futures\": \"JP?\", \"data_affectedtickers_1_details_symbol_value_date_time\": \"?-01-30T?:?:?.?Z\", \"data_affectedtickers_1_details_volatility_diff_median\": ?.?, \"data_affectedtickers_2_details_symbol_value_date_time\": \"?-02-17T?:?:?.?Z\", \"data_affectedtickers_2_details_volatility_diff_median\": ?.?, \"dictionary_High impact economic events for this week:\": \"\\\\uc774\\\\ubc88 \\\\uc8fc \\\\uc911\\\\uc694\\\\ud55c \\\\uacbd\\\\uc81c \\\\uc774\\\\ubca4\\\\ud2b8:\", \"data_affectedtickers_1_details_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_1_details_volatility_diff_std_dev\": ?.?, \"data_affectedtickers_2_details_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_2_details_volatility_diff_std_dev\": ?.?e-06, \"dictionary_E-mini Dow Jones Industrial Average Futures\": \"E-mini Dow Jones Industrial Average Futures\", \"data_affectedtickers_1_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_2_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_1_details_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_value_max\": ?, \"data_affectedtickers_1_details_volatility_diff_value_min\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_value_max\": ?, \"data_affectedtickers_2_details_volatility_diff_value_min\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_3_volatility_increase_label.visible\": false, \"data_affectedtickers_4_volatility_increase_label.visible\": false, \"data_affectedtickers_5_volatility_increase_label.visible\": false, \"data_affectedtickers_6_volatility_increase_label.visible\": false, \"data_affectedtickers_7_volatility_increase_label.visible\": false, \"data_affectedtickers_8_volatility_increase_label.visible\": false, \"data_affectedtickers_9_volatility_increase_label.visible\": false, \"data_affectedtickers_10_volatility_increase_label.visible\": false, \"data_affectedtickers_1_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_event_volatility_value_p75\": ?, \"data_affectedtickers_1_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_mean\": ?, \"data_affectedtickers_2_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_mean\": ?, \"dictionary_No earnings announcements scheduled this week.\": \"\\\\uc774\\\\ubc88 \\\\uc8fc\\\\uc5d0\\\\ub294 \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c\\\\uac00 \\\\uc608\\\\uc815\\\\ub418\\\\uc5b4 \\\\uc788\\\\uc9c0 \\\\uc54a\\\\uc2b5\\\\ub2c8\\\\ub2e4.\", \"dictionary_Upcoming earnings announcements for this week:\": \"\\\\uc774\\\\ubc88 \\\\uc8fc \\\\uc608\\\\uc815\\\\ub41c \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c:\", \"data_affectedtickers_1_details_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_2_details_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_1_details_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_median\": ?, \"data_affectedtickers_2_details_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_median\": ?, \"data_affectedtickers_1_details_event_volatility_value_median\": ?, \"data_affectedtickers_1_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p75\": ?, \"dictionary_These are the market movements for the last week:\": \"\\\\uc9c0\\\\ub09c \\\\uc8fc \\\\ub3d9\\\\uc548\\\\uc758 \\\\uc2dc\\\\uc7a5 \\\\uc6c0\\\\uc9c1\\\\uc784\\\\uc740 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"data_affectedtickers_1_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_median\": ?, \"dictionary_These are the market movements for the last ? hours:\": \"\\\\uc9c0\\\\ub09c ?\\\\uc2dc\\\\uac04 \\\\ub3d9\\\\uc548\\\\uc758 \\\\uc2dc\\\\uc7a5 \\\\uc6c0\\\\uc9c1\\\\uc784\\\\uc740 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\"}}?}", "trace": "traceback (most recent call last):\n file \"/var/task/lambda_function.py\", line ?, in lambda_handler\n results[?creatomate_response?] = webhook_creatomate.render(processlog, templateid,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n file \"/var/task/Webhook_Creatomate.py\", line ?, in render\n webhook_creatomate.waitforrender(apikey, creatomate_response[?][?id?], timeout)\n file \"/var/task/Webhook_Creatomate.py\", line ?, in waitforrender\n raise exception(status[?error_message?] +\". Render details: \" + json.dumps(status))\nexception: an http ? status was received while trying to download the file: https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg (element data_countryicon). render details: {\"id\": \"?e9f3dd-01fa-4f6c-8bbb-14d125195639\", \"status\": \"failed\", \"error_message\": \"An HTTP ? status was received while trying to download the file: https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg (element data_CountryIcon)\", \"url\": \"https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/?e9f3dd-01fa-4f6c-8bbb-14d125195639.png\", \"template_id\": \"?aa08409-8a69-4164-bd5d-c37b5949c5df\", \"template_name\": \"Volatility Warning PNG ? KR\", \"template_tags\": [], \"output_format\": \"png\", \"modifications\": {\"data_Date\": \"? ?\\uc6d4, ?AM UTC\", \"data_name\": \"\\uc2e4\\uc5c5\\ub960\", \"text_title\": \"?\\uc2dc\\uac04 \\ub0b4 \\uc5f0\\ud569 \\uc655\\uad6d\\uc5d0\\uc11c ?\\uc2e4\\uc5c5\\ub960?\\uc73c\\ub85c \\uc778\\ud55c \\ubcc0\\ub3d9\\uc131\\uc774 \\uc608\\uc0c1\\ub429\\ub2c8\\ub2e4.\", \"has_results\": true, \"data_Heading\": \"\\ubcc0\\ub3d9\\uc131 \\uacbd\\uace0\", \"data_country\": \"GB\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"\\uc2e4\\uc5c5\\ub960 (\\uc5f0\\ud569 \\uc655\\uad6d)\\ub85c \\uc778\\ud574 \\ubcc0\\ub3d9\\uc131\\uc774 \\ud06c\\uac8c \\uc99d\\uac00\\ud560 \\uac83\\uc785\\ub2c8\\ub2e4. EURUSD - ?.?\\nGBPUSD - ?.?\\n\", \"dictionary_Corn\": \"\\uc625\\uc218\\uc218\", \"dictionary_Gold\": \"\\uae08\", \"dictionary_Hour\": \"\\uc2dc\\uac04\", \"dictionary_Open\": \"\\uac1c\\uc7a5\", \"dictionary_Time\": \"\\uc2dc\\uac04\", \"text_short_text\": \"\\uc2e4\\uc5c5\\ub960 (\\uc5f0\\ud569 \\uc655\\uad6d)\\ub85c \\uc778\\ud574 \\ubcc0\\ub3d9\\uc131\\uc774 \\ud06c\\uac8c \\uc99d\\uac00\\ud560 \\uac83\\uc785\\ub2c8\\ub2e4. EURUSD - ?.?\\nGBPUSD - ?.?\\n\", \"data_CountryIcon\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg\", \"data_description\": \"In the United Kingdom, the unemployment rate measures the number of people actively looking for a job as a percentage of the labour force.\", \"data_released_at\": \"?-02-17T?:?\", \"dictionary_Close\": \"\\ud3d0\\uc7a5\", \"dictionary_Daily\": \"\\ub9e4\\uc77c\", \"dictionary_Event\": \"\\uc774\\ubca4\\ud2b8\", \"dictionary_Hours\": \"\\uc2dc\\uac04\", \"dictionary_Price\": \"\\uac00\\uaca9\", \"dictionary_Wheat\": \"\\ubc00\", \"quantity_results\": ?, \"data_country_name\": \"\\uc5f0\\ud569 \\uc655\\uad6d\", \"dictionary_Actual\": \"\\uc2e4\\uc81c\", \"dictionary_Change\": \"\", \"dictionary_Coffee\": \"\\ucee4\\ud53c\", \"dictionary_Friday\": \"\\uae08\\uc694\\uc77c\", \"dictionary_Monday\": \"\\uc6d4\\uc694\\uc77c\", \"dictionary_Silver\": \"\\uc740\", \"dictionary_Sunday\": \"\\uc77c\\uc694\\uc77c\", \"dictionary_Target\": \"\\ubaa9\\ud45c\", \"dictionary_dd MMM\": \"MMM d\\uc77c\", \"params_mode_value\": ?, \"params_uuid_value\": \"?c17f79-1688-4262-b2cc-bb8a7d82e0de\", \"dictionary_909_DAX\": \"GER?\", \"dictionary_AUD/USD\": \"AUD/USD\", \"dictionary_Company\": \"\\ud68c\\uc0ac\", \"dictionary_EUR/USD\": \"EUR/USD\", \"dictionary_GBP/USD\": \"GBP/USD\", \"dictionary_Indices\": \"\\uc9c0\\uc218\", \"dictionary_Minutes\": \"\\ubd84\", \"dictionary_NZD/USD\": \"NZD/USD\", \"dictionary_Tuesday\": \"\\ud654\\uc694\\uc77c\", \"dictionary_USD/CAD\": \"USD/CAD\", \"dictionary_USD/CHF\": \"USD/CHF\", \"dictionary_USD/JPY\": \"USD/JPY\", \"dictionary_909_Gold\": \"XAUUSD\", \"dictionary_Estimate\": \"\\ucd94\\uc815\", \"dictionary_Expected\": \"\\uc608\\uc0c1\\uce58\", \"dictionary_FTSE ?\": \"FTSE ?\", \"dictionary_Interval\": \"\\ucc28\\ud2b8 \\uc8fc\\uae30\", \"dictionary_KeyLevel\": \"\\ud575\\uc2ec \\uc218\\uc900\", \"dictionary_Previous\": \"\\uc774\\uc804\", \"dictionary_Saturday\": \"\\ud1a0\\uc694\\uc77c\", \"dictionary_Thursday\": \"\\ubaa9\\uc694\\uc77c\", \"dictionary_estimate\": \"\\ucd94\\uc815\\uce58\", \"dictionary_previous\": \"\\uc774\\uc804\", \"params_locale_value\": \"ko\", \"params_region_value\": \"ko\", \"data_country_name_en\": \"Great Britain\", \"dictionary_Consensus\": \"\\ud569\\uc758\", \"dictionary_Crude oil\": \"\\uc6d0\\uc720\", \"dictionary_Fibonacci\": \"\\ud53c\\ubcf4\\ub098\\uce58 \\ud328\\ud134\", \"dictionary_Hang Seng\": \"Hang Seng\", \"dictionary_Stop_Loss\": \"\\uc190\\uc808\\ub9e4\", \"dictionary_Wednesday\": \"\\uc218\\uc694\\uc77c\", \"dictionary_consensus\": \"\\ucee8\\uc13c\\uc11c\\uc2a4\", \"dictionary_dd MMM, h\": \"MMM d\\uc77c, h\", \"params_request_value\": \"VolatilityWarning\", \"params_tickers_value\": \"EURUSD,USDJPY,GBPUSD,AUDUSD,USDCHF,USDCAD,NZDUSD,EURJPY,GBPJPY,EURGBP,AUDJPY,EURAUD,CHFJPY,EURCHF\", \"params_webhook_value\": \"https://hooks.zapier.com/hooks/catch/?/?l0rz5n/\", 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Futures\", \"dictionary_impactofearningsrelease_title\": \"{earningscompany_name} ({earningscompany}) \\uc2e4\\uc801 \\ubc1c\\ud45c\\uc758 \\uc601\\ud5a5\", \"data_affectedtickers_10_normal_to.visible\": false, \"data_affectedtickers_1_details_symbol_pip\": ?, \"data_affectedtickers_2_details_symbol_pip\": ?, \"data_affectedtickers_3_event_from.visible\": false, \"data_affectedtickers_4_event_from.visible\": false, \"data_affectedtickers_5_event_from.visible\": false, \"data_affectedtickers_6_event_from.visible\": false, \"data_affectedtickers_7_event_from.visible\": false, \"data_affectedtickers_8_event_from.visible\": false, \"data_affectedtickers_9_event_from.visible\": false, \"data_affectedtickers_10_event_from.visible\": false, \"data_affectedtickers_1_details_symbol_name\": \"GBPUSD\", \"data_affectedtickers_2_details_symbol_name\": \"EURUSD\", \"data_affectedtickers_3_description.visible\": false, \"data_affectedtickers_3_normal_from.visible\": false, \"data_affectedtickers_4_description.visible\": false, \"data_affectedtickers_4_normal_from.visible\": false, \"data_affectedtickers_5_description.visible\": false, \"data_affectedtickers_5_normal_from.visible\": false, \"data_affectedtickers_6_description.visible\": false, \"data_affectedtickers_6_normal_from.visible\": false, \"data_affectedtickers_7_description.visible\": false, \"data_affectedtickers_7_normal_from.visible\": false, \"data_affectedtickers_8_description.visible\": false, \"data_affectedtickers_8_normal_from.visible\": false, \"data_affectedtickers_9_description.visible\": false, \"data_affectedtickers_9_normal_from.visible\": false, \"dictionary_Market moves for the last week:\": \"\\uc9c0\\ub09c \\uc8fc \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\ubcc0\\ub3d9:\", \"dictionary_This week?s biggest movers are:\": \"\\uc774\\ubc88 \\uc8fc \\uac00\\uc7a5 \\ud070 \\uc6c0\\uc9c1\\uc784\\uc740 \\ub2e4\\uc74c\\uacfc \\uac19\\uc2b5\\ub2c8\\ub2e4:\", \"dictionary_UpcomingHighImpactEconomicEvent\": \"\\ub2e4\\uac00\\uc624\\ub294 \\uc601\\ud5a5\\ub825 \\uc788\\ub294 \\uacbd\\uc81c \\uc774\\ubca4\\ud2b8\", \"data_affectedtickers_10_description.visible\": false, \"data_affectedtickers_10_normal_from.visible\": false, \"data_affectedtickers_3_diff_percent.visible\": false, \"data_affectedtickers_4_diff_percent.visible\": false, \"data_affectedtickers_5_diff_percent.visible\": false, \"data_affectedtickers_6_diff_percent.visible\": false, \"data_affectedtickers_7_diff_percent.visible\": false, \"data_affectedtickers_8_diff_percent.visible\": false, \"data_affectedtickers_9_diff_percent.visible\": false, \"dictionary_Zeromarkets ?_Platinum Futures\": \"XPTUSD\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany})\\uac00 \\uc624\\ub298 \\uc2e4\\uc801\\uc744 \\ubc1c\\ud45c\\ud569\\ub2c8\\ub2e4. EPS\\uac00 \\uc608\\uc0c1\\ubcf4\\ub2e4 {delta_sign}\\uc77c \\uacbd\\uc6b0, {earningscompany}\\ub294 \\ub2e4\\uc74c \\ub2ec \\ub3d9\\uc548 {mean_mov_percent} \\ubcc0\\ub3d9 \\uac00\\ub2a5\\uc131\\uc774 {probability}\\uc785\\ub2c8\\ub2e4.\\\\n\\uc774\\uc804 EPS\\uac00 \\uc608\\uc0c1\\ubcf4\\ub2e4 {delta_sign}\\uc77c \\ub54c \\uc601\\ud5a5\\uc744 \\ubc1b\\uc740 \\uc8fc\\uc2dd\\uc740 \\ub2e4\\uc74c\\uacfc \\uac19\\uc2b5\\ub2c8\\ub2e4:\", \"data_affectedtickers_10_diff_percent.visible\": false, \"data_affectedtickers_1_details_symbol_ticker\": \"GBPUSD\", \"data_affectedtickers_2_details_symbol_ticker\": \"EURUSD\", \"dictionary_2-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_E-mini Russell ? Index Futures\": \"E-mini Russell ? Index Futures\", \"dictionary_impactofearningsrelease_shorttext\": \"{earningscompany_name} ({earningscompany})\\uac00 \\uc624\\ub298 \\uc2e4\\uc801\\uc744 \\ubc1c\\ud45c\\ud558\\uba70 \\ub2e4\\uc74c \\uc8fc\\uc2dd\\uc5d0 \\uc601\\ud5a5\\uc744 \\ubbf8\\uce60 \\uac83\\uc785\\ub2c8\\ub2e4:\", \"dictionary_upcomingearnings_title_noearnings\": \"{fromdate}\\ubd80\\ud130 {todate}\\uae4c\\uc9c0\\ub294 \\uc2e4\\uc801 \\ubc1c\\ud45c\\uac00 \\uc608\\uc815\\ub418\\uc5b4 \\uc788\\uc9c0 \\uc54a\\uc2b5\\ub2c8\\ub2e4.\", \"dictionary_10-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_CalendarofHighimpactEconomicEvents\": \"\\uc601\\ud5a5\\ub825\\uc774 \\ud070 \\uacbd\\uc81c \\uc774\\ubca4\\ud2b8 \\uc77c\\uc815\", \"data_affectedtickers_1_details_symbol_exchange\": \"FOREX\", \"data_affectedtickers_1_details_symbol_interval\": ?, \"data_affectedtickers_1_details_symbol_pip_size\": ?.?, \"data_affectedtickers_2_details_symbol_exchange\": \"FOREX\", \"data_affectedtickers_2_details_symbol_interval\": ?, \"data_affectedtickers_2_details_symbol_pip_size\": ?.?, \"dictionary_Market moves for the last ? hours:\": \"\\uc9c0\\ub09c ?\\uc2dc\\uac04 \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\ubcc0\\ub3d9:\", \"data_affectedtickers_1_details_symbol_pip_value\": ?, \"data_affectedtickers_2_details_symbol_pip_value\": ?, \"data_affectedtickers_3_diff_percent_str.visible\": false, \"data_affectedtickers_4_diff_percent_str.visible\": false, \"data_affectedtickers_5_diff_percent_str.visible\": false, \"data_affectedtickers_6_diff_percent_str.visible\": false, \"data_affectedtickers_7_diff_percent_str.visible\": false, \"data_affectedtickers_8_diff_percent_str.visible\": false, \"data_affectedtickers_9_diff_percent_str.visible\": false, \"params_creatomate_templateselectionscheme_value\": \"random\", \"data_affectedtickers_10_diff_percent_str.visible\": false, \"data_affectedtickers_1_volatility_increase_label\": \"\\ubcc0\\ub3d9\\uc131 \\uc99d\\uac00\", \"data_affectedtickers_2_volatility_increase_label\": \"\\ubcc0\\ub3d9\\uc131 \\uc99d\\uac00\", \"dictionary_Zeromarkets ?_WTI Crude Oil Futures\": \"WTI\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): {mean_mov_percent} \\ubcc0\\ub3d9 \\uac00\\ub2a5\\uc131\\uc774 {probability}\\uc785\\ub2c8\\ub2e4.\", \"dictionary_Market movements in the last ? hours.\": \"\\uc9c0\\ub09c ?\\uc2dc\\uac04 \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\uc6c0\\uc9c1\\uc784.\", \"data_affectedtickers_1_details_volatility_diff_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p75\": ?.?, \"dictionary_Companies releasing earnings this week:\": \"\\uc774\\ubc88 \\uc8fc \\uc2e4\\uc801 \\ubc1c\\ud45c \\uae30\\uc5c5:\", \"dictionary_Zeromarkets ?_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_Zeromarkets ?_US Dollar Index Futures\": \"USDX\", \"data_affectedtickers_1_details_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_2_details_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_1_details_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_symbol_value_currency\": \"USD\", \"data_affectedtickers_1_details_symbol_value_lot_size\": ?, \"data_affectedtickers_2_details_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_symbol_value_currency\": \"USD\", \"data_affectedtickers_2_details_symbol_value_lot_size\": ?, \"dictionary_Zeromarkets ?_Nikkei ? Dollar Futures\": \"JP?\", \"data_affectedtickers_1_details_symbol_value_date_time\": \"?-01-30T?:?:?.?Z\", \"data_affectedtickers_1_details_volatility_diff_median\": ?.?, \"data_affectedtickers_2_details_symbol_value_date_time\": \"?-02-17T?:?:?.?Z\", \"data_affectedtickers_2_details_volatility_diff_median\": ?.?, \"dictionary_High impact economic events for this week:\": \"\\uc774\\ubc88 \\uc8fc \\uc911\\uc694\\ud55c \\uacbd\\uc81c \\uc774\\ubca4\\ud2b8:\", \"data_affectedtickers_1_details_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_1_details_volatility_diff_std_dev\": ?.?, \"data_affectedtickers_2_details_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_2_details_volatility_diff_std_dev\": ?.?e-06, \"dictionary_E-mini Dow Jones Industrial Average Futures\": \"E-mini Dow Jones Industrial Average Futures\", \"data_affectedtickers_1_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_2_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_1_details_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_value_max\": ?, \"data_affectedtickers_1_details_volatility_diff_value_min\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_value_max\": ?, \"data_affectedtickers_2_details_volatility_diff_value_min\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_3_volatility_increase_label.visible\": false, \"data_affectedtickers_4_volatility_increase_label.visible\": false, \"data_affectedtickers_5_volatility_increase_label.visible\": false, \"data_affectedtickers_6_volatility_increase_label.visible\": false, \"data_affectedtickers_7_volatility_increase_label.visible\": false, \"data_affectedtickers_8_volatility_increase_label.visible\": false, \"data_affectedtickers_9_volatility_increase_label.visible\": false, \"data_affectedtickers_10_volatility_increase_label.visible\": false, \"data_affectedtickers_1_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_event_volatility_value_p75\": ?, \"data_affectedtickers_1_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_mean\": ?, \"data_affectedtickers_2_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_mean\": ?, \"dictionary_No earnings announcements scheduled this week.\": \"\\uc774\\ubc88 \\uc8fc\\uc5d0\\ub294 \\uc2e4\\uc801 \\ubc1c\\ud45c\\uac00 \\uc608\\uc815\\ub418\\uc5b4 \\uc788\\uc9c0 \\uc54a\\uc2b5\\ub2c8\\ub2e4.\", \"dictionary_Upcoming earnings announcements for this week:\": \"\\uc774\\ubc88 \\uc8fc \\uc608\\uc815\\ub41c \\uc2e4\\uc801 \\ubc1c\\ud45c:\", \"data_affectedtickers_1_details_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_2_details_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_1_details_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_median\": ?, \"data_affectedtickers_2_details_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_median\": ?, \"data_affectedtickers_1_details_event_volatility_value_median\": ?, \"data_affectedtickers_1_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p75\": ?, \"dictionary_These are the market movements for the last week:\": \"\\uc9c0\\ub09c \\uc8fc \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\uc6c0\\uc9c1\\uc784\\uc740 \\ub2e4\\uc74c\\uacfc \\uac19\\uc2b5\\ub2c8\\ub2e4:\", \"data_affectedtickers_1_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_median\": ?, \"dictionary_These are the market movements for the last ? hours:\": \"\\uc9c0\\ub09c ?\\uc2dc\\uac04 \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\uc6c0\\uc9c1\\uc784\\uc740 \\ub2e4\\uc74c\\uacfc \\uac19\\uc2b5\\ub2c8\\ub2e4:\"}}" } }?json');Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Feb 17 07 1 0ms 0ms 15 0ms 56 0ms 0ms 0ms select * from ( select pricedatetime, open, high, low, close, volume, bsf from t60 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Feb 17 07 56 0ms 0ms 16 0ms 2 0ms 0ms 0ms with pre_symbols as ( select s.symbolid, s.symbol, s.timegranularity, dss.downloadersymbol, dtt.timezone, s.exchange, s.longname from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join brokersymbollist bsl on bsl.symbolid = s.symbolid and brokerid = ? where dss.classname in (...) and (dss.downloadersymbol in (...) or s.symbol in (...)) and dss.enabled = ? and s.nonliquid = ? and s.deleted = ? ), report_symbols as ( select ps1.*, ps2.symbolid as price_symbol_id from pre_symbols ps1 inner join pre_symbols ps2 on ps1.symbol = ps2.symbol and ps1.downloadersymbol = ps2.downloadersymbol and ps2.timegranularity = ? ), rbr_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.resultuid as ruid, ? as direction, rs.symbol as sym, rs.downloadersymbol, rs.symbolid as sid, rs.timegranularity as tg, rs.timezone, rs.exchange as e, rs.longname, bmr.patternendtime as pet, lpi.latestpricedatetime as lpdt, bmr.patternlengthbars as l, bmr.breakout >= ? as complete, rbr.age as age from bigmovement_results bmr inner join report_symbols rs on rs.symbolid = bmr.symbolid inner join autochartist_symbolupdates lpi on lpi.symbolid = rs.price_symbol_id inner join rbr_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid where patternendtime >= (now() - ? * interval ?) -- results can't be more than ? days old and (bmr.resultuid > rm.resultuid or rbr.relevant = ?) ;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Feb 17 07 2 0ms 0ms 17 0ms 331 0ms 0ms 0ms commit;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Feb 17 07 331 0ms 0ms 18 0ms 327 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ), kr as ( select a.