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Global information
- Generated on Mon Feb 16 23:59:32 2026
- Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-02-17_010000.log
- Parsed 1,175,256 log entries in 31s
- Log start from 2026-02-17 01:00:00 to 2026-02-17 01:59:29
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Overview
Global Stats
- 214 Number of unique normalized queries
- 142,855 Number of queries
- 1h48m18s Total query duration
- 2026-02-17 01:00:00 First query
- 2026-02-17 01:59:28 Last query
- 2,221 queries/s at 2026-02-17 01:15:04 Query peak
- 1h48m18s Total query duration
- 6s537ms Prepare/parse total duration
- 40s224ms Bind total duration
- 1h47m32s 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
- 42 Total number of automatic vacuums
- 57 Total number of automatic analyzes
- 671 Number temporary file
- 154.77 MiB Max size of temporary file
- 8.45 MiB Average size of temporary file
- 2,803 Total number of sessions
- 13 sessions at 2026-02-17 01:55:35 Session peak
- 2d12h11m22s Total duration of sessions
- 1m17s Average duration of sessions
- 50 Average queries per session
- 2s318ms Average queries duration per session
- 1m14s Average idle time per session
- 2,803 Total number of connections
- 27 connections/s at 2026-02-17 01:03:48 Connection peak
- 3 Total number of databases
SQL Traffic
Key values
- 2,221 queries/s Query Peak
- 2026-02-17 01:15:04 Date
SELECT Traffic
Key values
- 1,065 queries/s Query Peak
- 2026-02-17 01:15:04 Date
INSERT/UPDATE/DELETE Traffic
Key values
- 153 queries/s Query Peak
- 2026-02-17 01:00:53 Date
Queries duration
Key values
- 1h48m18s 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 01 142,855 0ms 45s421ms 45ms 3m59s 4m32s 4m50s Day Hour SELECT COPY TO Average Duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Feb 17 01 35,567 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 01 25,250 1,665 16 96 0ms 0ms 0ms 0ms Day Hour Prepare Bind Bind/Prepare Percentage of prepare Feb 17 01 18,340 48,034 2.62 27.30% Day Hour Count Average / Second Feb 17 01 2,803 0.78/s Day Hour Count Average Duration Average idle time Feb 17 01 2,803 1m17s 1m15s -
Connections
Established Connections
Key values
- 27 connections Connection Peak
- 2026-02-17 01:03:48 Date
Connections per database
Key values
- acaweb_fx Main Database
- 2,803 connections Total
Connections per user
Key values
- postgres Main User
- 2,803 connections Total
Connections per host
Key values
- 192.168.4.142 Main host with 1132 connections
- 2,803 Total connections
Host Count 127.0.0.1 114 192.168.0.114 5 192.168.0.216 107 192.168.0.74 41 192.168.1.145 145 192.168.1.15 252 192.168.1.20 160 192.168.1.231 20 192.168.1.239 13 192.168.1.90 50 192.168.2.126 36 192.168.2.182 12 192.168.3.199 36 192.168.4.117 1 192.168.4.142 1,132 192.168.4.150 10 192.168.4.222 1 192.168.4.224 6 192.168.4.238 12 192.168.4.33 70 192.168.4.81 6 192.168.4.98 330 [local] 244 -
Sessions
Simultaneous sessions
Key values
- 13 sessions Session Peak
- 2026-02-17 01:55:35 Date
Histogram of session times
Key values
- 2,335 0-500ms duration
Sessions per database
Key values
- acaweb_fx Main Database
- 2,803 sessions Total
Sessions per user
Key values
- postgres Main User
- 2,803 sessions Total
Sessions per host
Key values
- 192.168.4.142 Main Host
- 2,803 sessions Total
Host Count Total Duration Average Duration 127.0.0.1 114 5s198ms 45ms 192.168.0.114 5 25m10s 5m2s 192.168.0.216 107 3m53s 2s186ms 192.168.0.74 41 2h5m7s 3m3s 192.168.1.145 145 7h16m28s 3m 192.168.1.15 252 7h17m2s 1m44s 192.168.1.20 160 12h51m9s 4m49s 192.168.1.231 20 9h52m30s 29m37s 192.168.1.239 13 105ms 8ms 192.168.1.90 50 33s999ms 679ms 192.168.2.126 36 5s825ms 161ms 192.168.2.182 12 1s804ms 150ms 192.168.3.199 36 1s340ms 37ms 192.168.4.117 1 185ms 185ms 192.168.4.142 1,132 10m54s 578ms 192.168.4.150 10 20h2m57s 2h17s 192.168.4.222 1 44s692ms 44s692ms 192.168.4.224 6 127ms 21ms 192.168.4.238 12 11s66ms 922ms 192.168.4.33 70 43s827ms 626ms 192.168.4.81 6 74ms 12ms 192.168.4.98 330 15s771ms 47ms [local] 244 3m23s 834ms -
Checkpoints / Restartpoints
Checkpoints Buffers
Key values
- 12,683 buffers Checkpoint Peak
- 2026-02-17 01:06:58 Date
- 209.972 seconds Highest write time
- 0.011 seconds Sync time
Checkpoints Wal files
Key values
- 5 files Wal files usage Peak
- 2026-02-17 01:06:58 Date
Checkpoints distance
Key values
- 154.72 Mo Distance Peak
- 2026-02-17 01:36:59 Date
Checkpoints Activity
↑ Back to the top of the Checkpoint Activity tableDay Hour Written buffers Write time Sync time Total time Feb 17 01 46,312 1,900.265s 0.046s 1,900.632s Day Hour Added Removed Recycled Synced files Longest sync Average sync Feb 17 01 0 0 24 2,062 0.011s 0s Day Hour Count Avg time (sec) Feb 17 01 0 0s Day Hour Mean distance Mean estimate Feb 17 01 31,597.67 kB 72,252.17 kB -
Temporary Files
Size of temporary files
Key values
- 184.68 MiB Temp Files size Peak
- 2026-02-17 01:50:08 Date
Number of temporary files
Key values
- 30 per second Temp Files Peak
- 2026-02-17 01:47:08 Date
Temporary Files Activity
↑ Back to the top of the Temporary Files Activity tableDay Hour Count Total size Average size Feb 17 01 671 5.53 GiB 8.45 MiB Queries generating the most temporary files (N)
Rank Count Total size Min size Max size Avg size Query 1 47 193.91 MiB 4.08 MiB 4.20 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 01:00:45 Duration: 0ms
2 38 226.06 MiB 3.61 MiB 9.53 MiB 5.95 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 01:02:06 Duration: 0ms
3 30 1.65 GiB 2.58 MiB 154.77 MiB 56.30 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 01:00:06 Duration: 0ms
4 16 738.50 MiB 46.16 MiB 46.16 MiB 46.16 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 01:01:12 Duration: 0ms
5 16 1.22 GiB 78.39 MiB 78.39 MiB 78.39 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 01:01:15 Duration: 0ms
6 9 27.37 MiB 3.03 MiB 3.05 MiB 3.04 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 01:16:05 Duration: 0ms
7 8 1.05 GiB 134.79 MiB 134.89 MiB 134.84 MiB select updateresultsmaterializedview ();-
select updateresultsmaterializedview ();
Date: 2026-02-17 01:02:15 Duration: 0ms
8 4 328.82 MiB 82.14 MiB 82.27 MiB 82.20 MiB select updateageforrelevantresults ();-
select updateageforrelevantresults ();
Date: 2026-02-17 01:02:06 Duration: 0ms
Queries generating the largest temporary files
Rank Size Query 1 154.77 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 01:20:04 ]
2 148.80 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 01:50:05 ]
3 146.21 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 01:00:06 ]
4 134.89 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 01:32:21 ]
5 134.88 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 01:47:13 ]
6 134.87 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 01:50:32 ]
7 134.86 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 01:35:32 ]
8 134.82 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 01:17:16 ]
9 134.81 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 01:20:32 ]
10 134.81 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 01:02:15 ]
11 134.79 MiB select updateresultsmaterializedview ();[ Date: 2026-02-17 01:05:32 ]
12 108.12 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 01:40:05 ]
13 95.26 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 01:10:04 ]
14 82.27 MiB select updateageforrelevantresults ();[ Date: 2026-02-17 01:32:06 ]
15 82.22 MiB select updateageforrelevantresults ();[ Date: 2026-02-17 01:02:06 ]
16 82.19 MiB select updateageforrelevantresults ();[ Date: 2026-02-17 01:47:04 ]
17 82.14 MiB select updateageforrelevantresults ();[ Date: 2026-02-17 01:17:05 ]
18 80.09 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 01:30:04 ]
19 78.55 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 01:40:05 ]
20 78.39 MiB 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 01:01:15 ]
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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)
- 57 analyzes Total
Table Number of analyzes acaweb_fx.public.solr_relevance_old 16 acaweb_fx.pg_catalog.pg_attribute 8 acaweb_fx.pg_catalog.pg_class 6 acaweb_fx.pg_catalog.pg_type 4 acaweb_fx.public.relevance_keylevels_results 4 acaweb_fx.public.relevance_autochartist_results 4 acaweb_fx.public.datafeeds_latestrun 3 acaweb_fx.public.autochartist_symbolupdates 2 acaweb_fx.public.latest_t15_candle_view 2 acaweb_fx.pg_catalog.pg_depend 2 acaweb_fx.public.relevance_fibonacci_results 2 acaweb_fx.pg_catalog.pg_index 1 acaweb_fx.public.symbollatestupdatetime 1 acaweb_fx.public.latest_candle_datetime_per_receng 1 acaweb_fx.public.correlating_signals 1 Total 57 Vacuums per table
Key values
- public.solr_relevance_old (17) Main table vacuumed on database acaweb_fx
- 42 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 17 16 13,437 0 56 0 0 8,868 296 2,559,192 acaweb_fx.pg_catalog.pg_attribute 4 4 3,314 0 608 0 268 1,483 524 2,930,804 acaweb_fx.public.datafeeds_latestrun 3 0 360 0 11 0 0 39 6 40,630 acaweb_fx.pg_catalog.pg_class 3 3 1,373 0 109 0 0 401 103 604,072 acaweb_fx.pg_toast.pg_toast_2619 2 2 311 0 63 0 0 214 59 199,148 acaweb_fx.pg_catalog.pg_type 2 2 324 0 53 0 0 148 44 223,928 acaweb_fx.public.relevance_keylevels_results 2 2 7,409 0 182 4 143 2,066 171 648,214 acaweb_fx.public.relevance_autochartist_results 2 2 6,640 0 309 2 466 1,249 295 741,437 acaweb_fx.public.relevance_fibonacci_results 2 2 2,439 0 88 0 86 420 175 634,504 acaweb_fx.public.autochartist_symbolupdates 1 1 21,944 0 1,169 4 38,516 5,329 1,110 871,792 acaweb_fx.public.solr_imports 1 1 65 0 2 0 0 6 2 14,137 acaweb_fx.pg_catalog.pg_statistic 1 1 997 0 186 0 594 502 177 731,806 acaweb_fx.pg_catalog.pg_depend 1 1 371 0 83 0 59 181 64 340,811 acaweb_fx.public.latest_t15_candle_view 1 1 66 0 1 0 0 6 1 9,037 Total 42 38 59,050 48,100 2,920 10 40,132 20,912 3,027 10,549,512 Tuples removed per table
Key values
- public.solr_relevance_old (42682) Main table with removed tuples on database acaweb_fx
- 61806 tuples Total removed
Index Tuples Pages Table Vacuums scans removed remain not yet removable removed remain acaweb_fx.public.solr_relevance_old 17 16 42,682 105,049 7,321 0 3,372 acaweb_fx.pg_catalog.pg_attribute 4 4 8,016 42,640 0 0 1,077 acaweb_fx.public.autochartist_symbolupdates 1 1 5,260 52,336 40 0 40,691 acaweb_fx.public.relevance_keylevels_results 2 2 2,380 26,265 0 0 558 acaweb_fx.public.relevance_autochartist_results 2 2 812 17,245 621 0 760 acaweb_fx.pg_catalog.pg_type 2 2 619 2,932 36 0 88 acaweb_fx.pg_catalog.pg_statistic 1 1 540 3,809 38 0 1,194 acaweb_fx.pg_catalog.pg_depend 1 1 519 14,429 0 0 142 acaweb_fx.pg_catalog.pg_class 3 3 334 4,950 0 0 450 acaweb_fx.public.relevance_fibonacci_results 2 2 209 3,105 0 0 204 acaweb_fx.public.datafeeds_latestrun 3 0 167 42 0 0 48 acaweb_fx.pg_toast.pg_toast_2619 2 2 160 336 0 3 103 acaweb_fx.public.latest_t15_candle_view 1 1 56 14 0 0 1 acaweb_fx.public.solr_imports 1 1 52 1 0 0 2 Total 42 38 61,806 273,153 8,056 3 48,690 Pages removed per table
Key values
- pg_toast.pg_toast_2619 (3) Main table with removed pages on database acaweb_fx
- 3 pages Total removed
Table Number of vacuums Index scans Tuples removed Pages removed acaweb_fx.pg_toast.pg_toast_2619 2 2 160 3 acaweb_fx.pg_catalog.pg_type 2 2 619 0 acaweb_fx.public.datafeeds_latestrun 3 0 167 0 acaweb_fx.public.autochartist_symbolupdates 1 1 5260 0 acaweb_fx.public.solr_imports 1 1 52 0 acaweb_fx.pg_catalog.pg_statistic 1 1 540 0 acaweb_fx.pg_catalog.pg_attribute 4 4 8016 0 acaweb_fx.pg_catalog.pg_depend 1 1 519 0 acaweb_fx.public.latest_t15_candle_view 1 1 56 0 acaweb_fx.public.relevance_keylevels_results 2 2 2380 0 acaweb_fx.public.solr_relevance_old 17 16 42682 0 acaweb_fx.public.relevance_autochartist_results 2 2 812 0 acaweb_fx.pg_catalog.pg_class 3 3 334 0 acaweb_fx.public.relevance_fibonacci_results 2 2 209 0 Total 42 38 61,806 3 Autovacuum Activity
↑ Back to the top of the Autovacuum Activity tableDay Hour VACUUMs ANALYZEs Feb 17 01 42 57 - 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
- 35,567 Total read queries
- 31,613 Total write queries
Queries by database
Key values
- unknown Main database
- 141,924 Requests
- 1h47m32s (unknown)
- Main time consuming database
