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Global information
- Generated on Fri Mar 6 07:59:59 2026
- Log file: /home/postgres/pg_data/data/pg_log/postgresql-2026-03-06_090000.log
- Parsed 2,514,023 log entries in 58s
- Log start from 2026-03-06 09:00:00 to 2026-03-06 09:59:57
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Overview
Global Stats
- 267 Number of unique normalized queries
- 271,951 Number of queries
- 1h39m26s Total query duration
- 2026-03-06 09:00:00 First query
- 2026-03-06 09:59:57 Last query
- 3,951 queries/s at 2026-03-06 09:45:04 Query peak
- 1h39m26s Total query duration
- 6s2ms Prepare/parse total duration
- 42s608ms Bind total duration
- 1h38m38s Execute total duration
- 412 Number of events
- 4 Number of unique normalized events
- 360 Max number of times the same event was reported
- 0 Number of cancellation
- 41 Total number of automatic vacuums
- 53 Total number of automatic analyzes
- 803 Number temporary file
- 615.96 MiB Max size of temporary file
- 7.96 MiB Average size of temporary file
- 2,585 Total number of sessions
- 13 sessions at 2026-03-06 09:53:48 Session peak
- 27d20h26m27s Total duration of sessions
- 15m30s Average duration of sessions
- 105 Average queries per session
- 2s308ms Average queries duration per session
- 15m28s Average idle time per session
- 2,572 Total number of connections
- 28 connections/s at 2026-03-06 09:48:48 Connection peak
- 3 Total number of databases
SQL Traffic
Key values
- 3,951 queries/s Query Peak
- 2026-03-06 09:45:04 Date
SELECT Traffic
Key values
- 1,962 queries/s Query Peak
- 2026-03-06 09:45:04 Date
INSERT/UPDATE/DELETE Traffic
Key values
- 206 queries/s Query Peak
- 2026-03-06 09:00:55 Date
Queries duration
Key values
- 1h39m26s 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) Mar 06 09 271,951 0ms 38s95ms 21ms 3m22s 4m20s 5m28s Day Hour SELECT COPY TO Average Duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Mar 06 09 94,880 26 0ms 0ms 0ms 0ms Day Hour INSERT UPDATE DELETE COPY FROM Average Duration Latency Percentile(90) Latency Percentile(95) Latency Percentile(99) Mar 06 09 23,052 1,766 16 96 0ms 0ms 0ms 0ms Day Hour Prepare Bind Bind/Prepare Percentage of prepare Mar 06 09 18,094 101,108 5.59 13.67% Day Hour Count Average / Second Mar 06 09 2,572 0.71/s Day Hour Count Average Duration Average idle time Mar 06 09 2,585 15m30s 15m28s -
Connections
Established Connections
Key values
- 28 connections Connection Peak
- 2026-03-06 09:48:48 Date
Connections per database
Key values
- acaweb_fx Main Database
- 2,572 connections Total
Connections per user
Key values
- postgres Main User
- 2,572 connections Total
Connections per host
Key values
- 192.168.4.142 Main host with 1049 connections
- 2,572 Total connections
Host Count 127.0.0.1 111 182.165.1.54 2 192.168.0.114 9 192.168.0.216 106 192.168.0.74 168 192.168.0.84 2 192.168.1.127 1 192.168.1.131 2 192.168.1.145 65 192.168.1.15 89 192.168.1.154 1 192.168.1.20 89 192.168.1.238 2 192.168.1.239 18 192.168.1.90 77 192.168.2.126 48 192.168.2.182 12 192.168.3.199 36 192.168.4.142 1,049 192.168.4.150 10 192.168.4.203 4 192.168.4.222 1 192.168.4.238 8 192.168.4.245 4 192.168.4.33 79 192.168.4.54 1 192.168.4.59 4 192.168.4.98 330 [local] 244 -
Sessions
Simultaneous sessions
Key values
- 13 sessions Session Peak
- 2026-03-06 09:53:48 Date
Histogram of session times
Key values
- 2,091 0-500ms duration
Sessions per database
Key values
- acaweb_fx Main Database
- 2,585 sessions Total
Sessions per user
Key values
- postgres Main User
- 2,585 sessions Total
Sessions per host
Key values
- 192.168.4.142 Main Host
- 2,585 sessions Total
Host Count Total Duration Average Duration 127.0.0.1 111 5s385ms 48ms 182.165.1.54 2 23h23m30s 11h41m45s 192.168.0.114 7 51m4s 7m17s 192.168.0.171 8 7d4h6m27s 21h30m48s 192.168.0.216 106 3m3s 1s729ms 192.168.0.74 169 2d12h26m31s 21m27s 192.168.0.84 2 23h59m21s 11h59m40s 192.168.1.127 1 114ms 114ms 192.168.1.131 2 23h59m20s 11h59m40s 192.168.1.145 64 1d20h59m20s 42m10s 192.168.1.15 89 1d22h49m36s 31m34s 192.168.1.154 9 7d7h44m15s 19h31m35s 192.168.1.20 88 2d3h38m23s 35m12s 192.168.1.238 2 23h59m28s 11h59m44s 192.168.1.239 18 104ms 5ms 192.168.1.90 77 37s502ms 487ms 192.168.2.126 48 17s937ms 373ms 192.168.2.182 12 2s216ms 184ms 192.168.3.199 36 1s728ms 48ms 192.168.4.142 1,049 6m47s 388ms 192.168.4.150 10 20h7m18s 2h43s 192.168.4.203 4 26s315ms 6s578ms 192.168.4.222 1 1m 1m 192.168.4.238 8 10s368ms 1s296ms 192.168.4.245 4 35ms 8ms 192.168.4.33 79 4m41s 3s564ms 192.168.4.54 1 226ms 226ms 192.168.4.59 4 39ms 9ms 192.168.4.98 330 13s273ms 40ms [local] 244 4m21s 1s71ms -
Checkpoints / Restartpoints
Checkpoints Buffers
Key values
- 9,672 buffers Checkpoint Peak
- 2026-03-06 09:05:05 Date
- 209.933 seconds Highest write time
- 0.069 seconds Sync time
Checkpoints Wal files
Key values
- 4 files Wal files usage Peak
- 2026-03-06 09:05:05 Date
Checkpoints distance
Key values
- 109.60 Mo Distance Peak
- 2026-03-06 09:05:05 Date
Checkpoints Activity
↑ Back to the top of the Checkpoint Activity tableDay Hour Written buffers Write time Sync time Total time Mar 06 09 40,330 1,966.195s 0.198s 1,966.723s Day Hour Added Removed Recycled Synced files Longest sync Average sync Mar 06 09 0 0 21 1,883 0.069s 0s Day Hour Count Avg time (sec) Mar 06 09 0 0s Day Hour Mean distance Mean estimate Mar 06 09 28,705.08 kB 152,360.17 kB -
Temporary Files
Size of temporary files
Key values
- 615.96 MiB Temp Files size Peak
- 2026-03-06 09:14:10 Date
Number of temporary files
Key values
- 30 per second Temp Files Peak
- 2026-03-06 09:32:08 Date
Temporary Files Activity
↑ Back to the top of the Temporary Files Activity tableDay Hour Count Total size Average size Mar 06 09 803 6.24 GiB 7.96 MiB Queries generating the most temporary files (N)
Rank Count Total size Min size Max size Avg size Query 1 64 318.53 MiB 4.96 MiB 4.99 MiB 4.98 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: 2026-03-06 09:00:48 Duration: 0ms
2 29 1.67 GiB 9.20 MiB 152.04 MiB 58.91 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: 2026-03-06 09:00:06 Duration: 0ms
3 25 82.44 MiB 3.14 MiB 3.45 MiB 3.30 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: 2026-03-06 09:01:08 Duration: 0ms
4 16 739.12 MiB 46.20 MiB 46.20 MiB 46.20 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: 2026-03-06 09:01:14 Duration: 0ms
5 16 1.23 GiB 78.44 MiB 78.44 MiB 78.44 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: 2026-03-06 09:01:18 Duration: 0ms
6 8 1.13 GiB 144.64 MiB 144.71 MiB 144.67 MiB select updateresultsmaterializedview ();-
select updateresultsmaterializedview ();
Date: 2026-03-06 09:02:12 Duration: 0ms
7 4 319.85 MiB 79.90 MiB 80.01 MiB 79.96 MiB select updateageforrelevantresults ();-
select updateageforrelevantresults ();
Date: 2026-03-06 09:02:04 Duration: 0ms
8 3 25.23 MiB 8.40 MiB 8.41 MiB 8.41 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: 2026-03-06 09:01:57 Duration: 0ms
Queries generating the largest temporary files
Rank Size Query 1 152.04 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: 2026-03-06 09:50:05 ]
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: 2026-03-06 09:00:06 ]
3 144.71 MiB select updateresultsmaterializedview ();[ Date: 2026-03-06 09:47:11 ]
4 144.70 MiB select updateresultsmaterializedview ();[ Date: 2026-03-06 09:50:32 ]
5 144.70 MiB select updateresultsmaterializedview ();[ Date: 2026-03-06 09:32:12 ]
6 144.67 MiB select updateresultsmaterializedview ();[ Date: 2026-03-06 09:35:33 ]
7 144.67 MiB select updateresultsmaterializedview ();[ Date: 2026-03-06 09:02:12 ]
8 144.66 MiB select updateresultsmaterializedview ();[ Date: 2026-03-06 09:17:17 ]
9 144.65 MiB select updateresultsmaterializedview ();[ Date: 2026-03-06 09:20:32 ]
10 144.64 MiB select updateresultsmaterializedview ();[ Date: 2026-03-06 09:05:32 ]
11 131.18 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: 2026-03-06 09:40:04 ]
12 113.85 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: 2026-03-06 09:20:04 ]
13 105.39 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: 2026-03-06 09:30:05 ]
14 88.48 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: 2026-03-06 09:10:07 ]
15 86.33 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: 2026-03-06 09:30:07 ]
16 80.01 MiB select updateageforrelevantresults ();[ Date: 2026-03-06 09:02:04 ]
17 80.00 MiB select updateageforrelevantresults ();[ Date: 2026-03-06 09:32:04 ]
18 79.94 MiB select updateageforrelevantresults ();[ Date: 2026-03-06 09:47:04 ]
19 79.90 MiB select updateageforrelevantresults ();[ Date: 2026-03-06 09:17:06 ]
20 78.44 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: 2026-03-06 09:01:18 ]
-
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)
- 53 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_type 4 acaweb_fx.public.relevance_keylevels_results 4 acaweb_fx.public.relevance_autochartist_results 4 acaweb_fx.pg_catalog.pg_class 4 acaweb_fx.pg_catalog.pg_index 2 acaweb_fx.public.datafeeds_latestrun 2 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.public.latest_candle_datetime_per_receng 1 Total 53 Vacuums per table
Key values
- public.solr_relevance_old (16) Main table vacuumed on database acaweb_fx
- 41 vacuums Total
Index Buffer usage Skipped WAL usage Table Vacuums scans hits misses dirtied pins frozen records full page bytes acaweb_fx.public.solr_relevance_old 16 16 12,124 0 71 0 0 9,039 2,004 9,102,439 acaweb_fx.pg_catalog.pg_attribute 4 4 3,531 0 407 0 268 1,452 367 2,188,413 acaweb_fx.pg_catalog.pg_type 3 3 477 0 89 0 0 226 56 315,681 acaweb_fx.public.datafeeds_latestrun 3 0 360 0 5 0 0 37 12 39,998 acaweb_fx.public.relevance_keylevels_results 3 3 10,904 0 547 5 298 2,335 464 1,702,309 acaweb_fx.pg_catalog.pg_class 3 3 1,381 0 140 0 0 402 131 665,063 acaweb_fx.public.relevance_fibonacci_results 3 3 3,632 0 46 0 147 410 26 126,642 acaweb_fx.pg_toast.pg_toast_2619 2 2 256 0 57 0 0 186 34 153,075 acaweb_fx.pg_catalog.pg_statistic 1 1 924 0 168 0 594 440 136 526,471 acaweb_fx.public.relevance_consecutivecandles_results 1 1 75 0 13 1 0 23 8 39,907 acaweb_fx.public.latest_t15_candle_view 1 1 66 0 2 0 0 6 4 11,363 acaweb_fx.public.relevance_autochartist_results 1 1 3,263 0 235 0 251 614 170 562,125 Total 41 38 36,993 20,525 1,780 6 1,558 15,170 3,412 15,433,486 Tuples removed per table
Key values
- public.solr_relevance_old (66679) Main table with removed tuples on database acaweb_fx
- 78546 tuples Total removed
Index Tuples Pages Table Vacuums scans removed remain not yet removable removed remain acaweb_fx.public.solr_relevance_old 16 16 66,679 85,785 0 0 2,932 acaweb_fx.pg_catalog.pg_attribute 4 4 7,241 42,971 504 22 1,074 acaweb_fx.public.relevance_keylevels_results 3 3 1,661 39,750 1,860 0 837 acaweb_fx.pg_catalog.pg_type 3 3 975 4,380 36 0 132 acaweb_fx.pg_catalog.pg_statistic 1 1 511 3,792 36 0 1,194 acaweb_fx.public.relevance_autochartist_results 1 1 448 8,106 0 0 380 acaweb_fx.pg_catalog.pg_class 3 3 347 5,004 54 0 450 acaweb_fx.public.relevance_fibonacci_results 3 3 238 4,940 277 0 306 acaweb_fx.public.datafeeds_latestrun 3 0 168 42 0 0 48 acaweb_fx.pg_toast.pg_toast_2619 2 2 131 348 16 0 98 acaweb_fx.public.relevance_consecutivecandles_results 1 1 85 221 0 0 7 acaweb_fx.public.latest_t15_candle_view 1 1 62 12 0 0 1 Total 41 38 78,546 195,351 2,783 22 7,459 Pages removed per table
Key values
- pg_catalog.pg_attribute (22) Main table with removed pages on database acaweb_fx
- 22 pages Total removed
Table Number of vacuums Index scans Tuples removed Pages removed acaweb_fx.pg_catalog.pg_attribute 4 4 7241 22 acaweb_fx.pg_toast.pg_toast_2619 2 2 131 0 acaweb_fx.pg_catalog.pg_type 3 3 975 0 acaweb_fx.public.datafeeds_latestrun 3 0 168 0 acaweb_fx.pg_catalog.pg_statistic 1 1 511 0 acaweb_fx.public.relevance_consecutivecandles_results 1 1 85 0 acaweb_fx.public.latest_t15_candle_view 1 1 62 0 acaweb_fx.public.relevance_keylevels_results 3 3 1661 0 acaweb_fx.public.relevance_autochartist_results 1 1 448 0 acaweb_fx.public.solr_relevance_old 16 16 66679 0 acaweb_fx.pg_catalog.pg_class 3 3 347 0 acaweb_fx.public.relevance_fibonacci_results 3 3 238 0 Total 41 38 78,546 22 Autovacuum Activity
↑ Back to the top of the Autovacuum Activity tableDay Hour VACUUMs ANALYZEs Mar 06 09 41 53 - 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
- 94,880 Total read queries
- 37,451 Total write queries
Queries by database
Key values
- unknown Main database
- 271,005 Requests
- 1h38m38s (unknown)
- Main time consuming database
Database Request type Count Duration acaweb_fx Total 857 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 176 0ms select 72 0ms tcl 330 0ms update 37 0ms socialmedia Total 89 0ms others 1 0ms select 79 0ms tcl 9 0ms unknown Total 271,005 1h38m38s copy from 16 0ms cte 11,483 0ms insert 23,052 0ms others 3,672 0ms select 94,729 0ms tcl 553 0ms update 1,729 0ms Queries by user
Key values
- unknown Main user
- 271,005 Requests
User Request type Count Duration postgres Total 946 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 177 0ms select 151 0ms tcl 339 0ms update 37 0ms unknown Total 271,005 1h38m38s copy from 16 0ms cte 11,483 0ms insert 23,052 0ms others 3,672 0ms select 94,729 0ms tcl 553 0ms update 1,729 0ms Duration by user
Key values
- 1h38m38s (unknown) Main time consuming user
User Request type Count Duration postgres Total 946 0ms copy from 80 0ms copy to 26 0ms cte 104 0ms ddl 16 0ms delete 16 0ms others 177 0ms select 151 0ms tcl 339 0ms update 37 0ms unknown Total 271,005 1h38m38s copy from 16 0ms cte 11,483 0ms insert 23,052 0ms others 3,672 0ms select 94,729 0ms tcl 553 0ms update 1,729 0ms Queries by host
Key values
- unknown Main host
- 271,951 Requests
- 1h38m38s (unknown)
- Main time consuming host
Queries by application
Key values
- unknown Main application
- 271,596 Requests
- 1h38m38s (unknown)
- Main time consuming application
Number of cancelled queries
Key values
- 0 per second Cancelled query Peak
- 2026-03-06 09:42:12 Date
Number of cancelled queries (5 minutes period)
NO DATASET
-
Top Queries
Histogram of query times
Key values
- 98,088 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 28 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 Mar 06 09 28 0ms 0ms 2 0ms 693 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 #2
Day Hour Count Duration Avg duration Mar 06 09 693 0ms 0ms 3 0ms 1,986 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 #3
Day Hour Count Duration Avg duration Mar 06 09 1,986 0ms 0ms 4 0ms 4 0ms 0ms 0ms select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Mar 06 09 4 0ms 0ms 5 0ms 4 0ms 0ms 0ms select updaterelevantforrelevantresults ();Times Reported Time consuming queries #5
Day Hour Count Duration Avg duration Mar 06 09 4 0ms 0ms 6 0ms 1 0ms 0ms 0ms set datestyle = iso;Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Mar 06 09 1 0ms 0ms 7 0ms 1 0ms 0ms 0ms set client_encoding to ?;Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Mar 06 09 1 0ms 0ms 8 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 #8