*, rr.age, rr.relevant from keylevels_results a left outer join relevance_keylevels_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_keylevels_results) end ), all_results as ( select kr.resultuid as resultuid, kr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, p.patternname as pattern_name, kr.breakout as breakout, kr.atbaridentified as identified, dtt.timezone as timezone, kr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when kr.age is not null then kr.age when kr.resultuid <= rm.resultuid then ? else ? end as age, case when kr.relevant is not null then kr.relevant when kr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from kr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = kr.symbolid inner join symbols s on bsl.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join hrspatterns p on kr.patternid = p.patternid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left outer join autochartist_symbolupdates au on dss.symbolid = au.symbolid left outer join relevance_keylevels_results rar on rar.resultuid = kr.resultuid left join lateral calc_kl_signal_filter (kr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where kr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (kr.simulation = ? or kr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or p.patternname in (...)) and (? = ? or kr.patternclassid in (...)) and (? = ? or kr.patternlengthbars <= ?) and kr.patternstarttime::timestamp without time zone >= coalesce(au.earliestpricedatetime, ?::timestamp without time zone) -- to make sure patternstarttime is in our t-tables ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc limit ?;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Feb 17 07 327 0ms 0ms 19 0ms 11 0ms 0ms 0ms select psd.symbolid, psd.hourlysymbolid as intervalsymbolid, s.symbol, dtt.timezone, dtt.absolutetimezoneoffset, psh.hour as index, psh.ave, psh.stddev, ((psh.ave - psh.stddev) / ?.?) as low, ((psh.ave + psh.stddev) / ?.?) as high, ee.timezone as exchangetimezone, ee.mon_t1start as exchangestart, ee.mon_t1end as exchangeend from powerstats_symboldata psd inner join powerstats_hourly psh on psd.hourlysymbolid = psh.symbolid inner join symbols s on psh.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join mat_ps_hourly_symbolid_max_enddate e on psh.enddate = e.enddate and psh.symbolid = e.symbolid inner join exchanges ee on ee.exchange = s.exchange where psd.symbolid = ? and dtt.dayofweek = ? order by hour asc;Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Feb 17 07 11 0ms 0ms 20 0ms 3 0ms 0ms 0ms select (cast(substring(tz.gmoffset from ? for ?) as float) * ? + cast(substring(tz.gmoffset from ? for ?) as float)) / ? as offset from timezones tz where tz.timezone = ?;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Feb 17 07 3 0ms 0ms Most frequent queries (N)
Rank Times executed Total duration Min duration Max duration Avg duration Query 1 14,369 0ms 0ms 0ms 0ms select ?;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Feb 17 07 14,369 0ms 0ms 2 7,271 0ms 0ms 0ms 0ms select distinct on (coalesce(bim.code, s.symbol) , s.exchange, s.timegranularity, df.timezone) s.symbolid as id, coalesce(bim.code, s.symbol) as name, s.symbol as symbol, dss.downloadersymbol as ticker, s.exchange as exchange, s.timegranularity as interval, df.timezone as timezone from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable df on df.classname ilike dss.classname left join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = s.symbolid left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = ? and bim.type = ? where s.symbolid = ?;Times Reported Time consuming queries #2
Day Hour Count Duration Avg duration Feb 17 07 7,271 0ms 0ms 3 5,666 0ms 0ms 0ms 0ms select s.symbolid as id, s.symbol as name, s.exchange as exchange, s.timegranularity as interval, dtt.timezone as timezone from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join brokersymbollist bsl on bsl.symbolid = s.symbolid where bsl.brokerid = ? and (? = ? or s.timegranularity = ?) and (s.symbol = ? or dss.downloadersymbol = ?) and dss.enabled = ?;Times Reported Time consuming queries #3
Day Hour Count Duration Avg duration Feb 17 07 5,666 0ms 0ms 4 5,160 0ms 0ms 0ms 0ms insert into t15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Feb 17 07 5,160 0ms 0ms 5 3,945 0ms 0ms 0ms 0ms insert into autochartist_results (resultid, symbolid, bandwidth, pattern, qtytp, gmttimefound, direction, initialtrend, breakout, volumeincrease, noise, symmetry, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimeto, patternstarttime, patternendtime, patternstartprice, patternendprice, resx0, resx1, supportx0, supportx1, resy0, resy1, supporty0, supporty1, supportgradient, resgradient, riskreward, patternquality, trendchange, maxmovementafterbreakout, latestbaratbreakouttime, latestbaratbreakoutprice, patternlengthbars, temporarypattern, relevancestartdistance, simulation, writtendatetime) values (?, ?, ?.?, ?, ?, ?::timestamp without time zone, ?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?, ?.?, ?::timestamp without time zone, ?.?, ?, ?, ?.?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;Times Reported Time consuming queries #5
Day Hour Count Duration Avg duration Feb 17 07 3,945 0ms 0ms 6 3,798 0ms 0ms 0ms 0ms insert into executionlogs (executionid, status, message, details, detailtype) values (null, ?, ?, null, null);Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Feb 17 07 3,798 0ms 0ms 7 3,132 0ms 0ms 0ms 0ms insert into t30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Feb 17 07 3,132 0ms 0ms 8 2,376 0ms 0ms 0ms 0ms insert into keylevels_results (bandwidth, breakout, patternid, gmttimefound, approachingtimestamp, approachingregion, qtytp, patternlengthbars, patternprice, x0, x1, x2, x3, x4, x5, x6, x7, x8, x9, breakoutbars, breakoutprice, patternendtime, atbaridentified, atpriceidentified, errormargin, direction, symbolid, predictionpricefrom, predictionpriceto, predictiontimefrom, predictiontimebars, uniquepointsvalue, furthestprice, relevancestartdistance, patternclassid, patternstarttime, stoplosslevel, simulation, writtendatetime) values (?.?, ?, ?, ?::timestamp without time zone, ?, ?.?, ?, ?, ?.?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?.?, ?::timestamp without time zone, ?, ?.?, ?.?, ?, ?, ?.?, ?.?, ?::timestamp without time zone, ?, ?, ?.?, ?.?, ?, ?, ?.?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Feb 17 07 2,376 0ms 0ms 9 2,224 0ms 0ms 0ms 0ms insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Feb 17 07 2,224 0ms 0ms 10 2,167 0ms 0ms 0ms 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Feb 17 07 2,167 0ms 0ms 11 1,897 0ms 0ms 0ms 0ms insert into fibonacci_results (bandwidth, pattern, gmttimefound, direction, patternstarttime, patternendtime, patternstartprice, patternendprice, qtytp, pricex, timex, pricea, timea, priceb, timeb, pricec, timec, priced, timed, averagequality, timequality, errormargin, patternlengthbars, target10, target06, target16, target07, target12, target05, target03, symbolid, noise, ratiosfound, temporarypattern, uniqueindex, completed, simulation, writtendatetime) values (?.?, ?, ?::timestamp without time zone, ?, ?::timestamp without time zone, ?::timestamp without time zone, ?.?, ?.?, ?, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?::timestamp without time zone, ?.?, ?.?, ?.?, ?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?.?, ?, ?.?, ?, ?, ?, ?, ?, current_timestamp::timestamp without time zone) on conflict do nothing;Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Feb 17 07 1,897 0ms 0ms 12 1,822 0ms 0ms 0ms 0ms set extra_float_digits = ?;Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Feb 17 07 1,822 0ms 0ms 13 1,796 0ms 0ms 0ms 0ms set application_name = ?;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Feb 17 07 1,796 0ms 0ms 14 1,002 0ms 0ms 0ms 0ms select symbolid, pricedatetime, classname, downloadfrequency, downloadersymbol, open, high, low, close, volume, bsf, sastdatetimereceived from ( select pricedatetime, dss.classname, dss.downloadfrequency, dss.symbolid, dss.downloadersymbol, t.open, t.high, t.low, t.close, t.volume, t.bsf, t.sastdatetimereceived, row_number() over (partition by t.symbolid order by t.pricedatetime desc) as rn from t15 t, downloadersymbolsettings dss, symbols s where dss.classname = ? and dss.downloadfrequency = ? and dss.symbolid = t.symbolid and s.symbolid = dss.symbolid and dss.enabled = ? and s.deleted = ? and dss.downloadersymbol in (...) and t.pricedatetime > now() - interval ?) as ranked_candles_table where rn = ?;Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Feb 17 07 1,002 0ms 0ms 15 774 0ms 0ms 0ms 0ms select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, pattern as patternname, timegranularity as interval, patternlengthbars as length, patternendtime, direction, breakout, predictiontimeto, predictionpricefrom, predictionpriceto, patternstartprice, resy1, supporty1, dtt.timezone, cps.pip, newlevels.profit from autochartist_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern left join currencypips cps on cps.symbol = s.symbol left join lateral calc_cp_signal (a.resultuid) newlevels on true where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Feb 17 07 774 0ms 0ms 16 700 0ms 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_autochartist_results order by resultuid desc limit ? ) select a.symbolid, pattern, patternid, resy0, resy1, resx0, resx1, supporty0, supporty1, supportx0, supportx1, predictiontimeto, patternstarttime, timegranularity, patternendtime, direction, trendchange, patternlengthbars, patternquality, case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, s.exchange, s.symbol, s.longname, s.shortname, breakout, dtt.timezone, patternstartprice, patternendprice, qtytp, newlevels.profit, newlevels.stop, newlevels.filtered, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from autochartist_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern inner join rar_max rm on ? = ? left outer join relevance_autochartist_results rar on rar.resultuid = a.resultuid left join lateral calc_cp_signal (a.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Feb 17 07 700 0ms 0ms 17 646 0ms 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ) select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as ruid, s.symbolid as sid, s.symbol as sym, longname, shortname, exchange as e, timegranularity as tg, a.patternid as pid, a.direction as d, a.patternprice as pp, atbaridentified as pet, case when (x9 != ?) then x9 when (x8 != ?) then x8 when (x7 != ?) then x7 when (x6 != ?) then x6 when (x5 != ?) then x5 when (x4 != ?) then x4 when (x3 != ?) then x3 when (x2 != ?) then x2 end as pst, patternprice as patp, x0, x1, x2, case when (x3 != ?) then x3 else ? end as x3, case when (x4 != ?) then x4 else ? end as x4, case when (x5 != ?) then x5 else ? end as x5, case when (x6 != ?) then x6 else ? end as x6, case when (x7 != ?) then x7 else ? end as x7, case when (x8 != ?) then x8 else ? end as x8, errormargin as erm, breakoutprice as pe, breakoutbars as be, breakout, atbaridentified as atbar, atpriceidentified as atprice, patternlengthbars as l, bandwidth as bw, qtytp as qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzos, dtt.timezone as timezone, approachingtimestamp as apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb, furthestprice as fp, newlevels.filtered, a.uniquepointsvalue as upv, case when rar.age is not null then rar.age when a.resultuid <= rm.resultuid then ? else ? end as age, case when rar.relevant is not null then rar.relevant when a.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from keylevels_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join hrspatterns p on a.patternid = p.patternid inner join rar_max rm on ? = ? left outer join relevance_keylevels_results rar on a.resultuid = rar.resultuid left join lateral calc_kl_signal_filter (a.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Feb 17 07 646 0ms 0ms 18 377 0ms 0ms 0ms 0ms select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, a.patternprice, atbaridentified as patternendtime, breakout, p.patternname, dtt.timezone, a.direction, case when a.patternclassid = ? then a.predictionpricefrom else a.patternprice end as predictionpricefrom, case when a.patternclassid = ? then a.predictionpriceto else a.patternprice end as predictionpriceto, s.timegranularity as interval, patternlengthbars as length, cps.pip from keylevels_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join hrspatterns p on a.patternid = p.patternid left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Feb 17 07 377 0ms 0ms 19 369 0ms 0ms 0ms 0ms select downloadersymbol, spike_threshold from price_datafeed_spike_threshold where classname = ?;Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Feb 17 07 369 0ms 0ms 20 369 0ms 0ms 0ms 0ms select s.symbolid, dss.downloadfrequency, dss.downloadersymbol from downloadersymbolsettings dss inner join symbols s on dss.symbolid = s.symbolid where dss.classname = ? and s.deleted = ? and dss.enabled = ?;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Feb 17 07 369 0ms 0ms Normalized slowest queries (N)
Rank Min duration Max duration Avg duration Times executed Total duration Query 1 0ms 0ms 0ms 20 0ms select key, value from datasources ds inner join datasourceparams dsp on ds.id = dsp.datasourceid where ds.name = ?;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Feb 17 07 20 0ms 0ms 2 0ms 0ms 0ms 232 0ms with rar_max as ( select resultuid from relevance_consecutivecandles_results order by resultuid desc limit ? ), all_results as ( select ccr.resultuid as resultuid, ccr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, ccr.patternendtime as identified, dtt.timezone as timezone, ccr.qtyconsecutivecandles as length, g.basegroupname, case when rcr.age is not null then rcr.age when ccr.resultuid <= rm.resultuid then ? else ? end as age, case when rcr.relevant is not null then rcr.relevant when ccr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip, newlevels.filtered from consecutivecandles_results ccr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = ccr.symbolid inner join symbols s on ccr.symbolid = s.symbolid and s.nonliquid = ? inner join downloadersymbolsettings dss on ccr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join symbolgroup sg on ccr.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join rar_max rm on ? = ? left outer join relevance_consecutivecandles_results rcr on rcr.resultuid = ccr.resultuid left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? left join lateral calc_cc_signal_filter (ccr.resultuid) newlevels on true where ccr.gmttimefound > now() - interval ? and s.deleted = ? and (ccr.simulation = ? or ccr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or ccr.patternlengthbars <= ?) ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc;Times Reported Time consuming queries #2
Day Hour Count Duration Avg duration Feb 17 07 232 0ms 0ms 3 0ms 0ms 0ms 5 0ms select trumpetsymbolid as sid, trumpettimegranularity as tg from brokersymbollist bsl left join powerstats_symboldata psd on bsl.symbolid = psd.symbolid left join downloadersymbolsettings dss on psd.symbolid = dss.symbolid left join symbols s on dss.symbolid = s.symbolid left outer join brokerinstrumentmap bdfi on code = ? and bdfi.brokerid = ? and dss.datafeedinstrumentid = bdfi.datafeedinstrumentid where (code = ? or s.symbol = ?) and bsl.brokerid = ? and dss.classname <> ? group by trumpetsymbolid, trumpettimegranularity;Times Reported Time consuming queries #3
Day Hour Count Duration Avg duration Feb 17 07 5 0ms 0ms 4 0ms 0ms 0ms 36 0ms with rar_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.symbolid, patternstarttime, patternendtime, timegranularity, ? as direction, case when bmr.old_resultuid = ? then bmr.old_resultuid else bmr.resultuid end as uid, s.exchange, s.symbol, s.longname, s.shortname, dtt.timezone, bmr.patternmovement, bmr.statisticalmovement, bmr.fromprice, bmr.toprice, bmr.percentile, bmr.patternlengthbars, case when rbr.age is not null then rbr.age when bmr.resultuid <= rm.resultuid then ? else ? end as age, case when rbr.relevant is not null then rbr.relevant when bmr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from bigmovement_results bmr inner join downloadersymbolsettings dss on bmr.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on bmr.symbolid = s.symbolid inner join rar_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid left join currencypips cps on cps.symbol = s.symbol where (bmr.old_resultuid = ? or bmr.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Feb 17 07 36 0ms 0ms 5 0ms 0ms 0ms 2,224 0ms insert into t60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) on conflict (pricedatetime, symbolid) do update set open = ?, high = ?, low = ?, close = ?, volume = ?, bsf = ?, sastdatetimewritten = ?, sastdatetimereceived = ?;Times Reported Time consuming queries #5
Day Hour Count Duration Avg duration Feb 17 07 2,224 0ms 0ms 6 0ms 0ms 0ms 4 0ms select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Feb 17 07 4 0ms 0ms 7 0ms 0ms 0ms 208 0ms select case when a.old_resultuid = ? then a.old_resultuid else a.resultuid end as resultuid, s.symbol, timegranularity as interval, direction as direction, patternendtime as patternendtime, patternstartprice as psp, patternendprice as pep, target03 as t03, target16 as t16, patternlengthbars as length, p.patternname as patternname, dtt.timezone, cps.pip from fibonacci_results a inner join downloadersymbolsettings dss on a.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join fibonaccipatterns p on a.pattern = p.patternname left join currencypips cps on cps.symbol = s.symbol where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Feb 17 07 208 0ms 0ms 8 0ms 0ms 0ms 4 0ms select updaterelevantforrelevantresults ();Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Feb 17 07 4 0ms 0ms 9 0ms 0ms 0ms 2,167 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Feb 17 07 2,167 0ms 0ms 10 0ms 0ms 0ms 8 0ms select t.pricedatetime as pricedatetime, t.open as open, t.high as high, t.low as low, t.close "..." close, t.volume as volume, t.bsf as bsf from t1440 t where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?;Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Feb 17 07 8 0ms 0ms 11 0ms 0ms 0ms 1 0ms insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ?united kingdom unemployment rate\?s biggest movers\": \"maiores movimenta\\\\u00e7\\\\u00f5es de hoje\", \"dictionary_zeromarkets ?_silver\": \"xagusd\", \"dictionary_upcomingearnings_title\": \"an\\\\u00fancios de lucros entre {fromdate} e {todate}.\", \"params_creatomate_snapshots_value\": null, \"data_affectedtickers_1_description\": \"a amplitude de movimento esperada durante o evento est\\\\u00e1 entre ?.? e ?.?\", \"data_affectedtickers_1_normal_from\": ?.?, \"data_affectedtickers_2_description\": \"a amplitude de movimento esperada durante o evento est\\\\u00e1 entre ?.? e ?.?\", \"data_affectedtickers_2_normal_from\": ?.?, \"dictionary_brent crude oil futures\": \"brent crude oil futures\", \"dictionary_the biggest losers are:\": \"os maiores perdedores s\\\\u00e3o:\", \"dictionary_thisweekseconomicevents\": \"eventos econ\\\\u00f4micos desta semana\", \"dictionary_us dollar index futures\": \"us dollar index futures\", \"dictionary_zeromarkets ?_aud/usd\": \"audusd\", \"dictionary_zeromarkets ?_eur/usd\": \"eurusd\", \"dictionary_zeromarkets ?_gbp/usd\": \"gbpusd\", \"dictionary_zeromarkets ?_nzd/usd\": \"nzdusd\", \"dictionary_zeromarkets ?_usd/cad\": \"usdcad\", \"dictionary_zeromarkets ?_usd/chf\": \"usdchf\", \"dictionary_zeromarkets ?_usd/jpy\": \"usdjpy\", \"dictionary_volatilitywarning_title\": \"volatilidade esperada como resultado de \? em {country_name} nas pr\\\\u00f3ximas {hours_ahead} horas.\", \"params_creatomate_templateid_value\": \"a53e03b0-3d70-4faa-b766-d634ad553fe6,b56bf30a-04c6-4a1d-a7ec-88bf873f10cd\\\\r\\\\n\\\\r\\\\n\", \"params_title_character_limit_value\": ?, \"data_affectedtickers_1_diff_percent\": ?.?, \"data_affectedtickers_2_diff_percent\": ?.?, \"data_affectedtickers_3_name.visible\": false, \"data_affectedtickers_4_name.visible\": false, \"data_affectedtickers_5_name.visible\": false, \"data_affectedtickers_6_name.visible\": false, \"data_affectedtickers_7_name.visible\": false, \"data_affectedtickers_8_name.visible\": false, \"data_affectedtickers_9_name.visible\": false, \"dictionary_the biggest winners are:\": \"os maiores vencedores s\\\\u00e3o:\", \"dictionary_zeromarkets ?