Database Request type Count Duration acaweb_fx Total 859 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 172 0ms select 72 0ms tcl 333 0ms update 40 0ms socialmedia Total 72 0ms select 70 0ms tcl 2 0ms unknown Total 141,924 1h47m32s copy from 16 0ms cte 3,722 0ms insert 25,250 0ms others 4,076 0ms select 35,425 0ms tcl 383 0ms update 1,625 0ms Queries by user
Key values
- unknown Main user
- 141,924 Requests
User Request type Count Duration postgres Total 931 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 172 0ms select 142 0ms tcl 335 0ms update 40 0ms unknown Total 141,924 1h47m32s copy from 16 0ms cte 3,722 0ms insert 25,250 0ms others 4,076 0ms select 35,425 0ms tcl 383 0ms update 1,625 0ms Duration by user
Key values
- 1h47m32s (unknown) Main time consuming user
User Request type Count Duration postgres Total 931 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 172 0ms select 142 0ms tcl 335 0ms update 40 0ms unknown Total 141,924 1h47m32s copy from 16 0ms cte 3,722 0ms insert 25,250 0ms others 4,076 0ms select 35,425 0ms tcl 383 0ms update 1,625 0ms Queries by host
Key values
- unknown Main host
- 142,855 Requests
- 1h47m32s (unknown)
- Main time consuming host
Queries by application
Key values
- unknown Main application
- 142,497 Requests
- 1h47m32s (unknown)
- Main time consuming application
Number of cancelled queries
Key values
- 0 per second Cancelled query Peak
- 2026-02-17 01:26:15 Date
Number of cancelled queries (5 minutes period)
NO DATASET
-
Top Queries
Histogram of query times
Key values
- 48,557 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 12 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 01 12 0ms 0ms 2 0ms 217 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 01 217 0ms 0ms 3 0ms 4 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 #3
Day Hour Count Duration Avg duration Feb 17 01 4 0ms 0ms 4 0ms 2,301 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 #4
Day Hour Count Duration Avg duration Feb 17 01 2,301 0ms 0ms 5 0ms 4 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 #5
Day Hour Count Duration Avg duration Feb 17 01 4 0ms 0ms 6 0ms 150 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 #6
Day Hour Count Duration Avg duration Feb 17 01 150 0ms 0ms 7 0ms 4 0ms 0ms 0ms select updaterelevantforrelevantresults ();Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Feb 17 01 4 0ms 0ms 8 0ms 1,183 0ms 0ms 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Feb 17 01 1,183 0ms 0ms 9 0ms 1 0ms 0ms 0ms insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ? is being released in Great Britain in the next ? hours.\", \"has_results\": true, \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"data_event_uid\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"United Kingdom Unemployment Rate\? is being released in Great Britain. The expected value for this release is ?.?%.\", \"data_CountryIcon\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg\", \"data_Last12Label\": \"Last ? events\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_ActualLegend\": \"Actual\", \"dictionary_Actual\": \"Actual\", \"dictionary_Change\": \"Change\", \"dictionary_Coffee\": \"Coffee\", \"dictionary_Friday\": \"Friday\", \"dictionary_Monday\": \"Monday\", \"dictionary_Silver\": \"Silver\", \"dictionary_Sunday\": \"Sunday\", \"dictionary_Target\": \"Target\", \"dictionary_dd MMM\": \"dd MMM\", \"params_mode_value\": ?, \"params_uuid_value\": \"?c3886b-4850-4dc8-b236-6ce891d3e8e9\", \"data_ExpectedLabel\": \"Expected\", \"data_ExpectedValue\": \"?.?%\", \"data_PreviousLabel\": \"Previous\", \"data_PreviousValue\": \"?.?%\", \"dictionary_909_DAX\": \"GER?\", \"dictionary_AUD/USD\": \"AUD/USD\", \"dictionary_Company\": \"Company\", \"dictionary_EUR/USD\": \"EUR/USD\", \"dictionary_GBP/USD\": \"GBP/USD\", \"dictionary_Indices\": \"Indices\", \"dictionary_Minutes\": \"Minutes\", \"dictionary_NZD/USD\": \"NZD/USD\", \"dictionary_Tuesday\": \"Tuesday\", \"dictionary_USD/CAD\": \"USD/CAD\", \"dictionary_USD/CHF\": \"USD/CHF\", \"dictionary_USD/JPY\": \"USD/JPY\", \"data_ExpectedLegend\": \"Expected\", \"dictionary_909_Gold\": \"XAUUSD\", \"dictionary_Estimate\": \"Estimate\", \"dictionary_Expected\": \"Expected\", \"dictionary_FTSE ?\": \"FTSE ?\", \"dictionary_Interval\": \"Interval\", \"dictionary_KeyLevel\": \"Key Level\", \"dictionary_Previous\": \"Previous\", \"dictionary_Saturday\": \"Saturday\", \"dictionary_Thursday\": \"Thursday\", \"dictionary_estimate\": \"estimate\", \"dictionary_previous\": \"previous\", \"params_locale_value\": \"en\", \"params_region_value\": null, \"dictionary_Consensus\": \"Consensus\", \"dictionary_Crude oil\": \"Crude oil\", \"dictionary_Fibonacci\": \"Fibonacci Patterns\", \"dictionary_Hang Seng\": \"Hang Seng\", \"dictionary_Stop_Loss\": \"Stop-Loss\", \"dictionary_Wednesday\": \"Wednesday\", \"dictionary_consensus\": \"consensus\", \"dictionary_dd MMM, h\": \"dd MMM, h\", \"params_request_value\": \"HighImpactEvent\", \"params_webhook_value\": \"https://hooks.zapier.com/hooks/catch/?/?zze/\", \"dictionary_909_Silver\": \"XAGUSD\", \"dictionary_Currencies\": \"Currencies\", \"dictionary_Nasdaq ?\": \"Nasdaq ?\", \"dictionary_Nikkei ?\": \"Nikkei ?\", \"dictionary_Stop_Level\": \"Stop Level\", \"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\": \"After Market \", \"dictionary_BigMovement\": \"Big Movement\", \"dictionary_Commodities\": \"Commodities\", \"dictionary_EEE, dd MMM\": \"EEE, dd MMM\", \"dictionary_Entry_Level\": \"Entry Level\", \"dictionary_FridayShort\": \"Fri\", \"dictionary_MarketAlert\": \"Market Alert\", \"dictionary_MondayShort\": \"Mon\", \"dictionary_Natural gas\": \"Natural gas\", \"dictionary_Probability\": \"Probability\", \"dictionary_Releasetime\": \"Release time\", \"dictionary_SundayShort\": \"Sun\", \"dictionary_probability\": \"If {ticker} earnings {deltasign} {consensus}\", \"params_RateLimit_value\": \"?,?,?\", \"params_processid_value\": ?, \"dictionary_909_FTSE ?\": \"UK?\", \"dictionary_BeforeMarket\": \"Before Market \", \"dictionary_ChartPattern\": \"Chart Pattern\", \"dictionary_Corn Futures\": \"Corn Futures\", \"dictionary_Gold Futures\": \"Gold Futures\", \"dictionary_Last12events\": \"Last ? events\", \"dictionary_RoundNumbers\": \"Round Number\", \"dictionary_Target_Level\": \"Target Level\", \"dictionary_TuesdayShort\": \"Tue\", \"params_DateFormat_value\": \"dd MMM, ha UTC\", \"params_plot_width_value\": \"?\", \"dictionary_909_Crude oil\": \"XTIUSD\", \"dictionary_909_Hang Seng\": \"HK?\", \"dictionary_BiggestLosers\": \"Biggest Losers\", \"dictionary_BiggestMovers\": \"Biggest Movers\", \"dictionary_MarketSummary\": \"Market Summary\", \"dictionary_SaturdayShort\": \"Sat\", \"dictionary_Target_Period\": \"Target Period\", \"dictionary_ThursdayShort\": \"Thu\", \"dictionary_Wheat Futures\": \"Wheat Futures\", \"params_hours_ahead_value\": ?, \"params_plot_height_value\": \"?\", \"dictionary_909_Nasdaq ?\": \"US?\", \"dictionary_909_Nikkei ?\": \"JP?\", \"dictionary_BiggestGainers\": \"Biggest Gainers\", \"dictionary_Copper Futures\": \"Copper Futures\", \"dictionary_EarningsImpact\": \"Earnings Impact\", \"dictionary_NYSE Composite\": \"NYSE Composite\", \"dictionary_Silver Futures\": \"Silver Futures\", \"dictionary_WednesdayShort\": \"Wed\", \"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\": \"Extreme Movement\", \"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\", \"dictionary_EarningsExceeded\": \"Historically, when earnings exceeded expectations, {probability} of the time, the stock rose by an average of {movement}\", \"dictionary_Exploring Assets\": \"Exploring Assets\", \"dictionary_Platinum Futures\": \"Platinum Futures\", \"data_ExpectedValue_sanitized\": ?.?, \"dictionary_EarningsConcensus\": \"If {ticker} earnings {deltasign} {consensus}\", \"dictionary_EarningsShortfall\": \"Historically, when earnings fell short of expectations, {probability} of the time, the stock fell by an average of {movement}\", \"dictionary_Fun Finance Facts\": \"Fun Finance Facts\", \"dictionary_Trading Terms ?\": \"Trading Terms ?\", \"dictionary_VolatilityWarning\": \"Volatility Warning\", \"params_blank_image_url_value\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg\", \"dictionary_ConsecutiveCandles\": \"Consecutive Candles\", \"dictionary_DailyMarketSummary\": \"Daily Market Summary\", \"dictionary_Noearningsreleases\": \"No earnings releases\", \"dictionary_Shanghai Composite\": \"Shanghai Composite\", \"dictionary_VolatilityIncrease\": \"volatility increase\", \"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\": \"There are no losers\", \"dictionary_Zeromarkets ?_DAX\": \"GER?\", \"params_HistoryDateFormat_value\": \"dd MMM\", \"params_creatomate_apikey_value\": \"f3baa30db2c546378092e775d62306703b6cac7e1f9b3109f1967057727e08462cbc08c86e5fb77c8691e18c63216f96\", \"dictionary_909_Platinum Futures\": \"XPTUSD\", \"dictionary_JapaneseCandleSticks\": \"Japanese Candlesticks\", \"dictionary_There are no winners\": \"There are no winners\", \"dictionary_TodaysEconomicEvents\": \"Today\? is being released in {countryname} in the next {hours_ahead} hours.\", \"params_DateFormat_DateOnly_value\": \"dd MMM\", \"dictionary_E-mini S&P ? Futures\": \"E-mini S&P ? Futures\", \"dictionary_ThisWeeksMarketSummary\": \"This Week\?s biggest movers\": \"Today\?s Economic Events\", \"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\": \"Upcoming volatility as a result of \? in {country_name} in the next {hours_ahead} hours.\", \"params_creatomate_templateid_value\": \"fd2869ca-ade5-4c4a-aa6c-873c5d414b1e\", \"params_quickchart_templateid_value\": null, \"params_title_character_limit_value\": ?, \"dictionary_The biggest winners are:\": \"The biggest winners are:\", \"dictionary_Zeromarkets ?_FTSE ?\": \"UK?\", \"dictionary_highimpactevent_longtext\": \"{eventname}\?s Earnings Releases\", \"dictionary_Zeromarkets ?_Crude oil\": \"XTIUSD\", \"dictionary_Zeromarkets ?_Hang Seng\": \"HK?\", \"dictionary_highimpactevent_shorttext\": \"{eventname}\?s biggest movers\": \"This week\? ({country_name}) is going to result in significant increases in volatility.\", \"params_creatomate_modifications_value\": null, \"params_longtext_character_limit_value\": ?, \"params_minimum_quantity_results_value\": ?, \"dictionary_909_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_909_US Dollar Index Futures\": \"USDX\", \"dictionary_Stocksimpactedbythisrelease\": \"Stocks potentially impacted by this release:\", \"dictionary_This weeks economic events:\": \"This week\? ({country_name}) is going to result in significant increases in volatility.\", \"params_shorttext_character_limit_value\": ?, \"dictionary_BiggestStockMoversfortheweek\": \"Biggest Stock Movers for the week\", \"dictionary_upcomingeconomicevents_title\": \"High-impact economic events between {fromdate} and {todate}.\", \"dictionary_909_Nikkei ? Dollar Futures\": \"JP?\", \"dictionary_E-mini S&P MidCap ? Futures\": \"E-mini S&P MidCap ? Futures\", \"dictionary_impactofearningsrelease_title\": \"Impact of {earningscompany_name} ({earningscompany}) earnings release\", \"dictionary_Market moves for the last week:\": \"Market moves for the last week:\", \"dictionary_This week\?s biggest movers are:\", \"dictionary_UpcomingHighImpactEconomicEvent\": \"Upcoming High-Impact Economic Event\", \"dictionary_Zeromarkets ?_Platinum Futures\": \"XPTUSD\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany}) is releasing earnings today. When the EPS is {delta_sign} than expected, {earningscompany} has a {probability} probability of a {mean_mov_percent} move for the month following this release.\\\\\\\\nIn past earnings releases where the EPS is {delta_sign} than expected, the following stocks have been impacted:\", \"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}) is releasing earnings today and will impact the following stocks:\", \"dictionary_upcomingearnings_title_noearnings\": \"There are no earnings announcements scheduled between {fromdate} and {todate}.