Day Hour Count Duration Avg duration Mar 06 09 18 0ms 0ms 9 0ms 1 0ms 0ms 0ms update executions set isrunning = false, has_results=false, response=?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Thryv Holdings Inc: +?.?%\\\\n - CS Disco LLC: +?.?%\\\\n - Sabre Corpo: +?.?%\\\\n - Babcock & Wilcox Enterprises Inc: +?.?%\\\\n - Wix.Com Ltd: +?.?%\\\\n - Gossamer Bio Inc: +?.?%\\\\n - Gogo Inc: +?.?%\\\\n - Evolus Inc: +?.?%\\\\n - Luna Innovations Incorporated: +?.?%\\\\n - Trade Desk Inc: +?.?%\\\\n. The biggest losers are: - Uniqure NV: (?.?%)\\\\n - Sunrun Inc: (?.?%)\\\\n - Grocery Outlet Holding Corp: (?.?%)\\\\n - Microvision Inc: (?.?%)\\\\n - ThredUp Inc: (?.?%)\\\\n - Sight Sciences Inc: (?.?%)\\\\n - Cerus Corporation: (?.?%)\\\\n - Alector Inc: (?.?%)\\\\n - American Eagle Outfitters Inc: (?.?%)\\\\n - Celsius Holdings Inc: (?.?%)\\\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Thryv Holdings Inc: +?.?%, CS Disco LLC: +?.?%, Sabre Corpo: +?.?%, Babcock & Wilcox Enterprises Inc: +?.?%, Wix.Com Ltd: +?.?%, Gossamer Bio Inc: +?.?%, Gogo Inc: +?.?%, Evolus Inc: +?.?%, Luna Innovations Incorporated: +?.?%, Trade Desk Inc: +?.?%. The biggest losers are: Uniqure NV: (?.?%), Sunrun Inc: (?.?%), Grocery Outlet Holding Corp: (?.?%), Microvision Inc: (?.?%), ThredUp Inc: (?.?%), Sight Sciences Inc: (?.?%), Cerus Corporation: (?.?%), Alector Inc: (?.?%), American Eagle Outfitters Inc: (?.?%), Celsius Holdings Inc: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": null, \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?SJ?CI?U?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": null, \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": \"?A?NIHHA?KOY?\", \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": null, \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", 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The company was incorporated in ? and is headquartered in Concord, California.\", \"data_losers_7_GicIndustry\": \"Health Care Equipment & Supplies\", \"data_losers_7_OpenHeading\": \"Open\", \"data_losers_8_CountryName\": \"USA\", \"data_losers_8_Description\": \"Alector, Inc., a clinical stage biotechnology company, develops therapies to counteract the progression of neurodegeneration in the United States. Its pipeline includes Nivisnebart, an investigational human recombinant monoclonal antibody for treating prevalent neurodegenerative diseases; AL?, an anti-amyloid beta antibody paired in preclinical development for the potential treatment of Alzheimer\?s disease and Lewy body dementia in patients having GBA? gene mutations. The company also develops its preclinical and research pipeline comprising AL?, a tau siRNA for Alzheimer\?s disease; and ADP?-ABC, an NLRP? siRNA for neurodegenerative conditions. It has a strategic collaboration agreement with GlaxoSmithKline plc for the development and commercialization of progranulin-elevating monoclonal antibodies, including Latozinemab and Nivisnebart. The company was founded in ? and is headquartered in South San Francisco, California.\", \"data_losers_8_GicIndustry\": \"Biotechnology\", \"data_losers_8_OpenHeading\": \"Open\", \"data_losers_9_CountryName\": \"USA\", \"data_losers_9_Description\": \"American Eagle Outfitters, Inc. operates as a multi-brand specialty retailer in the United States and internationally. The company provides jeans, apparel and accessories, and personal care products for women and men under the American Eagle brand; and intimates, apparel, activewear, and swim collections under the Aerie and OFFLINE by Aerie brands. It also offers menswear products under the Todd Snyder New York brand; and fashion clothing and accessories under the Unsubscribed brand. 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American Eagle Outfitters, Inc. was founded in ? and is headquartered in Pittsburgh, Pennsylvania.\", \"data_losers_9_GicIndustry\": \"Specialty Retail\", \"data_losers_9_OpenHeading\": \"Open\", \"data_winners_10_GicSector\": \"Information Technology\", \"data_winners_10_UpdatedAt\": \"?-03-05\", \"data_winners_1_CountryISO\": \"US\", \"data_winners_1_IsDelisted\": false, \"data_winners_1_change_str\": \"+?.?%\", \"data_winners_2_CountryISO\": \"US\", \"data_winners_2_IsDelisted\": false, \"data_winners_2_change_str\": \"+?.?%\", \"data_winners_3_CountryISO\": \"US\", \"data_winners_3_IsDelisted\": false, \"data_winners_3_change_str\": \"+?.?%\", \"data_winners_4_CountryISO\": \"US\", \"data_winners_4_IsDelisted\": false, \"data_winners_4_change_str\": \"+?.?%\", \"data_winners_5_CountryISO\": \"US\", \"data_winners_5_IsDelisted\": false, \"data_winners_5_change_str\": \"+?.?%\", \"data_winners_6_CountryISO\": \"US\", \"data_winners_6_IsDelisted\": false, \"data_winners_6_change_str\": \"+?.?%\", \"data_winners_7_CountryISO\": \"US\", \"data_winners_7_IsDelisted\": false, \"data_winners_7_change_str\": \"+?.?%\", \"data_winners_8_CountryISO\": \"US\", \"data_winners_8_IsDelisted\": false, \"data_winners_8_change_str\": \"+?.?%\", \"data_winners_9_CountryISO\": \"US\", \"data_winners_9_IsDelisted\": false, \"data_winners_9_change_str\": \"+?.?%\", \"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_ignore_logos_value\": false, \"data_losers_10_CountryName\": \"USA\", \"data_losers_10_Description\": \"Celsius Holdings, Inc. develops, processes, manufactures, markets, sells, and distributes functional energy drinks in the United States, North America, Europe, the Asia Pacific, and internationally. 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Celsius Holdings, Inc. was founded in ? and is headquartered in Boca Raton, Florida.\", \"data_losers_10_GicIndustry\": \"Beverages\", \"data_losers_10_OpenHeading\": \"Open\", \"data_losers_1_CloseHeading\": \"Close\", \"data_losers_1_CurrencyCode\": \"USD\", \"data_losers_1_CurrencyName\": \"US Dollar\", \"data_losers_1_HomeCategory\": \"Domestic\", \"data_losers_2_CloseHeading\": \"Close\", \"data_losers_2_CurrencyCode\": \"USD\", \"data_losers_2_CurrencyName\": \"US Dollar\", \"data_losers_2_HomeCategory\": \"Domestic\", \"data_losers_3_CloseHeading\": \"Close\", \"data_losers_3_CurrencyCode\": \"USD\", \"data_losers_3_CurrencyName\": \"US Dollar\", \"data_losers_3_HomeCategory\": \"Domestic\", \"data_losers_4_CloseHeading\": \"Close\", \"data_losers_4_CurrencyCode\": \"USD\", \"data_losers_4_CurrencyName\": \"US Dollar\", \"data_losers_4_HomeCategory\": \"Domestic\", \"data_losers_5_CloseHeading\": \"Close\", \"data_losers_5_CurrencyCode\": \"USD\", \"data_losers_5_CurrencyName\": \"US Dollar\", \"data_losers_5_HomeCategory\": null, \"data_losers_6_CloseHeading\": \"Close\", \"data_losers_6_CurrencyCode\": \"USD\", \"data_losers_6_CurrencyName\": \"US Dollar\", \"data_losers_6_HomeCategory\": null, \"data_losers_7_CloseHeading\": \"Close\", \"data_losers_7_CurrencyCode\": \"USD\", \"data_losers_7_CurrencyName\": \"US Dollar\", \"data_losers_7_HomeCategory\": \"Domestic\", \"data_losers_8_CloseHeading\": \"Close\", \"data_losers_8_CurrencyCode\": \"USD\", \"data_losers_8_CurrencyName\": \"US Dollar\", \"data_losers_8_HomeCategory\": \"Domestic\", \"data_losers_9_CloseHeading\": \"Close\", \"data_losers_9_CurrencyCode\": \"USD\", \"data_losers_9_CurrencyName\": \"US Dollar\", \"data_losers_9_HomeCategory\": \"Domestic\", \"data_winners_10_CountryISO\": \"US\", \"data_winners_10_IsDelisted\": false, \"data_winners_10_change_str\": \"+?.?%\", \"data_winners_1_CountryName\": \"USA\", \"data_winners_1_Description\": \"Thryv Holdings, Inc. provides digital marketing solutions and cloud-based tools to the small-to-medium-sized businesses in the United States. 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Thryv Holdings, Inc. was incorporated in ? and is headquartered in DFW Airport, Texas.\", \"data_winners_1_GicIndustry\": \"Media\", \"data_winners_1_OpenHeading\": \"Open\", \"data_winners_2_CountryName\": \"USA\", \"data_winners_2_Description\": \"CS Disco, Inc. provides cloud-native and artificial intelligence-powered legal products for legal hold, legal request, ediscovery, legal document review, and case management in the United States and internationally. 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The company offers in-flight systems; in-flight services; aviation partner support; and engineering, design, and development services, as well as production operations functions. It offers voice and data, in-flight entertainment, and other services. In addition, the company engages in the development, deployment, and operation of networks, towers, cyber security software and data centers to support in-flight connectivity services, as well as in the provision of telecommunications services. It sells its products primarily to aircraft operators and original equipment manufacturers of business aviation aircraft through a distribution network of independent dealers. Gogo Inc. was founded in ? and is headquartered in Broomfield, Colorado.\", \"data_winners_7_GicIndustry\": \"Wireless Telecommunication Services\", \"data_winners_7_OpenHeading\": \"Open\", \"data_winners_8_CountryName\": \"USA\", \"data_winners_8_Description\": \"Evolus, Inc., a performance beauty company, delivers products in the cash-pay aesthetic market in the United States, Canada, Europe, and Australia. It offers Jeuveau, a proprietary ? kilodalton purified botulinum toxin type A formulation for the temporary improvement in the appearance of moderate to severe glabellar lines in adults; and Evolysse, a collection of injectable hyaluronic acid gels. 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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\": \"ebdfce84-ee3f-4a36-be67-2554576d3ff0\", \"params_title_character_limit_value\": ?, \"data_losers_1_InternationalDomestic\": \"International/Domestic\", \"data_losers_1_change_str.fill_color\": \"#?\", \"data_losers_2_InternationalDomestic\": \"Domestic\", \"data_losers_2_change_str.fill_color\": \"#?\", \"data_losers_3_InternationalDomestic\": null, \"data_losers_3_change_str.fill_color\": \"#?\", \"data_losers_4_InternationalDomestic\": \"International/Domestic\", \"data_losers_4_change_str.fill_color\": \"#?\", \"data_losers_5_InternationalDomestic\": null, \"data_losers_5_change_str.fill_color\": \"#?\", \"data_losers_6_InternationalDomestic\": null, \"data_losers_6_change_str.fill_color\": \"#?\", \"data_losers_7_InternationalDomestic\": \"Domestic\", \"data_losers_7_change_str.fill_color\": \"#?\", \"data_losers_8_InternationalDomestic\": \"Domestic\", \"data_losers_8_change_str.fill_color\": \"#?\", \"data_losers_9_InternationalDomestic\": \"Domestic\", \"data_losers_9_change_str.fill_color\": \"#?\", \"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_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.\", \"params_creatomate_modifications_value_frame_rate\": ?, \"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, 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: a file could not be downloaded: https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg (element data_winners_1_logourl). render details: {\"id\": \"?a5904a9-ce2c-4cfe-9113-462ab55342f0\", \"status\": \"failed\", \"error_message\": \"A file could not be downloaded: https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg (element data_winners_1_LogoURL)\", \"url\": \"https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/?a5904a9-ce2c-4cfe-9113-462ab55342f0.png\", \"template_id\": \"ebdfce84-ee3f-4a36-be67-2554576d3ff0\", \"template_name\": \"Biggest stock gainers and losers MP?\", \"template_tags\": [], \"output_format\": \"png\", \"frame_rate\": ?, \"modifications\": {\"data_Date\": \"? Mar ?\", \"frame_rate\": ?, \"text_title\": \"This week?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Thryv Holdings Inc: +?.?%\\n - CS Disco LLC: +?.?%\\n - Sabre Corpo: +?.?%\\n - Babcock & Wilcox Enterprises Inc: +?.?%\\n - Wix.Com Ltd: +?.?%\\n - Gossamer Bio Inc: +?.?%\\n - Gogo Inc: +?.?%\\n - Evolus Inc: +?.?%\\n - Luna Innovations Incorporated: +?.?%\\n - Trade Desk Inc: +?.?%\\n. The biggest losers are: - Uniqure NV: (?.?%)\\n - Sunrun Inc: (?.?%)\\n - Grocery Outlet Holding Corp: (?.?%)\\n - Microvision Inc: (?.?%)\\n - ThredUp Inc: (?.?%)\\n - Sight Sciences Inc: (?.?%)\\n - Cerus Corporation: (?.?%)\\n - Alector Inc: (?.?%)\\n - American Eagle Outfitters Inc: (?.?%)\\n - Celsius Holdings Inc: (?.?%)\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Thryv Holdings Inc: +?.?%, CS Disco LLC: +?.?%, Sabre Corpo: +?.?%, Babcock & Wilcox Enterprises Inc: +?.?%, Wix.Com Ltd: +?.?%, Gossamer Bio Inc: +?.?%, Gogo Inc: +?.?%, Evolus Inc: +?.?%, Luna Innovations Incorporated: +?.?%, Trade Desk Inc: +?.?%. The biggest losers are: Uniqure NV: (?.?%), Sunrun Inc: (?.?%), Grocery Outlet Holding Corp: (?.?%), Microvision Inc: (?.?%), ThredUp Inc: (?.?%), Sight Sciences Inc: (?.?%), Cerus Corporation: (?.?%), Alector Inc: (?.?%), American Eagle Outfitters Inc: (?.?%), Celsius Holdings Inc: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": null, \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?SJ?CI?U?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": null, \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": \"?A?NIHHA?KOY?\", \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": null, \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?BIEY?XIDA?Q?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?Z?RQOIY?JMHC?\", \"data_losers_9_CIK\": \"?\", \"data_losers_9_LEI\": \"?Z?HXK?DHW?\", \"dictionary_Actual\": \"Actual\", \"dictionary_chang[...];Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Mar 06 09 1 0ms 0ms 10 0ms 1 0ms 0ms 0ms insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Thryv Holdings Inc: +?.?%\\\\n - CS Disco LLC: +?.?%\\\\n - Sabre Corpo: +?.?%\\\\n - Babcock & Wilcox Enterprises Inc: +?.?%\\\\n - Wix.Com Ltd: +?.?%\\\\n - Gossamer Bio Inc: +?.?%\\\\n - Gogo Inc: +?.?%\\\\n - Evolus Inc: +?.?%\\\\n - Luna Innovations Incorporated: +?.?%\\\\n - Trade Desk Inc: +?.?%\\\\n. The biggest losers are: - Uniqure NV: (?.?%)\\\\n - Sunrun Inc: (?.?%)\\\\n - Grocery Outlet Holding Corp: (?.?%)\\\\n - Microvision Inc: (?.?%)\\\\n - ThredUp Inc: (?.?%)\\\\n - Sight Sciences Inc: (?.?%)\\\\n - Cerus Corporation: (?.?%)\\\\n - Alector Inc: (?.?%)\\\\n - American Eagle Outfitters Inc: (?.?%)\\\\n - Celsius Holdings Inc: (?.?%)\\\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Thryv Holdings Inc: +?.?%, CS Disco LLC: +?.?%, Sabre Corpo: +?.?%, Babcock & Wilcox Enterprises Inc: +?.?%, Wix.Com Ltd: +?.?%, Gossamer Bio Inc: +?.?%, Gogo Inc: +?.?%, Evolus Inc: +?.?%, Luna Innovations Incorporated: +?.?%, Trade Desk Inc: +?.?%. The biggest losers are: Uniqure NV: (?.?%), Sunrun Inc: (?.?%), Grocery Outlet Holding Corp: (?.?%), Microvision Inc: (?.?%), ThredUp Inc: (?.?%), Sight Sciences Inc: (?.?%), Cerus Corporation: (?.?%), Alector Inc: (?.?%), American Eagle Outfitters Inc: (?.?%), Celsius Holdings Inc: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": null, \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?SJ?CI?U?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": null, \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": \"?A?NIHHA?KOY?\", \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": null, \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?BIEY?XIDA?Q?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?Z?RQOIY?JMHC?\", \"data_losers_9_CIK\": \"?\", \"data_losers_9_LEI\": \"?Z?HXK?DHW?\", \"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\": \"fb8e9876-24a7-455a-b06b-3e2adb8b3970\", \"data_ChangeHeading\": \"Change\", \"data_losers_10_CIK\": \"?\", \"data_losers_10_LEI\": null, \"data_losers_1_Code\": \"QURE\", \"data_losers_1_ISIN\": \"NL?\", \"data_losers_1_Name\": \"Uniqure NV\", \"data_losers_1_Type\": \"Common Stock\", \"data_losers_1_code\": \"QURE\", \"data_losers_1_open\": ?.?, \"data_losers_2_Code\": \"RUN\", \"data_losers_2_ISIN\": \"US?W?\", \"data_losers_2_Name\": \"Sunrun Inc\", \"data_losers_2_Type\": \"Common Stock\", \"data_losers_2_code\": \"RUN\", \"data_losers_2_open\": ?.?, \"data_losers_3_Code\": \"GO\", \"data_losers_3_ISIN\": \"US?R?\", \"data_losers_3_Name\": \"Grocery Outlet Holding Corp\", \"data_losers_3_Type\": \"Common Stock\", \"data_losers_3_code\": \"GO\", \"data_losers_3_open\": ?.?, \"data_losers_4_Code\": \"MVIS\", \"data_losers_4_ISIN\": \"US?\", \"data_losers_4_Name\": \"Microvision Inc\", \"data_losers_4_Type\": \"Common Stock\", \"data_losers_4_code\": \"MVIS\", \"data_losers_4_open\": ?.?, \"data_losers_5_Code\": \"TDUP\", \"data_losers_5_ISIN\": \"US?E?