_ftse ?\": \"uk100\", \"dictionary_highimpactevent_longtext\": \"{eventname} ser\\\\u00e1 divulgado em {countryname} nas pr\\\\u00f3ximas {hours_ahead} horas. o valor esperado para esta divulga\\\\u00e7\\\\u00e3o \\\\u00e9 {consensus}. {description}\", \"params_probability_of_posting_value\": ?, \"data_affectedtickers_10_name.visible\": false, \"data_affectedtickers_3_shape.visible\": false, \"data_affectedtickers_4_shape.visible\": false, \"data_affectedtickers_5_shape.visible\": false, \"data_affectedtickers_6_shape.visible\": false, \"data_affectedtickers_7_shape.visible\": false, \"data_affectedtickers_8_shape.visible\": false, \"data_affectedtickers_9_shape.visible\": false, \"dictionary_909_wti crude oil futures\": \"wti\", \"dictionary_e-mini nasdaq-100 futures\": \"e-mini nasdaq-100 futures\", \"dictionary_nikkei ? dollar futures\": \"nikkei ? dollar futures\", \"dictionary_thisweeksearningsreleases\": \"divulga\\\\u00e7\\\\u00f5es de altas desta semana\", \"dictionary_zeromarkets ?_crude oil\": \"xtiusd\", \"dictionary_zeromarkets ?_hang seng\": \"hk50\", \"dictionary_highimpactevent_shorttext\": \"{eventname} ser\\\\u00e1 divulgado em {countryname}. o valor esperado para esta divulga\\\\u00e7\\\\u00e3o \\\\u00e9 {consensus}.\", \"data_affectedtickers_10_shape.visible\": false, \"data_affectedtickers_3_ticker.visible\": false, \"data_affectedtickers_4_ticker.visible\": false, \"data_affectedtickers_5_ticker.visible\": false, \"data_affectedtickers_6_ticker.visible\": false, \"data_affectedtickers_7_ticker.visible\": false, \"data_affectedtickers_8_ticker.visible\": false, \"data_affectedtickers_9_ticker.visible\": false, \"dictionary_nohighimpacteconomicevents\": \"sem eventos econ\\\\u00f4micos de alta volatilidade\", \"dictionary_this week\?s biggest movers are:\": \"as maiores movimenta\\\\u00e7\\\\u00f5es desta semana s\\\\u00e3o:\", \"dictionary_upcominghighimpacteconomicevent\": \"pr\\\\u00f3ximo evento econ\\\\u00f4mico de alta volatilidade\", \"data_affectedtickers_10_description.visible\": false, \"data_affectedtickers_10_normal_from.visible\": false, \"data_affectedtickers_3_diff_percent.visible\": false, \"data_affectedtickers_4_diff_percent.visible\": false, \"data_affectedtickers_5_diff_percent.visible\": false, \"data_affectedtickers_6_diff_percent.visible\": false, \"data_affectedtickers_7_diff_percent.visible\": false, \"data_affectedtickers_8_diff_percent.visible\": false, \"data_affectedtickers_9_diff_percent.visible\": false, \"dictionary_zeromarkets ?_platinum futures\": \"xptusd\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany}) est\\\\u00e1 divulgando os lucros hoje. quando o lpa \\\\u00e9 {delta_sign} do esperado, {earningscompany} tem uma probabilidade de {probability} de uma varia\\\\u00e7\\\\u00e3o de {mean_mov_percent} no m\\\\u00eas seguinte a esta divulga\\\\u00e7\\\\u00e3o.\\\\\\\\nem divulga\\\\u00e7\\\\u00f5es anteriores de lucros onde o lpa foi {delta_sign} do esperado, as seguintes a\\\\u00e7\\\\u00f5es foram impactadas:\", \"data_affectedtickers_10_diff_percent.visible\": false, \"data_affectedtickers_1_details_symbol_ticker\": \"gbpusd\", \"data_affectedtickers_2_details_symbol_ticker\": \"eurusd\", \"dictionary_2-year u.s. treasury note futures\": \"?-year u.s. treasury note futures\", \"dictionary_e-mini russell ? index futures\": \"e-mini russell ? index futures\", \"dictionary_impactofearningsrelease_shorttext\": \"{earningscompany_name} ({earningscompany}) est\\\\u00e1 divulgando os lucros hoje e impactar\\\\u00e1 as seguintes a\\\\u00e7\\\\u00f5es:\", \"dictionary_upcomingearnings_title_noearnings\": \"nenhum an\\\\u00fancio de lucros programado entre {fromdate} e {todate}.\", \"dictionary_10-year u.s. treasury note futures\": \"?-year u.s. treasury note futures\", \"dictionary_calendarofhighimpacteconomicevents\": \"calend\\\\u00e1rio de eventos econ\\\\u00f4micos importantes\", \"data_affectedtickers_1_details_symbol_exchange\": \"forex\", \"data_affectedtickers_1_details_symbol_interval\": ?, \"data_affectedtickers_1_details_symbol_pip_size\": ?.?, \"data_affectedtickers_2_details_symbol_exchange\": \"forex\", \"data_affectedtickers_2_details_symbol_interval\": ?, \"data_affectedtickers_2_details_symbol_pip_size\": ?.?, \"dictionary_market moves for the last ? hours:\": \"movimenta\\\\u00e7\\\\u00f5es do mercado nas \\\\u00faltimas ? horas:\", \"data_affectedtickers_1_details_symbol_pip_value\": ?, \"data_affectedtickers_2_details_symbol_pip_value\": ?, \"data_affectedtickers_3_diff_percent_str.visible\": false, \"data_affectedtickers_4_diff_percent_str.visible\": false, \"data_affectedtickers_5_diff_percent_str.visible\": false, \"data_affectedtickers_6_diff_percent_str.visible\": false, \"data_affectedtickers_7_diff_percent_str.visible\": false, \"data_affectedtickers_8_diff_percent_str.visible\": false, \"data_affectedtickers_9_diff_percent_str.visible\": false, \"params_creatomate_templateselectionscheme_value\": \"random\", \"data_affectedtickers_10_diff_percent_str.visible\": false, \"data_affectedtickers_1_volatility_increase_label\": \"aumento de volatilidade\", \"data_affectedtickers_2_volatility_increase_label\": \"aumento de volatilidade\", \"dictionary_zeromarkets ?_wti crude oil futures\": \"wti\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): probabilidade de {probability} de uma varia\\\\u00e7\\\\u00e3o de {mean_mov_percent} no m\\\\u00eas seguinte a esta divulga\\\\u00e7\\\\u00e3o.\", \"dictionary_market movements in the last ? hours.\": \"movimenta\\\\u00e7\\\\u00f5es do mercado nas \\\\u00faltimas ? horas.\", \"data_affectedtickers_1_details_volatility_diff_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p75\": ?.?, \"dictionary_companies releasing earnings this week:\": \"empresas divulgando lucros nesta semana:\", \"dictionary_zeromarkets ?_brent crude oil futures\": \"xbrusd\", \"dictionary_zeromarkets ?_us dollar index futures\": \"usdx\", \"data_affectedtickers_1_details_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_2_details_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_1_details_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_symbol_value_currency\": \"usd\", \"data_affectedtickers_1_details_symbol_value_lot_size\": ?, \"data_affectedtickers_2_details_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_symbol_value_currency\": \"usd\", \"data_affectedtickers_2_details_symbol_value_lot_size\": ?, \"dictionary_zeromarkets ?_nikkei ? dollar futures\": \"jp225\", \"data_affectedtickers_1_details_symbol_value_date_time\": \"?-01-30t21:?:?.?z\", \"data_affectedtickers_1_details_volatility_diff_median\": ?.?, \"data_affectedtickers_2_details_symbol_value_date_time\": \"?-02-17t04:?:?.?z\", \"data_affectedtickers_2_details_volatility_diff_median\": ?.?, \"dictionary_high impact economic events for this week:\": \"eventos econ\\\\u00f4micos de alto impacto para esta semana:\", \"data_affectedtickers_1_details_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_1_details_volatility_diff_std_dev\": ?.?, \"data_affectedtickers_2_details_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_2_details_volatility_diff_std_dev\": ?.?e-06, \"dictionary_e-mini dow jones industrial average futures\": \"e-mini dow jones industrial average futures\", \"data_affectedtickers_1_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_2_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_1_details_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_value_max\": ?, \"data_affectedtickers_1_details_volatility_diff_value_min\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_value_max\": ?, \"data_affectedtickers_2_details_volatility_diff_value_min\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_3_volatility_increase_label.visible\": false, \"data_affectedtickers_4_volatility_increase_label.visible\": false, \"data_affectedtickers_5_volatility_increase_label.visible\": false, \"data_affectedtickers_6_volatility_increase_label.visible\": false, \"data_affectedtickers_7_volatility_increase_label.visible\": false, \"data_affectedtickers_8_volatility_increase_label.visible\": false, \"data_affectedtickers_9_volatility_increase_label.visible\": false, \"data_affectedtickers_10_volatility_increase_label.visible\": false, \"data_affectedtickers_1_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_event_volatility_value_p75\": ?, \"data_affectedtickers_1_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_mean\": ?, \"data_affectedtickers_2_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_mean\": ?, \"dictionary_no earnings announcements scheduled this week.\": \"nenhum an\\\\u00fancio de lucros programado para esta semana.\", \"dictionary_upcoming earnings announcements for this week:\": \"pr\\\\u00f3ximos an\\\\u00fancios de lucros para esta semana:\", \"data_affectedtickers_1_details_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_2_details_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_1_details_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_median\": ?, \"data_affectedtickers_2_details_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_median\": ?, \"data_affectedtickers_1_details_event_volatility_value_median\": ?, \"data_affectedtickers_1_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p75\": ?, \"dictionary_these are the market movements for the last week:\": \"estas s\\\\u00e3o as movimenta\\\\u00e7\\\\u00f5es do mercado na \\\\u00faltima semana:\", \"data_affectedtickers_1_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_median\": ?, \"dictionary_these are the market movements for the last ? hours:\": \"estas s\\\\u00e3o as movimenta\\\\u00e7\\\\u00f5es do mercado nas \\\\u00faltimas ? horas:\"}}?}", "trace": "Traceback (most recent call last):\n File \"/var/task/lambda_function.py\", line ?, in lambda_handler\n results[?creatomate_response?] = Webhook_Creatomate.render(processlog, templateid,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/var/task/webhook_creatomate.py\", line ?, in render\n Webhook_Creatomate.waitForRender(apikey, creatomate_response[?][?id?], timeout)\n File \"/var/task/webhook_creatomate.py\", line ?, in waitForRender\n raise Exception(status[?error_message?] +\". render details: \" + json.dumps(status))\nException: An HTTP ? status was received while trying to download the file: https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg (element data_CountryIcon). Render details: {\"id\": \"?a249b0d-2f5f-48e3-b32e-37ce15035571\", \"status\": \"failed\", \"error_message\": \"an http ? status was received while trying to download the file: https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg (element data_countryicon)\", \"url\": \"https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/?a249b0d-2f5f-48e3-b32e-37ce15035571.png\", \"template_id\": \"a53e03b0-3d70-4faa-b766-d634ad553fe6\", \"template_name\": \"volatility warning png ? en pt\", \"template_tags\": [], \"output_format\": \"png\", \"modifications\": {\"data_date\": \"? fev., ?am utc\", \"data_name\": \"united kingdom unemployment rate\", \"text_title\": \"volatilidade esperada como resultado de ?united kingdom unemployment rate? em great britain nas pr\\u00f3ximas ? horas.\", \"has_results\": true, \"data_heading\": \"aviso de volatilidade\", \"data_country\": \"gb\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"dictionary_dax\": \"dax\", \"dictionary_utc\": \"utc\", \"text_long_text\": \"united kingdom unemployment rate (great britain) resultar\\u00e1 em aumentos significativos na volatilidade. eurusd - ?.?\\ngbpusd - ?.?\\n\", \"dictionary_corn\": \"milho\", \"dictionary_gold\": \"ouro\", \"dictionary_hour\": \"hora\", \"dictionary_open\": \"abrir\", \"dictionary_time\": \"tempo\", \"text_short_text\": \"united kingdom unemployment rate (great britain) resultar\\u00e1 em aumentos significativos na volatilidade. eurusd - ?.?\\ngbpusd - ?.?\\n\", \"data_countryicon\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg\", \"data_description\": \"in the united kingdom, the unemployment rate measures the number of people actively looking for a job as a percentage of the labour force.\", \"data_released_at\": \"?-02-17t07:?\", \"dictionary_close\": \"fechar\", \"dictionary_daily\": \"diariamente\", \"dictionary_event\": \"evento\", \"dictionary_hours\": \"horas\", \"dictionary_price\": \"pre\\u00e7o\", \"dictionary_wheat\": \"trigo\", \"quantity_results\": ?, \"data_country_name\": \"great britain\", \"dictionary_actual\": \"real\", \"dictionary_change\": \"mudan\\u00e7a\", \"dictionary_coffee\": \"caf\\u00e9\", \"dictionary_friday\": \"sexta-feira\", \"dictionary_monday\": \"segunda-feira\", \"dictionary_silver\": \"prata\", \"dictionary_sunday\": \"domingo\", \"dictionary_target\": \"objetivo\", \"dictionary_dd mmm\": \"dd mmm\", \"params_mode_value\": ?, \"params_uuid_value\": \"d2077974-89e3-4245-97da-ec0589ca8f12\", \"dictionary_909_dax\": \"ger40\", \"dictionary_aud/usd\": \"aud/usd\", \"dictionary_company\": \"empresa\", \"dictionary_eur/usd\": \"eur/usd\", \"dictionary_gbp/usd\": \"gbp/usd\", \"dictionary_indices\": \"\\u00cdndices\", \"dictionary_minutes\": \"minutos\", \"dictionary_nzd/usd\": \"nzd/usd\", \"dictionary_tuesday\": \"ter\\u00e7a-feira\", \"dictionary_usd/cad\": \"usd/cad\", \"dictionary_usd/chf\": \"usd/chf\", \"dictionary_usd/jpy\": \"usd/jpy\", \"dictionary_909_gold\": \"xauusd\", \"dictionary_estimate\": \"estimativa\", \"dictionary_expected\": \"esperado\", \"dictionary_ftse ?\": \"ftse ?\", \"dictionary_interval\": \"intervalo\", \"dictionary_keylevel\": \"n\\u00edvel-chave\", \"dictionary_previous\": \"pr\\u00e9via\", \"dictionary_saturday\": \"s\\u00e1bado\", \"dictionary_thursday\": \"quinta-feira\", \"dictionary_estimate\": \"estimativa\", \"dictionary_previous\": \"pr\\u00e9via\", \"params_locale_value\": \"pt_br\", \"params_region_value\": \"pt\", \"data_country_name_en\": \"great britain\", \"dictionary_consensus\": \"consenso\", \"dictionary_crude oil\": \"petr\\u00f3leo bruto\", \"dictionary_fibonacci\": \"padr\\u00f5es de fibonacci\", \"dictionary_hang seng\": \"hang seng\", \"dictionary_stop_loss\": \"stop loss\", \"dictionary_wednesday\": \"quarta-feira\", \"dictionary_consensus\": \"consenso\", \"dictionary_dd mmm, h\": \"dd mmm, h\", 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\"pre\\u00e7o de entrada\", \"dictionary_fridayshort\": \"sex\", \"dictionary_marketalert\": \"alerta de mercado\", \"dictionary_mondayshort\": \"seg\", \"dictionary_natural gas\": \"g\\u00e1s natural\", \"dictionary_probability\": \"probabilidade\", \"dictionary_releasetime\": \"hora de divulga\\u00e7\\u00e3o\", \"dictionary_sundayshort\": \"dom\", \"dictionary_probability\": \"se {ticker} ganhos {deltasign} {consensus}\", \"params_ratelimit_value\": \"?,?,?\", \"params_processid_value\": ?, \"params_threshold_value\": ?, \"dictionary_909_ftse ?\": \"uk100\", \"dictionary_beforemarket\": \"pr\\u00e9 mercado\", \"dictionary_chartpattern\": \"padr\\u00e3o gr\\u00e1fico\", \"dictionary_corn futures\": \"corn futures\", \"dictionary_gold futures\": \"gold futures\", \"dictionary_last12events\": \"\\u00daltimos ? eventos\", \"dictionary_roundnumbers\": \"n\\u00famero redondo\", \"dictionary_target_level\": \"n\\u00edvel visado\", \"dictionary_tuesdayshort\": \"ter\", \"params_dateformat_value\": \"dd mmm, ha utc\", \"params_disclaimer_value\": \"\\u26a0\\ufe0f negociar forex e cfd envolve alto risco e alavancagem significativa, podendo n\\u00e3o ser adequado para todos os investidores.\", \"dictionary_909_crude oil\": \"xtiusd\", \"dictionary_909_hang seng\": \"hk50\", \"dictionary_biggestlosers\": \"maiores baixas\", \"dictionary_biggestmovers\": \"maiores varia\\u00e7\\u00f5es\", \"dictionary_marketsummary\": \"resumo do mercado\", \"dictionary_saturdayshort\": \"s\\u00e1b\", \"dictionary_target_period\": \"validade da opera\\u00e7\\u00e3o\", \"dictionary_thursdayshort\": \"qui\", \"dictionary_wheat futures\": \"wheat futures\", \"params_broker_name_value\": \"zeromarkets ?\", \"params_hours_ahead_value\": ?, \"dictionary_909_nasdaq ?\": \"us100\", \"dictionary_909_nikkei ?\": \"jp225\", \"dictionary_biggestgainers\": \"maiores altas\", \"dictionary_copper futures\": \"copper futures\", \"dictionary_earningsimpact\": \"impacto dos ganhos\", \"dictionary_nyse composite\": \"\\u00edndice nyse\", \"dictionary_silver futures\": \"silver futures\", \"dictionary_wednesdayshort\": \"qua\", \"params_dynamic_text_value\": false, \"params_flaglocation_value\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/\", \"dictionary_909_natural gas\": \"xngusd\", \"dictionary_aud/usd futures\": \"aud/usd futures\", \"dictionary_eur/usd futures\": \"eur/usd futures\", \"dictionary_extrememovement\": \"movimento extremo\", \"dictionary_gbp/usd futures\": \"gbp/usd futures\", \"dictionary_nzd/usd futures\": \"nzd/usd futures\", \"dictionary_soybean futures\": \"soybean futures\", \"dictionary_usd/cad futures\": \"usd/cad futures\", \"dictionary_usd/chf futures\": \"usd/chf futures\", \"dictionary_usd/jpy futures\": \"usd/jpy futures\", \"data_affectedtickers_1_name\": \"gbpusd\", \"data_affectedtickers_2_name\": \"eurusd\", \"dictionary_earningsexceeded\": \"historicamente, quando os lucros superaram as expectativas do passado, 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mercado\", \"dictionary_noearningsreleases\": \"n\\u00e3o h\\u00e1 divulga\\u00e7\\u00e3o de resultados\", \"dictionary_shanghai composite\": \"shanghai composite\", \"dictionary_volatilityincrease\": \"aumento de volatilidade\", \"params_do_not_translate_value\": true, \"dictionary_heating oil futures\": \"heating oil futures\", \"dictionary_natural gas futures\": \"natural gas futures\", \"dictionary_there are no losers\": \"n\\u00e3o h\\u00e1 perdedores\", \"dictionary_zeromarkets ?_dax\": \"ger40\", \"params_creatomate_apikey_value\": \"fd5bebb849e74f8a96e1577a5c9a472b58b31c8326087e0128edcc0c34032721ed6f3fc82151d24819397aad6c23335d\", \"data_affectedtickers_1_event_to\": ?.?, \"data_affectedtickers_2_event_to\": ?.?, \"dictionary_909_platinum futures\": \"xptusd\", \"dictionary_japanesecandlesticks\": \"vela japonesa\", \"dictionary_there are no winners\": \"n\\u00e3o h\\u00e1 vencedores\", \"dictionary_todayseconomicevents\": \"os acontecimento econ\\u00f4micos de hoje\", \"dictionary_zeromarkets ?_gold\": \"xauusd\", \"data_affectedtickers_1_normal_to\": ?.?, \"data_affectedtickers_2_normal_to\": ?.?, \"dictionary_expectedmovementrange\": \"a amplitude de movimento esperada durante o evento est\\u00e1 entre {from} e {to}\", \"dictionary_wti crude oil futures\": \"wti crude oil futures\", \"dictionary_highimpactevent_title\": \"{eventname} ser\\u00e1 divulgado em {countryname} nas pr\\u00f3ximas {hours_ahead} horas.\", \"params_dateformat_dateonly_value\": \"dd mmm\", \"data_affectedtickers_1_event_from\": ?.?, \"data_affectedtickers_2_event_from\": ?.?, \"dictionary_e-mini s&p ? futures\": \"e-mini s&p ? futures\", \"dictionary_thisweeksmarketsummary\": \"resumo do mercado desta semana\", \"dictionary_today?s biggest movers\": \"maiores movimenta\\u00e7\\u00f5es de hoje\", \"dictionary_zeromarkets ?_silver\": \"xagusd\", \"dictionary_upcomingearnings_title\": \"an\\u00fancios de lucros entre {fromdate} e {todate}.\", \"params_creatomate_snapshots_value\": null, \"data_affectedtickers_1_description\": \"a amplitude de movimento esperada durante o evento est\\u00e1 entre ?.? e ?.?\", \"data_affectedtickers_1_normal_from\": ?.?, \"data_affectedtickers_2_description\": \"a amplitude de movimento esperada durante o evento est\\u00e1 entre ?.? e ?.?\", \"data_affectedtickers_2_normal_from\": ?.?, \"dictionary_brent crude oil futures\": \"brent crude oil futures\", \"dictionary_the biggest losers are:\": \"os maiores perdedores s\\u00e3o:\", \"dictionary_thisweekseconomicevents\": \"eventos econ\\u00f4micos desta semana\", \"dictionary_us dollar index futures\": \"us dollar index futures\", \"dictionary_zeromarkets ?_aud/usd\": \"audusd\", \"dictionary_zeromarkets ?_eur/usd\": \"eurusd\", \"dictionary_zeromarkets ?_gbp/usd\": \"gbpusd\", \"dictionary_zeromarkets ?_nzd/usd\": \"nzdusd\", \"dictionary_zeromarkets ?_usd/cad\": \"usdcad\", \"dictionary_zeromarkets ?_usd/chf\": \"usdchf\", \"dictionary_zeromarkets ?_usd/jpy\": \"usdjpy\", \"dictionary_volatilitywarning_title\": \"volatilidade esperada como resultado de ?{eventname}? em {country_name} nas pr\\u00f3ximas {hours_ahead} horas.