\", \"dictionary_10-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_CalendarofHighimpactEconomicEvents\": \"Calendar of High impact Economic Events \", \"dictionary_Market moves for the last ? hours:\": \"Market moves for the last ? hours:\", \"params_creatomate_templateselectionscheme_value\": \"random\", \"dictionary_Zeromarkets ?_WTI Crude Oil Futures\": \"WTI\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): {probability} probability of a {mean_mov_percent} move for the month following this release.\", \"dictionary_Market movements in the last ? hours.\": \"Market movements in the last ? hours.\", \"dictionary_Companies releasing earnings this week:\": \"Companies releasing earnings this week:\", \"dictionary_Zeromarkets ?_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_Zeromarkets ?_US Dollar Index Futures\": \"USDX\", \"dictionary_Zeromarkets ?_Nikkei ? Dollar Futures\": \"JP?\", \"dictionary_High impact economic events for this week:\": \"High-impact economic events for this week:\", \"dictionary_E-mini Dow Jones Industrial Average Futures\": \"E-mini Dow Jones Industrial Average Futures\", \"dictionary_No earnings announcements scheduled this week.\": \"No earnings announcements scheduled this week.\", \"dictionary_Upcoming earnings announcements for this week:\": \"Upcoming earnings announcements for this week:\", \"dictionary_These are the market movements for the last week:\": \"These are the market movements for the last week:\", \"dictionary_These are the market movements for the last ? hours:\": \"These are the market movements for the last ? hours:\"}}?}", "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\": \"d05ca1f9-f065-49a1-a076-8194c927039e\", \"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/d05ca1f9-f065-49a1-a076-8194c927039e.jpg\", \"template_id\": \"fd2869ca-ade5-4c4a-aa6c-873c5d414b1e\", \"template_name\": \"Single Economic Event with Historical Values PNG\", \"template_tags\": [], \"output_format\": \"jpg\", \"modifications\": {\"data_Date\": \"? Feb, ?AM UTC\", \"data_Name\": \"United Kingdom Unemployment Rate\", \"text_title\": \"United Kingdom Unemployment Rate? is being released in Great Britain in the next ? hours.\", \"has_results\": true, \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"data_event_uid\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"United Kingdom Unemployment Rate? is being released in Great Britain in the next ? hours. The expected value for this release is ?.?%. In the United Kingdom, the unemployment rate measures the number of people actively looking for a job as a percentage of the labour force.\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"United Kingdom Unemployment Rate? is being released in Great Britain. The expected value for this release is ?.?%.\", \"data_CountryIcon\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg\", \"data_Last12Label\": \"Last ? events\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_ActualLegend\": \"Actual\", \"dictionary_Actual\": \"Actual\", \"dictionary_Change\": \"Change\", \"dictionary_Coffee\": \"Coffee\", \"dictionary_Friday\": \"Friday\", \"dictionary_Monday\": \"Monday\", \"dictionary_Silver\": \"Silver\", \"dictionary_Sunday\": \"Sunday\", \"dictionary_Target\": \"Target\", \"dictionary_dd MMM\": \"dd MMM\", \"params_mode_value\": ?, \"params_uuid_value\": \"?c3886b-4850-4dc8-b236-6ce891d3e8e9\", \"data_ExpectedLabel\": \"Expected\", \"data_ExpectedValue\": \"?.?%\", \"data_PreviousLabel\": \"Previous\", \"data_PreviousValue\": \"?.?%\", \"dictionary_909_DAX\": \"GER?\", \"dictionary_AUD/USD\": \"AUD/USD\", \"dictionary_Company\": \"Company\", \"dictionary_EUR/USD\": \"EUR/USD\", \"dictionary_GBP/USD\": \"GBP/USD\", \"dictionary_Indices\": \"Indices\", \"dictionary_Minutes\": \"Minutes\", \"dictionary_NZD/USD\": \"NZD/USD\", \"dictionary_Tuesday\": \"Tuesday\", \"dictionary_USD/CAD\": \"USD/CAD\", \"dictionary_USD/CHF\": \"USD/CHF\", \"dictionary_USD/JPY\": \"USD/JPY\", \"data_ExpectedLegend\": \"Expected\", \"dictionary_909_Gold\": \"XAUUSD\", \"dictionary_Estimate\": \"Estimate\", \"dictionary_Expected\": \"Expected\", \"dictionary_FTSE ?\": \"FTSE ?\", \"dictionary_Interval\": \"Interval\", \"dictionary_KeyLevel\": \"Key Level\", \"dictionary_Previous\": \"Previous\", \"dictionary_Saturday\": \"Saturday\", \"dictionary_Thursday\": \"Thursday\", \"dictionary_estimate\": \"estimate\", \"dictionary_previous\": \"previous\", \"params_locale_value\": \"en\", \"params_region_value\": null, \"dictionary_Consensus\": \"Consensus\", \"dictionary_Crude oil\": \"Crude oil\", \"dictionary_Fibonacci\": \"Fibonacci Patterns\", \"dictionary_Hang Seng\": \"Hang Seng\", \"dictionary_Stop_Loss\": \"Stop-Loss\", \"dictionary_Wednesday\": \"Wednesday\", \"dictionary_consensus\": \"consensus\", \"dictionary_dd MMM, h\": \"dd MMM, h\", \"params_request_value\": \"HighImpactEvent\", \"params_webhook_value\": \"https://hooks.zapier.com/hooks/catch/?/?zze/\", \"dictionary_909_Silver\": \"XAGUSD\", \"dictionary_Currencies\": \"Currencies\", \"dictionary_Nasdaq ?\": \"Nasdaq ?\", \"dictionary_Nikkei ?\": \"Nikkei ?\", \"dictionary_Stop_Level\": \"Stop Level\", \"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\": \"After Market \", \"dictionary_BigMovement\": \"Big Movement\", \"dictionary_Commodities\": \"Commodities\", \"dictionary_EEE, dd MMM\": \"EEE, dd MMM\", \"dictionary_Entry_Level\": \"Entry Level\", \"dictionary_FridayShort\": \"Fri\", \"dictionary_MarketAlert\": \"Market Alert\", \"dictionary_MondayShort\": \"Mon\", \"dictionary_Natural gas\": \"Natural gas\", \"dictionary_Probability\": \"Probability\", \"dictionary_Releasetime\": \"Release time\", \"dictionary_SundayShort\": \"Sun\", \"dictionary_probability\": \"If {ticker} earnings {deltasign} {consensus}\", \"params_RateLimit_value\": \"?,?,?\", \"params_processid_value\": ?, \"dictionary_909_FTSE ?\": \"UK?\", \"dictionary_BeforeMarket\": \"Before Market \", \"dictionary_ChartPattern\": \"Chart Pattern\", \"dictionary_Corn Futures\": \"Corn Futures\", \"dictionary_Gold Futures\": \"Gold Futures\", \"dictionary_Last12events\": \"Last ? events\", \"dictionary_RoundNumbers\": \"Round Number\", \"dictionary_Target_Level\": \"Target Level\", \"dictionary_TuesdayShort\": \"Tue\", \"params_DateFormat_value\": \"dd MMM, ha UTC\", \"params_plot_width_value\": \"?\", \"dictionary_909_Crude oil\": \"XTIUSD\", \"dictionary_909_Hang Seng\": \"HK?\", \"dictionary_BiggestLosers\": \"Biggest Losers\", \"dictionary_BiggestMovers\": \"Biggest Movers\", \"dictionary_MarketSummary\": \"Market Summary\", \"dictionary_SaturdayShort\": \"Sat\", \"dictionary_Target_Period\": \"Target Period\", \"dictionary_ThursdayShort\": \"Thu\", \"dictionary_Wheat Futures\": \"Wheat Futures\", \"params_hours_ahead_value\": ?, \"params_plot_height_value\": \"?\", \"dictionary_909_Nasdaq ?\": \"US?\", \"dictionary_909_Nikkei ?\": \"JP?\", \"dictionary_BiggestGainers\": \"Biggest Gainers\", \"dictionary_Copper Futures\": \"Copper Futures\", \"dictionary_EarningsImpact\": \"Earnings Impact\", \"dictionary_NYSE Composite\": \"NYSE Composite\", \"dictionary_Silver Futures\": \"Silver Futures\", \"dictionary_WednesdayShort\": \"Wed\", \"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\": \"Extreme Movement\", \"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\", \"dictionary_EarningsExceeded\": \"Historically, when earnings exceeded expectations, {probability} of the time, the stock rose by an average of {movement}\", \"dictionary_Exploring Assets\": \"Exploring Assets\", \"dictionary_Platinum Futures\": \"Platinum Futures\", \"data_ExpectedValue_sanitized\": ?.?, \"dictionary_EarningsConcensus\": \"If {ticker} earnings {deltasign} {consensus}\", \"dictionary_EarningsShortfall\": \"Historically, when earnings fell short of expectations, {probability} of the time, the stock fell by an average of {movement}\", \"dictionary_Fun Finance Facts\": \"Fun Finance Facts\", \"dictionary_Trading Terms ?\": \"Trading Terms ?\", \"dictionary_VolatilityWarning\": \"Volatility Warning\", \"params_blank_image_url_value\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg\", \"dictionary_ConsecutiveCandles\": \"Consecutive Candles\", \"dictionary_DailyMarketSummary\": \"Daily Market Summary\", \"dictionary_Noearningsreleases\": \"No earnings releases\", \"dictionary_Shanghai Composite\": \"Shanghai Composite\", \"dictionary_VolatilityIncrease\": \"volatility increase\", \"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\": \"There are no losers\", \"dictionary_Zeromarkets ?_DAX\": \"GER?\", \"params_HistoryDateFormat_value\": \"dd MMM\", \"params_creatomate_apikey_value\": \"f3baa30db2c546378092e775d62306703b6cac7e1f9b3109f1967057727e08462cbc08c86e5fb77c8691e18c63216f96\", \"dictionary_909_Platinum Futures\": \"XPTUSD\", \"dictionary_JapaneseCandleSticks\": \"Japanese Candlesticks\", \"dictionary_There are no winners\": \"There are no winners\", \"dictionary_TodaysEconomicEvents\": \"Today?s Economic Events\", \"dictionary_Zeromarkets ?_Gold\": \"XAUUSD\", \"dictionary_ExpectedMovementRange\": \"Expected movement range during event is between {from} and {to}\", \"dictionary_WTI Crude Oil Futures\": \"WTI Crude Oil Futures\", \"dictionary_highimpactevent_title\": \"{eventname}? is being released in {countryname} in the next {hours_ahead} hours.\", \"params_DateFormat_DateOnly_value\": \"dd MMM\", \"dictionary_E-mini S&P ? Futures\": \"E-mini S&P ? Futures\", \"dictionary_ThisWeeksMarketSummary\": \"This Week?s Market Summary\", \"dictionary_Today?s biggest movers\": \"Today?s biggest movers\", \"dictionary_Zeromarkets ?_Silver\": \"XAGUSD\", \"dictionary_upcomingearnings_title\": \"Earnings announcements between {fromdate} and {todate}.\", \"params_creatomate_snapshots_value\": null, \"dictionary_Brent Crude Oil Futures\": \"Brent Crude Oil Futures\", \"dictionary_The biggest losers are:\": \"The biggest losers are:\", \"dictionary_ThisWeeksEconomicEvents\": \"This Week?s Economic Events\", \"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\": \"Upcoming volatility as a result of ?{eventname}? in {country_name} in the next {hours_ahead} hours.\", \"params_creatomate_templateid_value\": \"fd2869ca-ade5-4c4a-aa6c-873c5d414b1e\", \"params_quickchart_templateid_value\": null, \"params_title_character_limit_value\": ?, \"dictionary_The biggest winners are:\": \"The biggest winners are:\", \"dictionary_Zeromarkets ?_FTSE ?\": \"UK?\", \"dictionary_highimpactevent_longtext\": \"{eventname}? is being released in {countryname} in the next {hours_ahead} hours. The expected value for this release is {consensus}. {description}\", \"params_probability_of_posting_value\": ?, \"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\": \"This Week?s Earnings Releases\", \"dictionary_Zeromarkets ?_Crude oil\": \"XTIUSD\", \"dictionary_Zeromarkets ?_Hang Seng\": \"HK?\", \"dictionary_highimpactevent_shorttext\": \"{eventname}? is being released in {countryname}. The expected value for this release is {consensus}.\", \"dictionary_Nohighimpacteconomicevents\": \"No high impact economic events\", \"dictionary_This week?s biggest movers\": \"This week?s biggest movers\", \"dictionary_Zeromarkets ?_Nasdaq ?\": \"US?\", \"dictionary_Zeromarkets ?_Nikkei ?\": \"JP?\", \"dictionary_volatilitywarning_longtext\": \"{eventname}? ({country_name}) is going to result in significant increases in volatility.\", \"params_creatomate_modifications_value\": null, \"params_longtext_character_limit_value\": ?, \"params_minimum_quantity_results_value\": ?, \"dictionary_909_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_909_US Dollar Index Futures\": \"USDX\", \"dictionary_Stocksimpactedbythisrelease\": \"Stocks potentially impacted by this release:\", \"dictionary_This weeks economic events:\": \"This week?s economic events:\", \"dictionary_Zeromarkets ?_Natural gas\": \"XNGUSD\", \"dictionary_volatilitywarning_shorttext\": \"{eventname}? ({country_name}) is going to result in significant increases in volatility.\", \"params_shorttext_character_limit_value\": ?, \"dictionary_BiggestStockMoversfortheweek\": \"Biggest Stock Movers for the week\", \"dictionary_upcomingeconomicevents_title\": \"High-impact economic events between {fromdate} and {todate}.\", \"dictionary_909_Nikkei ? Dollar Futures\": \"JP?