\", \"data_losers_5_Name\": \"ThredUp Inc\", \"data_losers_5_Type\": \"Common Stock\", \"data_losers_5_code\": \"TDUP\", \"data_losers_5_open\": ?.?, \"data_losers_6_Code\": \"SGHT\", \"data_losers_6_ISIN\": \"US?M?\", \"data_losers_6_Name\": \"Sight Sciences Inc\", \"data_losers_6_Type\": \"Common Stock\", \"data_losers_6_code\": \"SGHT\", \"data_losers_6_open\": ?.?, \"data_losers_7_Code\": \"CERS\", \"data_losers_7_ISIN\": \"US?\", \"data_losers_7_Name\": \"Cerus Corporation\", \"data_losers_7_Type\": \"Common Stock\", \"data_losers_7_code\": \"CERS\", \"data_losers_7_open\": ?.?, \"data_losers_8_Code\": \"ALEC\", \"data_losers_8_ISIN\": \"US?\", \"data_losers_8_Name\": \"Alector Inc\", \"data_losers_8_Type\": \"Common Stock\", \"data_losers_8_code\": \"ALEC\", \"data_losers_8_open\": ?.?, \"data_losers_9_Code\": \"AEO\", \"data_losers_9_ISIN\": \"US?E?\", \"data_losers_9_Name\": \"American Eagle Outfitters Inc\", \"data_losers_9_Type\": \"Common Stock\", \"data_losers_9_code\": \"AEO\", \"data_losers_9_open\": ?.?, \"data_winners_1_CIK\": \"?\", \"data_winners_1_LEI\": null, \"data_winners_2_CIK\": \"?\", \"data_winners_2_LEI\": null, \"data_winners_3_CIK\": \"?\", \"data_winners_3_LEI\": \"?VKCYZW?GZ?WW?\", \"data_winners_4_CIK\": \"?\", \"data_winners_4_LEI\": \"?H?OZN?C?T?\", \"data_winners_5_CIK\": \"?\", \"data_winners_5_LEI\": null, \"data_winners_6_CIK\": \"?\", \"data_winners_6_LEI\": \"?ZOC?N?W?PSFR?\", \"data_winners_7_CIK\": \"?\", \"data_winners_7_LEI\": \"?VL?DIHSFHYTO?\", \"data_winners_8_CIK\": \"?\", \"data_winners_8_LEI\": \"?VYQRYBQOT?V?\", \"data_winners_9_CIK\": \"?\", \"data_winners_9_LEI\": null, \"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_losers_10_Code\": \"CELH\", \"data_losers_10_ISIN\": \"US?V?\", \"data_losers_10_Name\": \"Celsius Holdings Inc\", \"data_losers_10_Type\": \"Common Stock\", \"data_losers_10_code\": \"CELH\", \"data_losers_10_open\": ?.?, \"data_losers_1_CUSIP\": \"N?\", \"data_losers_1_Phone\": \"? ? ? ?\", \"data_losers_1_close\": ?.?, \"data_losers_2_CUSIP\": \"?W?\", \"data_losers_2_Phone\": \"? ? ?\", \"data_losers_2_close\": ?.?, \"data_losers_3_CUSIP\": \"?R?\", \"data_losers_3_Phone\": \"? ? ?\", \"data_losers_3_close\": ?.?, \"data_losers_4_CUSIP\": \"?\", \"data_losers_4_Phone\": \"? ? ?\", \"data_losers_4_close\": ?.?, \"data_losers_5_CUSIP\": \"?E?\", \"data_losers_5_Phone\": \"? ? ?\", \"data_losers_5_close\": ?.?, \"data_losers_6_CUSIP\": \"?M?\", \"data_losers_6_Phone\": \"? ? ?\", \"data_losers_6_close\": ?.?, \"data_losers_7_CUSIP\": \"?\", \"data_losers_7_Phone\": \"? ? ?\", \"data_losers_7_close\": ?.?, \"data_losers_8_CUSIP\": \"?\", \"data_losers_8_Phone\": \"? ? ?\", \"data_losers_8_close\": ?.?, \"data_losers_9_CUSIP\": \"?D?\", \"data_losers_9_Phone\": \"? ? ?\", \"data_losers_9_close\": ?.?, \"data_winners_10_CIK\": \"?\", \"data_winners_10_LEI\": \"?GXPD?VT?E?P?\", \"data_winners_1_Code\": \"THRY\", \"data_winners_1_ISIN\": \"US?\", \"data_winners_1_Name\": \"Thryv Holdings Inc\", \"data_winners_1_Type\": \"Common Stock\", \"data_winners_1_code\": \"THRY\", \"data_winners_1_open\": ?.?, \"data_winners_2_Code\": \"LAW\", \"data_winners_2_ISIN\": \"US?\", \"data_winners_2_Name\": \"CS Disco LLC\", \"data_winners_2_Type\": \"Common Stock\", \"data_winners_2_code\": \"LAW\", \"data_winners_2_open\": ?.?, \"data_winners_3_Code\": \"SABR\", \"data_winners_3_ISIN\": \"US?M?\", \"data_winners_3_Name\": \"Sabre Corpo\", \"data_winners_3_Type\": \"Common Stock\", \"data_winners_3_code\": \"SABR\", \"data_winners_3_open\": ?.?, \"data_winners_4_Code\": \"BW\", \"data_winners_4_ISIN\": \"US?L?\", \"data_winners_4_Name\": \"Babcock & Wilcox Enterprises Inc\", \"data_winners_4_Type\": \"Common Stock\", \"data_winners_4_code\": \"BW\", \"data_winners_4_open\": ?.?, \"data_winners_5_Code\": \"WIX\", \"data_winners_5_ISIN\": \"USM?\", \"data_winners_5_Name\": \"Wix.Com Ltd\", \"data_winners_5_Type\": \"Common Stock\", \"data_winners_5_code\": \"WIX\", \"data_winners_5_open\": ?.?, \"data_winners_6_Code\": \"GOSS\", \"data_winners_6_ISIN\": \"US?P?\", \"data_winners_6_Name\": \"Gossamer Bio Inc\", \"data_winners_6_Type\": \"Common Stock\", \"data_winners_6_code\": \"GOSS\", \"data_winners_6_open\": ?.?, \"data_winners_7_Code\": \"GOGO\", \"data_winners_7_ISIN\": \"US?C?\", \"data_winners_7_Name\": \"Gogo Inc\", \"data_winners_7_Type\": \"Common Stock\", \"data_winners_7_code\": \"GOGO\", \"data_winners_7_open\": ?.?, \"data_winners_8_Code\": \"EOLS\", \"data_winners_8_ISIN\": \"US?C?\", \"data_winners_8_Name\": \"Evolus Inc\", \"data_winners_8_Type\": \"Common Stock\", \"data_winners_8_code\": \"EOLS\", \"data_winners_8_open\": ?.?, \"data_winners_9_Code\": \"LUNA\", \"data_winners_9_ISIN\": \"US?\", \"data_winners_9_Name\": \"Luna Innovations Incorporated\", \"data_winners_9_Type\": \"Common Stock\", \"data_winners_9_code\": \"LUNA\", \"data_winners_9_open\": ?.?, \"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\": \"Insta EN\", \"data_losers_10_CUSIP\": \"?V?\", \"data_losers_10_Phone\": \"? ? ?\", \"data_losers_10_close\": ?.?, \"data_losers_1_Sector\": \"Healthcare\", \"data_losers_1_WebURL\": \"https://www.uniqure.com\", \"data_losers_1_change\": ?.?, \"data_losers_1_ticker\": \"QURE.US\", \"data_losers_2_Sector\": \"Technology\", \"data_losers_2_WebURL\": \"https://www.sunrun.com\", \"data_losers_2_change\": ?.?, \"data_losers_2_ticker\": \"RUN.US\", \"data_losers_3_Sector\": \"Consumer Defensive\", \"data_losers_3_WebURL\": \"https://www.groceryoutlet.com\", \"data_losers_3_change\": ?.?, \"data_losers_3_ticker\": \"GO.US\", \"data_losers_4_Sector\": \"Technology\", \"data_losers_4_WebURL\": \"https://www.microvision.com\", \"data_losers_4_change\": ?.?, \"data_losers_4_ticker\": \"MVIS.US\", \"data_losers_5_Sector\": \"Consumer Cyclical\", \"data_losers_5_WebURL\": \"https://www.thredup.com\", \"data_losers_5_change\": ?.?, \"data_losers_5_ticker\": \"TDUP.US\", \"data_losers_6_Sector\": \"Healthcare\", \"data_losers_6_WebURL\": \"https://www.sightsciences.com\", \"data_losers_6_change\": ?.?, \"data_losers_6_ticker\": \"SGHT.US\", \"data_losers_7_Sector\": \"Healthcare\", \"data_losers_7_WebURL\": \"https://www.cerus.com\", \"data_losers_7_change\": ?.?, \"data_losers_7_ticker\": \"CERS.US\", \"data_losers_8_Sector\": \"Healthcare\", \"data_losers_8_WebURL\": \"https://www.alector.com\", \"data_losers_8_change\": ?.?, \"data_losers_8_ticker\": \"ALEC.US\", \"data_losers_9_Sector\": \"Consumer 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The company was incorporated in ? and is headquartered in Concord, California.\", \"data_losers_7_GicIndustry\": \"Health Care Equipment & Supplies\", \"data_losers_7_OpenHeading\": \"Open\", \"data_losers_8_CountryName\": \"USA\", \"data_losers_8_Description\": \"Alector, Inc., a clinical stage biotechnology company, develops therapies to counteract the progression of neurodegeneration in the United States. Its pipeline includes Nivisnebart, an investigational human recombinant monoclonal antibody for treating prevalent neurodegenerative diseases; AL?, an anti-amyloid beta antibody paired in preclinical development for the potential treatment of Alzheimer\?s disease and Lewy body dementia in patients having GBA? gene mutations. The company also develops its preclinical and research pipeline comprising AL?, a tau siRNA for Alzheimer\?s disease; and ADP?-ABC, an NLRP? siRNA for neurodegenerative conditions. It has a strategic collaboration agreement with GlaxoSmithKline plc for the development and commercialization of progranulin-elevating monoclonal antibodies, including Latozinemab and Nivisnebart. The company was founded in ? and is headquartered in South San Francisco, California.\", \"data_losers_8_GicIndustry\": \"Biotechnology\", \"data_losers_8_OpenHeading\": \"Open\", \"data_losers_9_CountryName\": \"USA\", \"data_losers_9_Description\": \"American Eagle Outfitters, Inc. operates as a multi-brand specialty retailer in the United States and internationally. The company provides jeans, apparel and accessories, and personal care products for women and men under the American Eagle brand; and intimates, apparel, activewear, and swim collections under the Aerie and OFFLINE by Aerie brands. It also offers menswear products under the Todd Snyder New York brand; and fashion clothing and accessories under the Unsubscribed brand. 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American Eagle Outfitters, Inc. was founded in ? and is headquartered in Pittsburgh, Pennsylvania.\", \"data_losers_9_GicIndustry\": \"Specialty Retail\", \"data_losers_9_OpenHeading\": \"Open\", \"data_winners_10_GicSector\": \"Information Technology\", \"data_winners_10_UpdatedAt\": \"?-03-05\", \"data_winners_1_CountryISO\": \"US\", \"data_winners_1_IsDelisted\": false, \"data_winners_1_change_str\": \"+?.?%\", \"data_winners_2_CountryISO\": \"US\", \"data_winners_2_IsDelisted\": false, \"data_winners_2_change_str\": \"+?.?%\", \"data_winners_3_CountryISO\": \"US\", \"data_winners_3_IsDelisted\": false, \"data_winners_3_change_str\": \"+?.?%\", \"data_winners_4_CountryISO\": \"US\", \"data_winners_4_IsDelisted\": false, \"data_winners_4_change_str\": \"+?.?%\", \"data_winners_5_CountryISO\": \"US\", \"data_winners_5_IsDelisted\": false, \"data_winners_5_change_str\": \"+?.?%\", \"data_winners_6_CountryISO\": \"US\", \"data_winners_6_IsDelisted\": false, \"data_winners_6_change_str\": \"+?.?%\", \"data_winners_7_CountryISO\": \"US\", \"data_winners_7_IsDelisted\": false, \"data_winners_7_change_str\": \"+?.?%\", \"data_winners_8_CountryISO\": \"US\", \"data_winners_8_IsDelisted\": false, \"data_winners_8_change_str\": \"+?.?%\", \"data_winners_9_CountryISO\": \"US\", \"data_winners_9_IsDelisted\": false, \"data_winners_9_change_str\": \"+?.?%\", \"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_ignore_logos_value\": false, \"data_losers_10_CountryName\": \"USA\", \"data_losers_10_Description\": \"Celsius Holdings, Inc. develops, processes, manufactures, markets, sells, and distributes functional energy drinks in the United States, North America, Europe, the Asia Pacific, and internationally. 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Celsius Holdings, Inc. was founded in ? and is headquartered in Boca Raton, Florida.\", \"data_losers_10_GicIndustry\": \"Beverages\", \"data_losers_10_OpenHeading\": \"Open\", \"data_losers_1_CloseHeading\": \"Close\", \"data_losers_1_CurrencyCode\": \"USD\", \"data_losers_1_CurrencyName\": \"US Dollar\", \"data_losers_1_HomeCategory\": \"Domestic\", \"data_losers_2_CloseHeading\": \"Close\", \"data_losers_2_CurrencyCode\": \"USD\", \"data_losers_2_CurrencyName\": \"US Dollar\", \"data_losers_2_HomeCategory\": \"Domestic\", \"data_losers_3_CloseHeading\": \"Close\", \"data_losers_3_CurrencyCode\": \"USD\", \"data_losers_3_CurrencyName\": \"US Dollar\", \"data_losers_3_HomeCategory\": \"Domestic\", \"data_losers_4_CloseHeading\": \"Close\", \"data_losers_4_CurrencyCode\": \"USD\", \"data_losers_4_CurrencyName\": \"US Dollar\", \"data_losers_4_HomeCategory\": \"Domestic\", \"data_losers_5_CloseHeading\": \"Close\", \"data_losers_5_CurrencyCode\": \"USD\", \"data_losers_5_CurrencyName\": \"US Dollar\", \"data_losers_5_HomeCategory\": null, \"data_losers_6_CloseHeading\": \"Close\", \"data_losers_6_CurrencyCode\": \"USD\", \"data_losers_6_CurrencyName\": \"US Dollar\", \"data_losers_6_HomeCategory\": null, \"data_losers_7_CloseHeading\": \"Close\", \"data_losers_7_CurrencyCode\": \"USD\", \"data_losers_7_CurrencyName\": \"US Dollar\", \"data_losers_7_HomeCategory\": \"Domestic\", \"data_losers_8_CloseHeading\": \"Close\", \"data_losers_8_CurrencyCode\": \"USD\", \"data_losers_8_CurrencyName\": \"US Dollar\", \"data_losers_8_HomeCategory\": \"Domestic\", \"data_losers_9_CloseHeading\": \"Close\", \"data_losers_9_CurrencyCode\": \"USD\", \"data_losers_9_CurrencyName\": \"US Dollar\", \"data_losers_9_HomeCategory\": \"Domestic\", \"data_winners_10_CountryISO\": \"US\", \"data_winners_10_IsDelisted\": false, \"data_winners_10_change_str\": \"+?.?%\", \"data_winners_1_CountryName\": \"USA\", \"data_winners_1_Description\": \"Thryv Holdings, Inc. provides digital marketing solutions and cloud-based tools to the small-to-medium-sized businesses in the United States. 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Thryv Holdings, Inc. was incorporated in ? and is headquartered in DFW Airport, Texas.\", \"data_winners_1_GicIndustry\": \"Media\", \"data_winners_1_OpenHeading\": \"Open\", \"data_winners_2_CountryName\": \"USA\", \"data_winners_2_Description\": \"CS Disco, Inc. provides cloud-native and artificial intelligence-powered legal products for legal hold, legal request, ediscovery, legal document review, and case management in the United States and internationally. 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The company offers in-flight systems; in-flight services; aviation partner support; and engineering, design, and development services, as well as production operations functions. It offers voice and data, in-flight entertainment, and other services. In addition, the company engages in the development, deployment, and operation of networks, towers, cyber security software and data centers to support in-flight connectivity services, as well as in the provision of telecommunications services. It sells its products primarily to aircraft operators and original equipment manufacturers of business aviation aircraft through a distribution network of independent dealers. Gogo Inc. was founded in ? and is headquartered in Broomfield, Colorado.\", \"data_winners_7_GicIndustry\": \"Wireless Telecommunication Services\", \"data_winners_7_OpenHeading\": \"Open\", \"data_winners_8_CountryName\": \"USA\", \"data_winners_8_Description\": \"Evolus, Inc., a performance beauty company, delivers products in the cash-pay aesthetic market in the United States, Canada, Europe, and Australia. It offers Jeuveau, a proprietary ? kilodalton purified botulinum toxin type A formulation for the temporary improvement in the appearance of moderate to severe glabellar lines in adults; and Evolysse, a collection of injectable hyaluronic acid gels. 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\"data_winners_5_nocolor_change_str\": \"+?.?%\", \"data_winners_6_nocolor_change_str\": \"+?.?%\", \"data_winners_7_nocolor_change_str\": \"+?.?%\", \"data_winners_8_nocolor_change_str\": \"+?.?%\", \"data_winners_9_nocolor_change_str\": \"+?.?%\", \"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\": \"?c270d7-883c-480e-8118-92af28ced303\", \"params_title_character_limit_value\": ?, \"data_losers_1_InternationalDomestic\": \"International/Domestic\", \"data_losers_1_change_str.fill_color\": \"#?\", \"data_losers_2_InternationalDomestic\": \"Domestic\", \"data_losers_2_change_str.fill_color\": \"#?\", \"data_losers_3_InternationalDomestic\": null, \"data_losers_3_change_str.fill_color\": \"#?\", \"data_losers_4_InternationalDomestic\": \"International/Domestic\", \"data_losers_4_change_str.fill_color\": \"#?\", \"data_losers_5_InternationalDomestic\": null, \"data_losers_5_change_str.fill_color\": \"#?\", \"data_losers_6_InternationalDomestic\": null, \"data_losers_6_change_str.fill_color\": \"#?\", \"data_losers_7_InternationalDomestic\": \"Domestic\", \"data_losers_7_change_str.fill_color\": \"#?\", \"data_losers_8_InternationalDomestic\": \"Domestic\", \"data_losers_8_change_str.fill_color\": \"#?\", \"data_losers_9_InternationalDomestic\": \"Domestic\", \"data_losers_9_change_str.fill_color\": \"#?\", \"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_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.