\", \"params_creatomate_templateid_value\": \"a53e03b0-3d70-4faa-b766-d634ad553fe6,b56bf30a-04c6-4a1d-a7ec-88bf873f10cd\\r\\n\\r\\n\", \"params_title_character_limit_value\": ?, \"data_affectedtickers_1_diff_percent\": ?.?, \"data_affectedtickers_2_diff_percent\": ?.?, \"data_affectedtickers_3_name.visible\": false, \"data_affectedtickers_4_name.visible\": false, \"data_affectedtickers_5_name.visible\": false, \"data_affectedtickers_6_name.visible\": false, \"data_affectedtickers_7_name.visible\": false, \"data_affectedtickers_8_name.visible\": false, \"data_affectedtickers_9_name.visible\": false, \"dictionary_the biggest winners are:\": \"os maiores vencedores s\\u00e3o:\", \"dictionary_zeromarkets ?_ftse ?\": \"uk100\", \"dictionary_highimpactevent_longtext\": \"{eventname} ser\\u00e1 divulgado em {countryname} nas pr\\u00f3ximas {hours_ahead} horas. o valor esperado para esta divulga\\u00e7\\u00e3o \\u00e9 {consensus}. {description}\", \"params_probability_of_posting_value\": ?, \"data_affectedtickers_10_name.visible\": false, \"data_affectedtickers_3_shape.visible\": false, \"data_affectedtickers_4_shape.visible\": false, \"data_affectedtickers_5_shape.visible\": false, \"data_affectedtickers_6_shape.visible\": false, \"data_affectedtickers_7_shape.visible\": false, \"data_affectedtickers_8_shape.visible\": false, \"data_affectedtickers_9_shape.visible\": false, \"dictionary_909_wti crude oil futures\": \"wti\", \"dictionary_e-mini nasdaq-100 futures\": \"e-mini nasdaq-100 futures\", \"dictionary_nikkei ? dollar futures\": \"nikkei ? dollar futures\", \"dictionary_thisweeksearningsreleases\": \"divulga\\u00e7\\u00f5es de altas desta semana\", \"dictionary_zeromarkets ?_crude oil\": \"xtiusd\", \"dictionary_zeromarkets ?_hang seng\": \"hk50\", \"dictionary_highimpactevent_shorttext\": \"{eventname} ser\\u00e1 divulgado em {countryname}. o valor esperado para esta divulga\\u00e7\\u00e3o \\u00e9 {consensus}.\", \"data_affectedtickers_10_shape.visible\": false, \"data_affectedtickers_3_ticker.visible\": false, \"data_affectedtickers_4_ticker.visible\": false, \"data_affectedtickers_5_ticker.visible\": false, \"data_affectedtickers_6_ticker.visible\": false, \"data_affectedtickers_7_ticker.visible\": false, \"data_affectedtickers_8_ticker.visible\": false, \"data_affectedtickers_9_ticker.visible\": false, \"dictionary_nohighimpacteconomicevents\": \"sem eventos econ\\u00f4micos de alta volatilidade\", \"dictionary_this week?s biggest movers\": \"maiores movimenta\\u00e7\\u00f5es desta semana\", \"dictionary_zeromarkets ?_nasdaq ?\": \"us100\", \"dictionary_zeromarkets ?_nikkei ?\": \"jp225\", \"dictionary_volatilitywarning_longtext\": \"{eventname} ({country_name}) resultar\\u00e1 em aumentos significativos na volatilidade.\", \"params_creatomate_modifications_value\": null, \"params_longtext_character_limit_value\": ?, \"params_minimum_quantity_results_value\": ?, \"data_affectedtickers_10_ticker.visible\": false, \"data_affectedtickers_3_bgshape.visible\": false, \"data_affectedtickers_4_bgshape.visible\": false, \"data_affectedtickers_5_bgshape.visible\": false, \"data_affectedtickers_6_bgshape.visible\": false, \"data_affectedtickers_7_bgshape.visible\": false, \"data_affectedtickers_8_bgshape.visible\": false, \"data_affectedtickers_9_bgshape.visible\": false, \"dictionary_909_brent crude oil futures\": \"xbrusd\", \"dictionary_909_us dollar index futures\": \"usdx\", \"dictionary_stocksimpactedbythisrelease\": \"a\\u00e7\\u00f5es mais impactadas por esta divulga\\u00e7\\u00e3o:\", \"dictionary_this weeks economic events:\": \"eventos econ\\u00f4micos desta semana:\", \"dictionary_zeromarkets ?_natural gas\": \"xngusd\", \"dictionary_volatilitywarning_shorttext\": \"{eventname} ({country_name}) resultar\\u00e1 em aumentos significativos na volatilidade.\", \"params_shorttext_character_limit_value\": ?, \"data_affectedtickers_10_bgshape.visible\": false, \"data_affectedtickers_1_diff_percent_str\": \"?%\", \"data_affectedtickers_2_diff_percent_str\": \"?%\", \"data_affectedtickers_3_event_to.visible\": false, \"data_affectedtickers_4_event_to.visible\": false, \"data_affectedtickers_5_event_to.visible\": false, \"data_affectedtickers_6_event_to.visible\": false, \"data_affectedtickers_7_event_to.visible\": false, \"data_affectedtickers_8_event_to.visible\": false, \"data_affectedtickers_9_event_to.visible\": false, \"dictionary_biggeststockmoversfortheweek\": \"a\\u00e7\\u00f5es com maiores varia\\u00e7\\u00f5es na semana\", \"dictionary_upcomingeconomicevents_title\": \"eventos econ\\u00f4micos de alto impacto entre {fromdate} e {todate}.\", \"data_affectedtickers_10_event_to.visible\": false, \"data_affectedtickers_1_details_symbol_id\": ?, \"data_affectedtickers_2_details_symbol_id\": ?, \"data_affectedtickers_3_normal_to.visible\": false, \"data_affectedtickers_4_normal_to.visible\": false, \"data_affectedtickers_5_normal_to.visible\": false, \"data_affectedtickers_6_normal_to.visible\": false, \"data_affectedtickers_7_normal_to.visible\": false, \"data_affectedtickers_8_normal_to.visible\": false, \"data_affectedtickers_9_normal_to.visible\": false, \"dictionary_909_nikkei ? dollar futures\": \"jp225\", \"dictionary_e-mini s&p midcap ? futures\": \"e-mini s&p midcap ? futures\", \"dictionary_impactofearningsrelease_title\": \"impacto do relat\\u00f3rio de lucros de {earningscompany_name} ({earningscompany})\", \"data_affectedtickers_10_normal_to.visible\": false, \"data_affectedtickers_1_details_symbol_pip\": ?, \"data_affectedtickers_2_details_symbol_pip\": ?, \"data_affectedtickers_3_event_from.visible\": false, \"data_affectedtickers_4_event_from.visible\": false, \"data_affectedtickers_5_event_from.visible\": false, \"data_affectedtickers_6_event_from.visible\": false, \"data_affectedtickers_7_event_from.visible\": false, \"data_affectedtickers_8_event_from.visible\": false, \"data_affectedtickers_9_event_from.visible\": false, \"data_affectedtickers_10_event_from.visible\": false, \"data_affectedtickers_1_details_symbol_name\": \"gbpusd\", \"data_affectedtickers_2_details_symbol_name\": \"eurusd\", \"data_affectedtickers_3_description.visible\": false, \"data_affectedtickers_3_normal_from.visible\": false, \"data_affectedtickers_4_description.visible\": false, \"data_affectedtickers_4_normal_from.visible\": false, \"data_affectedtickers_5_description.visible\": false, \"data_affectedtickers_5_normal_from.visible\": false, \"data_affectedtickers_6_description.visible\": false, \"data_affectedtickers_6_normal_from.visible\": false, \"data_affectedtickers_7_description.visible\": false, \"data_affectedtickers_7_normal_from.visible\": false, \"data_affectedtickers_8_description.visible\": false, \"data_affectedtickers_8_normal_from.visible\": false, \"data_affectedtickers_9_description.visible\": false, \"data_affectedtickers_9_normal_from.visible\": false, \"dictionary_market moves for the last week:\": \"movimenta\\u00e7\\u00f5es do mercado na \\u00faltima semana:\", \"dictionary_this week?s biggest movers are:\": \"as maiores movimenta\\u00e7\\u00f5es desta semana s\\u00e3o:\", \"dictionary_upcominghighimpacteconomicevent\": \"pr\\u00f3ximo evento econ\\u00f4mico de alta volatilidade\", \"data_affectedtickers_10_description.visible\": false, \"data_affectedtickers_10_normal_from.visible\": false, \"data_affectedtickers_3_diff_percent.visible\": false, \"data_affectedtickers_4_diff_percent.visible\": false, \"data_affectedtickers_5_diff_percent.visible\": false, \"data_affectedtickers_6_diff_percent.visible\": false, \"data_affectedtickers_7_diff_percent.visible\": false, \"data_affectedtickers_8_diff_percent.visible\": false, \"data_affectedtickers_9_diff_percent.visible\": false, \"dictionary_zeromarkets ?_platinum futures\": \"xptusd\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany}) est\\u00e1 divulgando os lucros hoje. quando o lpa \\u00e9 {delta_sign} do esperado, {earningscompany} tem uma probabilidade de {probability} de uma varia\\u00e7\\u00e3o de {mean_mov_percent} no m\\u00eas seguinte a esta divulga\\u00e7\\u00e3o.\\\\nem divulga\\u00e7\\u00f5es anteriores de lucros onde o lpa foi {delta_sign} do esperado, as seguintes a\\u00e7\\u00f5es foram impactadas:\", \"data_affectedtickers_10_diff_percent.visible\": false, \"data_affectedtickers_1_details_symbol_ticker\": \"gbpusd\", \"data_affectedtickers_2_details_symbol_ticker\": \"eurusd\", \"dictionary_2-year u.s. treasury note futures\": \"?-year u.s. treasury note futures\", \"dictionary_e-mini russell ? index futures\": \"e-mini russell ? index futures\", \"dictionary_impactofearningsrelease_shorttext\": \"{earningscompany_name} ({earningscompany}) est\\u00e1 divulgando os lucros hoje e impactar\\u00e1 as seguintes a\\u00e7\\u00f5es:\", \"dictionary_upcomingearnings_title_noearnings\": \"nenhum an\\u00fancio de lucros programado entre {fromdate} e {todate}.\", \"dictionary_10-year u.s. treasury note futures\": \"?-year u.s. treasury note futures\", \"dictionary_calendarofhighimpacteconomicevents\": \"calend\\u00e1rio de eventos econ\\u00f4micos importantes\", \"data_affectedtickers_1_details_symbol_exchange\": \"forex\", \"data_affectedtickers_1_details_symbol_interval\": ?, \"data_affectedtickers_1_details_symbol_pip_size\": ?.?, \"data_affectedtickers_2_details_symbol_exchange\": \"forex\", \"data_affectedtickers_2_details_symbol_interval\": ?, \"data_affectedtickers_2_details_symbol_pip_size\": ?.?, \"dictionary_market moves for the last ? hours:\": \"movimenta\\u00e7\\u00f5es do mercado nas \\u00faltimas ? horas:\", \"data_affectedtickers_1_details_symbol_pip_value\": ?, \"data_affectedtickers_2_details_symbol_pip_value\": ?, \"data_affectedtickers_3_diff_percent_str.visible\": false, \"data_affectedtickers_4_diff_percent_str.visible\": false, \"data_affectedtickers_5_diff_percent_str.visible\": false, \"data_affectedtickers_6_diff_percent_str.visible\": false, \"data_affectedtickers_7_diff_percent_str.visible\": false, \"data_affectedtickers_8_diff_percent_str.visible\": false, \"data_affectedtickers_9_diff_percent_str.visible\": false, \"params_creatomate_templateselectionscheme_value\": \"random\", \"data_affectedtickers_10_diff_percent_str.visible\": false, \"data_affectedtickers_1_volatility_increase_label\": \"aumento de volatilidade\", \"data_affectedtickers_2_volatility_increase_label\": \"aumento de volatilidade\", \"dictionary_zeromarkets ?_wti crude oil futures\": \"wti\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): probabilidade de {probability} de uma varia\\u00e7\\u00e3o de {mean_mov_percent} no m\\u00eas seguinte a esta divulga\\u00e7\\u00e3o.\", \"dictionary_market movements in the last ? hours.\": \"movimenta\\u00e7\\u00f5es do mercado nas \\u00faltimas ? horas.\", \"data_affectedtickers_1_details_volatility_diff_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p75\": ?.?, \"dictionary_companies releasing earnings this week:\": \"empresas divulgando lucros nesta semana:\", \"dictionary_zeromarkets ?_brent crude oil futures\": \"xbrusd\", \"dictionary_zeromarkets ?_us dollar index futures\": \"usdx\", \"data_affectedtickers_1_details_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_2_details_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_1_details_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_symbol_value_currency\": \"usd\", \"data_affectedtickers_1_details_symbol_value_lot_size\": ?, \"data_affectedtickers_2_details_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_symbol_value_currency\": \"usd\", \"data_affectedtickers_2_details_symbol_value_lot_size\": ?, \"dictionary_zeromarkets ?_nikkei ? dollar futures\": \"jp225\", \"data_affectedtickers_1_details_symbol_value_date_time\": \"?-01-30t21:?:?.?z\", \"data_affectedtickers_1_details_volatility_diff_median\": ?.?, \"data_affectedtickers_2_details_symbol_value_date_time\": \"?-02-17t04:?:?.?z\", \"data_affectedtickers_2_details_volatility_diff_median\": ?.?, \"dictionary_high impact economic events for this week:\": \"eventos econ\\u00f4micos de alto impacto para esta semana:\", \"data_affectedtickers_1_details_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_1_details_volatility_diff_std_dev\": ?.?, \"data_affectedtickers_2_details_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_2_details_volatility_diff_std_dev\": ?.?e-06, \"dictionary_e-mini dow jones industrial average futures\": \"e-mini dow jones industrial average futures\", \"data_affectedtickers_1_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_2_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_1_details_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_value_max\": ?, \"data_affectedtickers_1_details_volatility_diff_value_min\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_value_max\": ?, \"data_affectedtickers_2_details_volatility_diff_value_min\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_3_volatility_increase_label.visible\": false, \"data_affectedtickers_4_volatility_increase_label.visible\": false, \"data_affectedtickers_5_volatility_increase_label.visible\": false, \"data_affectedtickers_6_volatility_increase_label.visible\": false, \"data_affectedtickers_7_volatility_increase_label.visible\": false, \"data_affectedtickers_8_volatility_increase_label.visible\": false, \"data_affectedtickers_9_volatility_increase_label.visible\": false, \"data_affectedtickers_10_volatility_increase_label.visible\": false, \"data_affectedtickers_1_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_event_volatility_value_p75\": ?, \"data_affectedtickers_1_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_mean\": ?, \"data_affectedtickers_2_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_mean\": ?, \"dictionary_no earnings announcements scheduled this week.\": \"nenhum an\\u00fancio de lucros programado para esta semana.\", \"dictionary_upcoming earnings announcements for this week:\": \"pr\\u00f3ximos an\\u00fancios de lucros para esta semana:\", \"data_affectedtickers_1_details_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_2_details_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_1_details_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_median\": ?, \"data_affectedtickers_2_details_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_median\": ?, \"data_affectedtickers_1_details_event_volatility_value_median\": ?, \"data_affectedtickers_1_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p75\": ?, \"dictionary_these are the market movements for the last week:\": \"estas s\\u00e3o as movimenta\\u00e7\\u00f5es do mercado na \\u00faltima semana:\", \"data_affectedtickers_1_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_median\": ?, \"dictionary_these are the market movements for the last ? hours:\": \"estas s\\u00e3o as movimenta\\u00e7\\u00f5es do mercado nas \\u00faltimas ? horas:\"}}" }?json');Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Feb 17 07 1 0ms 0ms 12 0ms 0ms 0ms 18 0ms select cast(count(*) / cast(setting as numeric) * ? as int) from pg_stat_activity, pg_settings where name = ? group by setting;Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Feb 17 07 18 0ms 0ms 13 0ms 0ms 0ms 238 0ms select v.datname, c.relname, v.phase, v.heap_blks_total, v.heap_blks_scanned, v.heap_blks_vacuumed, v.index_vacuum_count, v.max_dead_tuples, v.num_dead_tuples from pg_stat_progress_vacuum as v join pg_class c on c.oid = v.relid;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Feb 17 07 238 0ms 0ms 14 0ms 0ms 0ms 1 0ms insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ?\\\\uc2e4\\\\uc5c5\\\\ub960\?s biggest movers\": \"\\\\uc624\\\\ub298 \\\\uac00\\\\uc7a5 \\\\ud070 \\\\uc6c0\\\\uc9c1\\\\uc784\", \"dictionary_Zeromarkets ?_Silver\": \"XAGUSD\", \"dictionary_upcomingearnings_title\": \"{fromdate}\\\\ubd80\\\\ud130 {todate}\\\\uae4c\\\\uc9c0\\\\uc758 \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c.\", \"params_creatomate_snapshots_value\": null, \"data_affectedtickers_1_description\": \"\\\\uc774\\\\ubca4\\\\ud2b8 \\\\uc911 \\\\uc608\\\\uc0c1 \\\\uc774\\\\ub3d9 \\\\ubc94\\\\uc704\\\\ub294 ?.?\\\\uc5d0\\\\uc11c ?.? \\\\uc0ac\\\\uc774\\\\uc785\\\\ub2c8\\\\ub2e4.\", \"data_affectedtickers_1_normal_from\": ?.?, \"data_affectedtickers_2_description\": \"\\\\uc774\\\\ubca4\\\\ud2b8 \\\\uc911 \\\\uc608\\\\uc0c1 \\\\uc774\\\\ub3d9 \\\\ubc94\\\\uc704\\\\ub294 ?.?\\\\uc5d0\\\\uc11c ?.? \\\\uc0ac\\\\uc774\\\\uc785\\\\ub2c8\\\\ub2e4.\", \"data_affectedtickers_2_normal_from\": ?.?, \"dictionary_Brent Crude Oil Futures\": \"Brent Crude Oil Futures\", \"dictionary_The biggest losers are:\": \"\\\\uac00\\\\uc7a5 \\\\ud070 \\\\ud328\\\\uc790\\\\ub294 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"dictionary_ThisWeeksEconomicEvents\": \"\\\\uae08\\\\uc8fc \\\\uacbd\\\\uc81c \\\\uc774\\\\ubca4\\\\ud2b8\", \"dictionary_US Dollar Index Futures\": \"US Dollar Index Futures\", \"dictionary_Zeromarkets ?_AUD/USD\": \"AUDUSD\", \"dictionary_Zeromarkets ?_EUR/USD\": \"EURUSD\", \"dictionary_Zeromarkets ?_GBP/USD\": \"GBPUSD\", \"dictionary_Zeromarkets ?_NZD/USD\": \"NZDUSD\", \"dictionary_Zeromarkets ?_USD/CAD\": \"USDCAD\", \"dictionary_Zeromarkets ?_USD/CHF\": \"USDCHF\", \"dictionary_Zeromarkets ?_USD/JPY\": \"USDJPY\", \"dictionary_volatilitywarning_title\": \"{hours_ahead}\\\\uc2dc\\\\uac04 \\\\ub0b4 {country_name}\\\\uc5d0\\\\uc11c \?\\\\uc73c\\\\ub85c \\\\uc778\\\\ud55c \\\\ubcc0\\\\ub3d9\\\\uc131\\\\uc774 \\\\uc608\\\\uc0c1\\\\ub429\\\\ub2c8\\\\ub2e4.\", \"params_creatomate_templateid_value\": \"?aa08409-8a69-4164-bd5d-c37b5949c5df,bcbd769a-c0a7-48bf-a9f3-9c7c6ffc975b\", \"params_title_character_limit_value\": ?, \"data_affectedtickers_1_diff_percent\": ?.?, \"data_affectedtickers_2_diff_percent\": ?.?, \"data_affectedtickers_3_name.visible\": false, \"data_affectedtickers_4_name.visible\": false, \"data_affectedtickers_5_name.visible\": false, \"data_affectedtickers_6_name.visible\": false, \"data_affectedtickers_7_name.visible\": false, \"data_affectedtickers_8_name.visible\": false, \"data_affectedtickers_9_name.visible\": false, \"dictionary_The biggest winners are:\": \"\\\\uac00\\\\uc7a5 \\\\ud070 \\\\uc2b9\\\\uc790\\\\ub294 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"dictionary_Zeromarkets ?_FTSE ?\": \"UK?\", \"dictionary_highimpactevent_longtext\": \"{eventname}\\\\uac00 {hours_ahead}\\\\uc2dc\\\\uac04 \\\\ub0b4 {countryname}\\\\uc5d0\\\\uc11c \\\\ubc1c\\\\ud45c\\\\ub429\\\\ub2c8\\\\ub2e4. \\\\uc608\\\\uc0c1\\\\uce58\\\\ub294 {consensus}\\\\uc785\\\\ub2c8\\\\ub2e4. {description}\", \"params_probability_of_posting_value\": ?, \"data_affectedtickers_10_name.visible\": false, \"data_affectedtickers_3_Shape.visible\": false, \"data_affectedtickers_4_Shape.visible\": false, \"data_affectedtickers_5_Shape.visible\": false, \"data_affectedtickers_6_Shape.visible\": false, \"data_affectedtickers_7_Shape.visible\": false, \"data_affectedtickers_8_Shape.visible\": false, \"data_affectedtickers_9_Shape.visible\": false, \"dictionary_909_WTI Crude Oil Futures\": \"WTI\", \"dictionary_E-mini NASDAQ? Futures\": \"E-mini NASDAQ? Futures\", \"dictionary_Nikkei ? Dollar Futures\": \"Nikkei ? Dollar Futures\", \"dictionary_ThisWeeksEarningsReleases\": \"\\\\uae08\\\\uc8fc \\\\uc218\\\\uc775 \\\\ubc1c\\\\ud45c\", \"dictionary_Zeromarkets ?_Crude oil\": \"XTIUSD\", \"dictionary_Zeromarkets ?_Hang Seng\": \"HK?\", \"dictionary_highimpactevent_shorttext\": \"{eventname}\\\\uac00 {countryname}\\\\uc5d0\\\\uc11c \\\\ubc1c\\\\ud45c\\\\ub429\\\\ub2c8\\\\ub2e4. \\\\uc608\\\\uc0c1\\\\uce58\\\\ub294 {consensus}\\\\uc785\\\\ub2c8\\\\ub2e4.\", \"data_affectedtickers_10_Shape.visible\": false, \"data_affectedtickers_3_ticker.visible\": false, \"data_affectedtickers_4_ticker.visible\": false, \"data_affectedtickers_5_ticker.visible\": false, \"data_affectedtickers_6_ticker.visible\": false, \"data_affectedtickers_7_ticker.visible\": false, \"data_affectedtickers_8_ticker.visible\": false, \"data_affectedtickers_9_ticker.visible\": false, \"dictionary_Nohighimpacteconomicevents\": \"\\\\ud070 \\\\uc601\\\\ud5a5\\\\uc744 \\\\ubbf8\\\\uce58\\\\ub294 \\\\uacbd\\\\uc81c \\\\uc0ac\\\\uac74 \\\\uc5c6\\\\uc74c\", \"dictionary_This week\?s biggest movers are:\": \"\\\\uc774\\\\ubc88 \\\\uc8fc \\\\uac00\\\\uc7a5 \\\\ud070 \\\\uc6c0\\\\uc9c1\\\\uc784\\\\uc740 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"dictionary_UpcomingHighImpactEconomicEvent\": \"\\\\ub2e4\\\\uac00\\\\uc624\\\\ub294 \\\\uc601\\\\ud5a5\\\\ub825 \\\\uc788\\\\ub294 \\\\uacbd\\\\uc81c \\\\uc774\\\\ubca4\\\\ud2b8\", \"data_affectedtickers_10_description.visible\": false, \"data_affectedtickers_10_normal_from.visible\": false, \"data_affectedtickers_3_diff_percent.visible\": false, \"data_affectedtickers_4_diff_percent.visible\": false, \"data_affectedtickers_5_diff_percent.visible\": false, \"data_affectedtickers_6_diff_percent.visible\": false, \"data_affectedtickers_7_diff_percent.visible\": false, \"data_affectedtickers_8_diff_percent.visible\": false, \"data_affectedtickers_9_diff_percent.visible\": false, \"dictionary_Zeromarkets ?