\", \"dictionary_E-mini S&P MidCap ? Futures\": \"E-mini S&P MidCap ? Futures\", \"dictionary_impactofearningsrelease_title\": \"Impact of {earningscompany_name} ({earningscompany}) earnings release\", \"dictionary_Market moves for the last week:\": \"Market moves for the last week:\", \"dictionary_This week?s biggest movers are:\": \"This week?s biggest movers are:\", \"dictionary_UpcomingHighImpactEconomicEvent\": \"Upcoming High-Impact Economic Event\", \"dictionary_Zeromarkets ?_Platinum Futures\": \"XPTUSD\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany}) is releasing earnings today. When the EPS is {delta_sign} than expected, {earningscompany} has a {probability} probability of a {mean_mov_percent} move for the month following this release.\\\\nIn past earnings releases where the EPS is {delta_sign} than expected, the following stocks have been impacted:\", \"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}) is releasing earnings today and will impact the following stocks:\", \"dictionary_upcomingearnings_title_noearnings\": \"There are no earnings announcements scheduled between {fromdate} and {todate}.\", \"dictionary_10-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_CalendarofHighimpactEconomicEvents\": \"Calendar of High impact Economic Events \", \"dictionary_Market moves for the last ? hours:\": \"Market moves for the last ? hours:\", \"params_creatomate_templateselectionscheme_value\": \"random\", \"dictionary_Zeromarkets ?_WTI Crude Oil Futures\": \"WTI\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): {probability} probability of a {mean_mov_percent} move for the month following this release.\", \"dictionary_Market movements in the last ? hours.\": \"Market movements in the last ? hours.\", \"dictionary_Companies releasing earnings this week:\": \"Companies releasing earnings this week:\", \"dictionary_Zeromarkets ?_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_Zeromarkets ?_US Dollar Index Futures\": \"USDX\", \"dictionary_Zeromarkets ?_Nikkei ? Dollar Futures\": \"JP?\", \"dictionary_High impact economic events for this week:\": \"High-impact economic events for this week:\", \"dictionary_E-mini Dow Jones Industrial Average Futures\": \"E-mini Dow Jones Industrial Average Futures\", \"dictionary_No earnings announcements scheduled this week.\": \"No earnings announcements scheduled this week.\", \"dictionary_Upcoming earnings announcements for this week:\": \"Upcoming earnings announcements for this week:\", \"dictionary_These are the market movements for the last week:\": \"These are the market movements for the last week:\", \"dictionary_These are the market movements for the last ? hours:\": \"These are the market movements for the last ? hours:\"}}" } }?json');Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Feb 17 01 1 0ms 0ms 10 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 #10
Day Hour Count Duration Avg duration Feb 17 01 18 0ms 0ms 11 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 #11
Day Hour Count Duration Avg duration Feb 17 01 238 0ms 0ms 12 0ms 29 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 #12
Day Hour Count Duration Avg duration Feb 17 01 29 0ms 0ms 13 0ms 359 0ms 0ms 0ms commit;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Feb 17 01 359 0ms 0ms 14 0ms 1 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 #14
Day Hour Count Duration Avg duration Feb 17 01 1 0ms 0ms 15 0ms 14 0ms 0ms 0ms update correlating_signals set sent = true where id = ?;Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Feb 17 01 14 0ms 0ms 16 0ms 290 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 #16
Day Hour Count Duration Avg duration Feb 17 01 290 0ms 0ms 17 0ms 13 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 #17
Day Hour Count Duration Avg duration Feb 17 01 13 0ms 0ms 18 0ms 2 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 #18
Day Hour Count Duration Avg duration Feb 17 01 2 0ms 0ms 19 0ms 238 0ms 0ms 0ms select count(*), sum(size), extract(epoch from now() - min(modification)) from pg_ls_waldir ();Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Feb 17 01 238 0ms 0ms 20 0ms 60 0ms 0ms 0ms select * from ( select pricedatetime, open, high, low, close, volume, bsf from t30 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Feb 17 01 60 0ms 0ms Most frequent queries (N)
Rank Times executed Total duration Min duration Max duration Avg duration Query 1 12,537 0ms 0ms 0ms 0ms select ?;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Feb 17 01 12,537 0ms 0ms 2 6,395 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 01 6,395 0ms 0ms 3 5,287 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 #3
Day Hour Count Duration Avg duration Feb 17 01 5,287 0ms 0ms 4 4,746 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 #4
Day Hour Count Duration Avg duration Feb 17 01 4,746 0ms 0ms 5 4,670 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 #5
Day Hour Count Duration Avg duration Feb 17 01 4,670 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 01 3,798 0ms 0ms 7 3,315 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 01 3,315 0ms 0ms 8 3,141 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 01 3,141 0ms 0ms 9 2,301 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 01 2,301 0ms 0ms 10 1,836 0ms 0ms 0ms 0ms set extra_float_digits = ?;Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Feb 17 01 1,836 0ms 0ms 11 1,810 0ms 0ms 0ms 0ms set application_name = ?;Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Feb 17 01 1,810 0ms 0ms 12 1,183 0ms 0ms 0ms 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Feb 17 01 1,183 0ms 0ms 13 1,110 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 #13
Day Hour Count Duration Avg duration Feb 17 01 1,110 0ms 0ms 14 936 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 01 936 0ms 0ms 15 888 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 #15
Day Hour Count Duration Avg duration Feb 17 01 888 0ms 0ms 16 745 0ms 0ms 0ms 0ms insert into t1440_underlying (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 #16
Day Hour Count Duration Avg duration Feb 17 01 745 0ms 0ms 17 637 0ms 0ms 0ms 0ms insert into t240 (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 #17
Day Hour Count Duration Avg duration Feb 17 01 637 0ms 0ms 18 510 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 #18
Day Hour Count Duration Avg duration Feb 17 01 510 0ms 0ms 19 377 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 #19
Day Hour Count Duration Avg duration Feb 17 01 377 0ms 0ms 20 377 0ms 0ms 0ms 0ms select t.pricedatetime, t.open, t.high, t.low, t.close, t.volume, t.symbolid, dss.downloadersymbol as symbol, dss.classname as datafeed, t.bsf, t.sastdatetimewritten, t.sastdatetimereceived from t15 t inner join downloadersymbolsettings dss on t.symbolid = dss.symbolid where dss.classname = ? and dss.downloadersymbol in (...) and dss.downloadfrequency = ? and t.pricedatetime between ?::timestamp and ?::timestamp order by t.pricedatetime;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Feb 17 01 377 0ms 0ms Normalized slowest queries (N)
Rank Min duration Max duration Avg duration Times executed Total duration Query 1 0ms 0ms 0ms 12 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 01 12 0ms 0ms 2 0ms 0ms 0ms 217 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 01 217 0ms 0ms 3 0ms 0ms 0ms 4 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 #3
Day Hour Count Duration Avg duration Feb 17 01 4 0ms 0ms 4 0ms 0ms 0ms 2,301 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 #4
Day Hour Count Duration Avg duration Feb 17 01 2,301 0ms 0ms 5 0ms 0ms 0ms 4 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 #5
Day Hour Count Duration Avg duration Feb 17 01 4 0ms 0ms 6 0ms 0ms 0ms 150 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 #6
Day Hour Count Duration Avg duration Feb 17 01 150 0ms 0ms 7 0ms 0ms 0ms 4 0ms select updaterelevantforrelevantresults ();Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Feb 17 01 4 0ms 0ms 8 0ms 0ms 0ms 1,183 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Feb 17 01 1,183 0ms 0ms 9 0ms 0ms 0ms 1 0ms insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ? is being released in Great Britain in the next ? hours.\", \"has_results\": true, \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"data_event_uid\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"United Kingdom Unemployment Rate\? is being released in Great Britain. The expected value for this release is ?.?%.\", \"data_CountryIcon\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg\", \"data_Last12Label\": \"Last ? events\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_ActualLegend\": \"Actual\", \"dictionary_Actual\": \"Actual\", \"dictionary_Change\": \"Change\", \"dictionary_Coffee\": \"Coffee\", \"dictionary_Friday\": \"Friday\", \"dictionary_Monday\": \"Monday\", \"dictionary_Silver\": \"Silver\", \"dictionary_Sunday\": \"Sunday\", \"dictionary_Target\": \"Target\", \"dictionary_dd MMM\": \"dd MMM\", \"params_mode_value\": ?, \"params_uuid_value\": \"?c3886b-4850-4dc8-b236-6ce891d3e8e9\", \"data_ExpectedLabel\": \"Expected\", \"data_ExpectedValue\": \"?.?%\", \"data_PreviousLabel\": \"Previous\", \"data_PreviousValue\": \"?.?%\", \"dictionary_909_DAX\": \"GER?\", \"dictionary_AUD/USD\": \"AUD/USD\", \"dictionary_Company\": \"Company\", \"dictionary_EUR/USD\": \"EUR/USD\", \"dictionary_GBP/USD\": \"GBP/USD\", \"dictionary_Indices\": \"Indices\", \"dictionary_Minutes\": \"Minutes\", \"dictionary_NZD/USD\": \"NZD/USD\", \"dictionary_Tuesday\": \"Tuesday\", \"dictionary_USD/CAD\": \"USD/CAD\", \"dictionary_USD/CHF\": \"USD/CHF\", \"dictionary_USD/JPY\": \"USD/JPY\", \"data_ExpectedLegend\": \"Expected\", \"dictionary_909_Gold\": \"XAUUSD\", \"dictionary_Estimate\": \"Estimate\", \"dictionary_Expected\": \"Expected\", \"dictionary_FTSE ?\": \"FTSE ?\", \"dictionary_Interval\": \"Interval\", \"dictionary_KeyLevel\": \"Key Level\", \"dictionary_Previous\": \"Previous\", \"dictionary_Saturday\": \"Saturday\", \"dictionary_Thursday\": \"Thursday\", \"dictionary_estimate\": \"estimate\", \"dictionary_previous\": \"previous\", \"params_locale_value\": \"en\", \"params_region_value\": null, \"dictionary_Consensus\": \"Consensus\", \"dictionary_Crude oil\": \"Crude oil\", \"dictionary_Fibonacci\": \"Fibonacci Patterns\", \"dictionary_Hang Seng\": \"Hang Seng\", \"dictionary_Stop_Loss\": \"Stop-Loss\", \"dictionary_Wednesday\": \"Wednesday\", \"dictionary_consensus\": \"consensus\", \"dictionary_dd MMM, h\": \"dd MMM, h\", \"params_request_value\": \"HighImpactEvent\", \"params_webhook_value\": \"https://hooks.zapier.com/hooks/catch/?/?zze/\", \"dictionary_909_Silver\": \"XAGUSD\", \"dictionary_Currencies\": \"Currencies\", \"dictionary_Nasdaq ?\": \"Nasdaq ?\", \"dictionary_Nikkei ?\": \"Nikkei ?\", \"dictionary_Stop_Level\": \"Stop Level\", \"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\": \"After Market \", \"dictionary_BigMovement\": \"Big Movement\", \"dictionary_Commodities\": \"Commodities\", \"dictionary_EEE, dd MMM\": \"EEE, dd MMM\", \"dictionary_Entry_Level\": \"Entry Level\", \"dictionary_FridayShort\": \"Fri\", \"dictionary_MarketAlert\": \"Market Alert\", \"dictionary_MondayShort\": \"Mon\", \"dictionary_Natural gas\": \"Natural gas\", \"dictionary_Probability\": \"Probability\", \"dictionary_Releasetime\": \"Release time\", \"dictionary_SundayShort\": \"Sun\", \"dictionary_probability\": \"If {ticker} earnings {deltasign} {consensus}\", \"params_RateLimit_value\": \"?,?,?\", \"params_processid_value\": ?, \"dictionary_909_FTSE ?\": \"UK?\", \"dictionary_BeforeMarket\": \"Before Market \", \"dictionary_ChartPattern\": \"Chart Pattern\", \"dictionary_Corn Futures\": \"Corn Futures\", \"dictionary_Gold Futures\": \"Gold Futures\", \"dictionary_Last12events\": \"Last ? events\", \"dictionary_RoundNumbers\": \"Round Number\", \"dictionary_Target_Level\": \"Target Level\", \"dictionary_TuesdayShort\": \"Tue\", \"params_DateFormat_value\": \"dd MMM, ha UTC\", \"params_plot_width_value\": \"?\", \"dictionary_909_Crude oil\": \"XTIUSD\", \"dictionary_909_Hang Seng\": \"HK?