\", \"params_creatomate_modifications_value_frame_rate\": ?, \"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, 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: a file could not be downloaded: https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg (element data_winners_1_logourl). render details: {\"id\": \"?c64966-6920-46a8-bf2d-99eb2cde5cc5\", \"status\": \"failed\", \"error_message\": \"A file could not be downloaded: https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg (element data_winners_1_LogoURL)\", \"url\": \"https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/?c64966-6920-46a8-bf2d-99eb2cde5cc5.png\", \"template_id\": \"?c270d7-883c-480e-8118-92af28ced303\", \"template_name\": \"Biggest stock gainers and losers MP? (Instagram ?) (EN)\", \"template_tags\": [], \"output_format\": \"png\", \"frame_rate\": ?, \"modifications\": {\"data_Date\": \"? Mar ?\", \"frame_rate\": ?, \"text_title\": \"This week?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Thryv Holdings Inc: +?.?%\\n - CS Disco LLC: +?.?%\\n - Sabre Corpo: +?.?%\\n - Babcock & Wilcox Enterprises Inc: +?.?%\\n - Wix.Com Ltd: +?.?%\\n - Gossamer Bio Inc: +?.?%\\n - Gogo Inc: +?.?%\\n - Evolus Inc: +?.?%\\n - Luna Innovations Incorporated: +?.?%\\n - Trade Desk Inc: +?.?%\\n. The biggest losers are: - Uniqure NV: (?.?%)\\n - Sunrun Inc: (?.?%)\\n - Grocery Outlet Holding Corp: (?.?%)\\n - Microvision Inc: (?.?%)\\n - ThredUp Inc: (?.?%)\\n - Sight Sciences Inc: (?.?%)\\n - Cerus Corporation: (?.?%)\\n - Alector Inc: (?.?%)\\n - American Eagle Outfitters Inc: (?.?%)\\n - Celsius Holdings Inc: (?.?%)\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Thryv Holdings Inc: +?.?%, CS Disco LLC: +?.?%, Sabre Corpo: +?.?%, Babcock & Wilcox Enterprises Inc: +?.?%, Wix.Com Ltd: +?.?%, Gossamer Bio Inc: +?.?%, Gogo Inc: +?.?%, Evolus Inc: +?.?%, Luna Innovations Incorporated: +?.?%, Trade Desk Inc: +?.?%. The biggest losers are: Uniqure NV: (?.?%), Sunrun Inc: (?.?%), Grocery Outlet Holding Corp: (?.?%), Microvision Inc: (?.?%), ThredUp Inc: (?.?%), Sight Sciences Inc: (?.?%), Cerus Corporation: (?.?%), Alector Inc: (?.?%), American Eagle Outfitters Inc: (?.?%), Celsius Holdings Inc: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": null, \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?SJ?CI?U?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": null, \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": \"?A?NIHHA?KOY?\", \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": null, \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?BIEY?XIDA?Q?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?Z?RQOIY?JMHC?\", \"data_losers_9_CIK\": \"?[...];Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Mar 06 09 1 0ms 0ms 11 0ms 446 0ms 0ms 0ms commit;Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Mar 06 09 446 0ms 0ms 12 0ms 228 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 #12
Day Hour Count Duration Avg duration Mar 06 09 228 0ms 0ms 13 0ms 240 0ms 0ms 0ms select count(*), sum(size), extract(epoch from now() - min(modification)) from pg_ls_waldir ();Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Mar 06 09 240 0ms 0ms 14 0ms 240 0ms 0ms 0ms select system_identifier from pg_control_system ();Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Mar 06 09 240 0ms 0ms 15 0ms 6 0ms 0ms 0ms select groupid, exchange, groupname, symbol, longname from prfsymboltree where brokerid = ? order by groupname, symbol;Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Mar 06 09 6 0ms 0ms 16 0ms 228 0ms 0ms 0ms with rar_max as ( 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;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Mar 06 09 228 0ms 0ms 17 0ms 48 0ms 0ms 0ms select distinct classname, to_char(created_datetime, ?), to_char(cleared_datetime, ?), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = ? or cleared_datetime > current_timestamp - interval ?) order by created_datetime desc;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Mar 06 09 48 0ms 0ms 18 0ms 16 0ms 0ms 0ms with max_ra as ( select resultuid from relevance_fibonacci_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 left outer join relevance_fibonacci_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 fibonacci_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null);Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Mar 06 09 16 0ms 0ms 19 0ms 288 0ms 0ms 0ms set statement_timeout = ?;Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Mar 06 09 288 0ms 0ms 20 0ms 60 0ms 0ms 0ms select id, schedule from processes where enabled = true and schedule is not null;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Mar 06 09 60 0ms 0ms Most frequent queries (N)
Rank Times executed Total duration Min duration Max duration Avg duration Query 1 38,066 0ms 0ms 0ms 0ms select ?;Times Reported Time consuming queries #1
Day Hour Count Duration Avg duration Mar 06 09 38,066 0ms 0ms 2 10,399 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 Mar 06 09 10,399 0ms 0ms 3 8,720 0ms 0ms 0ms 0ms select s.symbolid as id, s.symbol as name, s.exchange as exchange, s.timegranularity as interval, dtt.timezone as timezone from symbols s inner join downloadersymbolsettings dss on dss.symbolid = s.symbolid inner join datafeedstimetable dtt on dss.classname = dtt.classname and dtt.dayofweek = ? inner join brokersymbollist bsl on bsl.symbolid = s.symbolid where bsl.brokerid = ? and (? = ? or s.timegranularity = ?) and (s.symbol = ? or dss.downloadersymbol = ?) and dss.enabled = ?;Times Reported Time consuming queries #3
Day Hour Count Duration Avg duration Mar 06 09 8,720 0ms 0ms 4 5,386 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 #4
Day Hour Count Duration Avg duration Mar 06 09 5,386 0ms 0ms 5 4,944 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 #5
Day Hour Count Duration Avg duration Mar 06 09 4,944 0ms 0ms 6 4,631 0ms 0ms 0ms 0ms select category, name, sum(total) as total, sum(correct) as correct, (cast(sum(correct) as float) / cast(sum(total) as float)) * ?.? as percentage, min("from") AS "from", max("to") AS "to" from ( select category, name, total, correct, percentage, "from", "to" from stats_summary where statsid = ? and category = lower(?) union select category, name, total, correct, percentage, "from", "to" from stats_hrs_summary where statsid = ? and category = lower(?) order by correct desc) as summdata group by category, name having sum(total) > ? order by name;Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Mar 06 09 4,631 0ms 0ms 7 4,487 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 #7
Day Hour Count Duration Avg duration Mar 06 09 4,487 0ms 0ms 8 3,816 0ms 0ms 0ms 0ms insert into executionlogs (executionid, status, message, details, detailtype) values (null, ?, ?, null, null);Times Reported Time consuming queries #8
Day Hour Count Duration Avg duration Mar 06 09 3,816 0ms 0ms 9 3,321 0ms 0ms 0ms 0ms select datid, datname, pid, usesysid, usename, application_name, client_addr, client_hostname, client_port, backend_start, xact_start, query_start, state_change, wait_event_type, wait_event, state, backend_xid, backend_xmin, query, backend_type from pg_stat_activity where backend_type != ? or (coalesce(trim(query), ?) != ? and pid != pg_backend_pid() and query_start is not null and datname not ilike ? and datname not ilike ? and datname not ilike ? and datname not ilike ? and not (query_start < ?::timestamptz and state = ?));Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Mar 06 09 3,321 0ms 0ms 10 2,904 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 #10
Day Hour Count Duration Avg duration Mar 06 09 2,904 0ms 0ms 11 2,879 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 #11
Day Hour Count Duration Avg duration Mar 06 09 2,879 0ms 0ms 12 2,525 0ms 0ms 0ms 0ms select category, name, sum(total) as total, sum(correct) as correct, (cast(sum(correct) as float) / cast(sum(total) as float)) * ?.? as percentage, min("from") AS "from", max("to") AS "to" from ( select category, name, total, correct, percentage, "from", "to" from stats_hrsapproaches_summary where statsid = ? and category = lower(?) order by correct desc) as summdata group by category, name having sum(total) > ? order by name;Times Reported Time consuming queries #12
Day Hour Count Duration Avg duration Mar 06 09 2,525 0ms 0ms 13 2,383 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 #13
Day Hour Count Duration Avg duration Mar 06 09 2,383 0ms 0ms 14 1,986 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 #14
Day Hour Count Duration Avg duration Mar 06 09 1,986 0ms 0ms 15 1,775 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 #15
Day Hour Count Duration Avg duration Mar 06 09 1,775 0ms 0ms 16 1,584 0ms 0ms 0ms 0ms set extra_float_digits = ?;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Mar 06 09 1,584 0ms 0ms 17 1,555 0ms 0ms 0ms 0ms with rar_max as ( select resultuid from relevance_fibonacci_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, exchange as e, longname as lo, shortname as sho, timegranularity as tg, p.patternid as pid, direction as d, patternstarttime as pst, patternendtime as pet, patternstartprice as psp, patternendprice as pep, pricex as px, timex as tx, pricea as pa, timea as ta, priceb as pb, timeb as tb, pricec as pc, timec as tc, priced as pd, timed as td, averagequality as aq, timequality as tq, ? - errormargin as rq, ? - noise as c, target10 as t10, target06 as t06, target16 as t16, target07 as t07, target12 as t12, target03 as t03, target05 as t05, patternlengthbars as l, temporarypattern as tp, bandwidth as bw, qtytp as qtp, p.patternname as patternname, dtt.absolutetimezoneoffset as tzos, dtt.timezone as tz, 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, newlevels.filtered 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 inner join rar_max rm on ? = ? left outer join relevance_fibonacci_results rar on a.resultuid = rar.resultuid left join currencypips cps on cps.symbol = s.symbol left join lateral calc_fib_signal_filter (a.resultuid) newlevels on true where (a.old_resultuid = ? or a.resultuid = ?) and dtt.dayofweek = ?;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Mar 06 09 1,555 0ms 0ms 18 1,548 0ms 0ms 0ms 0ms set application_name = ?;Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Mar 06 09 1,548 0ms 0ms 19 1,402 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 #19
Day Hour Count Duration Avg duration Mar 06 09 1,402 0ms 0ms 20 1,288 0ms 0ms 0ms 0ms update patternresultsrelevance set relevant = ?, saxo_relevant = ?, notrelevantpricedatetime = ?, reason = ? where uniqueindex = ? and relevant = ?;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Mar 06 09 1,288 0ms 0ms Normalized slowest queries (N)
Rank Min duration Max duration Avg duration Times executed Total duration Query 1 0ms 0ms 0ms 28 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 Mar 06 09 28 0ms 0ms 2 0ms 0ms 0ms 693 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 #2
Day Hour Count Duration Avg duration Mar 06 09 693 0ms 0ms 3 0ms 0ms 0ms 1,986 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 #3
Day Hour Count Duration Avg duration Mar 06 09 1,986 0ms 0ms 4 0ms 0ms 0ms 4 0ms select count(*) from datafeeds_latestrun where feedname ilike ? and ((latestrxtime > current_timestamp - interval ? and latestdbwritetime > current_timestamp - interval ?) or (latestdbwritetime > current_timestamp - interval ? and lateststartuptime > current_timestamp - interval ?));Times Reported Time consuming queries #4
Day Hour Count Duration Avg duration Mar 06 09 4 0ms 0ms 5 0ms 0ms 0ms 4 0ms select updaterelevantforrelevantresults ();Times Reported Time consuming queries #5
Day Hour Count Duration Avg duration Mar 06 09 4 0ms 0ms 6 0ms 0ms 0ms 1 0ms set datestyle = iso;Times Reported Time consuming queries #6
Day Hour Count Duration Avg duration Mar 06 09 1 0ms 0ms 7 0ms 0ms 0ms 1 0ms set client_encoding to ?;Times Reported Time consuming queries #7
Day Hour Count Duration Avg duration Mar 06 09 1 0ms 0ms 8 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 #8
Day Hour Count Duration Avg duration Mar 06 09 18 0ms 0ms 9 0ms 0ms 0ms 1 0ms update executions set isrunning = false, has_results=false, response=?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Thryv Holdings Inc: +?.?%\\\\n - CS Disco LLC: +?.?%\\\\n - Sabre Corpo: +?.?%\\\\n - Babcock & Wilcox Enterprises Inc: +?.?%\\\\n - Wix.Com Ltd: +?.?%\\\\n - Gossamer Bio Inc: +?.?%\\\\n - Gogo Inc: +?.?%\\\\n - Evolus Inc: +?.?%\\\\n - Luna Innovations Incorporated: +?.?%\\\\n - Trade Desk Inc: +?.?%\\\\n. The biggest losers are: - Uniqure NV: (?.?%)\\\\n - Sunrun Inc: (?.?%)\\\\n - Grocery Outlet Holding Corp: (?.?%)\\\\n - Microvision Inc: (?.?%)\\\\n - ThredUp Inc: (?.?%)\\\\n - Sight Sciences Inc: (?.?%)\\\\n - Cerus Corporation: (?.?%)\\\\n - Alector Inc: (?.?%)\\\\n - American Eagle Outfitters Inc: (?.?%)\\\\n - Celsius Holdings Inc: (?.?%)\\\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Thryv Holdings Inc: +?.?%, CS Disco LLC: +?.?%, Sabre Corpo: +?.?%, Babcock & Wilcox Enterprises Inc: +?.?%, Wix.Com Ltd: +?.?%, Gossamer Bio Inc: +?.?%, Gogo Inc: +?.?%, Evolus Inc: +?.?%, Luna Innovations Incorporated: +?.?%, Trade Desk Inc: +?.?%. The biggest losers are: Uniqure NV: (?.?%), Sunrun Inc: (?.?%), Grocery Outlet Holding Corp: (?.?%), Microvision Inc: (?.?%), ThredUp Inc: (?.?%), Sight Sciences Inc: (?.?%), Cerus Corporation: (?.?%), Alector Inc: (?.?%), American Eagle Outfitters Inc: (?.?%), Celsius Holdings Inc: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": null, \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?SJ?CI?U?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": null, \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": \"?A?NIHHA?KOY?\", \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": null, \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?BIEY?XIDA?Q?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?Z?RQOIY?JMHC?\", \"data_losers_9_CIK\": \"?\", \"data_losers_9_LEI\": \"?Z?HXK?DHW?\", \"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\": \"e9e91047-c9ea-4f5b-9289-dc8d4069ffab\", \"data_ChangeHeading\": \"Change\", \"data_losers_10_CIK\": \"?\", \"data_losers_10_LEI\": null, \"data_losers_1_Code\": \"QURE\", \"data_losers_1_ISIN\": \"NL?\", \"data_losers_1_Name\": \"Uniqure NV\", \"data_losers_1_Type\": \"Common Stock\", \"data_losers_1_code\": \"QURE\", \"data_losers_1_open\": ?.?, \"data_losers_2_Code\": \"RUN\", \"data_losers_2_ISIN\": \"US?W?\", \"data_losers_2_Name\": \"Sunrun Inc\", \"data_losers_2_Type\": \"Common Stock\", \"data_losers_2_code\": \"RUN\", \"data_losers_2_open\": ?.?, \"data_losers_3_Code\": \"GO\", \"data_losers_3_ISIN\": \"US?R?\", \"data_losers_3_Name\": \"Grocery Outlet Holding Corp\", \"data_losers_3_Type\": \"Common Stock\", \"data_losers_3_code\": \"GO\", \"data_losers_3_open\": ?.?, \"data_losers_4_Code\": \"MVIS\", \"data_losers_4_ISIN\": \"US?\", \"data_losers_4_Name\": \"Microvision Inc\", \"data_losers_4_Type\": \"Common Stock\", \"data_losers_4_code\": \"MVIS\", \"data_losers_4_open\": ?.?, \"data_losers_5_Code\": \"TDUP\", \"data_losers_5_ISIN\": \"US?E?\", \"data_losers_5_Name\": \"ThredUp Inc\", \"data_losers_5_Type\": \"Common Stock\", \"data_losers_5_code\": \"TDUP\", \"data_losers_5_open\": ?.?, \"data_losers_6_Code\": \"SGHT\", \"data_losers_6_ISIN\": \"US?M?\", \"data_losers_6_Name\": \"Sight Sciences Inc\", \"data_losers_6_Type\": \"Common Stock\", \"data_losers_6_code\": \"SGHT\", \"data_losers_6_open\": ?.?, \"data_losers_7_Code\": \"CERS\", \"data_losers_7_ISIN\": \"US?\", \"data_losers_7_Name\": \"Cerus Corporation\", \"data_losers_7_Type\": \"Common Stock\", \"data_losers_7_code\": \"CERS\", \"data_losers_7_open\": ?.?, \"data_losers_8_Code\": \"ALEC\", \"data_losers_8_ISIN\": \"US?