_Platinum Futures\": \"XPTUSD\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany})\\\\uac00 \\\\uc624\\\\ub298 \\\\uc2e4\\\\uc801\\\\uc744 \\\\ubc1c\\\\ud45c\\\\ud569\\\\ub2c8\\\\ub2e4. EPS\\\\uac00 \\\\uc608\\\\uc0c1\\\\ubcf4\\\\ub2e4 {delta_sign}\\\\uc77c \\\\uacbd\\\\uc6b0, {earningscompany}\\\\ub294 \\\\ub2e4\\\\uc74c \\\\ub2ec \\\\ub3d9\\\\uc548 {mean_mov_percent} \\\\ubcc0\\\\ub3d9 \\\\uac00\\\\ub2a5\\\\uc131\\\\uc774 {probability}\\\\uc785\\\\ub2c8\\\\ub2e4.\\\\\\\\n\\\\uc774\\\\uc804 EPS\\\\uac00 \\\\uc608\\\\uc0c1\\\\ubcf4\\\\ub2e4 {delta_sign}\\\\uc77c \\\\ub54c \\\\uc601\\\\ud5a5\\\\uc744 \\\\ubc1b\\\\uc740 \\\\uc8fc\\\\uc2dd\\\\uc740 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"data_affectedtickers_10_diff_percent.visible\": false, \"data_affectedtickers_1_details_symbol_ticker\": \"GBPUSD\", \"data_affectedtickers_2_details_symbol_ticker\": \"EURUSD\", \"dictionary_2-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_E-mini Russell ? Index Futures\": \"E-mini Russell ? Index Futures\", \"dictionary_impactofearningsrelease_shorttext\": \"{earningscompany_name} ({earningscompany})\\\\uac00 \\\\uc624\\\\ub298 \\\\uc2e4\\\\uc801\\\\uc744 \\\\ubc1c\\\\ud45c\\\\ud558\\\\uba70 \\\\ub2e4\\\\uc74c \\\\uc8fc\\\\uc2dd\\\\uc5d0 \\\\uc601\\\\ud5a5\\\\uc744 \\\\ubbf8\\\\uce60 \\\\uac83\\\\uc785\\\\ub2c8\\\\ub2e4:\", \"dictionary_upcomingearnings_title_noearnings\": \"{fromdate}\\\\ubd80\\\\ud130 {todate}\\\\uae4c\\\\uc9c0\\\\ub294 \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c\\\\uac00 \\\\uc608\\\\uc815\\\\ub418\\\\uc5b4 \\\\uc788\\\\uc9c0 \\\\uc54a\\\\uc2b5\\\\ub2c8\\\\ub2e4.\", \"dictionary_10-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_CalendarofHighimpactEconomicEvents\": \"\\\\uc601\\\\ud5a5\\\\ub825\\\\uc774 \\\\ud070 \\\\uacbd\\\\uc81c \\\\uc774\\\\ubca4\\\\ud2b8 \\\\uc77c\\\\uc815\", \"data_affectedtickers_1_details_symbol_exchange\": \"FOREX\", \"data_affectedtickers_1_details_symbol_interval\": ?, \"data_affectedtickers_1_details_symbol_pip_size\": ?.?, \"data_affectedtickers_2_details_symbol_exchange\": \"FOREX\", \"data_affectedtickers_2_details_symbol_interval\": ?, \"data_affectedtickers_2_details_symbol_pip_size\": ?.?, \"dictionary_Market moves for the last ? hours:\": \"\\\\uc9c0\\\\ub09c ?\\\\uc2dc\\\\uac04 \\\\ub3d9\\\\uc548\\\\uc758 \\\\uc2dc\\\\uc7a5 \\\\ubcc0\\\\ub3d9:\", \"data_affectedtickers_1_details_symbol_pip_value\": ?, \"data_affectedtickers_2_details_symbol_pip_value\": ?, \"data_affectedtickers_3_diff_percent_str.visible\": false, \"data_affectedtickers_4_diff_percent_str.visible\": false, \"data_affectedtickers_5_diff_percent_str.visible\": false, \"data_affectedtickers_6_diff_percent_str.visible\": false, \"data_affectedtickers_7_diff_percent_str.visible\": false, \"data_affectedtickers_8_diff_percent_str.visible\": false, \"data_affectedtickers_9_diff_percent_str.visible\": false, \"params_creatomate_templateselectionscheme_value\": \"random\", \"data_affectedtickers_10_diff_percent_str.visible\": false, \"data_affectedtickers_1_volatility_increase_label\": \"\\\\ubcc0\\\\ub3d9\\\\uc131 \\\\uc99d\\\\uac00\", \"data_affectedtickers_2_volatility_increase_label\": \"\\\\ubcc0\\\\ub3d9\\\\uc131 \\\\uc99d\\\\uac00\", \"dictionary_Zeromarkets ?_WTI Crude Oil Futures\": \"WTI\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): {mean_mov_percent} \\\\ubcc0\\\\ub3d9 \\\\uac00\\\\ub2a5\\\\uc131\\\\uc774 {probability}\\\\uc785\\\\ub2c8\\\\ub2e4.\", \"dictionary_Market movements in the last ? hours.\": \"\\\\uc9c0\\\\ub09c ?\\\\uc2dc\\\\uac04 \\\\ub3d9\\\\uc548\\\\uc758 \\\\uc2dc\\\\uc7a5 \\\\uc6c0\\\\uc9c1\\\\uc784.\", \"data_affectedtickers_1_details_volatility_diff_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p75\": ?.?, \"dictionary_Companies releasing earnings this week:\": \"\\\\uc774\\\\ubc88 \\\\uc8fc \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c \\\\uae30\\\\uc5c5:\", \"dictionary_Zeromarkets ?_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_Zeromarkets ?_US Dollar Index Futures\": \"USDX\", \"data_affectedtickers_1_details_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_2_details_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_1_details_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_symbol_value_currency\": \"USD\", \"data_affectedtickers_1_details_symbol_value_lot_size\": ?, \"data_affectedtickers_2_details_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_symbol_value_currency\": \"USD\", \"data_affectedtickers_2_details_symbol_value_lot_size\": ?, \"dictionary_Zeromarkets ?_Nikkei ? Dollar Futures\": \"JP?\", \"data_affectedtickers_1_details_symbol_value_date_time\": \"?-01-30T?:?:?.?Z\", \"data_affectedtickers_1_details_volatility_diff_median\": ?.?, \"data_affectedtickers_2_details_symbol_value_date_time\": \"?-02-17T?:?:?.?Z\", \"data_affectedtickers_2_details_volatility_diff_median\": ?.?, \"dictionary_High impact economic events for this week:\": \"\\\\uc774\\\\ubc88 \\\\uc8fc \\\\uc911\\\\uc694\\\\ud55c \\\\uacbd\\\\uc81c \\\\uc774\\\\ubca4\\\\ud2b8:\", \"data_affectedtickers_1_details_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_1_details_volatility_diff_std_dev\": ?.?, \"data_affectedtickers_2_details_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_2_details_volatility_diff_std_dev\": ?.?e-06, \"dictionary_E-mini Dow Jones Industrial Average Futures\": \"E-mini Dow Jones Industrial Average Futures\", \"data_affectedtickers_1_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_2_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_1_details_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_value_max\": ?, \"data_affectedtickers_1_details_volatility_diff_value_min\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_value_max\": ?, \"data_affectedtickers_2_details_volatility_diff_value_min\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_3_volatility_increase_label.visible\": false, \"data_affectedtickers_4_volatility_increase_label.visible\": false, \"data_affectedtickers_5_volatility_increase_label.visible\": false, \"data_affectedtickers_6_volatility_increase_label.visible\": false, \"data_affectedtickers_7_volatility_increase_label.visible\": false, \"data_affectedtickers_8_volatility_increase_label.visible\": false, \"data_affectedtickers_9_volatility_increase_label.visible\": false, \"data_affectedtickers_10_volatility_increase_label.visible\": false, \"data_affectedtickers_1_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_event_volatility_value_p75\": ?, \"data_affectedtickers_1_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_mean\": ?, \"data_affectedtickers_2_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_mean\": ?, \"dictionary_No earnings announcements scheduled this week.\": \"\\\\uc774\\\\ubc88 \\\\uc8fc\\\\uc5d0\\\\ub294 \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c\\\\uac00 \\\\uc608\\\\uc815\\\\ub418\\\\uc5b4 \\\\uc788\\\\uc9c0 \\\\uc54a\\\\uc2b5\\\\ub2c8\\\\ub2e4.\", \"dictionary_Upcoming earnings announcements for this week:\": \"\\\\uc774\\\\ubc88 \\\\uc8fc \\\\uc608\\\\uc815\\\\ub41c \\\\uc2e4\\\\uc801 \\\\ubc1c\\\\ud45c:\", \"data_affectedtickers_1_details_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_2_details_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_1_details_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_median\": ?, \"data_affectedtickers_2_details_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_median\": ?, \"data_affectedtickers_1_details_event_volatility_value_median\": ?, \"data_affectedtickers_1_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p75\": ?, \"dictionary_These are the market movements for the last week:\": \"\\\\uc9c0\\\\ub09c \\\\uc8fc \\\\ub3d9\\\\uc548\\\\uc758 \\\\uc2dc\\\\uc7a5 \\\\uc6c0\\\\uc9c1\\\\uc784\\\\uc740 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\", \"data_affectedtickers_1_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_median\": ?, \"dictionary_These are the market movements for the last ? hours:\": \"\\\\uc9c0\\\\ub09c ?\\\\uc2dc\\\\uac04 \\\\ub3d9\\\\uc548\\\\uc758 \\\\uc2dc\\\\uc7a5 \\\\uc6c0\\\\uc9c1\\\\uc784\\\\uc740 \\\\ub2e4\\\\uc74c\\\\uacfc \\\\uac19\\\\uc2b5\\\\ub2c8\\\\ub2e4:\"}}?}", "trace": "traceback (most recent call last):\n file \"/var/task/lambda_function.py\", line ?, in lambda_handler\n results[?creatomate_response?] = webhook_creatomate.render(processlog, templateid,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n file \"/var/task/Webhook_Creatomate.py\", line ?, in render\n webhook_creatomate.waitforrender(apikey, creatomate_response[?][?id?], timeout)\n file \"/var/task/Webhook_Creatomate.py\", line ?, in waitforrender\n raise exception(status[?error_message?] +\". Render details: \" + json.dumps(status))\nexception: an http ? status was received while trying to download the file: https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg (element data_countryicon). render details: {\"id\": \"?e9f3dd-01fa-4f6c-8bbb-14d125195639\", \"status\": \"failed\", \"error_message\": \"An HTTP ? status was received while trying to download the file: https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg (element data_CountryIcon)\", \"url\": \"https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/?e9f3dd-01fa-4f6c-8bbb-14d125195639.png\", \"template_id\": \"?aa08409-8a69-4164-bd5d-c37b5949c5df\", \"template_name\": \"Volatility Warning PNG ? KR\", \"template_tags\": [], \"output_format\": \"png\", \"modifications\": {\"data_Date\": \"? ?\\uc6d4, ?AM UTC\", \"data_name\": \"\\uc2e4\\uc5c5\\ub960\", \"text_title\": \"?\\uc2dc\\uac04 \\ub0b4 \\uc5f0\\ud569 \\uc655\\uad6d\\uc5d0\\uc11c ?\\uc2e4\\uc5c5\\ub960?\\uc73c\\ub85c \\uc778\\ud55c \\ubcc0\\ub3d9\\uc131\\uc774 \\uc608\\uc0c1\\ub429\\ub2c8\\ub2e4.\", \"has_results\": true, \"data_Heading\": \"\\ubcc0\\ub3d9\\uc131 \\uacbd\\uace0\", \"data_country\": \"GB\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"\\uc2e4\\uc5c5\\ub960 (\\uc5f0\\ud569 \\uc655\\uad6d)\\ub85c \\uc778\\ud574 \\ubcc0\\ub3d9\\uc131\\uc774 \\ud06c\\uac8c \\uc99d\\uac00\\ud560 \\uac83\\uc785\\ub2c8\\ub2e4. EURUSD - ?.?\\nGBPUSD - ?.?\\n\", \"dictionary_Corn\": \"\\uc625\\uc218\\uc218\", \"dictionary_Gold\": \"\\uae08\", \"dictionary_Hour\": \"\\uc2dc\\uac04\", \"dictionary_Open\": \"\\uac1c\\uc7a5\", \"dictionary_Time\": \"\\uc2dc\\uac04\", \"text_short_text\": \"\\uc2e4\\uc5c5\\ub960 (\\uc5f0\\ud569 \\uc655\\uad6d)\\ub85c \\uc778\\ud574 \\ubcc0\\ub3d9\\uc131\\uc774 \\ud06c\\uac8c \\uc99d\\uac00\\ud560 \\uac83\\uc785\\ub2c8\\ub2e4. EURUSD - ?.?\\nGBPUSD - ?.?\\n\", \"data_CountryIcon\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg\", \"data_description\": \"In the United Kingdom, the unemployment rate measures the number of people actively looking for a job as a percentage of the labour force.\", \"data_released_at\": \"?-02-17T?:?\", \"dictionary_Close\": \"\\ud3d0\\uc7a5\", \"dictionary_Daily\": \"\\ub9e4\\uc77c\", \"dictionary_Event\": \"\\uc774\\ubca4\\ud2b8\", \"dictionary_Hours\": \"\\uc2dc\\uac04\", \"dictionary_Price\": \"\\uac00\\uaca9\", \"dictionary_Wheat\": \"\\ubc00\", \"quantity_results\": ?, \"data_country_name\": \"\\uc5f0\\ud569 \\uc655\\uad6d\", \"dictionary_Actual\": \"\\uc2e4\\uc81c\", \"dictionary_Change\": \"\", \"dictionary_Coffee\": \"\\ucee4\\ud53c\", \"dictionary_Friday\": \"\\uae08\\uc694\\uc77c\", \"dictionary_Monday\": \"\\uc6d4\\uc694\\uc77c\", \"dictionary_Silver\": \"\\uc740\", \"dictionary_Sunday\": \"\\uc77c\\uc694\\uc77c\", \"dictionary_Target\": \"\\ubaa9\\ud45c\", \"dictionary_dd MMM\": \"MMM d\\uc77c\", \"params_mode_value\": ?, \"params_uuid_value\": \"?c17f79-1688-4262-b2cc-bb8a7d82e0de\", \"dictionary_909_DAX\": \"GER?\", \"dictionary_AUD/USD\": \"AUD/USD\", \"dictionary_Company\": \"\\ud68c\\uc0ac\", \"dictionary_EUR/USD\": \"EUR/USD\", \"dictionary_GBP/USD\": \"GBP/USD\", \"dictionary_Indices\": \"\\uc9c0\\uc218\", \"dictionary_Minutes\": \"\\ubd84\", \"dictionary_NZD/USD\": \"NZD/USD\", \"dictionary_Tuesday\": \"\\ud654\\uc694\\uc77c\", \"dictionary_USD/CAD\": \"USD/CAD\", \"dictionary_USD/CHF\": \"USD/CHF\", \"dictionary_USD/JPY\": \"USD/JPY\", \"dictionary_909_Gold\": \"XAUUSD\", \"dictionary_Estimate\": \"\\ucd94\\uc815\", \"dictionary_Expected\": \"\\uc608\\uc0c1\\uce58\", \"dictionary_FTSE ?\": \"FTSE ?\", \"dictionary_Interval\": \"\\ucc28\\ud2b8 \\uc8fc\\uae30\", \"dictionary_KeyLevel\": \"\\ud575\\uc2ec \\uc218\\uc900\", \"dictionary_Previous\": \"\\uc774\\uc804\", \"dictionary_Saturday\": \"\\ud1a0\\uc694\\uc77c\", \"dictionary_Thursday\": \"\\ubaa9\\uc694\\uc77c\", \"dictionary_estimate\": \"\\ucd94\\uc815\\uce58\", \"dictionary_previous\": \"\\uc774\\uc804\", \"params_locale_value\": \"ko\", \"params_region_value\": \"ko\", \"data_country_name_en\": \"Great Britain\", \"dictionary_Consensus\": \"\\ud569\\uc758\", \"dictionary_Crude oil\": \"\\uc6d0\\uc720\", \"dictionary_Fibonacci\": \"\\ud53c\\ubcf4\\ub098\\uce58 \\ud328\\ud134\", \"dictionary_Hang Seng\": \"Hang Seng\", \"dictionary_Stop_Loss\": \"\\uc190\\uc808\\ub9e4\", \"dictionary_Wednesday\": \"\\uc218\\uc694\\uc77c\", \"dictionary_consensus\": \"\\ucee8\\uc13c\\uc11c\\uc2a4\", \"dictionary_dd MMM, h\": \"MMM d\\uc77c, h\", \"params_request_value\": \"VolatilityWarning\", \"params_tickers_value\": \"EURUSD,USDJPY,GBPUSD,AUDUSD,USDCHF,USDCAD,NZDUSD,EURJPY,GBPJPY,EURGBP,AUDJPY,EURAUD,CHFJPY,EURCHF\", \"params_webhook_value\": \"https://hooks.zapier.com/hooks/catch/?/?l0rz5n/\", \"dictionary_909_Silver\": \"XAGUSD\", \"dictionary_Currencies\": \"\\ud1b5\\ud654\", \"dictionary_Nasdaq ?\": \"Nasdaq ?\", \"dictionary_Nikkei ?\": \"Nikkei ?\", \"dictionary_Stop_Level\": \"\\t\\uc2a4\\ud1b1 \\ub808\\ubca8\", \"params_brokerid_value\": \"?\", \"dictionary_909_AUD/USD\": \"AUDUSD\", \"dictionary_909_EUR/USD\": \"EURUSD\", \"dictionary_909_GBP/USD\": \"GBPUSD\", \"dictionary_909_NZD/USD\": \"NZDUSD\", \"dictionary_909_USD/CAD\": \"USDCAD\", \"dictionary_909_USD/CHF\": \"USDCHF\", \"dictionary_909_USD/JPY\": \"USDJPY\", \"dictionary_AfterMarket\": \"\\uc7a5 \\ub9c8\\uac10 \\ud6c4\", \"dictionary_BigMovement\": \"\\ud070 \\ub3d9\\ud5a5\", \"dictionary_Commodities\": \"\\uc6d0\\uc790\\uc7ac\", \"dictionary_EEE, dd MMM\": \"EEE, MMM d\\uc77c\", \"dictionary_Entry_Level\": \"\\uc9c4\\uc785 \\uac00\\uaca9 \", \"dictionary_FridayShort\": \"\\uae08\\uc694\\uc77c\", \"dictionary_MarketAlert\": \"\\uc2dc\\uc7a5 \\uacbd\\uace0\", \"dictionary_MondayShort\": \"\\uc6d4\\uc694\\uc77c\", \"dictionary_Natural gas\": \"\\ucc9c\\uc5f0 \\uac00\\uc2a4\", \"dictionary_Probability\": \"\\ud655\\ub960\", \"dictionary_Releasetime\": \"\\ubc1c\\ud45c \\uc2dc\\uac01\", \"dictionary_SundayShort\": \"\\uc77c\\uc694\\uc77c\", \"dictionary_probability\": \"{ticker} \\uc218\\uc775 {deltasign} {consensus}\\uc778 \\uacbd\\uc6b0\", \"params_RateLimit_value\": \"?,?,?\", \"params_processid_value\": ?, \"params_threshold_value\": ?, \"dictionary_909_FTSE ?\": \"UK?\", \"dictionary_BeforeMarket\": \"\\uc7a5 \\uc2dc\\uc791 \\uc804\", \"dictionary_ChartPattern\": \"\\ucc28\\ud2b8 \\ud328\\ud134\", \"dictionary_Corn Futures\": \"Corn Futures\", \"dictionary_Gold Futures\": \"Gold Futures\", \"dictionary_Last12events\": \"\\uc9c0\\ub09c ?\\uac1c \\uc774\\ubca4\\ud2b8\", \"dictionary_RoundNumbers\": \"\\ubc18\\uc62c\\ub9bc \\uc218\", \"dictionary_Target_Level\": \"\\ubaa9\\ud45c \\uc218\\uc900\", \"dictionary_TuesdayShort\": \"\\ud654\\uc694\\uc77c\", 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Futures\", \"dictionary_impactofearningsrelease_title\": \"{earningscompany_name} ({earningscompany}) \\uc2e4\\uc801 \\ubc1c\\ud45c\\uc758 \\uc601\\ud5a5\", \"data_affectedtickers_10_normal_to.visible\": false, \"data_affectedtickers_1_details_symbol_pip\": ?, \"data_affectedtickers_2_details_symbol_pip\": ?, \"data_affectedtickers_3_event_from.visible\": false, \"data_affectedtickers_4_event_from.visible\": false, \"data_affectedtickers_5_event_from.visible\": false, \"data_affectedtickers_6_event_from.visible\": false, \"data_affectedtickers_7_event_from.visible\": false, \"data_affectedtickers_8_event_from.visible\": false, \"data_affectedtickers_9_event_from.visible\": false, \"data_affectedtickers_10_event_from.visible\": false, \"data_affectedtickers_1_details_symbol_name\": \"GBPUSD\", \"data_affectedtickers_2_details_symbol_name\": \"EURUSD\", \"data_affectedtickers_3_description.visible\": false, \"data_affectedtickers_3_normal_from.visible\": false, \"data_affectedtickers_4_description.visible\": false, \"data_affectedtickers_4_normal_from.visible\": false, \"data_affectedtickers_5_description.visible\": false, \"data_affectedtickers_5_normal_from.visible\": false, \"data_affectedtickers_6_description.visible\": false, \"data_affectedtickers_6_normal_from.visible\": false, \"data_affectedtickers_7_description.visible\": false, \"data_affectedtickers_7_normal_from.visible\": false, \"data_affectedtickers_8_description.visible\": false, \"data_affectedtickers_8_normal_from.visible\": false, \"data_affectedtickers_9_description.visible\": false, \"data_affectedtickers_9_normal_from.visible\": false, \"dictionary_Market moves for the last week:\": \"\\uc9c0\\ub09c \\uc8fc \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\ubcc0\\ub3d9:\", \"dictionary_This week?s biggest movers are:\": \"\\uc774\\ubc88 \\uc8fc \\uac00\\uc7a5 \\ud070 \\uc6c0\\uc9c1\\uc784\\uc740 \\ub2e4\\uc74c\\uacfc \\uac19\\uc2b5\\ub2c8\\ub2e4:\", \"dictionary_UpcomingHighImpactEconomicEvent\": \"\\ub2e4\\uac00\\uc624\\ub294 \\uc601\\ud5a5\\ub825 \\uc788\\ub294 \\uacbd\\uc81c \\uc774\\ubca4\\ud2b8\", \"data_affectedtickers_10_description.visible\": false, \"data_affectedtickers_10_normal_from.visible\": false, \"data_affectedtickers_3_diff_percent.visible\": false, \"data_affectedtickers_4_diff_percent.visible\": false, \"data_affectedtickers_5_diff_percent.visible\": false, \"data_affectedtickers_6_diff_percent.visible\": false, \"data_affectedtickers_7_diff_percent.visible\": false, \"data_affectedtickers_8_diff_percent.visible\": false, \"data_affectedtickers_9_diff_percent.visible\": false, \"dictionary_Zeromarkets ?_Platinum Futures\": \"XPTUSD\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany})\\uac00 \\uc624\\ub298 \\uc2e4\\uc801\\uc744 \\ubc1c\\ud45c\\ud569\\ub2c8\\ub2e4. EPS\\uac00 \\uc608\\uc0c1\\ubcf4\\ub2e4 {delta_sign}\\uc77c \\uacbd\\uc6b0, {earningscompany}\\ub294 \\ub2e4\\uc74c \\ub2ec \\ub3d9\\uc548 {mean_mov_percent} \\ubcc0\\ub3d9 \\uac00\\ub2a5\\uc131\\uc774 {probability}\\uc785\\ub2c8\\ub2e4.\\\\n\\uc774\\uc804 EPS\\uac00 \\uc608\\uc0c1\\ubcf4\\ub2e4 {delta_sign}\\uc77c \\ub54c \\uc601\\ud5a5\\uc744 \\ubc1b\\uc740 \\uc8fc\\uc2dd\\uc740 \\ub2e4\\uc74c\\uacfc \\uac19\\uc2b5\\ub2c8\\ub2e4:\", \"data_affectedtickers_10_diff_percent.visible\": false, \"data_affectedtickers_1_details_symbol_ticker\": \"GBPUSD\", \"data_affectedtickers_2_details_symbol_ticker\": \"EURUSD\", \"dictionary_2-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_E-mini Russell ? Index Futures\": \"E-mini Russell ? Index Futures\", \"dictionary_impactofearningsrelease_shorttext\": \"{earningscompany_name} ({earningscompany})\\uac00 \\uc624\\ub298 \\uc2e4\\uc801\\uc744 \\ubc1c\\ud45c\\ud558\\uba70 \\ub2e4\\uc74c \\uc8fc\\uc2dd\\uc5d0 \\uc601\\ud5a5\\uc744 \\ubbf8\\uce60 \\uac83\\uc785\\ub2c8\\ub2e4:\", \"dictionary_upcomingearnings_title_noearnings\": \"{fromdate}\\ubd80\\ud130 {todate}\\uae4c\\uc9c0\\ub294 \\uc2e4\\uc801 \\ubc1c\\ud45c\\uac00 \\uc608\\uc815\\ub418\\uc5b4 \\uc788\\uc9c0 \\uc54a\\uc2b5\\ub2c8\\ub2e4.\", \"dictionary_10-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_CalendarofHighimpactEconomicEvents\": \"\\uc601\\ud5a5\\ub825\\uc774 \\ud070 \\uacbd\\uc81c \\uc774\\ubca4\\ud2b8 \\uc77c\\uc815\", \"data_affectedtickers_1_details_symbol_exchange\": \"FOREX\", \"data_affectedtickers_1_details_symbol_interval\": ?, \"data_affectedtickers_1_details_symbol_pip_size\": ?.?, \"data_affectedtickers_2_details_symbol_exchange\": \"FOREX\", \"data_affectedtickers_2_details_symbol_interval\": ?, \"data_affectedtickers_2_details_symbol_pip_size\": ?.?, \"dictionary_Market moves for the last ? hours:\": \"\\uc9c0\\ub09c ?