\", \"dictionary_BiggestLosers\": \"Biggest Losers\", \"dictionary_BiggestMovers\": \"Biggest Movers\", \"dictionary_MarketSummary\": \"Market Summary\", \"dictionary_SaturdayShort\": \"Sat\", \"dictionary_Target_Period\": \"Target Period\", \"dictionary_ThursdayShort\": \"Thu\", \"dictionary_Wheat Futures\": \"Wheat Futures\", \"params_hours_ahead_value\": ?, \"params_plot_height_value\": \"?\", \"dictionary_909_Nasdaq ?\": \"US?\", \"dictionary_909_Nikkei ?\": \"JP?\", \"dictionary_BiggestGainers\": \"Biggest Gainers\", \"dictionary_Copper Futures\": \"Copper Futures\", \"dictionary_EarningsImpact\": \"Earnings Impact\", \"dictionary_NYSE Composite\": \"NYSE Composite\", \"dictionary_Silver Futures\": \"Silver Futures\", \"dictionary_WednesdayShort\": \"Wed\", \"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\": \"Extreme Movement\", \"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\", \"dictionary_EarningsExceeded\": \"Historically, when earnings exceeded expectations, {probability} of the time, the stock rose by an average of {movement}\", \"dictionary_Exploring Assets\": \"Exploring Assets\", \"dictionary_Platinum Futures\": \"Platinum Futures\", \"data_ExpectedValue_sanitized\": ?.?, \"dictionary_EarningsConcensus\": \"If {ticker} earnings {deltasign} {consensus}\", \"dictionary_EarningsShortfall\": \"Historically, when earnings fell short of expectations, {probability} of the time, the stock fell by an average of {movement}\", \"dictionary_Fun Finance Facts\": \"Fun Finance Facts\", \"dictionary_Trading Terms ?\": \"Trading Terms ?\", \"dictionary_VolatilityWarning\": \"Volatility Warning\", \"params_blank_image_url_value\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg\", \"dictionary_ConsecutiveCandles\": \"Consecutive Candles\", \"dictionary_DailyMarketSummary\": \"Daily Market Summary\", \"dictionary_Noearningsreleases\": \"No earnings releases\", \"dictionary_Shanghai Composite\": \"Shanghai Composite\", \"dictionary_VolatilityIncrease\": \"volatility increase\", \"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\": \"There are no losers\", \"dictionary_Zeromarkets ?_DAX\": \"GER?\", \"params_HistoryDateFormat_value\": \"dd MMM\", \"params_creatomate_apikey_value\": \"f3baa30db2c546378092e775d62306703b6cac7e1f9b3109f1967057727e08462cbc08c86e5fb77c8691e18c63216f96\", \"dictionary_909_Platinum Futures\": \"XPTUSD\", \"dictionary_JapaneseCandleSticks\": \"Japanese Candlesticks\", \"dictionary_There are no winners\": \"There are no winners\", \"dictionary_TodaysEconomicEvents\": \"Today\? is being released in {countryname} in the next {hours_ahead} hours.\", \"params_DateFormat_DateOnly_value\": \"dd MMM\", \"dictionary_E-mini S&P ? Futures\": \"E-mini S&P ? Futures\", \"dictionary_ThisWeeksMarketSummary\": \"This Week\?s biggest movers\": \"Today\?s Economic Events\", \"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\": \"Upcoming volatility as a result of \? in {country_name} in the next {hours_ahead} hours.\", \"params_creatomate_templateid_value\": \"fd2869ca-ade5-4c4a-aa6c-873c5d414b1e\", \"params_quickchart_templateid_value\": null, \"params_title_character_limit_value\": ?, \"dictionary_The biggest winners are:\": \"The biggest winners are:\", \"dictionary_Zeromarkets ?_FTSE ?\": \"UK?\", \"dictionary_highimpactevent_longtext\": \"{eventname}\?s Earnings Releases\", \"dictionary_Zeromarkets ?_Crude oil\": \"XTIUSD\", \"dictionary_Zeromarkets ?_Hang Seng\": \"HK?\", \"dictionary_highimpactevent_shorttext\": \"{eventname}\?s biggest movers\": \"This week\? ({country_name}) is going to result in significant increases in volatility.\", \"params_creatomate_modifications_value\": null, \"params_longtext_character_limit_value\": ?, \"params_minimum_quantity_results_value\": ?, \"dictionary_909_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_909_US Dollar Index Futures\": \"USDX\", \"dictionary_Stocksimpactedbythisrelease\": \"Stocks potentially impacted by this release:\", \"dictionary_This weeks economic events:\": \"This week\? ({country_name}) is going to result in significant increases in volatility.\", \"params_shorttext_character_limit_value\": ?, \"dictionary_BiggestStockMoversfortheweek\": \"Biggest Stock Movers for the week\", \"dictionary_upcomingeconomicevents_title\": \"High-impact economic events between {fromdate} and {todate}.\", \"dictionary_909_Nikkei ? Dollar Futures\": \"JP?\", \"dictionary_E-mini S&P MidCap ? Futures\": \"E-mini S&P MidCap ? Futures\", \"dictionary_impactofearningsrelease_title\": \"Impact of {earningscompany_name} ({earningscompany}) earnings release\", \"dictionary_Market moves for the last week:\": \"Market moves for the last week:\", \"dictionary_This week\?s biggest movers are:\", \"dictionary_UpcomingHighImpactEconomicEvent\": \"Upcoming High-Impact Economic Event\", \"dictionary_Zeromarkets ?_Platinum Futures\": \"XPTUSD\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany}) is releasing earnings today. When the EPS is {delta_sign} than expected, {earningscompany} has a {probability} probability of a {mean_mov_percent} move for the month following this release.\\\\\\\\nIn past earnings releases where the EPS is {delta_sign} than expected, the following stocks have been impacted:\", \"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}) is releasing earnings today and will impact the following stocks:\", \"dictionary_upcomingearnings_title_noearnings\": \"There are no earnings announcements scheduled between {fromdate} and {todate}.\", \"dictionary_10-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_CalendarofHighimpactEconomicEvents\": \"Calendar of High impact Economic Events \", \"dictionary_Market moves for the last ? hours:\": \"Market moves for the last ? hours:\", \"params_creatomate_templateselectionscheme_value\": \"random\", \"dictionary_Zeromarkets ?_WTI Crude Oil Futures\": \"WTI\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): {probability} probability of a {mean_mov_percent} move for the month following this release.\", \"dictionary_Market movements in the last ? hours.\": \"Market movements in the last ? hours.\", \"dictionary_Companies releasing earnings this week:\": \"Companies releasing earnings this week:\", \"dictionary_Zeromarkets ?_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_Zeromarkets ?_US Dollar Index Futures\": \"USDX\", \"dictionary_Zeromarkets ?_Nikkei ? Dollar Futures\": \"JP?\", \"dictionary_High impact economic events for this week:\": \"High-impact economic events for this week:\", \"dictionary_E-mini Dow Jones Industrial Average Futures\": \"E-mini Dow Jones Industrial Average Futures\", \"dictionary_No earnings announcements scheduled this week.\": \"No earnings announcements scheduled this week.\", \"dictionary_Upcoming earnings announcements for this week:\": \"Upcoming earnings announcements for this week:\", \"dictionary_These are the market movements for the last week:\": \"These are the market movements for the last week:\", \"dictionary_These are the market movements for the last ? hours:\": \"These are the market movements for the last ? hours:\"}}?}", "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\": \"d05ca1f9-f065-49a1-a076-8194c927039e\", \"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/d05ca1f9-f065-49a1-a076-8194c927039e.jpg\", \"template_id\": \"fd2869ca-ade5-4c4a-aa6c-873c5d414b1e\", \"template_name\": \"Single Economic Event with Historical Values PNG\", \"template_tags\": [], \"output_format\": \"jpg\", \"modifications\": {\"data_Date\": \"? Feb, ?AM UTC\", \"data_Name\": \"United Kingdom Unemployment Rate\", \"text_title\": \"United Kingdom Unemployment Rate? is being released in Great Britain in the next ? hours.\", \"has_results\": true, \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"data_event_uid\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"United Kingdom Unemployment Rate? is being released in Great Britain in the next ? hours. The expected value for this release is ?.?%. In the United Kingdom, the unemployment rate measures the number of people actively looking for a job as a percentage of the labour force.\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"United Kingdom Unemployment Rate? is being released in Great Britain. The expected value for this release is ?.?%.\", \"data_CountryIcon\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/great britain.svg\", \"data_Last12Label\": \"Last ? events\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_ActualLegend\": \"Actual\", \"dictionary_Actual\": \"Actual\", \"dictionary_Change\": \"Change\", \"dictionary_Coffee\": \"Coffee\", \"dictionary_Friday\": \"Friday\", \"dictionary_Monday\": \"Monday\", \"dictionary_Silver\": \"Silver\", \"dictionary_Sunday\": \"Sunday\", \"dictionary_Target\": \"Target\", \"dictionary_dd MMM\": \"dd MMM\", \"params_mode_value\": ?, \"params_uuid_value\": \"?c3886b-4850-4dc8-b236-6ce891d3e8e9\", \"data_ExpectedLabel\": \"Expected\", \"data_ExpectedValue\": \"?.?%\", \"data_PreviousLabel\": \"Previous\", \"data_PreviousValue\": \"?.?%\", \"dictionary_909_DAX\": \"GER?\", \"dictionary_AUD/USD\": \"AUD/USD\", \"dictionary_Company\": \"Company\", \"dictionary_EUR/USD\": \"EUR/USD\", \"dictionary_GBP/USD\": \"GBP/USD\", \"dictionary_Indices\": \"Indices\", \"dictionary_Minutes\": \"Minutes\", \"dictionary_NZD/USD\": \"NZD/USD\", \"dictionary_Tuesday\": \"Tuesday\", \"dictionary_USD/CAD\": \"USD/CAD\", \"dictionary_USD/CHF\": \"USD/CHF\", \"dictionary_USD/JPY\": \"USD/JPY\", \"data_ExpectedLegend\": \"Expected\", \"dictionary_909_Gold\": \"XAUUSD\", \"dictionary_Estimate\": \"Estimate\", \"dictionary_Expected\": \"Expected\", \"dictionary_FTSE ?\": \"FTSE ?\", \"dictionary_Interval\": \"Interval\", \"dictionary_KeyLevel\": \"Key Level\", \"dictionary_Previous\": \"Previous\", \"dictionary_Saturday\": \"Saturday\", \"dictionary_Thursday\": \"Thursday\", \"dictionary_estimate\": \"estimate\", \"dictionary_previous\": \"previous\", \"params_locale_value\": \"en\", \"params_region_value\": null, \"dictionary_Consensus\": \"Consensus\", \"dictionary_Crude oil\": \"Crude oil\", \"dictionary_Fibonacci\": \"Fibonacci Patterns\", \"dictionary_Hang Seng\": \"Hang Seng\", \"dictionary_Stop_Loss\": \"Stop-Loss\", \"dictionary_Wednesday\": \"Wednesday\", \"dictionary_consensus\": \"consensus\", \"dictionary_dd MMM, h\": \"dd MMM, h\", \"params_request_value\": \"HighImpactEvent\", \"params_webhook_value\": \"https://hooks.zapier.com/hooks/catch/?/?zze/\", \"dictionary_909_Silver\": \"XAGUSD\", \"dictionary_Currencies\": \"Currencies\", \"dictionary_Nasdaq ?\": \"Nasdaq ?\", \"dictionary_Nikkei ?\": \"Nikkei ?\", \"dictionary_Stop_Level\": \"Stop Level\", \"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\": \"After Market \", \"dictionary_BigMovement\": \"Big Movement\", \"dictionary_Commodities\": \"Commodities\", \"dictionary_EEE, dd MMM\": \"EEE, dd MMM\", \"dictionary_Entry_Level\": \"Entry Level\", \"dictionary_FridayShort\": \"Fri\", \"dictionary_MarketAlert\": \"Market Alert\", \"dictionary_MondayShort\": \"Mon\", \"dictionary_Natural gas\": \"Natural gas\", \"dictionary_Probability\": \"Probability\", \"dictionary_Releasetime\": \"Release time\", \"dictionary_SundayShort\": \"Sun\", \"dictionary_probability\": \"If {ticker} earnings {deltasign} {consensus}\", \"params_RateLimit_value\": \"?,?,?\", \"params_processid_value\": ?, \"dictionary_909_FTSE ?\": \"UK?\", \"dictionary_BeforeMarket\": \"Before Market \", \"dictionary_ChartPattern\": \"Chart Pattern\", \"dictionary_Corn Futures\": \"Corn Futures\", \"dictionary_Gold Futures\": \"Gold Futures\", \"dictionary_Last12events\": \"Last ? events\", \"dictionary_RoundNumbers\": \"Round Number\", \"dictionary_Target_Level\": \"Target Level\", \"dictionary_TuesdayShort\": \"Tue\", \"params_DateFormat_value\": \"dd MMM, ha UTC\", \"params_plot_width_value\": \"?\", \"dictionary_909_Crude oil\": \"XTIUSD\", \"dictionary_909_Hang Seng\": \"HK?\", \"dictionary_BiggestLosers\": \"Biggest Losers\", \"dictionary_BiggestMovers\": \"Biggest Movers\", \"dictionary_MarketSummary\": \"Market Summary\", \"dictionary_SaturdayShort\": \"Sat\", \"dictionary_Target_Period\": \"Target Period\", \"dictionary_ThursdayShort\": \"Thu\", \"dictionary_Wheat Futures\": \"Wheat Futures\", \"params_hours_ahead_value\": ?, \"params_plot_height_value\": \"?