\", \"data_losers_8_Name\": \"Alector Inc\", \"data_losers_8_Type\": \"Common Stock\", \"data_losers_8_code\": \"ALEC\", \"data_losers_8_open\": ?.?, \"data_losers_9_Code\": \"AEO\", \"data_losers_9_ISIN\": \"US?E?\", \"data_losers_9_Name\": \"American Eagle Outfitters Inc\", \"data_losers_9_Type\": \"Common Stock\", \"data_losers_9_code\": \"AEO\", \"data_losers_9_open\": ?.?, \"data_winners_1_CIK\": \"?\", \"data_winners_1_LEI\": null, \"data_winners_2_CIK\": \"?\", \"data_winners_2_LEI\": null, \"data_winners_3_CIK\": \"?\", \"data_winners_3_LEI\": \"?VKCYZW?GZ?WW?\", \"data_winners_4_CIK\": \"?\", \"data_winners_4_LEI\": \"?H?OZN?C?T?\", \"data_winners_5_CIK\": \"?\", \"data_winners_5_LEI\": null, \"data_winners_6_CIK\": \"?\", \"data_winners_6_LEI\": \"?ZOC?N?W?PSFR?\", \"data_winners_7_CIK\": \"?\", \"data_winners_7_LEI\": \"?VL?DIHSFHYTO?\", \"data_winners_8_CIK\": \"?\", \"data_winners_8_LEI\": \"?VYQRYBQOT?V?\", \"data_winners_9_CIK\": \"?\", \"data_winners_9_LEI\": null, \"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_losers_10_Code\": \"CELH\", \"data_losers_10_ISIN\": \"US?V?\", \"data_losers_10_Name\": \"Celsius Holdings Inc\", \"data_losers_10_Type\": \"Common Stock\", \"data_losers_10_code\": \"CELH\", \"data_losers_10_open\": ?.?, \"data_losers_1_CUSIP\": \"N?\", \"data_losers_1_Phone\": \"? ? ? ?\", \"data_losers_1_close\": ?.?, \"data_losers_2_CUSIP\": \"?W?\", \"data_losers_2_Phone\": \"? ? ?\", \"data_losers_2_close\": ?.?, \"data_losers_3_CUSIP\": \"?R?\", \"data_losers_3_Phone\": \"? ? ?\", \"data_losers_3_close\": ?.?, \"data_losers_4_CUSIP\": \"?\", \"data_losers_4_Phone\": \"? ? ?\", \"data_losers_4_close\": ?.?, \"data_losers_5_CUSIP\": \"?E?\", \"data_losers_5_Phone\": \"? ? ?\", \"data_losers_5_close\": ?.?, \"data_losers_6_CUSIP\": \"?M?\", \"data_losers_6_Phone\": \"? ? ?\", \"data_losers_6_close\": ?.?, \"data_losers_7_CUSIP\": \"?\", \"data_losers_7_Phone\": \"? ? ?\", \"data_losers_7_close\": ?.?, \"data_losers_8_CUSIP\": \"?\", \"data_losers_8_Phone\": \"? ? ?\", \"data_losers_8_close\": ?.?, \"data_losers_9_CUSIP\": \"?D?\", \"data_losers_9_Phone\": \"? ? ?\", \"data_losers_9_close\": ?.?, \"data_winners_10_CIK\": \"?\", \"data_winners_10_LEI\": \"?GXPD?VT?E?P?\", \"data_winners_1_Code\": \"THRY\", \"data_winners_1_ISIN\": \"US?\", \"data_winners_1_Name\": \"Thryv Holdings Inc\", \"data_winners_1_Type\": \"Common Stock\", \"data_winners_1_code\": \"THRY\", \"data_winners_1_open\": ?.?, \"data_winners_2_Code\": \"LAW\", \"data_winners_2_ISIN\": \"US?\", \"data_winners_2_Name\": \"CS Disco LLC\", \"data_winners_2_Type\": \"Common Stock\", \"data_winners_2_code\": \"LAW\", \"data_winners_2_open\": ?.?, \"data_winners_3_Code\": \"SABR\", \"data_winners_3_ISIN\": \"US?M?\", \"data_winners_3_Name\": \"Sabre Corpo\", \"data_winners_3_Type\": \"Common Stock\", \"data_winners_3_code\": \"SABR\", \"data_winners_3_open\": ?.?, \"data_winners_4_Code\": \"BW\", \"data_winners_4_ISIN\": \"US?L?\", \"data_winners_4_Name\": \"Babcock & Wilcox Enterprises Inc\", \"data_winners_4_Type\": \"Common Stock\", \"data_winners_4_code\": \"BW\", \"data_winners_4_open\": ?.?, \"data_winners_5_Code\": \"WIX\", \"data_winners_5_ISIN\": \"USM?\", \"data_winners_5_Name\": \"Wix.Com Ltd\", \"data_winners_5_Type\": \"Common Stock\", \"data_winners_5_code\": \"WIX\", \"data_winners_5_open\": ?.?, \"data_winners_6_Code\": \"GOSS\", \"data_winners_6_ISIN\": \"US?P?\", \"data_winners_6_Name\": \"Gossamer Bio Inc\", \"data_winners_6_Type\": \"Common Stock\", \"data_winners_6_code\": \"GOSS\", \"data_winners_6_open\": ?.?, \"data_winners_7_Code\": \"GOGO\", \"data_winners_7_ISIN\": \"US?C?\", \"data_winners_7_Name\": \"Gogo Inc\", \"data_winners_7_Type\": \"Common Stock\", \"data_winners_7_code\": \"GOGO\", \"data_winners_7_open\": ?.?, \"data_winners_8_Code\": \"EOLS\", \"data_winners_8_ISIN\": \"US?C?\", \"data_winners_8_Name\": \"Evolus Inc\", \"data_winners_8_Type\": \"Common Stock\", \"data_winners_8_code\": \"EOLS\", \"data_winners_8_open\": ?.?, \"data_winners_9_Code\": \"LUNA\", \"data_winners_9_ISIN\": \"US?\", \"data_winners_9_Name\": \"Luna Innovations Incorporated\", \"data_winners_9_Type\": \"Common Stock\", \"data_winners_9_code\": \"LUNA\", \"data_winners_9_open\": ?.?, \"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\": \"GLOBAL\", \"data_losers_10_CUSIP\": \"?V?\", \"data_losers_10_Phone\": \"? ? ?\", \"data_losers_10_close\": ?.?, \"data_losers_1_Sector\": \"Healthcare\", \"data_losers_1_WebURL\": \"https://www.uniqure.com\", \"data_losers_1_change\": ?.?, \"data_losers_1_ticker\": \"QURE.US\", \"data_losers_2_Sector\": \"Technology\", \"data_losers_2_WebURL\": \"https://www.sunrun.com\", \"data_losers_2_change\": ?.?, \"data_losers_2_ticker\": \"RUN.US\", \"data_losers_3_Sector\": \"Consumer Defensive\", \"data_losers_3_WebURL\": 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The company was incorporated in ? and is headquartered in Concord, California.\", \"data_losers_7_GicIndustry\": \"Health Care Equipment & Supplies\", \"data_losers_7_OpenHeading\": \"Open\", \"data_losers_8_CountryName\": \"USA\", \"data_losers_8_Description\": \"Alector, Inc., a clinical stage biotechnology company, develops therapies to counteract the progression of neurodegeneration in the United States. Its pipeline includes Nivisnebart, an investigational human recombinant monoclonal antibody for treating prevalent neurodegenerative diseases; AL?, an anti-amyloid beta antibody paired in preclinical development for the potential treatment of Alzheimer\?s disease and Lewy body dementia in patients having GBA? gene mutations. The company also develops its preclinical and research pipeline comprising AL?, a tau siRNA for Alzheimer\?s disease; and ADP?-ABC, an NLRP? siRNA for neurodegenerative conditions. It has a strategic collaboration agreement with GlaxoSmithKline plc for the development and commercialization of progranulin-elevating monoclonal antibodies, including Latozinemab and Nivisnebart. The company was founded in ? and is headquartered in South San Francisco, California.\", \"data_losers_8_GicIndustry\": \"Biotechnology\", \"data_losers_8_OpenHeading\": \"Open\", \"data_losers_9_CountryName\": \"USA\", \"data_losers_9_Description\": \"American Eagle Outfitters, Inc. operates as a multi-brand specialty retailer in the United States and internationally. The company provides jeans, apparel and accessories, and personal care products for women and men under the American Eagle brand; and intimates, apparel, activewear, and swim collections under the Aerie and OFFLINE by Aerie brands. It also offers menswear products under the Todd Snyder New York brand; and fashion clothing and accessories under the Unsubscribed brand. 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American Eagle Outfitters, Inc. was founded in ? and is headquartered in Pittsburgh, Pennsylvania.\", \"data_losers_9_GicIndustry\": \"Specialty Retail\", \"data_losers_9_OpenHeading\": \"Open\", \"data_winners_10_GicSector\": \"Information Technology\", \"data_winners_10_UpdatedAt\": \"?-03-05\", \"data_winners_1_CountryISO\": \"US\", \"data_winners_1_IsDelisted\": false, \"data_winners_1_change_str\": \"+?.?%\", \"data_winners_2_CountryISO\": \"US\", \"data_winners_2_IsDelisted\": false, \"data_winners_2_change_str\": \"+?.?%\", \"data_winners_3_CountryISO\": \"US\", \"data_winners_3_IsDelisted\": false, \"data_winners_3_change_str\": \"+?.?%\", \"data_winners_4_CountryISO\": \"US\", \"data_winners_4_IsDelisted\": false, \"data_winners_4_change_str\": \"+?.?%\", \"data_winners_5_CountryISO\": \"US\", \"data_winners_5_IsDelisted\": false, \"data_winners_5_change_str\": \"+?.?%\", \"data_winners_6_CountryISO\": \"US\", \"data_winners_6_IsDelisted\": false, \"data_winners_6_change_str\": \"+?.?%\", \"data_winners_7_CountryISO\": \"US\", \"data_winners_7_IsDelisted\": false, \"data_winners_7_change_str\": \"+?.?%\", \"data_winners_8_CountryISO\": \"US\", \"data_winners_8_IsDelisted\": false, \"data_winners_8_change_str\": \"+?.?%\", \"data_winners_9_CountryISO\": \"US\", \"data_winners_9_IsDelisted\": false, \"data_winners_9_change_str\": \"+?.?%\", \"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_ignore_logos_value\": false, \"data_losers_10_CountryName\": \"USA\", \"data_losers_10_Description\": \"Celsius Holdings, Inc. develops, processes, manufactures, markets, sells, and distributes functional energy drinks in the United States, North America, Europe, the Asia Pacific, and internationally. 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Celsius Holdings, Inc. was founded in ? and is headquartered in Boca Raton, Florida.\", \"data_losers_10_GicIndustry\": \"Beverages\", \"data_losers_10_OpenHeading\": \"Open\", \"data_losers_1_CloseHeading\": \"Close\", \"data_losers_1_CurrencyCode\": \"USD\", \"data_losers_1_CurrencyName\": \"US Dollar\", \"data_losers_1_HomeCategory\": \"Domestic\", \"data_losers_2_CloseHeading\": \"Close\", \"data_losers_2_CurrencyCode\": \"USD\", \"data_losers_2_CurrencyName\": \"US Dollar\", \"data_losers_2_HomeCategory\": \"Domestic\", \"data_losers_3_CloseHeading\": \"Close\", \"data_losers_3_CurrencyCode\": \"USD\", \"data_losers_3_CurrencyName\": \"US Dollar\", \"data_losers_3_HomeCategory\": \"Domestic\", \"data_losers_4_CloseHeading\": \"Close\", \"data_losers_4_CurrencyCode\": \"USD\", \"data_losers_4_CurrencyName\": \"US Dollar\", \"data_losers_4_HomeCategory\": \"Domestic\", \"data_losers_5_CloseHeading\": \"Close\", \"data_losers_5_CurrencyCode\": \"USD\", \"data_losers_5_CurrencyName\": \"US Dollar\", \"data_losers_5_HomeCategory\": null, \"data_losers_6_CloseHeading\": \"Close\", \"data_losers_6_CurrencyCode\": \"USD\", \"data_losers_6_CurrencyName\": \"US Dollar\", \"data_losers_6_HomeCategory\": null, \"data_losers_7_CloseHeading\": \"Close\", \"data_losers_7_CurrencyCode\": \"USD\", \"data_losers_7_CurrencyName\": \"US Dollar\", \"data_losers_7_HomeCategory\": \"Domestic\", \"data_losers_8_CloseHeading\": \"Close\", \"data_losers_8_CurrencyCode\": \"USD\", \"data_losers_8_CurrencyName\": \"US Dollar\", \"data_losers_8_HomeCategory\": \"Domestic\", \"data_losers_9_CloseHeading\": \"Close\", \"data_losers_9_CurrencyCode\": \"USD\", \"data_losers_9_CurrencyName\": \"US Dollar\", \"data_losers_9_HomeCategory\": \"Domestic\", \"data_winners_10_CountryISO\": \"US\", \"data_winners_10_IsDelisted\": false, \"data_winners_10_change_str\": \"+?.?%\", \"data_winners_1_CountryName\": \"USA\", \"data_winners_1_Description\": \"Thryv Holdings, Inc. provides digital marketing solutions and cloud-based tools to the small-to-medium-sized businesses in the United States. 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Thryv Holdings, Inc. was incorporated in ? and is headquartered in DFW Airport, Texas.\", \"data_winners_1_GicIndustry\": \"Media\", \"data_winners_1_OpenHeading\": \"Open\", \"data_winners_2_CountryName\": \"USA\", \"data_winners_2_Description\": \"CS Disco, Inc. provides cloud-native and artificial intelligence-powered legal products for legal hold, legal request, ediscovery, legal document review, and case management in the United States and internationally. 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The company offers in-flight systems; in-flight services; aviation partner support; and engineering, design, and development services, as well as production operations functions. It offers voice and data, in-flight entertainment, and other services. In addition, the company engages in the development, deployment, and operation of networks, towers, cyber security software and data centers to support in-flight connectivity services, as well as in the provision of telecommunications services. It sells its products primarily to aircraft operators and original equipment manufacturers of business aviation aircraft through a distribution network of independent dealers. Gogo Inc. was founded in ? and is headquartered in Broomfield, Colorado.\", \"data_winners_7_GicIndustry\": \"Wireless Telecommunication Services\", \"data_winners_7_OpenHeading\": \"Open\", \"data_winners_8_CountryName\": \"USA\", \"data_winners_8_Description\": \"Evolus, Inc., a performance beauty company, delivers products in the cash-pay aesthetic market in the United States, Canada, Europe, and Australia. It offers Jeuveau, a proprietary ? kilodalton purified botulinum toxin type A formulation for the temporary improvement in the appearance of moderate to severe glabellar lines in adults; and Evolysse, a collection of injectable hyaluronic acid gels. 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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\": \"ebdfce84-ee3f-4a36-be67-2554576d3ff0\", \"params_title_character_limit_value\": ?, \"data_losers_1_InternationalDomestic\": \"International/Domestic\", \"data_losers_1_change_str.fill_color\": \"#?\", \"data_losers_2_InternationalDomestic\": \"Domestic\", \"data_losers_2_change_str.fill_color\": \"#?\", \"data_losers_3_InternationalDomestic\": null, \"data_losers_3_change_str.fill_color\": \"#?\", \"data_losers_4_InternationalDomestic\": \"International/Domestic\", \"data_losers_4_change_str.fill_color\": \"#?\", \"data_losers_5_InternationalDomestic\": null, \"data_losers_5_change_str.fill_color\": \"#?\", \"data_losers_6_InternationalDomestic\": null, \"data_losers_6_change_str.fill_color\": \"#?\", \"data_losers_7_InternationalDomestic\": \"Domestic\", \"data_losers_7_change_str.fill_color\": \"#?\", \"data_losers_8_InternationalDomestic\": \"Domestic\", \"data_losers_8_change_str.fill_color\": \"#?\", \"data_losers_9_InternationalDomestic\": \"Domestic\", \"data_losers_9_change_str.fill_color\": \"#?\", \"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_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.\", \"params_creatomate_modifications_value_frame_rate\": ?, \"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, 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: a file could not be downloaded: https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg (element data_winners_1_logourl). render details: {\"id\": \"?a5904a9-ce2c-4cfe-9113-462ab55342f0\", \"status\": \"failed\", \"error_message\": \"A file could not be downloaded: https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg (element data_winners_1_LogoURL)\", \"url\": \"https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/?a5904a9-ce2c-4cfe-9113-462ab55342f0.png\", \"template_id\": \"ebdfce84-ee3f-4a36-be67-2554576d3ff0\", \"template_name\": \"Biggest stock gainers and losers MP?\", \"template_tags\": [], \"output_format\": \"png\", \"frame_rate\": ?, \"modifications\": {\"data_Date\": \"? Mar ?\", \"frame_rate\": ?, \"text_title\": \"This week?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Thryv Holdings Inc: +?.?%\\n - CS Disco LLC: +?.?%\\n - Sabre Corpo: +?.?%\\n - Babcock & Wilcox Enterprises Inc: +?.?%\\n - Wix.Com Ltd: +?.?%\\n - Gossamer Bio Inc: +?.?%\\n - Gogo Inc: +?.?%\\n - Evolus Inc: +?.?%\\n - Luna Innovations Incorporated: +?.?%\\n - Trade Desk Inc: +?.?%\\n. The biggest losers are: - Uniqure NV: (?.?%)\\n - Sunrun Inc: (?.?%)\\n - Grocery Outlet Holding Corp: (?.?%)\\n - Microvision Inc: (?.?%)\\n - ThredUp Inc: (?.?%)\\n - Sight Sciences Inc: (?.?%)\\n - Cerus Corporation: (?.?%)\\n - Alector Inc: (?.?%)\\n - American Eagle Outfitters Inc: (?.?%)\\n - Celsius Holdings Inc: (?.?%)\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Thryv Holdings Inc: +?.?%, CS Disco LLC: +?.?%, Sabre Corpo: +?.?%, Babcock & Wilcox Enterprises Inc: +?.?%, Wix.Com Ltd: +?.?%, Gossamer Bio Inc: +?.?%, Gogo Inc: +?.?%, Evolus Inc: +?.?%, Luna Innovations Incorporated: +?.?%, Trade Desk Inc: +?.?%. The biggest losers are: Uniqure NV: (?.?%), Sunrun Inc: (?.?%), Grocery Outlet Holding Corp: (?.?%), Microvision Inc: (?.?%), ThredUp Inc: (?.?%), Sight Sciences Inc: (?.?%), Cerus Corporation: (?.?%), Alector Inc: (?.?%), American Eagle Outfitters Inc: (?.?%), Celsius Holdings Inc: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": null, \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?SJ?CI?U?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": null, \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": \"?A?NIHHA?KOY?\", \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": null, \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?BIEY?XIDA?Q?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?Z?RQOIY?JMHC?\", \"data_losers_9_CIK\": \"?\", \"data_losers_9_LEI\": \"?Z?HXK?DHW?\", \"dictionary_Actual\": \"Actual\", \"dictionary_chang[...];Times Reported Time consuming queries #9