\\uc2dc\\uac04 \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\ubcc0\\ub3d9:\", \"data_affectedtickers_1_details_symbol_pip_value\": ?, \"data_affectedtickers_2_details_symbol_pip_value\": ?, \"data_affectedtickers_3_diff_percent_str.visible\": false, \"data_affectedtickers_4_diff_percent_str.visible\": false, \"data_affectedtickers_5_diff_percent_str.visible\": false, \"data_affectedtickers_6_diff_percent_str.visible\": false, \"data_affectedtickers_7_diff_percent_str.visible\": false, \"data_affectedtickers_8_diff_percent_str.visible\": false, \"data_affectedtickers_9_diff_percent_str.visible\": false, \"params_creatomate_templateselectionscheme_value\": \"random\", \"data_affectedtickers_10_diff_percent_str.visible\": false, \"data_affectedtickers_1_volatility_increase_label\": \"\\ubcc0\\ub3d9\\uc131 \\uc99d\\uac00\", \"data_affectedtickers_2_volatility_increase_label\": \"\\ubcc0\\ub3d9\\uc131 \\uc99d\\uac00\", \"dictionary_Zeromarkets ?_WTI Crude Oil Futures\": \"WTI\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): {mean_mov_percent} \\ubcc0\\ub3d9 \\uac00\\ub2a5\\uc131\\uc774 {probability}\\uc785\\ub2c8\\ub2e4.\", \"dictionary_Market movements in the last ? hours.\": \"\\uc9c0\\ub09c ?\\uc2dc\\uac04 \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\uc6c0\\uc9c1\\uc784.\", \"data_affectedtickers_1_details_volatility_diff_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p25\": ?.?, \"data_affectedtickers_2_details_volatility_diff_p75\": ?.?, \"dictionary_Companies releasing earnings this week:\": \"\\uc774\\ubc88 \\uc8fc \\uc2e4\\uc801 \\ubc1c\\ud45c \\uae30\\uc5c5:\", \"dictionary_Zeromarkets ?_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_Zeromarkets ?_US Dollar Index Futures\": \"USDX\", \"data_affectedtickers_1_details_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_2_details_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_volatility_diff_mean\": ?.?, \"data_affectedtickers_1_details_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_symbol_value_currency\": \"USD\", \"data_affectedtickers_1_details_symbol_value_lot_size\": ?, \"data_affectedtickers_2_details_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_symbol_value_currency\": \"USD\", \"data_affectedtickers_2_details_symbol_value_lot_size\": ?, \"dictionary_Zeromarkets ?_Nikkei ? Dollar Futures\": \"JP?\", \"data_affectedtickers_1_details_symbol_value_date_time\": \"?-01-30T?:?:?.?Z\", \"data_affectedtickers_1_details_volatility_diff_median\": ?.?, \"data_affectedtickers_2_details_symbol_value_date_time\": \"?-02-17T?:?:?.?Z\", \"data_affectedtickers_2_details_volatility_diff_median\": ?.?, \"dictionary_High impact economic events for this week:\": \"\\uc774\\ubc88 \\uc8fc \\uc911\\uc694\\ud55c \\uacbd\\uc81c \\uc774\\ubca4\\ud2b8:\", \"data_affectedtickers_1_details_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_1_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_1_details_volatility_diff_std_dev\": ?.?, \"data_affectedtickers_2_details_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_max\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_min\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p25\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_p75\": ?.?, \"data_affectedtickers_2_details_symbol_is_outbound_code\": ?, \"data_affectedtickers_2_details_volatility_diff_std_dev\": ?.?e-06, \"dictionary_E-mini Dow Jones Industrial Average Futures\": \"E-mini Dow Jones Industrial Average Futures\", \"data_affectedtickers_1_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_2_details_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_max_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_min_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p25_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_p75_pips\": ?, \"data_affectedtickers_1_details_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_value_max\": ?, \"data_affectedtickers_1_details_volatility_diff_value_min\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_1_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_mean_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_value_max\": ?, \"data_affectedtickers_2_details_volatility_diff_value_min\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_value_p75\": ?, \"data_affectedtickers_3_volatility_increase_label.visible\": false, \"data_affectedtickers_4_volatility_increase_label.visible\": false, \"data_affectedtickers_5_volatility_increase_label.visible\": false, \"data_affectedtickers_6_volatility_increase_label.visible\": false, \"data_affectedtickers_7_volatility_increase_label.visible\": false, \"data_affectedtickers_8_volatility_increase_label.visible\": false, \"data_affectedtickers_9_volatility_increase_label.visible\": false, \"data_affectedtickers_10_volatility_increase_label.visible\": false, \"data_affectedtickers_1_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_event_volatility_value_p75\": ?, \"data_affectedtickers_1_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_mean\": ?, \"data_affectedtickers_2_details_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_no_event_volatility_median\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_mean\": ?, \"dictionary_No earnings announcements scheduled this week.\": \"\\uc774\\ubc88 \\uc8fc\\uc5d0\\ub294 \\uc2e4\\uc801 \\ubc1c\\ud45c\\uac00 \\uc608\\uc815\\ub418\\uc5b4 \\uc788\\uc9c0 \\uc54a\\uc2b5\\ub2c8\\ub2e4.\", \"dictionary_Upcoming earnings announcements for this week:\": \"\\uc774\\ubc88 \\uc8fc \\uc608\\uc815\\ub41c \\uc2e4\\uc801 \\ubc1c\\ud45c:\", \"data_affectedtickers_1_details_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_1_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p25\": ?.?, \"data_affectedtickers_1_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_2_details_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_no_event_volatility_std_dev\": ?.?, \"data_affectedtickers_2_details_volatility_diff_median_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_max\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_min\": ?.?, \"data_affectedtickers_2_details_volatility_diff_percent_p25\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_p75\": ?.?, \"data_affectedtickers_1_details_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_1_details_volatility_diff_value_median\": ?, \"data_affectedtickers_2_details_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_no_event_volatility_max_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_min_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p25_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_p75_pips\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_mean\": ?.?, \"data_affectedtickers_2_details_volatility_diff_value_median\": ?, \"data_affectedtickers_1_details_event_volatility_value_median\": ?, \"data_affectedtickers_1_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_1_details_no_event_volatility_value_p75\": ?, \"data_affectedtickers_2_details_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_mean_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_max\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_min\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p25\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_p75\": ?, \"dictionary_These are the market movements for the last week:\": \"\\uc9c0\\ub09c \\uc8fc \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\uc6c0\\uc9c1\\uc784\\uc740 \\ub2e4\\uc74c\\uacfc \\uac19\\uc2b5\\ub2c8\\ub2e4:\", \"data_affectedtickers_1_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_value_mean\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_median\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_1_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_1_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_2_details_no_event_volatility_median_pips\": ?, \"data_affectedtickers_2_details_no_event_volatility_sample_size\": ?, \"data_affectedtickers_2_details_volatility_diff_percent_std_dev\": ?.?, \"data_affectedtickers_1_details_no_event_volatility_value_median\": ?, \"data_affectedtickers_2_details_no_event_volatility_value_median\": ?, \"dictionary_These are the market movements for the last ? hours:\": \"\\uc9c0\\ub09c ?\\uc2dc\\uac04 \\ub3d9\\uc548\\uc758 \\uc2dc\\uc7a5 \\uc6c0\\uc9c1\\uc784\\uc740 \\ub2e4\\uc74c\\uacfc \\uac19\\uc2b5\\ub2c8\\ub2e4:\"}}" } }?json');Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Feb 17 07 1 0ms 0ms 15 0ms 0ms 0ms 56 0ms select * from ( select pricedatetime, open, high, low, close, volume, bsf from t60 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Feb 17 07 56 0ms 0ms 16 0ms 0ms 0ms 2 0ms with pre_symbols as ( select s.symbolid, s.symbol, s.timegranularity, dss.downloadersymbol, dtt.timezone, s.exchange, s.longname from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join brokersymbollist bsl on bsl.symbolid = s.symbolid and brokerid = ? where dss.classname in (...) and (dss.downloadersymbol in (...) or s.symbol in (...)) and dss.enabled = ? and s.nonliquid = ? and s.deleted = ? ), report_symbols as ( select ps1.*, ps2.symbolid as price_symbol_id from pre_symbols ps1 inner join pre_symbols ps2 on ps1.symbol = ps2.symbol and ps1.downloadersymbol = ps2.downloadersymbol and ps2.timegranularity = ? ), rbr_max as ( select resultuid from relevance_bigmovement_results order by resultuid desc limit ? ) select bmr.resultuid as ruid, ? as direction, rs.symbol as sym, rs.downloadersymbol, rs.symbolid as sid, rs.timegranularity as tg, rs.timezone, rs.exchange as e, rs.longname, bmr.patternendtime as pet, lpi.latestpricedatetime as lpdt, bmr.patternlengthbars as l, bmr.breakout >= ? as complete, rbr.age as age from bigmovement_results bmr inner join report_symbols rs on rs.symbolid = bmr.symbolid inner join autochartist_symbolupdates lpi on lpi.symbolid = rs.price_symbol_id inner join rbr_max rm on ? = ? left outer join relevance_bigmovement_results rbr on rbr.resultuid = bmr.resultuid where patternendtime >= (now() - ? * interval ?) -- results can't be more than ? days old and (bmr.resultuid > rm.resultuid or rbr.relevant = ?) ;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Feb 17 07 2 0ms 0ms 17 0ms 0ms 0ms 331 0ms commit;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Feb 17 07 331 0ms 0ms 18 0ms 0ms 0ms 327 0ms with rar_max as ( select resultuid from relevance_keylevels_results order by resultuid desc limit ? ), kr as ( select a.*, rr.age, rr.relevant from keylevels_results a left outer join relevance_keylevels_results rr on a.resultuid = rr.resultuid where case when false = ? then true else a.resultuid > ( select min(resultuid) from relevance_keylevels_results) end ), all_results as ( select kr.resultuid as resultuid, kr.direction as direction, s.exchange as exchange, s.symbolid as symbolid, coalesce(bim.code, s.symbol) as symbol_code, s.longname as symbol_name, s.timegranularity as interval, p.patternname as pattern_name, kr.breakout as breakout, kr.atbaridentified as identified, dtt.timezone as timezone, kr.patternlengthbars as length, g.basegroupname, newlevels.filtered, case when kr.age is not null then kr.age when kr.resultuid <= rm.resultuid then ? else ? end as age, case when kr.relevant is not null then kr.relevant when kr.resultuid <= rm.resultuid then ? else ? end as relevant, cps.pip from kr inner join brokersymbollist bsl on bsl.brokerid = ? and bsl.symbolid = kr.symbolid inner join symbols s on bsl.symbolid = s.symbolid and s.nonliquid = ? inner join symbolgroup sg on s.symbolid = sg.symbolid inner join groups g on sg.groupid = g.groupid inner join brokergroups bg on g.groupid = bg.groupid and bsl.brokerid = bg.brokerid inner join hrspatterns p on kr.patternid = p.patternid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join rar_max rm on ? = ? left outer join autochartist_symbolupdates au on dss.symbolid = au.symbolid left outer join relevance_keylevels_results rar on rar.resultuid = kr.resultuid left join lateral calc_kl_signal_filter (kr.resultuid) newlevels on true left join currencypips cps on cps.symbol = s.symbol left outer join brokerinstrumentmap bim on dss.datafeedinstrumentid = bim.datafeedinstrumentid and bim.brokerid = bsl.brokerid and bim.type = ? where kr.gmttimefound > now() - interval ? and dss.enabled = ? and s.deleted = ? and (kr.simulation = ? or kr.simulation is null) and (? = ? or s.timegranularity in (...)) and (? = ? or s.exchange in (...)) and (? = ? or coalesce(bim.code, s.symbol) in (...)) and (? = ? or p.patternname in (...)) and (? = ? or kr.patternclassid in (...)) and (? = ? or kr.patternlengthbars <= ?) and kr.patternstarttime::timestamp without time zone >= coalesce(au.earliestpricedatetime, ?::timestamp without time zone) -- to make sure patternstarttime is in our t-tables ), results as ( select distinct on (symbolid) * from all_results where (false = ? or relevant = ?) and (? = ? or age <= ?) order by symbolid, resultuid ) select * from results order by identified desc, length desc limit ?;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Feb 17 07 327 0ms 0ms 19 0ms 0ms 0ms 11 0ms select psd.symbolid, psd.hourlysymbolid as intervalsymbolid, s.symbol, dtt.timezone, dtt.absolutetimezoneoffset, psh.hour as index, psh.ave, psh.stddev, ((psh.ave - psh.stddev) / ?.?) as low, ((psh.ave + psh.stddev) / ?.?) as high, ee.timezone as exchangetimezone, ee.mon_t1start as exchangestart, ee.mon_t1end as exchangeend from powerstats_symboldata psd inner join powerstats_hourly psh on psd.hourlysymbolid = psh.symbolid inner join symbols s on psh.symbolid = s.symbolid inner join downloadersymbolsettings dss on s.symbolid = dss.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname inner join mat_ps_hourly_symbolid_max_enddate e on psh.enddate = e.enddate and psh.symbolid = e.symbolid inner join exchanges ee on ee.exchange = s.exchange where psd.symbolid = ? and dtt.dayofweek = ? order by hour asc;Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Feb 17 07 11 0ms 0ms 20 0ms 0ms 0ms 3 0ms select (cast(substring(tz.gmoffset from ? for ?) as float) * ? + cast(substring(tz.gmoffset from ? for ?) as float)) / ? as offset from timezones tz where tz.timezone = ?;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Feb 17 07 3 0ms 0ms Time consuming prepare
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 2s677ms 2,275 0ms 12ms 1ms WITH rar_max as ( ;Times Reported Time consuming prepare #1
Day Hour Count Duration Avg duration Feb 17 07 2,275 2s677ms 1ms -
WITH rar_max as ( ;
Date: 2026-02-17 07:15:43 Duration: 12ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-02-17 07:30:43 Duration: 11ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-02-17 07:52:18 Duration: 11ms Database: postgres
2 1s685ms 1,108 0ms 10ms 1ms SELECT symbolid, ;Times Reported Time consuming prepare #2
Day Hour Count Duration Avg duration 07 1,108 1s685ms 1ms -
SELECT symbolid, ;
Date: 2026-02-17 07:15:51 Duration: 10ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-02-17 07:01:07 Duration: 7ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-02-17 07:45:54 Duration: 5ms Database: postgres
3 792ms 2,815 0ms 12ms 0ms SELECT ;Times Reported Time consuming prepare #3
Day Hour Count Duration Avg duration 07 2,815 792ms 0ms -
SELECT ;
Date: 2026-02-17 07:15:52 Duration: 12ms Database: postgres
-
SELECT ;
Date: 2026-02-17 07:40:25 Duration: 10ms Database: postgres
-
SELECT ;
Date: 2026-02-17 07:52:30 Duration: 8ms Database: postgres
4 407ms 369 0ms 2ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming prepare #4
Day Hour Count Duration Avg duration 07 369 407ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 07:47:12 Duration: 2ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 07:30:53 Duration: 1ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 07:45:12 Duration: 1ms Database: postgres
5 274ms 1,822 0ms 2ms 0ms SET extra_float_digits = 3;Times Reported Time consuming prepare #5
Day Hour Count Duration Avg duration 07 1,822 274ms 0ms -
SET extra_float_digits = 3;
Date: 2026-02-17 07:45:59 Duration: 2ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-02-17 07:00:44 Duration: 1ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-02-17 07:46:02 Duration: 1ms Database: postgres
6 268ms 2,994 0ms 2ms 0ms INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming prepare #6
Day Hour Count Duration Avg duration 07 2,994 268ms 0ms -
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:01:06 Duration: 2ms Database: postgres
-
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:40:50 Duration: 0ms Database: postgres
-
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:41:48 Duration: 0ms Database: postgres
7 220ms 2,071 0ms 0ms 0ms INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming prepare #7
Day Hour Count Duration Avg duration 07 2,071 220ms 0ms -
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:00:45 Duration: 0ms Database: postgres
-
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:00:45 Duration: 0ms Database: postgres
-
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:11:25 Duration: 0ms Database: postgres
8 170ms 1,038 0ms 0ms 0ms INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming prepare #8
Day Hour Count Duration Avg duration 07 1,038 170ms 0ms -
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:32:17 Duration: 0ms Database: postgres
-
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:16:46 Duration: 0ms Database: postgres
-
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:46:28 Duration: 0ms Database: postgres
9 105ms 16 4ms 7ms 6ms with sym_info as ( ;Times Reported Time consuming prepare #9
Day Hour Count Duration Avg duration 07 16 105ms 6ms -
with sym_info as ( ;
Date: 2026-02-17 07:51:49 Duration: 7ms Database: postgres
-
with sym_info as ( ;
Date: 2026-02-17 07:51:46 Duration: 7ms Database: postgres
-
with sym_info as ( ;
Date: 2026-02-17 07:06:46 Duration: 7ms Database: postgres
10 77ms 1,521 0ms 2ms 0ms select 1;Times Reported Time consuming prepare #10
Day Hour Count Duration Avg duration 07 1,521 77ms 0ms -
select 1;
Date: 2026-02-17 07:30:53 Duration: 2ms Database: postgres
-
select 1;
Date: 2026-02-17 07:57:32 Duration: 2ms Database: postgres
-
select 1;
Date: 2026-02-17 07:51:36 Duration: 2ms Database: postgres
11 49ms 18 1ms 4ms 2ms select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;Times Reported Time consuming prepare #11
Day Hour Count Duration Avg duration 07 18 49ms 2ms -
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;
Date: 2026-02-17 07:01:19 Duration: 4ms Database: postgres
-
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;
Date: 2026-02-17 07:51:10 Duration: 3ms Database: postgres
-
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;
Date: 2026-02-17 07:11:01 Duration: 3ms Database: postgres
12 34ms 296 0ms 0ms 0ms INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming prepare #12
Day Hour Count Duration Avg duration 07 296 34ms 0ms -
INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:02:33 Duration: 0ms Database: postgres
-
INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:46:30 Duration: 0ms Database: postgres
-
INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:46:34 Duration: 0ms Database: postgres
13 30ms 159 0ms 0ms 0ms SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;Times Reported Time consuming prepare #13
Day Hour Count Duration Avg duration 07 159 30ms 0ms -
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-17 07:13:10 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-17 07:13:11 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-17 07:13:11 Duration: 0ms Database: postgres
14 28ms 204 0ms 0ms 0ms INSERT INTO T1440_underlying (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming prepare #14
Day Hour Count Duration Avg duration 07 204 28ms 0ms -
INSERT INTO T1440_underlying (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:45:57 Duration: 0ms Database: postgres
-
INSERT INTO T1440_underlying (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:47:07 Duration: 0ms Database: postgres
-
INSERT INTO T1440_underlying (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:00:54 Duration: 0ms Database: postgres
15 27ms 24 0ms 3ms 1ms WITH last_candle AS ( ;Times Reported Time consuming prepare #15
Day Hour Count Duration Avg duration 07 24 27ms 1ms -
WITH last_candle AS ( ;
Date: 2026-02-17 07:36:04 Duration: 3ms Database: postgres
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WITH last_candle AS ( ;
Date: 2026-02-17 07:52:04 Duration: 3ms Database: postgres
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WITH last_candle AS ( ;
Date: 2026-02-17 07:04:00 Duration: 2ms Database: postgres
16 23ms 1,796 0ms 0ms 0ms SET application_name = 'PostgreSQL JDBC Driver';Times Reported Time consuming prepare #16
Day Hour Count Duration Avg duration 07 1,796 23ms 0ms -
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-02-17 07:40:25 Duration: 0ms Database: postgres
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SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-02-17 07:01:08 Duration: 0ms Database: postgres
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SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-02-17 07:46:02 Duration: 0ms Database: postgres