\", \"dictionary_909_Nasdaq ?\": \"US?\", \"dictionary_909_Nikkei ?\": \"JP?\", \"dictionary_BiggestGainers\": \"Biggest Gainers\", \"dictionary_Copper Futures\": \"Copper Futures\", \"dictionary_EarningsImpact\": \"Earnings Impact\", \"dictionary_NYSE Composite\": \"NYSE Composite\", \"dictionary_Silver Futures\": \"Silver Futures\", \"dictionary_WednesdayShort\": \"Wed\", \"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\": \"Extreme Movement\", \"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\", \"dictionary_EarningsExceeded\": \"Historically, when earnings exceeded expectations, {probability} of the time, the stock rose by an average of {movement}\", \"dictionary_Exploring Assets\": \"Exploring Assets\", \"dictionary_Platinum Futures\": \"Platinum Futures\", \"data_ExpectedValue_sanitized\": ?.?, \"dictionary_EarningsConcensus\": \"If {ticker} earnings {deltasign} {consensus}\", \"dictionary_EarningsShortfall\": \"Historically, when earnings fell short of expectations, {probability} of the time, the stock fell by an average of {movement}\", \"dictionary_Fun Finance Facts\": \"Fun Finance Facts\", \"dictionary_Trading Terms ?\": \"Trading Terms ?\", \"dictionary_VolatilityWarning\": \"Volatility Warning\", \"params_blank_image_url_value\": \"https://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg\", \"dictionary_ConsecutiveCandles\": \"Consecutive Candles\", \"dictionary_DailyMarketSummary\": \"Daily Market Summary\", \"dictionary_Noearningsreleases\": \"No earnings releases\", \"dictionary_Shanghai Composite\": \"Shanghai Composite\", \"dictionary_VolatilityIncrease\": \"volatility increase\", \"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\": \"There are no losers\", \"dictionary_Zeromarkets ?_DAX\": \"GER?\", \"params_HistoryDateFormat_value\": \"dd MMM\", \"params_creatomate_apikey_value\": \"f3baa30db2c546378092e775d62306703b6cac7e1f9b3109f1967057727e08462cbc08c86e5fb77c8691e18c63216f96\", \"dictionary_909_Platinum Futures\": \"XPTUSD\", \"dictionary_JapaneseCandleSticks\": \"Japanese Candlesticks\", \"dictionary_There are no winners\": \"There are no winners\", \"dictionary_TodaysEconomicEvents\": \"Today?s Economic Events\", \"dictionary_Zeromarkets ?_Gold\": \"XAUUSD\", \"dictionary_ExpectedMovementRange\": \"Expected movement range during event is between {from} and {to}\", \"dictionary_WTI Crude Oil Futures\": \"WTI Crude Oil Futures\", \"dictionary_highimpactevent_title\": \"{eventname}? is being released in {countryname} in the next {hours_ahead} hours.\", \"params_DateFormat_DateOnly_value\": \"dd MMM\", \"dictionary_E-mini S&P ? Futures\": \"E-mini S&P ? Futures\", \"dictionary_ThisWeeksMarketSummary\": \"This Week?s Market Summary\", \"dictionary_Today?s biggest movers\": \"Today?s biggest movers\", \"dictionary_Zeromarkets ?_Silver\": \"XAGUSD\", \"dictionary_upcomingearnings_title\": \"Earnings announcements between {fromdate} and {todate}.\", \"params_creatomate_snapshots_value\": null, \"dictionary_Brent Crude Oil Futures\": \"Brent Crude Oil Futures\", \"dictionary_The biggest losers are:\": \"The biggest losers are:\", \"dictionary_ThisWeeksEconomicEvents\": \"This Week?s Economic Events\", \"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\": \"Upcoming volatility as a result of ?{eventname}? in {country_name} in the next {hours_ahead} hours.\", \"params_creatomate_templateid_value\": \"fd2869ca-ade5-4c4a-aa6c-873c5d414b1e\", \"params_quickchart_templateid_value\": null, \"params_title_character_limit_value\": ?, \"dictionary_The biggest winners are:\": \"The biggest winners are:\", \"dictionary_Zeromarkets ?_FTSE ?\": \"UK?\", \"dictionary_highimpactevent_longtext\": \"{eventname}? is being released in {countryname} in the next {hours_ahead} hours. The expected value for this release is {consensus}. {description}\", \"params_probability_of_posting_value\": ?, \"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\": \"This Week?s Earnings Releases\", \"dictionary_Zeromarkets ?_Crude oil\": \"XTIUSD\", \"dictionary_Zeromarkets ?_Hang Seng\": \"HK?\", \"dictionary_highimpactevent_shorttext\": \"{eventname}? is being released in {countryname}. The expected value for this release is {consensus}.\", \"dictionary_Nohighimpacteconomicevents\": \"No high impact economic events\", \"dictionary_This week?s biggest movers\": \"This week?s biggest movers\", \"dictionary_Zeromarkets ?_Nasdaq ?\": \"US?\", \"dictionary_Zeromarkets ?_Nikkei ?\": \"JP?\", \"dictionary_volatilitywarning_longtext\": \"{eventname}? ({country_name}) is going to result in significant increases in volatility.\", \"params_creatomate_modifications_value\": null, \"params_longtext_character_limit_value\": ?, \"params_minimum_quantity_results_value\": ?, \"dictionary_909_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_909_US Dollar Index Futures\": \"USDX\", \"dictionary_Stocksimpactedbythisrelease\": \"Stocks potentially impacted by this release:\", \"dictionary_This weeks economic events:\": \"This week?s economic events:\", \"dictionary_Zeromarkets ?_Natural gas\": \"XNGUSD\", \"dictionary_volatilitywarning_shorttext\": \"{eventname}? ({country_name}) is going to result in significant increases in volatility.\", \"params_shorttext_character_limit_value\": ?, \"dictionary_BiggestStockMoversfortheweek\": \"Biggest Stock Movers for the week\", \"dictionary_upcomingeconomicevents_title\": \"High-impact economic events between {fromdate} and {todate}.\", \"dictionary_909_Nikkei ? Dollar Futures\": \"JP?\", \"dictionary_E-mini S&P MidCap ? Futures\": \"E-mini S&P MidCap ? Futures\", \"dictionary_impactofearningsrelease_title\": \"Impact of {earningscompany_name} ({earningscompany}) earnings release\", \"dictionary_Market moves for the last week:\": \"Market moves for the last week:\", \"dictionary_This week?s biggest movers are:\": \"This week?s biggest movers are:\", \"dictionary_UpcomingHighImpactEconomicEvent\": \"Upcoming High-Impact Economic Event\", \"dictionary_Zeromarkets ?_Platinum Futures\": \"XPTUSD\", \"dictionary_impactofearningsrelease_longtext\": \"{earningscompany_name} ({earningscompany}) is releasing earnings today. When the EPS is {delta_sign} than expected, {earningscompany} has a {probability} probability of a {mean_mov_percent} move for the month following this release.\\\\nIn past earnings releases where the EPS is {delta_sign} than expected, the following stocks have been impacted:\", \"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}) is releasing earnings today and will impact the following stocks:\", \"dictionary_upcomingearnings_title_noearnings\": \"There are no earnings announcements scheduled between {fromdate} and {todate}.\", \"dictionary_10-Year U.S. Treasury Note Futures\": \"?-Year U.S. Treasury Note Futures\", \"dictionary_CalendarofHighimpactEconomicEvents\": \"Calendar of High impact Economic Events \", \"dictionary_Market moves for the last ? hours:\": \"Market moves for the last ? hours:\", \"params_creatomate_templateselectionscheme_value\": \"random\", \"dictionary_Zeromarkets ?_WTI Crude Oil Futures\": \"WTI\", \"dictionary_impactofearningsrelease_longtext_item\": \"{impactcompany_name} ({impactcompany}): {probability} probability of a {mean_mov_percent} move for the month following this release.\", \"dictionary_Market movements in the last ? hours.\": \"Market movements in the last ? hours.\", \"dictionary_Companies releasing earnings this week:\": \"Companies releasing earnings this week:\", \"dictionary_Zeromarkets ?_Brent Crude Oil Futures\": \"XBRUSD\", \"dictionary_Zeromarkets ?_US Dollar Index Futures\": \"USDX\", \"dictionary_Zeromarkets ?_Nikkei ? Dollar Futures\": \"JP?\", \"dictionary_High impact economic events for this week:\": \"High-impact economic events for this week:\", \"dictionary_E-mini Dow Jones Industrial Average Futures\": \"E-mini Dow Jones Industrial Average Futures\", \"dictionary_No earnings announcements scheduled this week.\": \"No earnings announcements scheduled this week.\", \"dictionary_Upcoming earnings announcements for this week:\": \"Upcoming earnings announcements for this week:\", \"dictionary_These are the market movements for the last week:\": \"These are the market movements for the last week:\", \"dictionary_These are the market movements for the last ? hours:\": \"These are the market movements for the last ? hours:\"}}" } }?json');Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Feb 17 01 1 0ms 0ms 10 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 #10
Day Hour Count Duration Avg duration Feb 17 01 18 0ms 0ms 11 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 #11
Day Hour Count Duration Avg duration Feb 17 01 238 0ms 0ms 12 0ms 0ms 0ms 29 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 #12
Day Hour Count Duration Avg duration Feb 17 01 29 0ms 0ms 13 0ms 0ms 0ms 359 0ms commit;Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Feb 17 01 359 0ms 0ms 14 0ms 0ms 0ms 1 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 #14
Day Hour Count Duration Avg duration Feb 17 01 1 0ms 0ms 15 0ms 0ms 0ms 14 0ms update correlating_signals set sent = true where id = ?;Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Feb 17 01 14 0ms 0ms 16 0ms 0ms 0ms 290 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 #16
Day Hour Count Duration Avg duration Feb 17 01 290 0ms 0ms 17 0ms 0ms 0ms 13 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 #17
Day Hour Count Duration Avg duration Feb 17 01 13 0ms 0ms 18 0ms 0ms 0ms 2 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 #18
Day Hour Count Duration Avg duration Feb 17 01 2 0ms 0ms 19 0ms 0ms 0ms 238 0ms select count(*), sum(size), extract(epoch from now() - min(modification)) from pg_ls_waldir ();Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Feb 17 01 238 0ms 0ms 20 0ms 0ms 0ms 60 0ms select * from ( select pricedatetime, open, high, low, close, volume, bsf from t30 where symbolid = ? and (bsf = ? or bsf is null) order by pricedatetime desc limit ?) a order by pricedatetime asc;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Feb 17 01 60 0ms 0ms Time consuming prepare
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 2s479ms 1,904 0ms 19ms 1ms WITH rar_max as ( ;Times Reported Time consuming prepare #1
Day Hour Count Duration Avg duration Feb 17 01 1,904 2s479ms 1ms -
WITH rar_max as ( ;
Date: 2026-02-17 01:30:54 Duration: 19ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-02-17 01:46:09 Duration: 15ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-02-17 01:30:43 Duration: 13ms Database: postgres
2 1s624ms 1,081 0ms 4ms 1ms SELECT symbolid, ;Times Reported Time consuming prepare #2
Day Hour Count Duration Avg duration 01 1,081 1s624ms 1ms -
SELECT symbolid, ;
Date: 2026-02-17 01:15:44 Duration: 4ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-02-17 01:15:53 Duration: 4ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-02-17 01:15:59 Duration: 3ms Database: postgres
3 698ms 2,351 0ms 12ms 0ms SELECT ;Times Reported Time consuming prepare #3
Day Hour Count Duration Avg duration 01 2,351 698ms 0ms -
SELECT ;
Date: 2026-02-17 01:30:58 Duration: 12ms Database: postgres
-
SELECT ;
Date: 2026-02-17 01:01:02 Duration: 9ms Database: postgres
-
SELECT ;
Date: 2026-02-17 01:41:37 Duration: 6ms Database: postgres
4 331ms 311 0ms 1ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming prepare #4
Day Hour Count Duration Avg duration 01 311 331ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 01:16:01 Duration: 1ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 01:16:02 Duration: 1ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 01:16:02 Duration: 1ms Database: postgres
5 271ms 1,836 0ms 2ms 0ms SET extra_float_digits = 3;Times Reported Time consuming prepare #5
Day Hour Count Duration Avg duration 01 1,836 271ms 0ms -
SET extra_float_digits = 3;
Date: 2026-02-17 01:16:00 Duration: 2ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-02-17 01:15:58 Duration: 1ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-02-17 01:00:47 Duration: 1ms Database: postgres