Day Hour Count Duration Avg duration Mar 06 09 1 0ms 0ms 10 0ms 0ms 0ms 1 0ms insert into executionlogs(executionid, status, message, details, detailtype) values(?, ?, ?, ?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Thryv Holdings Inc: +?.?%\\\\n - CS Disco LLC: +?.?%\\\\n - Sabre Corpo: +?.?%\\\\n - Babcock & Wilcox Enterprises Inc: +?.?%\\\\n - Wix.Com Ltd: +?.?%\\\\n - Gossamer Bio Inc: +?.?%\\\\n - Gogo Inc: +?.?%\\\\n - Evolus Inc: +?.?%\\\\n - Luna Innovations Incorporated: +?.?%\\\\n - Trade Desk Inc: +?.?%\\\\n. The biggest losers are: - Uniqure NV: (?.?%)\\\\n - Sunrun Inc: (?.?%)\\\\n - Grocery Outlet Holding Corp: (?.?%)\\\\n - Microvision Inc: (?.?%)\\\\n - ThredUp Inc: (?.?%)\\\\n - Sight Sciences Inc: (?.?%)\\\\n - Cerus Corporation: (?.?%)\\\\n - Alector Inc: (?.?%)\\\\n - American Eagle Outfitters Inc: (?.?%)\\\\n - Celsius Holdings Inc: (?.?%)\\\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Thryv Holdings Inc: +?.?%, CS Disco LLC: +?.?%, Sabre Corpo: +?.?%, Babcock & Wilcox Enterprises Inc: +?.?%, Wix.Com Ltd: +?.?%, Gossamer Bio Inc: +?.?%, Gogo Inc: +?.?%, Evolus Inc: +?.?%, Luna Innovations Incorporated: +?.?%, Trade Desk Inc: +?.?%. The biggest losers are: Uniqure NV: (?.?%), Sunrun Inc: (?.?%), Grocery Outlet Holding Corp: (?.?%), Microvision Inc: (?.?%), ThredUp Inc: (?.?%), Sight Sciences Inc: (?.?%), Cerus Corporation: (?.?%), Alector Inc: (?.?%), American Eagle Outfitters Inc: (?.?%), Celsius Holdings Inc: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": null, \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?SJ?CI?U?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": null, \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": \"?A?NIHHA?KOY?\", \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": null, \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?BIEY?XIDA?Q?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?Z?RQOIY?JMHC?\", \"data_losers_9_CIK\": \"?\", \"data_losers_9_LEI\": \"?Z?HXK?DHW?\", \"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\": \"fb8e9876-24a7-455a-b06b-3e2adb8b3970\", \"data_ChangeHeading\": \"Change\", \"data_losers_10_CIK\": \"?\", \"data_losers_10_LEI\": null, \"data_losers_1_Code\": \"QURE\", \"data_losers_1_ISIN\": \"NL?\", \"data_losers_1_Name\": \"Uniqure NV\", \"data_losers_1_Type\": \"Common Stock\", \"data_losers_1_code\": \"QURE\", \"data_losers_1_open\": ?.?, \"data_losers_2_Code\": \"RUN\", \"data_losers_2_ISIN\": \"US?W?\", \"data_losers_2_Name\": \"Sunrun Inc\", \"data_losers_2_Type\": \"Common Stock\", \"data_losers_2_code\": \"RUN\", \"data_losers_2_open\": ?.?, \"data_losers_3_Code\": \"GO\", \"data_losers_3_ISIN\": \"US?R?\", \"data_losers_3_Name\": \"Grocery Outlet Holding Corp\", \"data_losers_3_Type\": \"Common Stock\", \"data_losers_3_code\": \"GO\", \"data_losers_3_open\": ?.?, \"data_losers_4_Code\": \"MVIS\", \"data_losers_4_ISIN\": \"US?\", \"data_losers_4_Name\": \"Microvision Inc\", \"data_losers_4_Type\": \"Common Stock\", \"data_losers_4_code\": \"MVIS\", \"data_losers_4_open\": ?.?, \"data_losers_5_Code\": \"TDUP\", \"data_losers_5_ISIN\": \"US?E?\", \"data_losers_5_Name\": \"ThredUp Inc\", \"data_losers_5_Type\": \"Common Stock\", \"data_losers_5_code\": \"TDUP\", \"data_losers_5_open\": ?.?, \"data_losers_6_Code\": \"SGHT\", \"data_losers_6_ISIN\": \"US?M?\", \"data_losers_6_Name\": \"Sight Sciences Inc\", \"data_losers_6_Type\": \"Common Stock\", \"data_losers_6_code\": \"SGHT\", \"data_losers_6_open\": ?.?, \"data_losers_7_Code\": \"CERS\", \"data_losers_7_ISIN\": \"US?\", \"data_losers_7_Name\": \"Cerus Corporation\", \"data_losers_7_Type\": \"Common Stock\", \"data_losers_7_code\": \"CERS\", \"data_losers_7_open\": ?.?, \"data_losers_8_Code\": \"ALEC\", \"data_losers_8_ISIN\": \"US?\", \"data_losers_8_Name\": \"Alector Inc\", \"data_losers_8_Type\": \"Common Stock\", \"data_losers_8_code\": \"ALEC\", \"data_losers_8_open\": ?.?, \"data_losers_9_Code\": \"AEO\", \"data_losers_9_ISIN\": \"US?E?\", \"data_losers_9_Name\": \"American Eagle Outfitters Inc\", \"data_losers_9_Type\": \"Common Stock\", \"data_losers_9_code\": \"AEO\", \"data_losers_9_open\": ?.?, \"data_winners_1_CIK\": \"?\", \"data_winners_1_LEI\": null, \"data_winners_2_CIK\": \"?\", \"data_winners_2_LEI\": null, \"data_winners_3_CIK\": \"?\", \"data_winners_3_LEI\": \"?VKCYZW?GZ?WW?\", \"data_winners_4_CIK\": \"?\", \"data_winners_4_LEI\": \"?H?OZN?C?T?\", \"data_winners_5_CIK\": \"?\", \"data_winners_5_LEI\": null, \"data_winners_6_CIK\": \"?\", \"data_winners_6_LEI\": \"?ZOC?N?W?PSFR?\", \"data_winners_7_CIK\": \"?\", \"data_winners_7_LEI\": \"?VL?DIHSFHYTO?\", \"data_winners_8_CIK\": \"?\", \"data_winners_8_LEI\": \"?VYQRYBQOT?V?\", \"data_winners_9_CIK\": \"?\", \"data_winners_9_LEI\": null, \"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_losers_10_Code\": \"CELH\", \"data_losers_10_ISIN\": \"US?V?\", \"data_losers_10_Name\": \"Celsius Holdings Inc\", \"data_losers_10_Type\": \"Common Stock\", \"data_losers_10_code\": \"CELH\", \"data_losers_10_open\": ?.?, \"data_losers_1_CUSIP\": \"N?\", \"data_losers_1_Phone\": \"? ? ? ?\", \"data_losers_1_close\": ?.?, \"data_losers_2_CUSIP\": \"?W?\", \"data_losers_2_Phone\": \"? ? ?\", \"data_losers_2_close\": ?.?, \"data_losers_3_CUSIP\": \"?R?\", \"data_losers_3_Phone\": \"? ? ?\", \"data_losers_3_close\": ?.?, \"data_losers_4_CUSIP\": \"?\", \"data_losers_4_Phone\": \"? ? ?\", \"data_losers_4_close\": ?.?, \"data_losers_5_CUSIP\": \"?E?\", \"data_losers_5_Phone\": \"? ? ?\", \"data_losers_5_close\": ?.?, \"data_losers_6_CUSIP\": \"?M?\", \"data_losers_6_Phone\": \"? ? ?\", \"data_losers_6_close\": ?.?, \"data_losers_7_CUSIP\": \"?\", \"data_losers_7_Phone\": \"? ? ?\", \"data_losers_7_close\": ?.?, \"data_losers_8_CUSIP\": \"?\", \"data_losers_8_Phone\": \"? ? ?\", \"data_losers_8_close\": ?.?, \"data_losers_9_CUSIP\": \"?D?\", \"data_losers_9_Phone\": \"? ? ?\", \"data_losers_9_close\": ?.?, \"data_winners_10_CIK\": \"?\", \"data_winners_10_LEI\": \"?GXPD?VT?E?P?\", \"data_winners_1_Code\": \"THRY\", \"data_winners_1_ISIN\": \"US?\", \"data_winners_1_Name\": \"Thryv Holdings Inc\", \"data_winners_1_Type\": \"Common Stock\", \"data_winners_1_code\": \"THRY\", \"data_winners_1_open\": ?.?, \"data_winners_2_Code\": \"LAW\", \"data_winners_2_ISIN\": \"US?\", \"data_winners_2_Name\": \"CS Disco LLC\", \"data_winners_2_Type\": \"Common Stock\", \"data_winners_2_code\": \"LAW\", \"data_winners_2_open\": ?.?, \"data_winners_3_Code\": \"SABR\", \"data_winners_3_ISIN\": \"US?M?\", \"data_winners_3_Name\": \"Sabre Corpo\", \"data_winners_3_Type\": \"Common Stock\", \"data_winners_3_code\": \"SABR\", \"data_winners_3_open\": ?.?, \"data_winners_4_Code\": \"BW\", \"data_winners_4_ISIN\": \"US?L?\", \"data_winners_4_Name\": \"Babcock & Wilcox Enterprises Inc\", \"data_winners_4_Type\": \"Common Stock\", \"data_winners_4_code\": \"BW\", \"data_winners_4_open\": ?.?, \"data_winners_5_Code\": \"WIX\", \"data_winners_5_ISIN\": \"USM?\", \"data_winners_5_Name\": \"Wix.Com Ltd\", \"data_winners_5_Type\": \"Common Stock\", \"data_winners_5_code\": \"WIX\", \"data_winners_5_open\": ?.?, \"data_winners_6_Code\": \"GOSS\", \"data_winners_6_ISIN\": \"US?P?\", \"data_winners_6_Name\": \"Gossamer Bio Inc\", \"data_winners_6_Type\": \"Common Stock\", \"data_winners_6_code\": \"GOSS\", \"data_winners_6_open\": ?.?, \"data_winners_7_Code\": \"GOGO\", \"data_winners_7_ISIN\": \"US?C?\", \"data_winners_7_Name\": \"Gogo Inc\", \"data_winners_7_Type\": \"Common Stock\", \"data_winners_7_code\": \"GOGO\", \"data_winners_7_open\": ?.?, \"data_winners_8_Code\": \"EOLS\", \"data_winners_8_ISIN\": \"US?C?\", \"data_winners_8_Name\": \"Evolus Inc\", \"data_winners_8_Type\": \"Common Stock\", \"data_winners_8_code\": \"EOLS\", \"data_winners_8_open\": ?.?, \"data_winners_9_Code\": \"LUNA\", \"data_winners_9_ISIN\": \"US?\", \"data_winners_9_Name\": \"Luna Innovations Incorporated\", \"data_winners_9_Type\": \"Common Stock\", \"data_winners_9_code\": \"LUNA\", \"data_winners_9_open\": ?.?, \"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\": \"Insta EN\", \"data_losers_10_CUSIP\": \"?V?\", \"data_losers_10_Phone\": \"? ? ?\", \"data_losers_10_close\": ?.?, \"data_losers_1_Sector\": \"Healthcare\", \"data_losers_1_WebURL\": \"https://www.uniqure.com\", \"data_losers_1_change\": ?.?, \"data_losers_1_ticker\": \"QURE.US\", \"data_losers_2_Sector\": \"Technology\", \"data_losers_2_WebURL\": \"https://www.sunrun.com\", \"data_losers_2_change\": ?.?, \"data_losers_2_ticker\": \"RUN.US\", \"data_losers_3_Sector\": \"Consumer Defensive\", \"data_losers_3_WebURL\": \"https://www.groceryoutlet.com\", \"data_losers_3_change\": ?.?, \"data_losers_3_ticker\": \"GO.US\", \"data_losers_4_Sector\": \"Technology\", \"data_losers_4_WebURL\": \"https://www.microvision.com\", \"data_losers_4_change\": ?.?, \"data_losers_4_ticker\": \"MVIS.US\", \"data_losers_5_Sector\": \"Consumer Cyclical\", \"data_losers_5_WebURL\": \"https://www.thredup.com\", \"data_losers_5_change\": ?.?, \"data_losers_5_ticker\": \"TDUP.US\", \"data_losers_6_Sector\": \"Healthcare\", \"data_losers_6_WebURL\": \"https://www.sightsciences.com\", \"data_losers_6_change\": ?.?, \"data_losers_6_ticker\": \"SGHT.US\", \"data_losers_7_Sector\": \"Healthcare\", \"data_losers_7_WebURL\": \"https://www.cerus.com\", \"data_losers_7_change\": ?.?, \"data_losers_7_ticker\": \"CERS.US\", \"data_losers_8_Sector\": \"Healthcare\", \"data_losers_8_WebURL\": \"https://www.alector.com\", \"data_losers_8_change\": ?.?, \"data_losers_8_ticker\": \"ALEC.US\", \"data_losers_9_Sector\": \"Consumer 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The company was incorporated in ? and is headquartered in Concord, California.\", \"data_losers_7_GicIndustry\": \"Health Care Equipment & Supplies\", \"data_losers_7_OpenHeading\": \"Open\", \"data_losers_8_CountryName\": \"USA\", \"data_losers_8_Description\": \"Alector, Inc., a clinical stage biotechnology company, develops therapies to counteract the progression of neurodegeneration in the United States. Its pipeline includes Nivisnebart, an investigational human recombinant monoclonal antibody for treating prevalent neurodegenerative diseases; AL?, an anti-amyloid beta antibody paired in preclinical development for the potential treatment of Alzheimer\?s disease and Lewy body dementia in patients having GBA? gene mutations. The company also develops its preclinical and research pipeline comprising AL?, a tau siRNA for Alzheimer\?s disease; and ADP?-ABC, an NLRP? siRNA for neurodegenerative conditions. It has a strategic collaboration agreement with GlaxoSmithKline plc for the development and commercialization of progranulin-elevating monoclonal antibodies, including Latozinemab and Nivisnebart. The company was founded in ? and is headquartered in South San Francisco, California.\", \"data_losers_8_GicIndustry\": \"Biotechnology\", \"data_losers_8_OpenHeading\": \"Open\", \"data_losers_9_CountryName\": \"USA\", \"data_losers_9_Description\": \"American Eagle Outfitters, Inc. operates as a multi-brand specialty retailer in the United States and internationally. The company provides jeans, apparel and accessories, and personal care products for women and men under the American Eagle brand; and intimates, apparel, activewear, and swim collections under the Aerie and OFFLINE by Aerie brands. It also offers menswear products under the Todd Snyder New York brand; and fashion clothing and accessories under the Unsubscribed brand. 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American Eagle Outfitters, Inc. was founded in ? and is headquartered in Pittsburgh, Pennsylvania.\", \"data_losers_9_GicIndustry\": \"Specialty Retail\", \"data_losers_9_OpenHeading\": \"Open\", \"data_winners_10_GicSector\": \"Information Technology\", \"data_winners_10_UpdatedAt\": \"?-03-05\", \"data_winners_1_CountryISO\": \"US\", \"data_winners_1_IsDelisted\": false, \"data_winners_1_change_str\": \"+?.?%\", \"data_winners_2_CountryISO\": \"US\", \"data_winners_2_IsDelisted\": false, \"data_winners_2_change_str\": \"+?.?%\", \"data_winners_3_CountryISO\": \"US\", \"data_winners_3_IsDelisted\": false, \"data_winners_3_change_str\": \"+?.?%\", \"data_winners_4_CountryISO\": \"US\", \"data_winners_4_IsDelisted\": false, \"data_winners_4_change_str\": \"+?.?%\", \"data_winners_5_CountryISO\": \"US\", \"data_winners_5_IsDelisted\": false, \"data_winners_5_change_str\": \"+?.?%\", \"data_winners_6_CountryISO\": \"US\", \"data_winners_6_IsDelisted\": false, \"data_winners_6_change_str\": \"+?.?%\", \"data_winners_7_CountryISO\": \"US\", \"data_winners_7_IsDelisted\": false, \"data_winners_7_change_str\": \"+?.?%\", \"data_winners_8_CountryISO\": \"US\", \"data_winners_8_IsDelisted\": false, \"data_winners_8_change_str\": \"+?.?%\", \"data_winners_9_CountryISO\": \"US\", \"data_winners_9_IsDelisted\": false, \"data_winners_9_change_str\": \"+?.?%\", \"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_ignore_logos_value\": false, \"data_losers_10_CountryName\": \"USA\", \"data_losers_10_Description\": \"Celsius Holdings, Inc. develops, processes, manufactures, markets, sells, and distributes functional energy drinks in the United States, North America, Europe, the Asia Pacific, and internationally. 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Celsius Holdings, Inc. was founded in ? and is headquartered in Boca Raton, Florida.\", \"data_losers_10_GicIndustry\": \"Beverages\", \"data_losers_10_OpenHeading\": \"Open\", \"data_losers_1_CloseHeading\": \"Close\", \"data_losers_1_CurrencyCode\": \"USD\", \"data_losers_1_CurrencyName\": \"US Dollar\", \"data_losers_1_HomeCategory\": \"Domestic\", \"data_losers_2_CloseHeading\": \"Close\", \"data_losers_2_CurrencyCode\": \"USD\", \"data_losers_2_CurrencyName\": \"US Dollar\", \"data_losers_2_HomeCategory\": \"Domestic\", \"data_losers_3_CloseHeading\": \"Close\", \"data_losers_3_CurrencyCode\": \"USD\", \"data_losers_3_CurrencyName\": \"US Dollar\", \"data_losers_3_HomeCategory\": \"Domestic\", \"data_losers_4_CloseHeading\": \"Close\", \"data_losers_4_CurrencyCode\": \"USD\", \"data_losers_4_CurrencyName\": \"US Dollar\", \"data_losers_4_HomeCategory\": \"Domestic\", \"data_losers_5_CloseHeading\": \"Close\", \"data_losers_5_CurrencyCode\": \"USD\", \"data_losers_5_CurrencyName\": \"US Dollar\", \"data_losers_5_HomeCategory\": null, \"data_losers_6_CloseHeading\": \"Close\", \"data_losers_6_CurrencyCode\": \"USD\", \"data_losers_6_CurrencyName\": \"US Dollar\", \"data_losers_6_HomeCategory\": null, \"data_losers_7_CloseHeading\": \"Close\", \"data_losers_7_CurrencyCode\": \"USD\", \"data_losers_7_CurrencyName\": \"US Dollar\", \"data_losers_7_HomeCategory\": \"Domestic\", \"data_losers_8_CloseHeading\": \"Close\", \"data_losers_8_CurrencyCode\": \"USD\", \"data_losers_8_CurrencyName\": \"US Dollar\", \"data_losers_8_HomeCategory\": \"Domestic\", \"data_losers_9_CloseHeading\": \"Close\", \"data_losers_9_CurrencyCode\": \"USD\", \"data_losers_9_CurrencyName\": \"US Dollar\", \"data_losers_9_HomeCategory\": \"Domestic\", \"data_winners_10_CountryISO\": \"US\", \"data_winners_10_IsDelisted\": false, \"data_winners_10_change_str\": \"+?.?