17 20ms 22 0ms 0ms 0ms select distinct classname, to_char(created_datetime, 'yyyy-mm-dd HH24:MI'), to_char(cleared_datetime, 'yyyy-mm-dd HH24:MI'), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = 1 OR cleared_datetime > current_timestamp - interval '17 hour') order by created_datetime desc;Times Reported Time consuming prepare #17
Day Hour Count Duration Avg duration 07 22 20ms 0ms -
select distinct classname, to_char(created_datetime, 'yyyy-mm-dd HH24:MI'), to_char(cleared_datetime, 'yyyy-mm-dd HH24:MI'), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = 1 OR cleared_datetime > current_timestamp - interval '17 hour') order by created_datetime desc;
Date: 2026-02-17 07:55:02 Duration: 0ms Database: postgres
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select distinct classname, to_char(created_datetime, 'yyyy-mm-dd HH24:MI'), to_char(cleared_datetime, 'yyyy-mm-dd HH24:MI'), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = 1 OR cleared_datetime > current_timestamp - interval '17 hour') order by created_datetime desc;
Date: 2026-02-17 07:15:01 Duration: 0ms Database: postgres
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select distinct classname, to_char(created_datetime, 'yyyy-mm-dd HH24:MI'), to_char(cleared_datetime, 'yyyy-mm-dd HH24:MI'), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = 1 OR cleared_datetime > current_timestamp - interval '17 hour') order by created_datetime desc;
Date: 2026-02-17 07:20:02 Duration: 0ms Database: postgres
18 16ms 22 0ms 1ms 0ms select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;Times Reported Time consuming prepare #18
Day Hour Count Duration Avg duration 07 22 16ms 0ms -
select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;
Date: 2026-02-17 07:45:06 Duration: 1ms Database: postgres
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select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;
Date: 2026-02-17 07:30:04 Duration: 0ms Database: postgres
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select feedname, to_char(latestrxtime, 'yyyy-mm-dd HH24:MI'), to_char(LatestDBWriteTime, 'yyyy-mm-dd HH24:MI'), to_char(LatestStartupTime, 'yyyy-mm-dd HH24:MI'), StartupTimeInMinutes, dm.source_type, dm.transport_type, case when latestrxtime < (CURRENT_TIMESTAMP - 5 * interval '1 minute') then 'X' else 'OK' end, case when (feedname ilike '%_EOD' OR feedname ilike 'IQFEED_DAILIES' or feedname ilike 'YAHOO%' or feedname ilike 'QUANDL_FUTURES%' or feedname ilike 'BAR_CHART') then case when LatestDBWriteTime < (CURRENT_TIMESTAMP - 24 * interval '1 hour') then 'X' else 'OK' end else case when (LatestDBWriteTime < (CURRENT_TIMESTAMP - 15 * interval '1 minute') and LatestStartupTime < (CURRENT_TIMESTAMP - 30 * interval '1 minute')) OR latestrxtime < CURRENT_TIMESTAMP - interval '2 hour' then 'X' else 'OK' end end as statusDB, comment from datafeeds_latestrun dlr left outer join datafeeds df on dlr.feedname ilike df.name inner join datafeeds_metadata dm on df.metadata_id = dm.id order by feedname;
Date: 2026-02-17 07:20:02 Duration: 0ms Database: postgres
19 15ms 6 2ms 3ms 2ms with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;Times Reported Time consuming prepare #19
Day Hour Count Duration Avg duration 07 6 15ms 2ms -
with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;
Date: 2026-02-17 07:30:02 Duration: 3ms Database: postgres
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with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;
Date: 2026-02-17 07:40:02 Duration: 2ms Database: postgres
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with rankedmt4 as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors ), last_feed_entry as ( select * from rankedmt4 where r = 1 ), ok_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where status = 'OK' ), earliest_entry_after_ok as ( select m.datafeedname, min(m.eventtimestamp) as eventtimestamp from mt4datafeederrors m left outer join ( select datafeedname, eventtimestamp from ok_entries where r = 1) oo on m.datafeedname = oo.datafeedname where m.eventtimestamp > coalesce(oo.eventtimestamp, '1900-01-01'::timestamp without time zone) group by m.datafeedname ), notified_entries as ( select *, row_number() over (partition by datafeedname order by eventtimestamp desc) r from mt4datafeederrors where notified is not null and notified <> '' ), broker as ( select *, row_number() over (partition by feedname order by brokerid) r from ( select distinct b.brokerid, b.name as brokername, dss.classname as feedname from downloadersymbolsettings dss inner join brokersymbollist bsl on dss.symbolid = bsl.symbolid inner join broker b on bsl.brokerid = b.brokerid where dss.enabled = 1) a ) select last.id, last.datafeedname, last.eventtimestamp, last.status, last.errordescription, last.serveraddress, last.username, note.notified, note.eventtimestamp, broker.brokername from last_feed_entry last inner join earliest_entry_after_ok after_ok on last.datafeedname = after_ok.datafeedname inner join broker on last.datafeedname = broker.feedname left outer join ok_entries ok on ok.datafeedname = last.datafeedname left outer join notified_entries note on note.datafeedname = last.datafeedname and note.r = 1 where (ok.r is null or ok.r = 1) and last.datafeedname not in ( select distinct datafeedname from last_feed_entry where status = 'OK') and extract(epoch from (last.eventtimestamp - after_ok.eventtimestamp)) > 60 * 60 and last.eventtimestamp > current_timestamp - interval '1 day' and (note.eventtimestamp is null or note.eventtimestamp < current_timestamp - interval '10 hours') and last.eventtimestamp > current_timestamp - interval '1 hour' and broker.r = 1;
Date: 2026-02-17 07:50:02 Duration: 2ms Database: postgres
20 14ms 24 0ms 0ms 0ms select count(*) from datafeed_restarter_events where is_current_entry = 1;Times Reported Time consuming prepare #20
Day Hour Count Duration Avg duration 07 24 14ms 0ms -
select count(*) from datafeed_restarter_events where is_current_entry = 1;
Date: 2026-02-17 07:55:00 Duration: 0ms Database: postgres
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select count(*) from datafeed_restarter_events where is_current_entry = 1;
Date: 2026-02-17 07:10:07 Duration: 0ms Database: postgres
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select count(*) from datafeed_restarter_events where is_current_entry = 1;
Date: 2026-02-17 07:45:00 Duration: 0ms Database: postgres
Time consuming bind
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 32s656ms 3,059 0ms 58ms 10ms WITH rar_max as ( ;Times Reported Time consuming bind #1
Day Hour Count Duration Avg duration Feb 17 07 3,059 32s656ms 10ms -
WITH rar_max as ( ;
Date: 2026-02-17 07:00:58 Duration: 58ms Database: postgres parameters: $1 = 't', $2 = '538', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '125', $14 = 'ANGLO', $15 = 'BARC', $16 = 'BAY', $17 = 'BPLON', $18 = 'HSBCL', $19 = 'LLOY', $20 = 'RIO', $21 = 'RollsRoyce', $22 = 'TESCO', $23 = 'VOD', $24 = 'AUDCAD', $25 = 'AUDCHF', $26 = 'AUDJPY', $27 = 'AUDNZD', $28 = 'AUDUSD', $29 = 'CADCHF', $30 = 'CADJPY', $31 = 'CHFJPY', $32 = 'EURAUD', $33 = 'EURCAD', $34 = 'EURCHF', $35 = 'EURDKK', $36 = 'EURGBP', $37 = 'EURHUF', $38 = 'EURJPY', $39 = 'EURNOK', $40 = 'EURNZD', $41 = 'EURPLN', $42 = 'EURUSD', $43 = 'GBPAUD', $44 = 'GBPCAD', $45 = 'GBPCHF', $46 = 'GBPJPY', $47 = 'GBPNZD', $48 = 'GBPUSD', $49 = 'GBPZAR', $50 = 'NZDCAD', $51 = 'NZDCHF', $52 = 'NZDJPY', $53 = 'NZDUSD', $54 = 'USDCAD', $55 = 'USDCHF', $56 = 'USDCNH', $57 = 'USDCZK', $58 = 'USDDKK', $59 = 'USDHKD', $60 = 'USDHUF', $61 = 'USDJPY', $62 = 'USDMXN', $63 = 'USDNOK', $64 = 'USDPLN', $65 = 'USDSEK', $66 = 'USDSGD', $67 = 'USDTRY', $68 = 'USDZAR', $69 = 'XAGEUR', $70 = 'XAGUSD', $71 = 'XAUEUR', $72 = 'XAUUSD', $73 = 'ZARJPY', $74 = 'Cocoa', $75 = 'Coffee', $76 = 'Copper', $77 = 'Palladium', $78 = 'Platinum', $79 = 'Sugar', $80 = 'UKOIL', $81 = 'USOIL', $82 = 'AUS200', $83 = 'FRA40', $84 = 'JPN225', $85 = 'NETH25', $86 = 'SPA35', $87 = 'SUI20', $88 = 'UK100', $89 = 'USA100', $90 = 'USA30', $91 = 'USDIndex', $92 = 'ALCOA', $93 = 'ALIBABA', $94 = 'AMAZON', $95 = 'AMEX', $96 = 'APPLE', $97 = 'BBVA', $98 = 'BOA', $99 = 'BOEING', $100 = 'CHEVRON', $101 = 'CISCO', $102 = 'COKE', $103 = 'EBAY', $104 = 'GE', $105 = 'GOOGLE', $106 = 'GS', $107 = 'HLT', $108 = 'IBM', $109 = 'ILMN', $110 = 'INTEL', $111 = 'Iberdrola', $112 = 'MCARD', $113 = 'MCDON', $114 = 'META', $115 = 'MSFT', $116 = 'Mapfre', $117 = 'Netflix', $118 = 'PFIZER', $119 = 'QCOM', $120 = 'Santander', $121 = 'TEVA', $122 = 'Telefonica', $123 = 'Tesla', $124 = 'AUDUSD', $125 = 'EURGBP', $126 = 'EURUSD', $127 = 'GBPUSD', $128 = 'NZDUSD', $129 = 'USDCAD', $130 = 'USDCHF', $131 = 'USDJPY', $132 = 'Adidas', $133 = 'Bayer', $134 = 'Daimler', $135 = 'Danone', $136 = 'LVMH', $137 = 'Lufthansa', $138 = 'Volksw', $139 = '0', $140 = '', $141 = '500', $142 = '500', $143 = '0', $144 = '0', $145 = '0', $146 = 't', $147 = '10', $148 = '10'
-
WITH rar_max as ( ;
Date: 2026-02-17 07:16:17 Duration: 56ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '0', $10 = 't', $11 = '0', $12 = '0'
-
WITH rar_max as ( ;
Date: 2026-02-17 07:15:54 Duration: 54ms Database: postgres parameters: $1 = 't', $2 = '538', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '125', $14 = 'ANGLO', $15 = 'BARC', $16 = 'BAY', $17 = 'BPLON', $18 = 'HSBCL', $19 = 'LLOY', $20 = 'RIO', $21 = 'RollsRoyce', $22 = 'TESCO', $23 = 'VOD', $24 = 'AUDCAD', $25 = 'AUDCHF', $26 = 'AUDJPY', $27 = 'AUDNZD', $28 = 'AUDUSD', $29 = 'CADCHF', $30 = 'CADJPY', $31 = 'CHFJPY', $32 = 'EURAUD', $33 = 'EURCAD', $34 = 'EURCHF', $35 = 'EURDKK', $36 = 'EURGBP', $37 = 'EURHUF', $38 = 'EURJPY', $39 = 'EURNOK', $40 = 'EURNZD', $41 = 'EURPLN', $42 = 'EURUSD', $43 = 'GBPAUD', $44 = 'GBPCAD', $45 = 'GBPCHF', $46 = 'GBPJPY', $47 = 'GBPNZD', $48 = 'GBPUSD', $49 = 'GBPZAR', $50 = 'NZDCAD', $51 = 'NZDCHF', $52 = 'NZDJPY', $53 = 'NZDUSD', $54 = 'USDCAD', $55 = 'USDCHF', $56 = 'USDCNH', $57 = 'USDCZK', $58 = 'USDDKK', $59 = 'USDHKD', $60 = 'USDHUF', $61 = 'USDJPY', $62 = 'USDMXN', $63 = 'USDNOK', $64 = 'USDPLN', $65 = 'USDSEK', $66 = 'USDSGD', $67 = 'USDTRY', $68 = 'USDZAR', $69 = 'XAGEUR', $70 = 'XAGUSD', $71 = 'XAUEUR', $72 = 'XAUUSD', $73 = 'ZARJPY', $74 = 'Cocoa', $75 = 'Coffee', $76 = 'Copper', $77 = 'Palladium', $78 = 'Platinum', $79 = 'Sugar', $80 = 'UKOIL', $81 = 'USOIL', $82 = 'AUS200', $83 = 'FRA40', $84 = 'JPN225', $85 = 'NETH25', $86 = 'SPA35', $87 = 'SUI20', $88 = 'UK100', $89 = 'USA100', $90 = 'USA30', $91 = 'USDIndex', $92 = 'ALCOA', $93 = 'ALIBABA', $94 = 'AMAZON', $95 = 'AMEX', $96 = 'APPLE', $97 = 'BBVA', $98 = 'BOA', $99 = 'BOEING', $100 = 'CHEVRON', $101 = 'CISCO', $102 = 'COKE', $103 = 'EBAY', $104 = 'GE', $105 = 'GOOGLE', $106 = 'GS', $107 = 'HLT', $108 = 'IBM', $109 = 'ILMN', $110 = 'INTEL', $111 = 'Iberdrola', $112 = 'MCARD', $113 = 'MCDON', $114 = 'META', $115 = 'MSFT', $116 = 'Mapfre', $117 = 'Netflix', $118 = 'PFIZER', $119 = 'QCOM', $120 = 'Santander', $121 = 'TEVA', $122 = 'Telefonica', $123 = 'Tesla', $124 = 'AUDUSD', $125 = 'EURGBP', $126 = 'EURUSD', $127 = 'GBPUSD', $128 = 'NZDUSD', $129 = 'USDCAD', $130 = 'USDCHF', $131 = 'USDJPY', $132 = 'Adidas', $133 = 'Bayer', $134 = 'Daimler', $135 = 'Danone', $136 = 'LVMH', $137 = 'Lufthansa', $138 = 'Volksw', $139 = '0', $140 = '', $141 = '500', $142 = '500', $143 = '0', $144 = '0', $145 = '0', $146 = 't', $147 = '10', $148 = '10'
2 6s430ms 16,361 0ms 12ms 0ms SELECT ;Times Reported Time consuming bind #2
Day Hour Count Duration Avg duration 07 16,361 6s430ms 0ms -
SELECT ;
Date: 2026-02-17 07:40:25 Duration: 12ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '515840243200566300'
-
SELECT ;
Date: 2026-02-17 07:45:52 Duration: 12ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '515840243253118300'
-
SELECT ;
Date: 2026-02-17 07:46:57 Duration: 10ms Database: postgres parameters: $1 = '515840233916163300'
3 2s860ms 1,108 1ms 6ms 2ms SELECT symbolid, ;Times Reported Time consuming bind #3
Day Hour Count Duration Avg duration 07 1,108 2s860ms 2ms -
SELECT symbolid, ;
Date: 2026-02-17 07:15:51 Duration: 6ms Database: postgres parameters: $1 = 'DXFEED_FX', $2 = '15', $3 = 'EUR/AUD', $4 = 'AUD/JPY', $5 = 'AUD/USD', $6 = 'AUD/CAD', $7 = 'EUR/JPY', $8 = 'AUD/CHF', $9 = 'CAD/CHF', $10 = 'EUR/CAD', $11 = 'CAD/JPY', $12 = 'AUD/NZD'
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SELECT symbolid, ;
Date: 2026-02-17 07:31:10 Duration: 5ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'EURUSD.ID', $4 = 'GBPAUD', $5 = 'GBPAUD.FX', $6 = 'GBPCAD.FX', $7 = 'GBPAUD.ID'
-
SELECT symbolid, ;
Date: 2026-02-17 07:47:10 Duration: 4ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'GBPCHF.FX', $4 = 'GBPCHF', $5 = 'GBPAUD', $6 = 'GBPAUD.FX', $7 = 'GBPCAD.FX', $8 = 'GBPAUD.ID'
4 715ms 16 28ms 93ms 44ms with sym_info as ( ;Times Reported Time consuming bind #4
Day Hour Count Duration Avg duration 07 16 715ms 44ms -
with sym_info as ( ;
Date: 2026-02-17 07:51:58 Duration: 93ms Database: postgres parameters: $1 = '692', $2 = 'Forex', $3 = 'Forex', $4 = '692', $5 = 'Forex', $6 = '692', $7 = '692', $8 = 'Forex', $9 = '692'
-
with sym_info as ( ;
Date: 2026-02-17 07:51:54 Duration: 46ms Database: postgres parameters: $1 = '627', $2 = 'Forex', $3 = 'Forex', $4 = '627', $5 = 'Forex', $6 = '627', $7 = '627', $8 = 'Forex', $9 = '627'
-
with sym_info as ( ;
Date: 2026-02-17 07:51:49 Duration: 45ms Database: postgres parameters: $1 = '617', $2 = 'Forex', $3 = 'Forex', $4 = '617', $5 = 'Forex', $6 = '617', $7 = '617', $8 = 'Forex', $9 = '617'
5 647ms 369 1ms 14ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming bind #5
Day Hour Count Duration Avg duration 07 369 647ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 07:15:43 Duration: 14ms Database: postgres parameters: $1 = 'AXIORY'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 07:00:44 Duration: 5ms Database: postgres parameters: $1 = 'ICMARKETS-AU-MT5'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 07:47:12 Duration: 3ms Database: postgres parameters: $1 = 'ICMARKETS-AU-MT5'
6 541ms 64 4ms 28ms 8ms WITH last_candle AS ( ;Times Reported Time consuming bind #6
Day Hour Count Duration Avg duration 07 64 541ms 8ms -
WITH last_candle AS ( ;
Date: 2026-02-17 07:52:04 Duration: 28ms Database: postgres parameters: $1 = '621', $2 = '621'
-
WITH last_candle AS ( ;
Date: 2026-02-17 07:52:00 Duration: 28ms Database: postgres parameters: $1 = '558', $2 = '558'
-
WITH last_candle AS ( ;
Date: 2026-02-17 07:52:00 Duration: 23ms Database: postgres parameters: $1 = '558', $2 = '558'
7 481ms 48 0ms 27ms 10ms WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;Times Reported Time consuming bind #7
Day Hour Count Duration Avg duration 07 48 481ms 10ms -
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-02-17 07:01:14 Duration: 27ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
-
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-02-17 07:16:29 Duration: 19ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
-
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-02-17 07:51:16 Duration: 18ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
8 470ms 19 0ms 48ms 24ms with wh_patitioned as ( ;Times Reported Time consuming bind #8
Day Hour Count Duration Avg duration 07 19 470ms 24ms -
with wh_patitioned as ( ;
Date: 2026-02-17 07:07:30 Duration: 48ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
-
with wh_patitioned as ( ;
Date: 2026-02-17 07:07:31 Duration: 44ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
-
with wh_patitioned as ( ;
Date: 2026-02-17 07:55:46 Duration: 41ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
9 462ms 14,266 0ms 15ms 0ms select 1;Times Reported Time consuming bind #9
Day Hour Count Duration Avg duration 07 14,266 462ms 0ms -
select 1;
Date: 2026-02-17 07:46:06 Duration: 15ms Database: postgres
-
select 1;
Date: 2026-02-17 07:46:06 Duration: 14ms Database: postgres
-
select 1;
Date: 2026-02-17 07:06:08 Duration: 9ms Database: postgres
10 247ms 3,132 0ms 1ms 0ms INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming bind #10
Day Hour Count Duration Avg duration 07 3,132 247ms 0ms -
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:00:47 Duration: 1ms Database: postgres parameters: $1 = '2026-02-17 06:30:00', $2 = '3.386', $3 = '3.3885', $4 = '3.381', $5 = '3.3845', $6 = '150', $7 = '605679104052913300', $8 = '0', $9 = '2026-02-17 07:00:47.237', $10 = '2026-02-17 07:00:47.134', $11 = '3.386', $12 = '3.3885', $13 = '3.381', $14 = '3.3845', $15 = '150', $16 = '0', $17 = '2026-02-17 07:00:47.237', $18 = '2026-02-17 07:00:47.134'
-
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:00:56 Duration: 0ms Database: postgres parameters: $1 = '2026-02-17 06:30:00', $2 = '4959.735', $3 = '4960.24', $4 = '4949.48', $5 = '4953.16', $6 = '3342', $7 = '515840230629656300', $8 = '0', $9 = '2026-02-17 07:00:56.262', $10 = '2026-02-17 07:00:56.237', $11 = '4959.735', $12 = '4960.24', $13 = '4949.48', $14 = '4953.16', $15 = '3342', $16 = '0', $17 = '2026-02-17 07:00:56.262', $18 = '2026-02-17 07:00:56.237'
-
INSERT INTO T30 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:30:48 Duration: 0ms Database: postgres parameters: $1 = '2026-02-17 07:00:00', $2 = '0.82315', $3 = '0.82318', $4 = '0.82258', $5 = '0.82307', $6 = '1594', $7 = '515840247885957300', $8 = '0', $9 = '2026-02-17 07:30:48.813', $10 = '2026-02-17 07:30:48.701', $11 = '0.82315', $12 = '0.82318', $13 = '0.82258', $14 = '0.82307', $15 = '1594', $16 = '0', $17 = '2026-02-17 07:30:48.813', $18 = '2026-02-17 07:30:48.701'
11 244ms 5,160 0ms 0ms 0ms INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming bind #11
Day Hour Count Duration Avg duration 07 5,160 244ms 0ms -
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:47:06 Duration: 0ms Database: postgres parameters: $1 = '2026-02-16 21:00:00', $2 = '45495.01', $3 = '45497.49', $4 = '45487.51', $5 = '45492.49', $6 = '11', $7 = '500991628285199200', $8 = '0', $9 = '2026-02-17 07:47:06.779', $10 = '2026-02-17 07:47:06.722', $11 = '45495.01', $12 = '45497.49', $13 = '45487.51', $14 = '45492.49', $15 = '11', $16 = '0', $17 = '2026-02-17 07:47:06.779', $18 = '2026-02-17 07:47:06.722'
-
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:56:49 Duration: 0ms Database: postgres parameters: $1 = '2026-02-17 07:30:00', $2 = '49318.65', $3 = '49341.05', $4 = '49291.55', $5 = '49339.55', $6 = '3605', $7 = '515840248000537300', $8 = '0', $9 = '2026-02-17 07:56:49.981', $10 = '2026-02-17 07:56:49.914', $11 = '49318.65', $12 = '49341.05', $13 = '49291.55', $14 = '49339.55', $15 = '3605', $16 = '0', $17 = '2026-02-17 07:56:49.981', $18 = '2026-02-17 07:56:49.914'
-
INSERT INTO T15 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:47:32 Duration: 0ms Database: postgres parameters: $1 = '2026-02-17 07:15:00', $2 = '7.46976', $3 = '7.4698', $4 = '7.46974', $5 = '7.46976', $6 = '91', $7 = '515840243190162300', $8 = '0', $9 = '2026-02-17 07:47:32.745', $10 = '2026-02-17 07:47:32.667', $11 = '7.46976', $12 = '7.4698', $13 = '7.46974', $14 = '7.46976', $15 = '91', $16 = '0', $17 = '2026-02-17 07:47:32.745', $18 = '2026-02-17 07:47:32.667'
12 193ms 2,224 0ms 1ms 0ms INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming bind #12
Day Hour Count Duration Avg duration 07 2,224 193ms 0ms -
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:15:43 Duration: 1ms Database: postgres parameters: $1 = '2026-02-17 06:00:00', $2 = '45.475', $3 = '45.48', $4 = '45.325', $5 = '45.415', $6 = '404', $7 = '606715251035251300', $8 = '0', $9 = '2026-02-17 07:15:43.471', $10 = '2026-02-17 07:15:43.471', $11 = '45.475', $12 = '45.48', $13 = '45.325', $14 = '45.415', $15 = '404', $16 = '0', $17 = '2026-02-17 07:15:43.471', $18 = '2026-02-17 07:15:43.471'
-
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:00:55 Duration: 0ms Database: postgres parameters: $1 = '2026-02-17 06:00:00', $2 = '4185.3', $3 = '4188.56', $4 = '4177.43', $5 = '4182.48', $6 = '6003', $7 = '515840247937295300', $8 = '0', $9 = '2026-02-17 07:00:55.579', $10 = '2026-02-17 07:00:55.429', $11 = '4185.3', $12 = '4188.56', $13 = '4177.43', $14 = '4182.48', $15 = '6003', $16 = '0', $17 = '2026-02-17 07:00:55.579', $18 = '2026-02-17 07:00:55.429'
-
INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:11:25 Duration: 0ms Database: postgres parameters: $1 = '2026-02-13 21:00:00', $2 = '262.7', $3 = '264.16', $4 = '262.49', $5 = '262.72', $6 = '1334', $7 = '515840247899857300', $8 = '0', $9 = '2026-02-17 07:11:25.422', $10 = '2026-02-17 07:11:25.379', $11 = '262.7', $12 = '264.16', $13 = '262.49', $14 = '262.72', $15 = '1334', $16 = '0', $17 = '2026-02-17 07:11:25.422', $18 = '2026-02-17 07:11:25.379'
13 100ms 100 0ms 1ms 1ms SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;Times Reported Time consuming bind #13