6 268ms 3,066 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 prepare #6
Day Hour Count Duration Avg duration 01 3,066 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 01:31:01 Duration: 1ms 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 01:11:51 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 01:41:47 Duration: 0ms Database: postgres
7 201ms 2,121 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 01 2,121 201ms 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 01:11:53 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 01:30:52 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 01:01:51 Duration: 0ms Database: postgres
8 157ms 975 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 01 975 157ms 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 01:56:48 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 01:47:55 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 01:41:47 Duration: 0ms Database: postgres
9 86ms 1,398 0ms 14ms 0ms select 1;Times Reported Time consuming prepare #9
Day Hour Count Duration Avg duration 01 1,398 86ms 0ms -
select 1;
Date: 2026-02-17 01:46:00 Duration: 14ms Database: postgres
-
select 1;
Date: 2026-02-17 01:30:52 Duration: 3ms Database: postgres
-
select 1;
Date: 2026-02-17 01:00:53 Duration: 1ms Database: postgres
10 64ms 600 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 #10
Day Hour Count Duration Avg duration 01 600 64ms 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 01:02:41 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 01:02:27 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 01:02:47 Duration: 0ms Database: postgres
11 57ms 637 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 #11
Day Hour Count Duration Avg duration 01 637 57ms 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 01:15:32 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 01:01:38 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 01:01:31 Duration: 0ms Database: postgres
12 46ms 8 4ms 7ms 5ms with sym_info as ( ;Times Reported Time consuming prepare #12
Day Hour Count Duration Avg duration 01 8 46ms 5ms -
with sym_info as ( ;
Date: 2026-02-17 01:36:43 Duration: 7ms Database: postgres
-
with sym_info as ( ;
Date: 2026-02-17 01:36:45 Duration: 6ms Database: postgres
-
with sym_info as ( ;
Date: 2026-02-17 01:36:51 Duration: 6ms Database: postgres
13 45ms 18 1ms 3ms 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 #13
Day Hour Count Duration Avg duration 01 18 45ms 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 01:51:16 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 01:11:01 Duration: 2ms 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 01:01:17 Duration: 2ms Database: postgres
14 24ms 24 0ms 1ms 1ms 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 #14
Day Hour Count Duration Avg duration 01 24 24ms 1ms -
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 01:27:24 Duration: 1ms Database: postgres
-
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 01:12:23 Duration: 1ms Database: postgres
-
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 01:52:26 Duration: 1ms Database: postgres
15 23ms 15 0ms 3ms 1ms WITH last_candle AS ( ;Times Reported Time consuming prepare #15
Day Hour Count Duration Avg duration 01 15 23ms 1ms -
WITH last_candle AS ( ;
Date: 2026-02-17 01:36:00 Duration: 3ms Database: postgres
-
WITH last_candle AS ( ;
Date: 2026-02-17 01:32:00 Duration: 3ms Database: postgres
-
WITH last_candle AS ( ;
Date: 2026-02-17 01:16:00 Duration: 3ms Database: postgres
16 22ms 1,810 0ms 0ms 0ms SET application_name = 'PostgreSQL JDBC Driver';Times Reported Time consuming prepare #16
Day Hour Count Duration Avg duration 01 1,810 22ms 0ms -
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-02-17 01:46:03 Duration: 0ms Database: postgres
-
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-02-17 01:30:43 Duration: 0ms Database: postgres
-
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-02-17 01:41:22 Duration: 0ms Database: postgres
17 20ms 24 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 #17
Day Hour Count Duration Avg duration 01 24 20ms 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 01:32:25 Duration: 1ms Database: postgres
-
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 01:27:24 Duration: 1ms Database: postgres
-
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 01:17:23 Duration: 1ms Database: postgres
18 14ms 24 0ms 0ms 0ms select count(*) from datafeed_restarter_events where is_current_entry = 1;Times Reported Time consuming prepare #18
Day Hour Count Duration Avg duration 01 24 14ms 0ms -
select count(*) from datafeed_restarter_events where is_current_entry = 1;
Date: 2026-02-17 01:20:04 Duration: 0ms Database: postgres
-
select count(*) from datafeed_restarter_events where is_current_entry = 1;
Date: 2026-02-17 01:05:31 Duration: 0ms Database: postgres
-
select count(*) from datafeed_restarter_events where is_current_entry = 1;
Date: 2026-02-17 01:10:07 Duration: 0ms Database: postgres
19 14ms 6 2ms 2ms 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 01 6 14ms 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 01:40:02 Duration: 2ms Database: postgres
-
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 01:30:03 Duration: 2ms Database: postgres
-
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 01:50:02 Duration: 2ms Database: postgres
20 14ms 6 2ms 2ms 2ms select client_addr, count(1) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by client_addr, setting having (client_addr is not null OR (client_addr is null and count(1) > (cast(setting as numeric) / 3 * 2))) order by count desc;Times Reported Time consuming prepare #20
Day Hour Count Duration Avg duration 01 6 14ms 2ms -
select client_addr, count(1) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by client_addr, setting having (client_addr is not null OR (client_addr is null and count(1) > (cast(setting as numeric) / 3 * 2))) order by count desc;
Date: 2026-02-17 01:10:04 Duration: 2ms Database: postgres
-
select client_addr, count(1) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by client_addr, setting having (client_addr is not null OR (client_addr is null and count(1) > (cast(setting as numeric) / 3 * 2))) order by count desc;
Date: 2026-02-17 01:20:04 Duration: 2ms Database: postgres
-
select client_addr, count(1) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by client_addr, setting having (client_addr is not null OR (client_addr is null and count(1) > (cast(setting as numeric) / 3 * 2))) order by count desc;
Date: 2026-02-17 01:00:04 Duration: 2ms Database: postgres
Time consuming bind
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 28s497ms 2,852 0ms 54ms 9ms WITH rar_max as ( ;Times Reported Time consuming bind #1
Day Hour Count Duration Avg duration Feb 17 01 2,852 28s497ms 9ms -
WITH rar_max as ( ;
Date: 2026-02-17 01:51:13 Duration: 54ms Database: postgres parameters: $1 = 't', $2 = '667', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '0', $14 = '', $15 = '0', $16 = '', $17 = '0', $18 = '0', $19 = '0', $20 = '700', $21 = '700', $22 = 't', $23 = '10', $24 = '10'
-
WITH rar_max as ( ;
Date: 2026-02-17 01:06:54 Duration: 54ms Database: postgres parameters: $1 = 't', $2 = '667', $3 = '0', $4 = '0', $5 = '0', $6 = '', $7 = '0', $8 = '', $9 = '0', $10 = '', $11 = '0', $12 = '0', $13 = '0', $14 = '0', $15 = '0', $16 = 't', $17 = '0', $18 = '0'
-
WITH rar_max as ( ;
Date: 2026-02-17 01:07:03 Duration: 47ms Database: postgres parameters: $1 = 't', $2 = '667', $3 = '0', $4 = '0', $5 = '0', $6 = '', $7 = '0', $8 = '', $9 = '0', $10 = '', $11 = '0', $12 = '0', $13 = '0', $14 = '0', $15 = '0', $16 = 't', $17 = '0', $18 = '0'
2 5s190ms 13,633 0ms 16ms 0ms SELECT ;Times Reported Time consuming bind #2
Day Hour Count Duration Avg duration 01 13,633 5s190ms 0ms -
SELECT ;
Date: 2026-02-17 01:41:22 Duration: 16ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '515840243225790300'
-
SELECT ;
Date: 2026-02-17 01:15:44 Duration: 15ms Database: postgres parameters: $1 = '667', $2 = '667', $3 = '500991628218508200'
-
SELECT ;
Date: 2026-02-17 01:32:12 Duration: 12ms Database: postgres parameters: $1 = '558', $2 = '0', $3 = '0', $4 = 'F40', $5 = 'F40'
3 2s756ms 1,081 1ms 5ms 2ms SELECT symbolid, ;Times Reported Time consuming bind #3
Day Hour Count Duration Avg duration 01 1,081 2s756ms 2ms -
SELECT symbolid, ;
Date: 2026-02-17 01:45:45 Duration: 5ms Database: postgres parameters: $1 = 'BDSWISS', $2 = '15', $3 = 'LTCUSD', $4 = 'NAS100', $5 = 'NEOUSD', $6 = 'IOTAUSD', $7 = 'LTCEUR', $8 = 'HKDJPY', $9 = 'NOKJPY'
-
SELECT symbolid, ;
Date: 2026-02-17 01:31:01 Duration: 5ms Database: postgres parameters: $1 = 'AXIORY', $2 = '15', $3 = 'GBPSGD', $4 = 'GBPNZD'
-
SELECT symbolid, ;
Date: 2026-02-17 01:15:59 Duration: 4ms Database: postgres parameters: $1 = 'AXIORY', $2 = '15', $3 = 'GBPJPY', $4 = 'GBPCHF', $5 = 'GBPAUD', $6 = 'GBPCAD'
4 556ms 22 0ms 47ms 25ms with wh_patitioned as ( ;Times Reported Time consuming bind #4
Day Hour Count Duration Avg duration 01 22 556ms 25ms -
with wh_patitioned as ( ;
Date: 2026-02-17 01:51:19 Duration: 47ms 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 01:36:34 Duration: 43ms 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 01:51:19 Duration: 42ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
5 548ms 311 1ms 19ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming bind #5
Day Hour Count Duration Avg duration 01 311 548ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 01:16:02 Duration: 19ms Database: postgres parameters: $1 = 'MILLENNIUMPF'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 01:30:43 Duration: 13ms Database: postgres parameters: $1 = 'AXIORY'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-02-17 01:16:15 Duration: 3ms Database: postgres parameters: $1 = 'AXIORY'
6 431ms 60 4ms 21ms 7ms WITH last_candle AS ( ;Times Reported Time consuming bind #6
Day Hour Count Duration Avg duration 01 60 431ms 7ms -
WITH last_candle AS ( ;
Date: 2026-02-17 01:36:00 Duration: 21ms Database: postgres parameters: $1 = '558', $2 = '558'
-
WITH last_candle AS ( ;
Date: 2026-02-17 01:32:00 Duration: 12ms Database: postgres parameters: $1 = '558', $2 = '558'
-
WITH last_candle AS ( ;
Date: 2026-02-17 01:48:00 Duration: 12ms Database: postgres parameters: $1 = '558', $2 = '558'
7 426ms 12,417 0ms 12ms 0ms select 1;Times Reported Time consuming bind #7
Day Hour Count Duration Avg duration 01 12,417 426ms 0ms -
select 1;
Date: 2026-02-17 01:51:00 Duration: 12ms Database: postgres
-
select 1;
Date: 2026-02-17 01:51:04 Duration: 9ms Database: postgres
-
select 1;
Date: 2026-02-17 01:37:09 Duration: 8ms Database: postgres
8 293ms 8 28ms 44ms 36ms with sym_info as ( ;Times Reported Time consuming bind #8
Day Hour Count Duration Avg duration 01 8 293ms 36ms -
with sym_info as ( ;
Date: 2026-02-17 01:36:51 Duration: 44ms 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 01:06:55 Duration: 44ms 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 01:36:45 Duration: 44ms Database: postgres parameters: $1 = '617', $2 = 'Forex', $3 = 'Forex', $4 = '617', $5 = 'Forex', $6 = '617', $7 = '617', $8 = 'Forex', $9 = '617'
9 247ms 3,315 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 #9
Day Hour Count Duration Avg duration 01 3,315 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 01:31:03 Duration: 1ms Database: postgres parameters: $1 = '2026-02-17 01:00:00', $2 = '1.675465', $3 = '1.67598', $4 = '1.67506', $5 = '1.67524', $6 = '1648', $7 = '515840230460425300', $8 = '0', $9 = '2026-02-17 01:31:03.33', $10 = '2026-02-17 01:31:03.33', $11 = '1.675465', $12 = '1.67598', $13 = '1.67506', $14 = '1.67524', $15 = '1648', $16 = '0', $17 = '2026-02-17 01:31:03.33', $18 = '2026-02-17 01:31:03.33'
-
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 01:31:01 Duration: 0ms Database: postgres parameters: $1 = '2026-02-17 01:00:00', $2 = '178.765', $3 = '179.255', $4 = '177.81', $5 = '178.2', $6 = '1002', $7 = '606715250665191300', $8 = '0', $9 = '2026-02-17 01:31:01.136', $10 = '2026-02-17 01:31:01.135', $11 = '178.765', $12 = '179.255', $13 = '177.81', $14 = '178.2', $15 = '1002', $16 = '0', $17 = '2026-02-17 01:31:01.136', $18 = '2026-02-17 01:31:01.135'