%\", \"data_winners_1_CountryName\": \"USA\", \"data_winners_1_Description\": \"Thryv Holdings, Inc. provides digital marketing solutions and cloud-based tools to the small-to-medium-sized businesses in the United States. 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Thryv Holdings, Inc. was incorporated in ? and is headquartered in DFW Airport, Texas.\", \"data_winners_1_GicIndustry\": \"Media\", \"data_winners_1_OpenHeading\": \"Open\", \"data_winners_2_CountryName\": \"USA\", \"data_winners_2_Description\": \"CS Disco, Inc. provides cloud-native and artificial intelligence-powered legal products for legal hold, legal request, ediscovery, legal document review, and case management in the United States and internationally. 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The company offers in-flight systems; in-flight services; aviation partner support; and engineering, design, and development services, as well as production operations functions. It offers voice and data, in-flight entertainment, and other services. In addition, the company engages in the development, deployment, and operation of networks, towers, cyber security software and data centers to support in-flight connectivity services, as well as in the provision of telecommunications services. It sells its products primarily to aircraft operators and original equipment manufacturers of business aviation aircraft through a distribution network of independent dealers. Gogo Inc. was founded in ? and is headquartered in Broomfield, Colorado.\", \"data_winners_7_GicIndustry\": \"Wireless Telecommunication Services\", \"data_winners_7_OpenHeading\": \"Open\", \"data_winners_8_CountryName\": \"USA\", \"data_winners_8_Description\": \"Evolus, Inc., a performance beauty company, delivers products in the cash-pay aesthetic market in the United States, Canada, Europe, and Australia. It offers Jeuveau, a proprietary ? kilodalton purified botulinum toxin type A formulation for the temporary improvement in the appearance of moderate to severe glabellar lines in adults; and Evolysse, a collection of injectable hyaluronic acid gels. 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\"data_winners_5_nocolor_change_str\": \"+?.?%\", \"data_winners_6_nocolor_change_str\": \"+?.?%\", \"data_winners_7_nocolor_change_str\": \"+?.?%\", \"data_winners_8_nocolor_change_str\": \"+?.?%\", \"data_winners_9_nocolor_change_str\": \"+?.?%\", \"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\": \"?c270d7-883c-480e-8118-92af28ced303\", \"params_title_character_limit_value\": ?, \"data_losers_1_InternationalDomestic\": \"International/Domestic\", \"data_losers_1_change_str.fill_color\": \"#?\", \"data_losers_2_InternationalDomestic\": \"Domestic\", \"data_losers_2_change_str.fill_color\": \"#?\", \"data_losers_3_InternationalDomestic\": null, \"data_losers_3_change_str.fill_color\": \"#?\", \"data_losers_4_InternationalDomestic\": \"International/Domestic\", \"data_losers_4_change_str.fill_color\": \"#?\", \"data_losers_5_InternationalDomestic\": null, \"data_losers_5_change_str.fill_color\": \"#?\", \"data_losers_6_InternationalDomestic\": null, \"data_losers_6_change_str.fill_color\": \"#?\", \"data_losers_7_InternationalDomestic\": \"Domestic\", \"data_losers_7_change_str.fill_color\": \"#?\", \"data_losers_8_InternationalDomestic\": \"Domestic\", \"data_losers_8_change_str.fill_color\": \"#?\", \"data_losers_9_InternationalDomestic\": \"Domestic\", \"data_losers_9_change_str.fill_color\": \"#?\", \"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_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.\", \"params_creatomate_modifications_value_frame_rate\": ?, \"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, 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: a file could not be downloaded: https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg (element data_winners_1_logourl). render details: {\"id\": \"?c64966-6920-46a8-bf2d-99eb2cde5cc5\", \"status\": \"failed\", \"error_message\": \"A file could not be downloaded: https://eodhistoricaldata.comhttps://acflags.s3.eu-west-1.amazonaws.com/flags/round-flags/blank.svg (element data_winners_1_LogoURL)\", \"url\": \"https://f002.backblazeb2.com/file/creatomate-c8xg3hsxdu/?c64966-6920-46a8-bf2d-99eb2cde5cc5.png\", \"template_id\": \"?c270d7-883c-480e-8118-92af28ced303\", \"template_name\": \"Biggest stock gainers and losers MP? (Instagram ?) (EN)\", \"template_tags\": [], \"output_format\": \"png\", \"frame_rate\": ?, \"modifications\": {\"data_Date\": \"? Mar ?\", \"frame_rate\": ?, \"text_title\": \"This week?s biggest movers\", \"has_results\": true, \"data_Heading\": \"Biggest Movers\", \"dictionary_AM\": \"AM\", \"dictionary_PM\": \"PM\", \"dictionary_am\": \"am\", \"dictionary_pm\": \"pm\", \"output_format\": \"png\", \"snapshot_time\": ?, \"dictionary_DAX\": \"DAX\", \"dictionary_UTC\": \"UTC\", \"text_long_text\": \"The biggest winners are: - Thryv Holdings Inc: +?.?%\\n - CS Disco LLC: +?.?%\\n - Sabre Corpo: +?.?%\\n - Babcock & Wilcox Enterprises Inc: +?.?%\\n - Wix.Com Ltd: +?.?%\\n - Gossamer Bio Inc: +?.?%\\n - Gogo Inc: +?.?%\\n - Evolus Inc: +?.?%\\n - Luna Innovations Incorporated: +?.?%\\n - Trade Desk Inc: +?.?%\\n. The biggest losers are: - Uniqure NV: (?.?%)\\n - Sunrun Inc: (?.?%)\\n - Grocery Outlet Holding Corp: (?.?%)\\n - Microvision Inc: (?.?%)\\n - ThredUp Inc: (?.?%)\\n - Sight Sciences Inc: (?.?%)\\n - Cerus Corporation: (?.?%)\\n - Alector Inc: (?.?%)\\n - American Eagle Outfitters Inc: (?.?%)\\n - Celsius Holdings Inc: (?.?%)\\n\", \"dictionary_Corn\": \"Corn\", \"dictionary_Gold\": \"Gold\", \"dictionary_Hour\": \"Hour\", \"dictionary_Open\": \"Open\", \"dictionary_Time\": \"Time\", \"text_short_text\": \"The biggest winners are: Thryv Holdings Inc: +?.?%, CS Disco LLC: +?.?%, Sabre Corpo: +?.?%, Babcock & Wilcox Enterprises Inc: +?.?%, Wix.Com Ltd: +?.?%, Gossamer Bio Inc: +?.?%, Gogo Inc: +?.?%, Evolus Inc: +?.?%, Luna Innovations Incorporated: +?.?%, Trade Desk Inc: +?.?%. The biggest losers are: Uniqure NV: (?.?%), Sunrun Inc: (?.?%), Grocery Outlet Holding Corp: (?.?%), Microvision Inc: (?.?%), ThredUp Inc: (?.?%), Sight Sciences Inc: (?.?%), Cerus Corporation: (?.?%), Alector Inc: (?.?%), American Eagle Outfitters Inc: (?.?%), Celsius Holdings Inc: (?.?%)\", \"data_OpenHeading\": \"Open\", \"dictionary_Close\": \"Close\", \"dictionary_Daily\": \"Daily\", \"dictionary_Event\": \"Event\", \"dictionary_Hours\": \"Hours\", \"dictionary_Price\": \"Price\", \"dictionary_Wheat\": \"Wheat\", \"quantity_results\": ?, \"data_CloseHeading\": \"Close\", \"data_losers_1_CIK\": \"?\", \"data_losers_1_LEI\": null, \"data_losers_2_CIK\": \"?\", \"data_losers_2_LEI\": \"?SJ?CI?U?\", \"data_losers_3_CIK\": \"?\", \"data_losers_3_LEI\": null, \"data_losers_4_CIK\": \"?\", \"data_losers_4_LEI\": \"?A?NIHHA?KOY?\", \"data_losers_5_CIK\": \"?\", \"data_losers_5_LEI\": null, \"data_losers_6_CIK\": \"?\", \"data_losers_6_LEI\": null, \"data_losers_7_CIK\": \"?\", \"data_losers_7_LEI\": \"?BIEY?XIDA?Q?\", \"data_losers_8_CIK\": \"?\", \"data_losers_8_LEI\": \"?Z?RQOIY?JMHC?\", \"data_losers_9_CIK\": \"?[...];Times Reported Time consuming queries #10
Day Hour Count Duration Avg duration Mar 06 09 1 0ms 0ms 11 0ms 0ms 0ms 446 0ms commit;Times Reported Time consuming queries #11
Day Hour Count Duration Avg duration Mar 06 09 446 0ms 0ms 12 0ms 0ms 0ms 228 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 #12
Day Hour Count Duration Avg duration Mar 06 09 228 0ms 0ms 13 0ms 0ms 0ms 240 0ms select count(*), sum(size), extract(epoch from now() - min(modification)) from pg_ls_waldir ();Times Reported Time consuming queries #13
Day Hour Count Duration Avg duration Mar 06 09 240 0ms 0ms 14 0ms 0ms 0ms 240 0ms select system_identifier from pg_control_system ();Times Reported Time consuming queries #14
Day Hour Count Duration Avg duration Mar 06 09 240 0ms 0ms 15 0ms 0ms 0ms 6 0ms select groupid, exchange, groupname, symbol, longname from prfsymboltree where brokerid = ? order by groupname, symbol;Times Reported Time consuming queries #15
Day Hour Count Duration Avg duration Mar 06 09 6 0ms 0ms 16 0ms 0ms 0ms 228 0ms with rar_max as ( 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;Times Reported Time consuming queries #16
Day Hour Count Duration Avg duration Mar 06 09 228 0ms 0ms 17 0ms 0ms 0ms 48 0ms select distinct classname, to_char(created_datetime, ?), to_char(cleared_datetime, ?), action_to_take, description, created_datetime from datafeed_restarter_events where (is_current_entry = ? or cleared_datetime > current_timestamp - interval ?) order by created_datetime desc;Times Reported Time consuming queries #17
Day Hour Count Duration Avg duration Mar 06 09 48 0ms 0ms 18 0ms 0ms 0ms 16 0ms with max_ra as ( select resultuid from relevance_fibonacci_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 left outer join relevance_fibonacci_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 fibonacci_results a on a.resultuid = s.result_uid where s.uuid ilike ? and a.resultuid is null);Times Reported Time consuming queries #18
Day Hour Count Duration Avg duration Mar 06 09 16 0ms 0ms 19 0ms 0ms 0ms 288 0ms set statement_timeout = ?;Times Reported Time consuming queries #19
Day Hour Count Duration Avg duration Mar 06 09 288 0ms 0ms 20 0ms 0ms 0ms 60 0ms select id, schedule from processes where enabled = true and schedule is not null;Times Reported Time consuming queries #20
Day Hour Count Duration Avg duration Mar 06 09 60 0ms 0ms Time consuming prepare
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 1s867ms 2,122 0ms 8ms 0ms WITH rar_max as ( ;Times Reported Time consuming prepare #1
Day Hour Count Duration Avg duration Mar 06 09 2,122 1s867ms 0ms -
WITH rar_max as ( ;
Date: 2026-03-06 09:50:41 Duration: 8ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-03-06 09:50:43 Duration: 8ms Database: postgres
-
WITH rar_max as ( ;
Date: 2026-03-06 09:50:41 Duration: 8ms Database: postgres
2 1s389ms 1,023 0ms 20ms 1ms SELECT symbolid, ;Times Reported Time consuming prepare #2
Day Hour Count Duration Avg duration 09 1,023 1s389ms 1ms -
SELECT symbolid, ;
Date: 2026-03-06 09:15:59 Duration: 20ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-03-06 09:01:36 Duration: 2ms Database: postgres
-
SELECT symbolid, ;
Date: 2026-03-06 09:10:35 Duration: 2ms Database: postgres
3 703ms 2,852 0ms 5ms 0ms SELECT ;Times Reported Time consuming prepare #3
Day Hour Count Duration Avg duration 09 2,852 703ms 0ms -
SELECT ;
Date: 2026-03-06 09:57:16 Duration: 5ms Database: postgres
-
SELECT ;
Date: 2026-03-06 09:50:05 Duration: 4ms Database: postgres
-
SELECT ;
Date: 2026-03-06 09:34:45 Duration: 4ms Database: postgres
4 413ms 377 0ms 1ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming prepare #4
Day Hour Count Duration Avg duration 09 377 413ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-06 09:30:52 Duration: 1ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-06 09:15:39 Duration: 1ms Database: postgres
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-06 09:30:53 Duration: 1ms Database: postgres
5 228ms 2,752 0ms 0ms 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 #5
Day Hour Count Duration Avg duration 09 2,752 228ms 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: 2026-03-06 09:10:47 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: 2026-03-06 09:01:36 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: 2026-03-06 09:31:15 Duration: 0ms Database: postgres
6 224ms 1,584 0ms 0ms 0ms SET extra_float_digits = 3;Times Reported Time consuming prepare #6
Day Hour Count Duration Avg duration 09 1,584 224ms 0ms -
SET extra_float_digits = 3;
Date: 2026-03-06 09:50:41 Duration: 0ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-03-06 09:57:16 Duration: 0ms Database: postgres
-
SET extra_float_digits = 3;
Date: 2026-03-06 09:34:41 Duration: 0ms Database: postgres
7 181ms 1,824 0ms 1ms 0ms INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming prepare #7
Day Hour Count Duration Avg duration 09 1,824 181ms 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: 2026-03-06 09:05:22 Duration: 1ms 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: 2026-03-06 09:10:43 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: 2026-03-06 09:11:25 Duration: 0ms Database: postgres
8 137ms 931 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 09 931 137ms 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: 2026-03-06 09:56:35 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: 2026-03-06 09:32:07 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: 2026-03-06 09:56:49 Duration: 0ms Database: postgres
9 129ms 666 0ms 14ms 0ms select category, ;Times Reported Time consuming prepare #9
Day Hour Count Duration Avg duration 09 666 129ms 0ms -
select category, ;
Date: 2026-03-06 09:01:14 Duration: 14ms Database: postgres
-
select category, ;
Date: 2026-03-06 09:10:54 Duration: 0ms Database: postgres
-
select category, ;
Date: 2026-03-06 09:10:55 Duration: 0ms Database: postgres
10 78ms 48 0ms 12ms 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 #10
Day Hour Count Duration Avg duration 09 48 78ms 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: 2026-03-06 09:41:13 Duration: 12ms 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: 2026-03-06 09:06:05 Duration: 9ms 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: 2026-03-06 09:40:51 Duration: 1ms Database: postgres
11 76ms 1,269 0ms 7ms 0ms select 1;Times Reported Time consuming prepare #11
Day Hour Count Duration Avg duration 09 1,269 76ms 0ms -
select 1;
Date: 2026-03-06 09:50:05 Duration: 7ms Database: postgres
-
select 1;
Date: 2026-03-06 09:50:43 Duration: 6ms Database: postgres
-
select 1;
Date: 2026-03-06 09:57:13 Duration: 4ms Database: postgres
12 63ms 41 0ms 4ms 1ms WITH last_candle AS ( ;Times Reported Time consuming prepare #12
Day Hour Count Duration Avg duration 09 41 63ms 1ms -
WITH last_candle AS ( ;
Date: 2026-03-06 09:36:00 Duration: 4ms Database: postgres
-
WITH last_candle AS ( ;
Date: 2026-03-06 09:36:01 Duration: 4ms Database: postgres
-
WITH last_candle AS ( ;
Date: 2026-03-06 09:36:00 Duration: 4ms Database: postgres
13 50ms 48 0ms 2ms 1ms 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 #13
Day Hour Count Duration Avg duration 09 48 50ms 1ms -
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: 2026-03-06 09:56:16 Duration: 2ms 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: 2026-03-06 09:41:13 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: 2026-03-06 09:40:51 Duration: 1ms Database: postgres
14 49ms 18 2ms 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 #14
Day Hour Count Duration Avg duration 09 18 49ms 2ms -
select cast(count(*) / cast(setting as numeric) * 100 as int) from pg_stat_activity, pg_settings WHERE name = 'max_connections' group by setting;
Date: 2026-03-06 09:11:06 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: 2026-03-06 09:41:03 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: 2026-03-06 09:50:02 Duration: 2ms Database: postgres
15 46ms 21 1ms 6ms 2ms with wh_patitioned as ( ;Times Reported Time consuming prepare #15
Day Hour Count Duration Avg duration 09 21 46ms 2ms -
with wh_patitioned as ( ;
Date: 2026-03-06 09:02:30 Duration: 6ms Database: postgres
-
with wh_patitioned as ( ;
Date: 2026-03-06 09:08:45 Duration: 4ms Database: postgres
-
with wh_patitioned as ( ;
Date: 2026-03-06 09:02:29 Duration: 4ms Database: postgres
16 40ms 8 4ms 7ms 5ms with sym_info as ( ;Times Reported Time consuming prepare #16