Day Hour Count Duration Avg duration 07 100 100ms 1ms -
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;
Date: 2026-02-17 07:25:46 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'XAUUSD', $3 = '538'
-
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;
Date: 2026-02-17 07:03:21 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'GBPUSD', $3 = '558'
-
SELECT timegranularity FROM brokersymbollist bsl INNER JOIN symbols s ON bsl.symbolid = s.symbolid INNER JOIN downloadersymbolsettings dss on s.symbolid = dss.symbolid LEFT OUTER JOIN brokerinstrumentmapping bdfi ON bdfi.brokerid = $1 AND dss.datafeedinstrumentid = bdfi.datafeedinstrumentid WHERE s.nonliquid = 0 and s.deleted = 0 and dss.enabled = 1 AND s.symbol ILIKE $2 AND bsl.brokerid = $3 AND timegranularity >= 15 ORDER BY timegranularity LIMIT 1;
Date: 2026-02-17 07:25:53 Duration: 1ms Database: postgres parameters: $1 = '489', $2 = 'XAUUSD', $3 = '489'
14 79ms 159 0ms 2ms 0ms SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;Times Reported Time consuming bind #14
Day Hour Count Duration Avg duration 07 159 79ms 0ms -
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-17 07:13:10 Duration: 2ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-17 07:13:10 Duration: 1ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-02-17 07:13:11 Duration: 1ms Database: postgres
15 47ms 186 0ms 4ms 0ms /*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;Times Reported Time consuming bind #15
Day Hour Count Duration Avg duration 07 186 47ms 0ms -
/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;
Date: 2026-02-17 07:30:48 Duration: 4ms Database: postgres parameters: $1 = '600687905493217303'
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/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;
Date: 2026-02-17 07:22:13 Duration: 4ms Database: postgres parameters: $1 = '607692701909571303'
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/*server.KeyLevelResult*/ SELECT ResultUID AS ruid, s.symbolid AS sid, symbol AS sym, longname, shortname, Exchange AS e, timegranularity AS tg, a.PatternID AS pid, a.direction AS d, a.patternprice as pp, atbaridentified AS pet, CASE WHEN (x9 != '') THEN x9 WHEN (x8 != '') THEN x8 WHEN (x7 != '') THEN x7 WHEN (x6 != '') THEN x6 WHEN (x5 != '') THEN x5 WHEN (x4 != '') THEN x4 WHEN (x3 != '') THEN x3 WHEN (x2 != '') THEN x2 END AS pst, PatternPrice AS patp, x0, x1, x2, CASE WHEN (x3 != '') THEN x3 ELSE '1900-01-01' END as x3, CASE WHEN (x4 != '') THEN x4 ELSE '1900-01-01' END as x4, CASE WHEN (x5 != '') THEN x5 ELSE '1900-01-01' END as x5, CASE WHEN (x6 != '') THEN x6 ELSE '1900-01-01' END as x6, CASE WHEN (x7 != '') THEN x7 ELSE '1900-01-01' END as x7, CASE WHEN (x8 != '') THEN x8 ELSE '1900-01-01' END as x8, CASE WHEN (x9 != '') THEN x9 ELSE '1900-01-01' END as x9, errorMargin as erm, breakoutprice as pE, breakoutbars as be, breakout, atbaridentified as atBar, PatternLengthBars AS l, Bandwidth AS bw, QtyTP AS qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz, approachingtimestamp AS apt, approachingregion as apr, predictionpricefrom as ppf, predictionpriceto as ppt, predictiontimefrom as ptf, predictiontimebars as ptb FROM keylevels_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid INNER JOIN hrspatterns p on a.patternid = p.patternid where resultuid = $1 and dtt.dayofweek = 3;
Date: 2026-02-17 07:07:11 Duration: 4ms Database: postgres parameters: $1 = '607692293592835303'
16 43ms 9 3ms 7ms 4ms SELECT DISTINCT ON (basegroupname, symbol) ;Times Reported Time consuming bind #16
Day Hour Count Duration Avg duration 07 9 43ms 4ms -
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-02-17 07:21:05 Duration: 7ms Database: postgres parameters: $1 = '627', $2 = '627'
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SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-02-17 07:21:02 Duration: 6ms Database: postgres parameters: $1 = '627', $2 = '627'
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SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-02-17 07:30:40 Duration: 6ms Database: postgres parameters: $1 = '667', $2 = '667'
17 37ms 8 2ms 6ms 4ms WITH pre_symbols AS ( /* find relevant symbols */ ;Times Reported Time consuming bind #17
Day Hour Count Duration Avg duration 07 8 37ms 4ms -
WITH pre_symbols AS ( /* find relevant symbols */ ;
Date: 2026-02-17 07:13:09 Duration: 6ms Database: postgres parameters: $1 = '1018', $2 = 'ICMARKETS-AU-MT5', $3 = 'AAPL.NAS', $4 = 'ABBV.NYSE', $5 = 'AMCR.NYSE', $6 = 'AMP.NYSE', $7 = 'AMZN.NAS', $8 = 'ANZ.ASX', $9 = 'AUDJPY', $10 = 'AUDUSD', $11 = 'AUS200', $12 = 'BABA.NYSE', $13 = 'BIIB.NAS', $14 = 'BXB.ASX', $15 = 'CBA.ASX', $16 = 'CHINA50', $17 = 'CSL.ASX', $18 = 'DE30', $19 = 'ES35', $20 = 'EURCHF', $21 = 'EURGBP', $22 = 'EURUSD', $23 = 'F40', $24 = 'FMG.ASX', $25 = 'GBPJPY', $26 = 'GBPUSD', $27 = 'GOOG.NAS', $28 = 'HK50', $29 = 'IT40', $30 = 'JP225', $31 = 'KO.NYSE', $32 = 'MQG.ASX', $33 = 'MSFT.NAS', $34 = 'NAB.ASX', $35 = 'NFLX.NAS', $36 = 'PYPL.NAS', $37 = 'QBE.ASX', $38 = 'STOXX50', $39 = 'SUN.ASX', $40 = 'TCL.ASX', $41 = 'TLS.ASX', $42 = 'TSLA.NAS', $43 = 'UK100', $44 = 'UNH.NYSE', $45 = 'US2000', $46 = 'US30', $47 = 'US500', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDCNH', $51 = 'USDJPY', $52 = 'USTEC', $53 = 'WBC.ASX', $54 = 'WES.ASX', $55 = 'WOW.ASX', $56 = 'WPL.ASX', $57 = 'XAUEUR', $58 = 'XAUUSD', $59 = 'XBRUSD', $60 = 'XTIUSD', $61 = 'AAPL.NAS', $62 = 'ABBV.NYSE', $63 = 'AMCR.NYSE', $64 = 'AMP.NYSE', $65 = 'AMZN.NAS', $66 = 'ANZ.ASX', $67 = 'AUDJPY', $68 = 'AUDUSD', $69 = 'AUS200', $70 = 'BABA.NYSE', $71 = 'BIIB.NAS', $72 = 'BXB.ASX', $73 = 'CBA.ASX', $74 = 'CHINA50', $75 = 'CSL.ASX', $76 = 'DE30', $77 = 'ES35', $78 = 'EURCHF', $79 = 'EURGBP', $80 = 'EURUSD', $81 = 'F40', $82 = 'FMG.ASX', $83 = 'GBPJPY', $84 = 'GBPUSD', $85 = 'GOOG.NAS', $86 = 'HK50', $87 = 'IT40', $88 = 'JP225', $89 = 'KO.NYSE', $90 = 'MQG.ASX', $91 = 'MSFT.NAS', $92 = 'NAB.ASX', $93 = 'NFLX.NAS', $94 = 'PYPL.NAS', $95 = 'QBE.ASX', $96 = 'STOXX50', $97 = 'SUN.ASX', $98 = 'TCL.ASX', $99 = 'TLS.ASX', $100 = 'TSLA.NAS', $101 = 'UK100', $102 = 'UNH.NYSE', $103 = 'US2000', $104 = 'US30', $105 = 'US500', $106 = 'USDCAD', $107 = 'USDCHF', $108 = 'USDCNH', $109 = 'USDJPY', $110 = 'USTEC', $111 = 'WBC.ASX', $112 = 'WES.ASX', $113 = 'WOW.ASX', $114 = 'WPL.ASX', $115 = 'XAUEUR', $116 = 'XAUUSD', $117 = 'XBRUSD', $118 = 'XTIUSD', $119 = '5'
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WITH pre_symbols AS ( /* find relevant symbols */ ;
Date: 2026-02-17 07:13:09 Duration: 6ms Database: postgres parameters: $1 = '1018', $2 = 'ICMARKETS-AU-MT5', $3 = 'AAPL.NAS', $4 = 'ABBV.NYSE', $5 = 'AMCR.NYSE', $6 = 'AMP.NYSE', $7 = 'AMZN.NAS', $8 = 'ANZ.ASX', $9 = 'AUDJPY', $10 = 'AUDUSD', $11 = 'AUS200', $12 = 'BABA.NYSE', $13 = 'BIIB.NAS', $14 = 'BXB.ASX', $15 = 'CBA.ASX', $16 = 'CHINA50', $17 = 'CSL.ASX', $18 = 'DE30', $19 = 'ES35', $20 = 'EURCHF', $21 = 'EURGBP', $22 = 'EURUSD', $23 = 'F40', $24 = 'FMG.ASX', $25 = 'GBPJPY', $26 = 'GBPUSD', $27 = 'GOOG.NAS', $28 = 'HK50', $29 = 'IT40', $30 = 'JP225', $31 = 'KO.NYSE', $32 = 'MQG.ASX', $33 = 'MSFT.NAS', $34 = 'NAB.ASX', $35 = 'NFLX.NAS', $36 = 'PYPL.NAS', $37 = 'QBE.ASX', $38 = 'STOXX50', $39 = 'SUN.ASX', $40 = 'TCL.ASX', $41 = 'TLS.ASX', $42 = 'TSLA.NAS', $43 = 'UK100', $44 = 'UNH.NYSE', $45 = 'US2000', $46 = 'US30', $47 = 'US500', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDCNH', $51 = 'USDJPY', $52 = 'USTEC', $53 = 'WBC.ASX', $54 = 'WES.ASX', $55 = 'WOW.ASX', $56 = 'WPL.ASX', $57 = 'XAUEUR', $58 = 'XAUUSD', $59 = 'XBRUSD', $60 = 'XTIUSD', $61 = 'AAPL.NAS', $62 = 'ABBV.NYSE', $63 = 'AMCR.NYSE', $64 = 'AMP.NYSE', $65 = 'AMZN.NAS', $66 = 'ANZ.ASX', $67 = 'AUDJPY', $68 = 'AUDUSD', $69 = 'AUS200', $70 = 'BABA.NYSE', $71 = 'BIIB.NAS', $72 = 'BXB.ASX', $73 = 'CBA.ASX', $74 = 'CHINA50', $75 = 'CSL.ASX', $76 = 'DE30', $77 = 'ES35', $78 = 'EURCHF', $79 = 'EURGBP', $80 = 'EURUSD', $81 = 'F40', $82 = 'FMG.ASX', $83 = 'GBPJPY', $84 = 'GBPUSD', $85 = 'GOOG.NAS', $86 = 'HK50', $87 = 'IT40', $88 = 'JP225', $89 = 'KO.NYSE', $90 = 'MQG.ASX', $91 = 'MSFT.NAS', $92 = 'NAB.ASX', $93 = 'NFLX.NAS', $94 = 'PYPL.NAS', $95 = 'QBE.ASX', $96 = 'STOXX50', $97 = 'SUN.ASX', $98 = 'TCL.ASX', $99 = 'TLS.ASX', $100 = 'TSLA.NAS', $101 = 'UK100', $102 = 'UNH.NYSE', $103 = 'US2000', $104 = 'US30', $105 = 'US500', $106 = 'USDCAD', $107 = 'USDCHF', $108 = 'USDCNH', $109 = 'USDJPY', $110 = 'USTEC', $111 = 'WBC.ASX', $112 = 'WES.ASX', $113 = 'WOW.ASX', $114 = 'WPL.ASX', $115 = 'XAUEUR', $116 = 'XAUUSD', $117 = 'XBRUSD', $118 = 'XTIUSD', $119 = '5'
-
WITH pre_symbols AS ( /* find relevant symbols */ ;
Date: 2026-02-17 07:13:10 Duration: 6ms Database: postgres parameters: $1 = '1018', $2 = 'ICMARKETS-AU-MT5', $3 = 'AAPL.NAS', $4 = 'ABBV.NYSE', $5 = 'AMCR.NYSE', $6 = 'AMP.NYSE', $7 = 'AMZN.NAS', $8 = 'ANZ.ASX', $9 = 'AUDJPY', $10 = 'AUDUSD', $11 = 'AUS200', $12 = 'BABA.NYSE', $13 = 'BIIB.NAS', $14 = 'BXB.ASX', $15 = 'CBA.ASX', $16 = 'CHINA50', $17 = 'CSL.ASX', $18 = 'DE30', $19 = 'ES35', $20 = 'EURCHF', $21 = 'EURGBP', $22 = 'EURUSD', $23 = 'F40', $24 = 'FMG.ASX', $25 = 'GBPJPY', $26 = 'GBPUSD', $27 = 'GOOG.NAS', $28 = 'HK50', $29 = 'IT40', $30 = 'JP225', $31 = 'KO.NYSE', $32 = 'MQG.ASX', $33 = 'MSFT.NAS', $34 = 'NAB.ASX', $35 = 'NFLX.NAS', $36 = 'PYPL.NAS', $37 = 'QBE.ASX', $38 = 'STOXX50', $39 = 'SUN.ASX', $40 = 'TCL.ASX', $41 = 'TLS.ASX', $42 = 'TSLA.NAS', $43 = 'UK100', $44 = 'UNH.NYSE', $45 = 'US2000', $46 = 'US30', $47 = 'US500', $48 = 'USDCAD', $49 = 'USDCHF', $50 = 'USDCNH', $51 = 'USDJPY', $52 = 'USTEC', $53 = 'WBC.ASX', $54 = 'WES.ASX', $55 = 'WOW.ASX', $56 = 'WPL.ASX', $57 = 'XAUEUR', $58 = 'XAUUSD', $59 = 'XBRUSD', $60 = 'XTIUSD', $61 = 'AAPL.NAS', $62 = 'ABBV.NYSE', $63 = 'AMCR.NYSE', $64 = 'AMP.NYSE', $65 = 'AMZN.NAS', $66 = 'ANZ.ASX', $67 = 'AUDJPY', $68 = 'AUDUSD', $69 = 'AUS200', $70 = 'BABA.NYSE', $71 = 'BIIB.NAS', $72 = 'BXB.ASX', $73 = 'CBA.ASX', $74 = 'CHINA50', $75 = 'CSL.ASX', $76 = 'DE30', $77 = 'ES35', $78 = 'EURCHF', $79 = 'EURGBP', $80 = 'EURUSD', $81 = 'F40', $82 = 'FMG.ASX', $83 = 'GBPJPY', $84 = 'GBPUSD', $85 = 'GOOG.NAS', $86 = 'HK50', $87 = 'IT40', $88 = 'JP225', $89 = 'KO.NYSE', $90 = 'MQG.ASX', $91 = 'MSFT.NAS', $92 = 'NAB.ASX', $93 = 'NFLX.NAS', $94 = 'PYPL.NAS', $95 = 'QBE.ASX', $96 = 'STOXX50', $97 = 'SUN.ASX', $98 = 'TCL.ASX', $99 = 'TLS.ASX', $100 = 'TSLA.NAS', $101 = 'UK100', $102 = 'UNH.NYSE', $103 = 'US2000', $104 = 'US30', $105 = 'US500', $106 = 'USDCAD', $107 = 'USDCHF', $108 = 'USDCNH', $109 = 'USDJPY', $110 = 'USTEC', $111 = 'WBC.ASX', $112 = 'WES.ASX', $113 = 'WOW.ASX', $114 = 'WPL.ASX', $115 = 'XAUEUR', $116 = 'XAUUSD', $117 = 'XBRUSD', $118 = 'XTIUSD', $119 = '5'
18 34ms 159 0ms 3ms 0ms /*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;Times Reported Time consuming bind #18
Day Hour Count Duration Avg duration 07 159 34ms 0ms -
/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;
Date: 2026-02-17 07:50:27 Duration: 3ms Database: postgres parameters: $1 = '607693174370548301'
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/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;
Date: 2026-02-17 07:50:14 Duration: 2ms Database: postgres parameters: $1 = '607693172752015301'
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/*server.CPResult*/ SELECT patternid, resy0, resy1, supporty0, supporty1, predictiontimeto, patternstarttime, s.symbolid, resx0, resx1, supportx0, supportx1, symbol, longname, shortname, timegranularity, patternendtime, pattern, a.direction, trendchange, patternlengthbars, patternquality, resultuid as uid, breakout, initialtrend, volumeincrease, symmetry as uniformity, predictionpricefrom, predictionpriceto, noise, exchange, breakout, dtt.absolutetimezoneoffset as tzOs, dtt.timezone as tz FROM autochartist_results a INNER JOIN downloadersymbolsettings dss on a.symbolid = dss.symbolid INNER JOIN datafeedstimetable dtt ON dss.classname = dtt.classname inner join symbols s on a.symbolid = s.symbolid inner join patterns p on p.patternname = a.pattern where resultuid = $1;
Date: 2026-02-17 07:05:18 Duration: 2ms Database: postgres parameters: $1 = '607692291735037301'
19 34ms 296 0ms 0ms 0ms INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming bind #19
Day Hour Count Duration Avg duration 07 296 34ms 0ms -
INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:16:04 Duration: 0ms Database: postgres parameters: $1 = '2026-02-16 20:00:00', $2 = '15.98463', $3 = '15.989925', $4 = '15.96849', $5 = '15.97879', $6 = '7056', $7 = '515840249466177300', $8 = '0', $9 = '2026-02-17 07:16:04.696', $10 = '2026-02-17 07:16:04.642', $11 = '15.98463', $12 = '15.989925', $13 = '15.96849', $14 = '15.97879', $15 = '7056', $16 = '0', $17 = '2026-02-17 07:16:04.696', $18 = '2026-02-17 07:16:04.642'
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INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:02:33 Duration: 0ms Database: postgres parameters: $1 = '2026-02-16 16:00:00', $2 = '56927.5', $3 = '56927.5', $4 = '56702.5', $5 = '56737.5', $6 = '2363', $7 = '515840230556263300', $8 = '0', $9 = '2026-02-17 07:02:33.651', $10 = '2026-02-17 07:02:33.651', $11 = '56927.5', $12 = '56927.5', $13 = '56702.5', $14 = '56737.5', $15 = '2363', $16 = '0', $17 = '2026-02-17 07:02:33.651', $18 = '2026-02-17 07:02:33.651'
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INSERT INTO T240 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;
Date: 2026-02-17 07:02:31 Duration: 0ms Database: postgres parameters: $1 = '2026-02-16 16:00:00', $2 = '56930', $3 = '56930', $4 = '56705', $5 = '56740', $6 = '2363', $7 = '515840230561612300', $8 = '0', $9 = '2026-02-17 07:02:31.244', $10 = '2026-02-17 07:02:31.244', $11 = '56930', $12 = '56930', $13 = '56705', $14 = '56740', $15 = '2363', $16 = '0', $17 = '2026-02-17 07:02:31.244', $18 = '2026-02-17 07:02:31.244'
20 32ms 100 0ms 3ms 0ms SELECT DISTINCT COALESCE(stddev_15 / 2, 0) AS low_15_stddev, COALESCE(ave_15 / 2, 0) AS low_15_ave, COALESCE(stddev_15 / 2, 0) AS high_15_stddev, COALESCE(ave_15 / 2, 0) AS high_15_ave, COALESCE(stddev_30 / 2, 0) AS low_30_stddev, COALESCE(ave_30 / 2, 0) AS low_30_ave, COALESCE(stddev_30 / 2, 0) AS high_30_stddev, COALESCE(ave_30 / 2, 0) AS high_30_ave, COALESCE(stddev_60 / 2, 0) AS low_60_stddev, COALESCE(ave_60 / 2, 0) AS low_60_ave, COALESCE(stddev_60 / 2, 0) AS high_60_stddev, COALESCE(ave_60 / 2, 0) AS high_60_ave, COALESCE(stddev_240 / 2, 0) AS low_240_stddev, COALESCE(ave_240 / 2, 0) AS low_240_ave, COALESCE(stddev_240 / 2, 0) AS high_240_stddev, COALESCE(ave_240 / 2, 0) AS high_240_ave, COALESCE(stddev_1440 / 2, 0) AS low_1440_stddev, COALESCE(ave_1440 / 2, 0) AS low_1440_ave, COALESCE(stddev_1440 / 2, 0) AS high_1440_stddev, COALESCE(ave_1440 / 2, 0) AS high_1440_ave, s.exchange AS exchange, s.symbol AS symbol, s.longname, ps.enddate AS executiondate, dtt.absolutetimezoneoffset AS datafeedtimezoneoffset, dtt.timezone AS datafeedtimezonename, dss.downloadersymbol FROM powerstats_trumpet ps INNER JOIN downloadersymbolsettings dss ON dss.symbolid = ps.symbolid INNER JOIN datafeedstimetable dtt ON dtt.classname = dss.classname INNER JOIN symbols s ON ps.symbolid = s.symbolid WHERE ps.dayofweek = $1 AND ps.fromtime = $2 AND ps.symbolid = $3 ORDER BY ps.enddate DESC LIMIT 1;Times Reported Time consuming bind #20
Day Hour Count Duration Avg duration 07 100 32ms 0ms -
SELECT DISTINCT COALESCE(stddev_15 / 2, 0) AS low_15_stddev, COALESCE(ave_15 / 2, 0) AS low_15_ave, COALESCE(stddev_15 / 2, 0) AS high_15_stddev, COALESCE(ave_15 / 2, 0) AS high_15_ave, COALESCE(stddev_30 / 2, 0) AS low_30_stddev, COALESCE(ave_30 / 2, 0) AS low_30_ave, COALESCE(stddev_30 / 2, 0) AS high_30_stddev, COALESCE(ave_30 / 2, 0) AS high_30_ave, COALESCE(stddev_60 / 2, 0) AS low_60_stddev, COALESCE(ave_60 / 2, 0) AS low_60_ave, COALESCE(stddev_60 / 2, 0) AS high_60_stddev, COALESCE(ave_60 / 2, 0) AS high_60_ave, COALESCE(stddev_240 / 2, 0) AS low_240_stddev, COALESCE(ave_240 / 2, 0) AS low_240_ave, COALESCE(stddev_240 / 2, 0) AS high_240_stddev, COALESCE(ave_240 / 2, 0) AS high_240_ave, COALESCE(stddev_1440 / 2, 0) AS low_1440_stddev, COALESCE(ave_1440 / 2, 0) AS low_1440_ave, COALESCE(stddev_1440 / 2, 0) AS high_1440_stddev, COALESCE(ave_1440 / 2, 0) AS high_1440_ave, s.exchange AS exchange, s.symbol AS symbol, s.longname, ps.enddate AS executiondate, dtt.absolutetimezoneoffset AS datafeedtimezoneoffset, dtt.timezone AS datafeedtimezonename, dss.downloadersymbol FROM powerstats_trumpet ps INNER JOIN downloadersymbolsettings dss ON dss.symbolid = ps.symbolid INNER JOIN datafeedstimetable dtt ON dtt.classname = dss.classname INNER JOIN symbols s ON ps.symbolid = s.symbolid WHERE ps.dayofweek = $1 AND ps.fromtime = $2 AND ps.symbolid = $3 ORDER BY ps.enddate DESC LIMIT 1;
Date: 2026-02-17 07:05:53 Duration: 3ms Database: postgres parameters: $1 = '2', $2 = '405', $3 = '515840230627575300'
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SELECT DISTINCT COALESCE(stddev_15 / 2, 0) AS low_15_stddev, COALESCE(ave_15 / 2, 0) AS low_15_ave, COALESCE(stddev_15 / 2, 0) AS high_15_stddev, COALESCE(ave_15 / 2, 0) AS high_15_ave, COALESCE(stddev_30 / 2, 0) AS low_30_stddev, COALESCE(ave_30 / 2, 0) AS low_30_ave, COALESCE(stddev_30 / 2, 0) AS high_30_stddev, COALESCE(ave_30 / 2, 0) AS high_30_ave, COALESCE(stddev_60 / 2, 0) AS low_60_stddev, COALESCE(ave_60 / 2, 0) AS low_60_ave, COALESCE(stddev_60 / 2, 0) AS high_60_stddev, COALESCE(ave_60 / 2, 0) AS high_60_ave, COALESCE(stddev_240 / 2, 0) AS low_240_stddev, COALESCE(ave_240 / 2, 0) AS low_240_ave, COALESCE(stddev_240 / 2, 0) AS high_240_stddev, COALESCE(ave_240 / 2, 0) AS high_240_ave, COALESCE(stddev_1440 / 2, 0) AS low_1440_stddev, COALESCE(ave_1440 / 2, 0) AS low_1440_ave, COALESCE(stddev_1440 / 2, 0) AS high_1440_stddev, COALESCE(ave_1440 / 2, 0) AS high_1440_ave, s.exchange AS exchange, s.symbol AS symbol, s.longname, ps.enddate AS executiondate, dtt.absolutetimezoneoffset AS datafeedtimezoneoffset, dtt.timezone AS datafeedtimezonename, dss.downloadersymbol FROM powerstats_trumpet ps INNER JOIN downloadersymbolsettings dss ON dss.symbolid = ps.symbolid INNER JOIN datafeedstimetable dtt ON dtt.classname = dss.classname INNER JOIN symbols s ON ps.symbolid = s.symbolid WHERE ps.dayofweek = $1 AND ps.fromtime = $2 AND ps.symbolid = $3 ORDER BY ps.enddate DESC LIMIT 1;
Date: 2026-02-17 07:25:53 Duration: 3ms Database: postgres parameters: $1 = '2', $2 = '420', $3 = '515840230627575300'
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SELECT DISTINCT COALESCE(stddev_15 / 2, 0) AS low_15_stddev, COALESCE(ave_15 / 2, 0) AS low_15_ave, COALESCE(stddev_15 / 2, 0) AS high_15_stddev, COALESCE(ave_15 / 2, 0) AS high_15_ave, COALESCE(stddev_30 / 2, 0) AS low_30_stddev, COALESCE(ave_30 / 2, 0) AS low_30_ave, COALESCE(stddev_30 / 2, 0) AS high_30_stddev, COALESCE(ave_30 / 2, 0) AS high_30_ave, COALESCE(stddev_60 / 2, 0) AS low_60_stddev, COALESCE(ave_60 / 2, 0) AS low_60_ave, COALESCE(stddev_60 / 2, 0) AS high_60_stddev, COALESCE(ave_60 / 2, 0) AS high_60_ave, COALESCE(stddev_240 / 2, 0) AS low_240_stddev, COALESCE(ave_240 / 2, 0) AS low_240_ave, COALESCE(stddev_240 / 2, 0) AS high_240_stddev, COALESCE(ave_240 / 2, 0) AS high_240_ave, COALESCE(stddev_1440 / 2, 0) AS low_1440_stddev, COALESCE(ave_1440 / 2, 0) AS low_1440_ave, COALESCE(stddev_1440 / 2, 0) AS high_1440_stddev, COALESCE(ave_1440 / 2, 0) AS high_1440_ave, s.exchange AS exchange, s.symbol AS symbol, s.longname, ps.enddate AS executiondate, dtt.absolutetimezoneoffset AS datafeedtimezoneoffset, dtt.timezone AS datafeedtimezonename, dss.downloadersymbol FROM powerstats_trumpet ps INNER JOIN downloadersymbolsettings dss ON dss.symbolid = ps.symbolid INNER JOIN datafeedstimetable dtt ON dtt.classname = dss.classname INNER JOIN symbols s ON ps.symbolid = s.symbolid WHERE ps.dayofweek = $1 AND ps.fromtime = $2 AND ps.symbolid = $3 ORDER BY ps.enddate DESC LIMIT 1;
Date: 2026-02-17 07:35:53 Duration: 2ms Database: postgres parameters: $1 = '2', $2 = '435', $3 = '515840230627575300'
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Events
Log levels
Key values
- 299,144 Log entries
Events distribution
Key values
- 0 PANIC entries
- 0 FATAL entries
- 1 ERROR entries
- 0 WARNING entries
Most Frequent Errors/Events
Key values
- 1 Max number of times the same event was reported
- 1 Total events found
Rank Times reported Error 1 1 ERROR: relation "..." does not exist
Times Reported Most Frequent Error / Event #1
Day Hour Count Feb 17 07 1 - ERROR: relation "t0" does not exist at character 83
Statement: SELECT * FROM ( SELECT PriceDateTime, Open, High, Low, Close, Volume, BSF FROM T0 WHERE symbolid = $1 AND (BSF = 0 OR BSF IS NULL) ORDER BY PriceDateTime DESC LIMIT 1050 ) a ORDER BY PriceDateTime ASC
Date: 2026-02-17 07:30:48