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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 01:11:51 Duration: 0ms Database: postgres parameters: $1 = '2026-02-16 21:00:00', $2 = '4994.32', $3 = '4996.08', $4 = '4990.28', $5 = '4993.23', $6 = '2677', $7 = '515840247906722300', $8 = '0', $9 = '2026-02-17 01:11:51.335', $10 = '2026-02-17 01:11:51.254', $11 = '4994.32', $12 = '4996.08', $13 = '4990.28', $14 = '4993.23', $15 = '2677', $16 = '0', $17 = '2026-02-17 01:11:51.335', $18 = '2026-02-17 01:11:51.254'
10 228ms 5,287 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 #10
Day Hour Count Duration Avg duration 01 5,287 228ms 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 01:47:55 Duration: 0ms Database: postgres parameters: $1 = '2026-02-17 01:30:00', $2 = '4994.57', $3 = '4995.925', $4 = '4987.905', $5 = '4990.005', $6 = '1320', $7 = '515840230628558300', $8 = '0', $9 = '2026-02-17 01:47:55.343', $10 = '2026-02-17 01:47:55.284', $11 = '4994.57', $12 = '4995.925', $13 = '4987.905', $14 = '4990.005', $15 = '1320', $16 = '0', $17 = '2026-02-17 01:47:55.343', $18 = '2026-02-17 01:47:55.284'
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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 01:41:35 Duration: 0ms Database: postgres parameters: $1 = '2026-02-17 01:15:00', $2 = '8971.4', $3 = '8978.4', $4 = '8968.9', $5 = '8972', $6 = '2525', $7 = '515840248015086300', $8 = '0', $9 = '2026-02-17 01:41:35.814', $10 = '2026-02-17 01:41:35.729', $11 = '8971.4', $12 = '8978.4', $13 = '8968.9', $14 = '8972', $15 = '2525', $16 = '0', $17 = '2026-02-17 01:41:35.814', $18 = '2026-02-17 01:41:35.729'
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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 01:47:41 Duration: 0ms Database: postgres parameters: $1 = '2026-02-17 01:30:00', $2 = '0.76973', $3 = '0.76986', $4 = '0.7697', $5 = '0.76981', $6 = '114', $7 = '515840230585517300', $8 = '0', $9 = '2026-02-17 01:47:41.206', $10 = '2026-02-17 01:47:41.146', $11 = '0.76973', $12 = '0.76986', $13 = '0.7697', $14 = '0.76981', $15 = '114', $16 = '0', $17 = '2026-02-17 01:47:41.206', $18 = '2026-02-17 01:47:41.146'
11 228ms 39 0ms 19ms 5ms WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;Times Reported Time consuming bind #11
Day Hour Count Duration Avg duration 01 39 228ms 5ms -
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-02-17 01:02:06 Duration: 19ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
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WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-02-17 01:33:01 Duration: 17ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
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WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-02-17 01:07:21 Duration: 11ms Database: postgres parameters: $1 = '667', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
12 172ms 2,301 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 bind #12
Day Hour Count Duration Avg duration 01 2,301 172ms 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 01:11:53 Duration: 0ms Database: postgres parameters: $1 = '2026-02-16 21:00:00', $2 = '2042.1', $3 = '2047.1', $4 = '2038.77', $5 = '2040.65', $6 = '1336', $7 = '515840248012940300', $8 = '0', $9 = '2026-02-17 01:11:53.399', $10 = '2026-02-17 01:11:53.294', $11 = '2042.1', $12 = '2047.1', $13 = '2038.77', $14 = '2040.65', $15 = '1336', $16 = '0', $17 = '2026-02-17 01:11:53.399', $18 = '2026-02-17 01:11:53.294'
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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 01:30:53 Duration: 0ms Database: postgres parameters: $1 = '2026-02-13 22:00:00', $2 = '206.84', $3 = '207.85', $4 = '206.55', $5 = '206.97', $6 = '2961', $7 = '515840249447294300', $8 = '0', $9 = '2026-02-17 01:30:53.03', $10 = '2026-02-17 01:30:53.03', $11 = '206.84', $12 = '207.85', $13 = '206.55', $14 = '206.97', $15 = '2961', $16 = '0', $17 = '2026-02-17 01:30:53.03', $18 = '2026-02-17 01:30:53.03'
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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 01:46:55 Duration: 0ms Database: postgres parameters: $1 = '2026-02-13 22:00:00', $2 = '92.345', $3 = '92.685', $4 = '92.04', $5 = '92.685', $6 = '674', $7 = '515840249458435300', $8 = '0', $9 = '2026-02-17 01:46:55.886', $10 = '2026-02-17 01:46:55.885', $11 = '92.345', $12 = '92.685', $13 = '92.04', $14 = '92.685', $15 = '674', $16 = '0', $17 = '2026-02-17 01:46:55.886', $18 = '2026-02-17 01:46:55.885'
13 57ms 745 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 bind #13
Day Hour Count Duration Avg duration 01 745 57ms 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 01:02:27 Duration: 0ms Database: postgres parameters: $1 = '2026-02-13 00:00:00', $2 = '45.37', $3 = '46.03', $4 = '45.29', $5 = '45.91', $6 = '16243', $7 = '515840247904846300', $8 = '0', $9 = '2026-02-17 01:02:27.438', $10 = '2026-02-17 01:02:27.4', $11 = '45.37', $12 = '46.03', $13 = '45.29', $14 = '45.91', $15 = '16243', $16 = '0', $17 = '2026-02-17 01:02:27.438', $18 = '2026-02-17 01:02:27.4'
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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 01:02:41 Duration: 0ms Database: postgres parameters: $1 = '2026-02-13 00:00:00', $2 = '8312.38', $3 = '8333.93', $4 = '8273.37', $5 = '8307.45', $6 = '226092', $7 = '515840247903003300', $8 = '0', $9 = '2026-02-17 01:02:41.635', $10 = '2026-02-17 01:02:41.592', $11 = '8312.38', $12 = '8333.93', $13 = '8273.37', $14 = '8307.45', $15 = '226092', $16 = '0', $17 = '2026-02-17 01:02:41.635', $18 = '2026-02-17 01:02:41.592'
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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 01:02:47 Duration: 0ms Database: postgres parameters: $1 = '2026-02-13 00:00:00', $2 = '49450.55', $3 = '49737.86', $4 = '49077.24', $5 = '49442.95', $6 = '468173', $7 = '515840248001217300', $8 = '0', $9 = '2026-02-17 01:02:47.784', $10 = '2026-02-17 01:02:47.716', $11 = '49450.55', $12 = '49737.86', $13 = '49077.24', $14 = '49442.95', $15 = '468173', $16 = '0', $17 = '2026-02-17 01:02:47.784', $18 = '2026-02-17 01:02:47.716'
14 53ms 637 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 #14
Day Hour Count Duration Avg duration 01 637 53ms 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 01:01:27 Duration: 0ms Database: postgres parameters: $1 = '2026-02-16 20:00:00', $2 = '1085244.495', $3 = '1095814.845', $4 = '1080721.75', $5 = '1095401.985', $6 = '20532', $7 = '515840249474385300', $8 = '0', $9 = '2026-02-17 01:01:27.731', $10 = '2026-02-17 01:01:27.596', $11 = '1085244.495', $12 = '1095814.845', $13 = '1080721.75', $14 = '1095401.985', $15 = '20532', $16 = '0', $17 = '2026-02-17 01:01:27.731', $18 = '2026-02-17 01:01:27.596'
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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 01:16:03 Duration: 0ms Database: postgres parameters: $1 = '2026-02-16 20:00:00', $2 = '377.343', $3 = '377.464', $4 = '377.279', $5 = '377.454', $6 = '1361', $7 = '500991628207793200', $8 = '0', $9 = '2026-02-17 01:16:03.158', $10 = '2026-02-17 01:16:03.044', $11 = '377.343', $12 = '377.464', $13 = '377.279', $14 = '377.454', $15 = '1361', $16 = '0', $17 = '2026-02-17 01:16:03.158', $18 = '2026-02-17 01:16:03.044'
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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 01:01:31 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 01:01:31.355', $10 = '2026-02-17 01:01:31.354', $11 = '56930', $12 = '56930', $13 = '56705', $14 = '56740', $15 = '2363', $16 = '0', $17 = '2026-02-17 01:01:31.355', $18 = '2026-02-17 01:01:31.354'
15 51ms 293 0ms 4ms 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 #15
Day Hour Count Duration Avg duration 01 293 51ms 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 01:35:25 Duration: 4ms Database: postgres parameters: $1 = '607690403401860301'
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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 01:35:41 Duration: 3ms Database: postgres parameters: $1 = '607691349073946301'
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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 01:05:10 Duration: 3ms Database: postgres parameters: $1 = '607691303454752301'
16 45ms 45 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 #16
Day Hour Count Duration Avg duration 01 45 45ms 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 01:36:38 Duration: 1ms Database: postgres parameters: $1 = '632', $2 = 'GBPUSD', $3 = '632'
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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 01:05:57 Duration: 1ms Database: postgres parameters: $1 = '538', $2 = 'XAUUSD', $3 = '538'
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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 01:55:26 Duration: 1ms Database: postgres parameters: $1 = '621', $2 = 'GBPAUD', $3 = '621'
17 43ms 146 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 #17
Day Hour Count Duration Avg duration 01 146 43ms 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 01:15:40 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 01:50:44 Duration: 4ms Database: postgres parameters: $1 = '607690406157486303'
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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 01:35:56 Duration: 2ms Database: postgres parameters: $1 = '607691348238258303'
18 37ms 209 0ms 0ms 0ms SELECT * FROM ( SELECT PriceDateTime, Open, High, Low, Close, Volume, BSF FROM T15 WHERE symbolid = $1 AND (BSF = 0 OR BSF IS NULL) ORDER BY PriceDateTime DESC LIMIT 1050) a ORDER BY PriceDateTime ASC;Times Reported Time consuming bind #18
Day Hour Count Duration Avg duration 01 209 37ms 0ms -
SELECT * FROM ( SELECT PriceDateTime, Open, High, Low, Close, Volume, BSF FROM T15 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 01:50:38 Duration: 0ms Database: postgres parameters: $1 = '515840243899754300'
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SELECT * FROM ( SELECT PriceDateTime, Open, High, Low, Close, Volume, BSF FROM T15 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 01:12:23 Duration: 0ms Database: postgres parameters: $1 = '515840243880248300'
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SELECT * FROM ( SELECT PriceDateTime, Open, High, Low, Close, Volume, BSF FROM T15 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 01:36:03 Duration: 0ms Database: postgres parameters: $1 = '515840243280258300'
19 35ms 8 3ms 6ms 4ms SELECT DISTINCT ON (basegroupname, symbol) ;Times Reported Time consuming bind #19
Day Hour Count Duration Avg duration 01 8 35ms 4ms -
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-02-17 01:06:00 Duration: 6ms Database: postgres parameters: $1 = '627', $2 = '627'
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SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-02-17 01:38:16 Duration: 6ms Database: postgres parameters: $1 = '667', $2 = '667'
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SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-02-17 01:06:10 Duration: 5ms Database: postgres parameters: $1 = '627', $2 = '627'
20 31ms 1 31ms 31ms 31ms with maxwhid as ( ;Times Reported Time consuming bind #20
Day Hour Count Duration Avg duration 01 1 31ms 31ms -
with maxwhid as ( ;
Date: 2026-02-17 01:12:46 Duration: 31ms Database: postgres parameters: $1 = '335', $2 = '621', $3 = '637', $4 = '642', $5 = '666', $6 = '660', $7 = '643', $8 = '630', $9 = '680', $10 = '641', $11 = '431', $12 = '622', $13 = '489', $14 = '529', $15 = '576', $16 = '665', $17 = '667', $18 = '558', $19 = '620', $20 = '125', $21 = '488', $22 = '567', $23 = '689', $24 = '700', $25 = '758', $26 = '763', $27 = '765', $28 = '817', $29 = '914', $30 = '972'
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Events
Log levels
Key values
- 283,045 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 01 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 01:15:40