Day Hour Count Duration Avg duration 09 8 40ms 5ms -
with sym_info as ( ;
Date: 2026-03-06 09:36:54 Duration: 7ms Database: postgres
-
with sym_info as ( ;
Date: 2026-03-06 09:06:43 Duration: 5ms Database: postgres
-
with sym_info as ( ;
Date: 2026-03-06 09:36:42 Duration: 4ms Database: postgres
17 35ms 231 0ms 0ms 0ms SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;Times Reported Time consuming prepare #17
Day Hour Count Duration Avg duration 09 231 35ms 0ms -
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-03-06 09:12:50 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-03-06 09:12:50 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-03-06 09:12:50 Duration: 0ms Database: postgres
18 26ms 40 0ms 2ms 0ms select distinct s.statsid as statsid, sy.exchange as name;Times Reported Time consuming prepare #18
Day Hour Count Duration Avg duration 09 40 26ms 0ms -
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-03-06 09:10:54 Duration: 2ms Database: postgres
-
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-03-06 09:10:54 Duration: 2ms Database: postgres
-
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-03-06 09:00:42 Duration: 2ms Database: postgres
19 17ms 1,548 0ms 0ms 0ms SET application_name = 'PostgreSQL JDBC Driver';Times Reported Time consuming prepare #19
Day Hour Count Duration Avg duration 09 1,548 17ms 0ms -
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-03-06 09:57:16 Duration: 0ms Database: postgres
-
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-03-06 09:15:59 Duration: 0ms Database: postgres
-
SET application_name = 'PostgreSQL JDBC Driver';
Date: 2026-03-06 09:17:38 Duration: 0ms Database: postgres
20 16ms 48 0ms 0ms 0ms select recognitionengine, to_char(datetimeupdate, 'yyyy-mm-dd HH24:MI') from latest_candle_datetime_per_receng;Times Reported Time consuming prepare #20
Day Hour Count Duration Avg duration 09 48 16ms 0ms -
select recognitionengine, to_char(datetimeupdate, 'yyyy-mm-dd HH24:MI') from latest_candle_datetime_per_receng;
Date: 2026-03-06 09:40:51 Duration: 0ms Database: postgres
-
select recognitionengine, to_char(datetimeupdate, 'yyyy-mm-dd HH24:MI') from latest_candle_datetime_per_receng;
Date: 2026-03-06 09:41:13 Duration: 0ms Database: postgres
-
select recognitionengine, to_char(datetimeupdate, 'yyyy-mm-dd HH24:MI') from latest_candle_datetime_per_receng;
Date: 2026-03-06 09:06:04 Duration: 0ms Database: postgres
Time consuming bind
Rank Total duration Times executed Min duration Max duration Avg duration Query 1 24s294ms 10,467 0ms 70ms 2ms WITH rar_max as ( ;Times Reported Time consuming bind #1
Day Hour Count Duration Avg duration Mar 06 09 10,467 24s294ms 2ms -
WITH rar_max as ( ;
Date: 2026-03-06 09:26:10 Duration: 70ms Database: postgres parameters: $1 = '627', $2 = '7', $3 = '15', $4 = '30', $5 = '60', $6 = '120', $7 = '240', $8 = '480', $9 = '1440', $10 = '0', $11 = '', $12 = '213', $13 = '#ADBE', $14 = '#ALVG', $15 = '#AMZN', $16 = '#APPL', $17 = '#BA', $18 = '#BABA', $19 = '#BAYGn', $20 = '#BMWG', $21 = '#BNPP', $22 = '#CAT', $23 = '#CBKG', $24 = '#DAIGn', $25 = '#DIS', $26 = '#EA', $27 = '#FB', $28 = '#FDX', $29 = '#GE', $30 = '#GM', $31 = '#GOOGL', $32 = '#GS', $33 = '#INTC', $34 = '#JPM', $35 = '#KO', $36 = '#META', $37 = '#MSFT', $38 = '#NFLX', $39 = '#TSLA', $40 = '#VOWG', $41 = '#WMT', $42 = '#XOM', $43 = 'AUDCAD', $44 = 'AUDCHF', $45 = 'AUDJPY', $46 = 'AUDNZD', $47 = 'AUDUSD', $48 = 'AUS_200', $49 = 'BTCEUR', $50 = 'BTCGBP', $51 = 'BTCUSD', $52 = 'CADCHF', $53 = 'CADJPY', $54 = 'CHFJPY', $55 = 'CL_BRENT', $56 = 'DASHUSD', $57 = 'EOSUSD', $58 = 'ESP_35', $59 = 'ETHEUR', $60 = 'ETHGBP', $61 = 'ETHUSD', $62 = 'EURAUD', $63 = 'EURCAD', $64 = 'EURCHF', $65 = 'EURGBP', $66 = 'EURJPY', $67 = 'EURMXN', $68 = 'EURNOK', $69 = 'EURNZD', $70 = 'EURPLN', $71 = 'EURSEK', $72 = 'EURTRY', $73 = 'EURUSD', $74 = 'EUR_50', $75 = 'FRA_40', $76 = 'GBPAUD', $77 = 'GBPCAD', $78 = 'GBPCHF', $79 = 'GBPJPY', $80 = 'GBPNZD', $81 = 'GBPUSD', $82 = 'GBPZAR', $83 = 'GBR_100', $84 = 'HKDJPY', $85 = 'HKG_50', $86 = 'IOTAUSD', $87 = 'LTCEUR', $88 = 'LTCUSD', $89 = 'NAS100', $90 = 'NEOUSD', $91 = 'NOKJPY', $92 = 'NZDCAD', $93 = 'NZDCHF', $94 = 'NZDJPY', $95 = 'NZDUSD', $96 = 'OMGUSD', $97 = 'SPX500', $98 = 'TRXUSD', $99 = 'US30', $100 = 'USDCAD', $101 = 'USDCHF', $102 = 'USDCNH', $103 = 'USDDKK', $104 = 'USDJPY', $105 = 'USDMXN', $106 = 'USDNOK', $107 = 'USDPLN', $108 = 'USDSEK', $109 = 'USDSGD', $110 = 'USDZAR', $111 = 'USOIL', $112 = 'XAGUSD', $113 = 'XAUEUR', $114 = 'XAUUSD', $115 = 'XMRUSD', $116 = 'XPTUSD', $117 = 'XRPUSD', $118 = 'ZARJPY', $119 = 'ZECUSD', $120 = 'AUDCAD', $121 = 'AUDCHF', $122 = 'AUDJPY', $123 = 'AUDNZD', $124 = 'AUDUSD', $125 = 'CADCHF', $126 = 'CADJPY', $127 = 'CHFJPY', $128 = 'EURAUD', $129 = 'EURCAD', $130 = 'EURCHF', $131 = 'EURGBP', $132 = 'EURJPY', $133 = 'EURMXN', $134 = 'EURNOK', $135 = 'EURNZD', $136 = 'EURPLN', $137 = 'EURSEK', $138 = 'EURTRY', $139 = 'EURUSD', $140 = 'GBPAUD', $141 = 'GBPCAD', $142 = 'GBPCHF', $143 = 'GBPJPY', $144 = 'GBPNZD', $145 = 'GBPUSD', $146 = 'GBPZAR', $147 = 'HKDJPY', $148 = 'NOKJPY', $149 = 'NZDCAD', $150 = 'NZDCHF', $151 = 'NZDJPY', $152 = 'NZDUSD', $153 = 'USDCAD', $154 = 'USDCHF', $155 = 'USDCNH', $156 = 'USDDKK', $157 = 'USDJPY', $158 = 'USDMXN', $159 = 'USDNOK', $160 = 'USDPLN', $161 = 'USDSEK', $162 = 'USDSGD', $163 = 'USDZAR', $164 = 'ZARJPY', $165 = 'BTCEUR', $166 = 'BTCGBP', $167 = 'BTCUSD', $168 = 'DASHUSD', $169 = 'EOSUSD', $170 = 'ETHEUR', $171 = 'ETHGBP', $172 = 'ETHUSD', $173 = 'IOTAUSD', $174 = 'LTCEUR', $175 = 'LTCUSD', $176 = 'NEOUSD', $177 = 'OMGUSD', $178 = 'TRXUSD', $179 = 'XMRUSD', $180 = 'XRPUSD', $181 = 'ZECUSD', $182 = 'XAGUSD', $183 = 'XAUEUR', $184 = 'XAUUSD', $185 = 'XPTUSD', $186 = 'CL_BRENT', $187 = 'USOIL', $188 = '#ALVG', $189 = '#BAYGn', $190 = '#BMWG', $191 = '#BNPP', $192 = '#CBKG', $193 = '#DAIGn', $194 = '#VOWG', $195 = 'AUS_200', $196 = 'ESP_35', $197 = 'EUR_50', $198 = 'FRA_40', $199 = 'GBR_100', $200 = 'HKG_50', $201 = 'NAS100', $202 = 'SPX500', $203 = 'US30', $204 = '#ADBE', $205 = '#AMZN', $206 = '#APPL', $207 = '#BA', $208 = '#BABA', $209 = '#CAT', $210 = '#DIS', $211 = '#EA', $212 = '#FB', $213 = '#FDX', $214 = '#GE', $215 = '#GM', $216 = '#GOOGL', $217 = '#GS', $218 = '#INTC', $219 = '#JPM', $220 = '#KO', $221 = '#MSFT', $222 = '#NFLX', $223 = '#TSLA', $224 = '#WMT', $225 = '#XOM', $226 = '500', $227 = '500', $228 = 't', $229 = '10', $230 = '10'
-
WITH rar_max as ( ;
Date: 2026-03-06 09:26:09 Duration: 50ms Database: postgres parameters: $1 = '627', $2 = '7', $3 = '15', $4 = '30', $5 = '60', $6 = '120', $7 = '240', $8 = '480', $9 = '1440', $10 = '0', $11 = '', $12 = '213', $13 = '#ADBE', $14 = '#ALVG', $15 = '#AMZN', $16 = '#APPL', $17 = '#BA', $18 = '#BABA', $19 = '#BAYGn', $20 = '#BMWG', $21 = '#BNPP', $22 = '#CAT', $23 = '#CBKG', $24 = '#DAIGn', $25 = '#DIS', $26 = '#EA', $27 = '#FB', $28 = '#FDX', $29 = '#GE', $30 = '#GM', $31 = '#GOOGL', $32 = '#GS', $33 = '#INTC', $34 = '#JPM', $35 = '#KO', $36 = '#META', $37 = '#MSFT', $38 = '#NFLX', $39 = '#TSLA', $40 = '#VOWG', $41 = '#WMT', $42 = '#XOM', $43 = 'AUDCAD', $44 = 'AUDCHF', $45 = 'AUDJPY', $46 = 'AUDNZD', $47 = 'AUDUSD', $48 = 'AUS_200', $49 = 'BTCEUR', $50 = 'BTCGBP', $51 = 'BTCUSD', $52 = 'CADCHF', $53 = 'CADJPY', $54 = 'CHFJPY', $55 = 'CL_BRENT', $56 = 'DASHUSD', $57 = 'EOSUSD', $58 = 'ESP_35', $59 = 'ETHEUR', $60 = 'ETHGBP', $61 = 'ETHUSD', $62 = 'EURAUD', $63 = 'EURCAD', $64 = 'EURCHF', $65 = 'EURGBP', $66 = 'EURJPY', $67 = 'EURMXN', $68 = 'EURNOK', $69 = 'EURNZD', $70 = 'EURPLN', $71 = 'EURSEK', $72 = 'EURTRY', $73 = 'EURUSD', $74 = 'EUR_50', $75 = 'FRA_40', $76 = 'GBPAUD', $77 = 'GBPCAD', $78 = 'GBPCHF', $79 = 'GBPJPY', $80 = 'GBPNZD', $81 = 'GBPUSD', $82 = 'GBPZAR', $83 = 'GBR_100', $84 = 'HKDJPY', $85 = 'HKG_50', $86 = 'IOTAUSD', $87 = 'LTCEUR', $88 = 'LTCUSD', $89 = 'NAS100', $90 = 'NEOUSD', $91 = 'NOKJPY', $92 = 'NZDCAD', $93 = 'NZDCHF', $94 = 'NZDJPY', $95 = 'NZDUSD', $96 = 'OMGUSD', $97 = 'SPX500', $98 = 'TRXUSD', $99 = 'US30', $100 = 'USDCAD', $101 = 'USDCHF', $102 = 'USDCNH', $103 = 'USDDKK', $104 = 'USDJPY', $105 = 'USDMXN', $106 = 'USDNOK', $107 = 'USDPLN', $108 = 'USDSEK', $109 = 'USDSGD', $110 = 'USDZAR', $111 = 'USOIL', $112 = 'XAGUSD', $113 = 'XAUEUR', $114 = 'XAUUSD', $115 = 'XMRUSD', $116 = 'XPTUSD', $117 = 'XRPUSD', $118 = 'ZARJPY', $119 = 'ZECUSD', $120 = 'AUDCAD', $121 = 'AUDCHF', $122 = 'AUDJPY', $123 = 'AUDNZD', $124 = 'AUDUSD', $125 = 'CADCHF', $126 = 'CADJPY', $127 = 'CHFJPY', $128 = 'EURAUD', $129 = 'EURCAD', $130 = 'EURCHF', $131 = 'EURGBP', $132 = 'EURJPY', $133 = 'EURMXN', $134 = 'EURNOK', $135 = 'EURNZD', $136 = 'EURPLN', $137 = 'EURSEK', $138 = 'EURTRY', $139 = 'EURUSD', $140 = 'GBPAUD', $141 = 'GBPCAD', $142 = 'GBPCHF', $143 = 'GBPJPY', $144 = 'GBPNZD', $145 = 'GBPUSD', $146 = 'GBPZAR', $147 = 'HKDJPY', $148 = 'NOKJPY', $149 = 'NZDCAD', $150 = 'NZDCHF', $151 = 'NZDJPY', $152 = 'NZDUSD', $153 = 'USDCAD', $154 = 'USDCHF', $155 = 'USDCNH', $156 = 'USDDKK', $157 = 'USDJPY', $158 = 'USDMXN', $159 = 'USDNOK', $160 = 'USDPLN', $161 = 'USDSEK', $162 = 'USDSGD', $163 = 'USDZAR', $164 = 'ZARJPY', $165 = 'BTCEUR', $166 = 'BTCGBP', $167 = 'BTCUSD', $168 = 'DASHUSD', $169 = 'EOSUSD', $170 = 'ETHEUR', $171 = 'ETHGBP', $172 = 'ETHUSD', $173 = 'IOTAUSD', $174 = 'LTCEUR', $175 = 'LTCUSD', $176 = 'NEOUSD', $177 = 'OMGUSD', $178 = 'TRXUSD', $179 = 'XMRUSD', $180 = 'XRPUSD', $181 = 'ZECUSD', $182 = 'XAGUSD', $183 = 'XAUEUR', $184 = 'XAUUSD', $185 = 'XPTUSD', $186 = 'CL_BRENT', $187 = 'USOIL', $188 = '#ALVG', $189 = '#BAYGn', $190 = '#BMWG', $191 = '#BNPP', $192 = '#CBKG', $193 = '#DAIGn', $194 = '#VOWG', $195 = 'AUS_200', $196 = 'ESP_35', $197 = 'EUR_50', $198 = 'FRA_40', $199 = 'GBR_100', $200 = 'HKG_50', $201 = 'NAS100', $202 = 'SPX500', $203 = 'US30', $204 = '#ADBE', $205 = '#AMZN', $206 = '#APPL', $207 = '#BA', $208 = '#BABA', $209 = '#CAT', $210 = '#DIS', $211 = '#EA', $212 = '#FB', $213 = '#FDX', $214 = '#GE', $215 = '#GM', $216 = '#GOOGL', $217 = '#GS', $218 = '#INTC', $219 = '#JPM', $220 = '#KO', $221 = '#MSFT', $222 = '#NFLX', $223 = '#TSLA', $224 = '#WMT', $225 = '#XOM', $226 = '500', $227 = '500', $228 = 't', $229 = '10', $230 = '10'
-
WITH rar_max as ( ;
Date: 2026-03-06 09:15:50 Duration: 45ms Database: postgres parameters: $1 = 't', $2 = '627', $3 = '7', $4 = '15', $5 = '30', $6 = '60', $7 = '120', $8 = '240', $9 = '480', $10 = '1440', $11 = '0', $12 = '', $13 = '213', $14 = '#ADBE', $15 = '#ALVG', $16 = '#AMZN', $17 = '#APPL', $18 = '#BA', $19 = '#BABA', $20 = '#BAYGn', $21 = '#BMWG', $22 = '#BNPP', $23 = '#CAT', $24 = '#CBKG', $25 = '#DAIGn', $26 = '#DIS', $27 = '#EA', $28 = '#FB', $29 = '#FDX', $30 = '#GE', $31 = '#GM', $32 = '#GOOGL', $33 = '#GS', $34 = '#INTC', $35 = '#JPM', $36 = '#KO', $37 = '#META', $38 = '#MSFT', $39 = '#NFLX', $40 = '#TSLA', $41 = '#VOWG', $42 = '#WMT', $43 = '#XOM', $44 = 'AUDCAD', $45 = 'AUDCHF', $46 = 'AUDJPY', $47 = 'AUDNZD', $48 = 'AUDUSD', $49 = 'AUS_200', $50 = 'BTCEUR', $51 = 'BTCGBP', $52 = 'BTCUSD', $53 = 'CADCHF', $54 = 'CADJPY', $55 = 'CHFJPY', $56 = 'CL_BRENT', $57 = 'DASHUSD', $58 = 'EOSUSD', $59 = 'ESP_35', $60 = 'ETHEUR', $61 = 'ETHGBP', $62 = 'ETHUSD', $63 = 'EURAUD', $64 = 'EURCAD', $65 = 'EURCHF', $66 = 'EURGBP', $67 = 'EURJPY', $68 = 'EURMXN', $69 = 'EURNOK', $70 = 'EURNZD', $71 = 'EURPLN', $72 = 'EURSEK', $73 = 'EURTRY', $74 = 'EURUSD', $75 = 'EUR_50', $76 = 'FRA_40', $77 = 'GBPAUD', $78 = 'GBPCAD', $79 = 'GBPCHF', $80 = 'GBPJPY', $81 = 'GBPNZD', $82 = 'GBPUSD', $83 = 'GBPZAR', $84 = 'GBR_100', $85 = 'HKDJPY', $86 = 'HKG_50', $87 = 'IOTAUSD', $88 = 'LTCEUR', $89 = 'LTCUSD', $90 = 'NAS100', $91 = 'NEOUSD', $92 = 'NOKJPY', $93 = 'NZDCAD', $94 = 'NZDCHF', $95 = 'NZDJPY', $96 = 'NZDUSD', $97 = 'OMGUSD', $98 = 'SPX500', $99 = 'TRXUSD', $100 = 'US30', $101 = 'USDCAD', $102 = 'USDCHF', $103 = 'USDCNH', $104 = 'USDDKK', $105 = 'USDJPY', $106 = 'USDMXN', $107 = 'USDNOK', $108 = 'USDPLN', $109 = 'USDSEK', $110 = 'USDSGD', $111 = 'USDZAR', $112 = 'USOIL', $113 = 'XAGUSD', $114 = 'XAUEUR', $115 = 'XAUUSD', $116 = 'XMRUSD', $117 = 'XPTUSD', $118 = 'XRPUSD', $119 = 'ZARJPY', $120 = 'ZECUSD', $121 = 'AUDCAD', $122 = 'AUDCHF', $123 = 'AUDJPY', $124 = 'AUDNZD', $125 = 'AUDUSD', $126 = 'CADCHF', $127 = 'CADJPY', $128 = 'CHFJPY', $129 = 'EURAUD', $130 = 'EURCAD', $131 = 'EURCHF', $132 = 'EURGBP', $133 = 'EURJPY', $134 = 'EURMXN', $135 = 'EURNOK', $136 = 'EURNZD', $137 = 'EURPLN', $138 = 'EURSEK', $139 = 'EURTRY', $140 = 'EURUSD', $141 = 'GBPAUD', $142 = 'GBPCAD', $143 = 'GBPCHF', $144 = 'GBPJPY', $145 = 'GBPNZD', $146 = 'GBPUSD', $147 = 'GBPZAR', $148 = 'HKDJPY', $149 = 'NOKJPY', $150 = 'NZDCAD', $151 = 'NZDCHF', $152 = 'NZDJPY', $153 = 'NZDUSD', $154 = 'USDCAD', $155 = 'USDCHF', $156 = 'USDCNH', $157 = 'USDDKK', $158 = 'USDJPY', $159 = 'USDMXN', $160 = 'USDNOK', $161 = 'USDPLN', $162 = 'USDSEK', $163 = 'USDSGD', $164 = 'USDZAR', $165 = 'ZARJPY', $166 = 'BTCEUR', $167 = 'BTCGBP', $168 = 'BTCUSD', $169 = 'DASHUSD', $170 = 'EOSUSD', $171 = 'ETHEUR', $172 = 'ETHGBP', $173 = 'ETHUSD', $174 = 'IOTAUSD', $175 = 'LTCEUR', $176 = 'LTCUSD', $177 = 'NEOUSD', $178 = 'OMGUSD', $179 = 'TRXUSD', $180 = 'XMRUSD', $181 = 'XRPUSD', $182 = 'ZECUSD', $183 = 'XAGUSD', $184 = 'XAUEUR', $185 = 'XAUUSD', $186 = 'XPTUSD', $187 = 'CL_BRENT', $188 = 'USOIL', $189 = '#ALVG', $190 = '#BAYGn', $191 = '#BMWG', $192 = '#BNPP', $193 = '#CBKG', $194 = '#DAIGn', $195 = '#VOWG', $196 = 'AUS_200', $197 = 'ESP_35', $198 = 'EUR_50', $199 = 'FRA_40', $200 = 'GBR_100', $201 = 'HKG_50', $202 = 'NAS100', $203 = 'SPX500', $204 = 'US30', $205 = '#ADBE', $206 = '#AMZN', $207 = '#APPL', $208 = '#BA', $209 = '#BABA', $210 = '#CAT', $211 = '#DIS', $212 = '#EA', $213 = '#FB', $214 = '#FDX', $215 = '#GE', $216 = '#GM', $217 = '#GOOGL', $218 = '#GS', $219 = '#INTC', $220 = '#JPM', $221 = '#KO', $222 = '#MSFT', $223 = '#NFLX', $224 = '#TSLA', $225 = '#WMT', $226 = '#XOM', $227 = '0', $228 = '', $229 = '500', $230 = '500', $231 = '0', $232 = '0', $233 = '0', $234 = 't', $235 = '10', $236 = '10'
2 8s848ms 25,396 0ms 28ms 0ms SELECT ;Times Reported Time consuming bind #2
Day Hour Count Duration Avg duration 09 25,396 8s848ms 0ms -
SELECT ;
Date: 2026-03-06 09:06:05 Duration: 28ms Database: postgres parameters: $1 = '515840233923405300'
-
SELECT ;
Date: 2026-03-06 09:06:05 Duration: 15ms Database: postgres parameters: $1 = '558', $2 = '0', $3 = '0', $4 = 'GBPAUD', $5 = 'GBPAUD'
-
SELECT ;
Date: 2026-03-06 09:06:05 Duration: 9ms Database: postgres parameters: $1 = '515840233923405300'
3 2s455ms 1,023 1ms 17ms 2ms SELECT symbolid, ;Times Reported Time consuming bind #3
Day Hour Count Duration Avg duration 09 1,023 2s455ms 2ms -
SELECT symbolid, ;
Date: 2026-03-06 09:15:51 Duration: 17ms Database: postgres parameters: $1 = 'AXIORY', $2 = '15', $3 = 'EURCAD', $4 = 'EURCNH', $5 = 'EURAUD', $6 = 'EURCHF'
-
SELECT symbolid, ;
Date: 2026-03-06 09:15:53 Duration: 11ms Database: postgres parameters: $1 = 'AXIORY', $2 = '15', $3 = 'EURCZK', $4 = 'EURDKK', $5 = 'EURGBP', $6 = 'EURHKD'
-
SELECT symbolid, ;
Date: 2026-03-06 09:01:20 Duration: 6ms Database: postgres parameters: $1 = 'MILLENNIUMPF', $2 = '15', $3 = 'GBPJPY', $4 = 'GBPJPY.FX'
4 1s412ms 232 0ms 37ms 6ms select distinct s.statsid as statsid, sy.exchange as name;Times Reported Time consuming bind #4
Day Hour Count Duration Avg duration 09 232 1s412ms 6ms -
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-03-06 09:10:54 Duration: 37ms Database: postgres parameters: $1 = '1436', $2 = '1436'
-
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-03-06 09:10:55 Duration: 31ms Database: postgres parameters: $1 = '1504', $2 = '1504'
-
select distinct s.statsid as statsid, sy.exchange as name;
Date: 2026-03-06 09:10:54 Duration: 30ms Database: postgres parameters: $1 = '1508', $2 = '1508'
5 748ms 37,958 0ms 6ms 0ms select 1;Times Reported Time consuming bind #5
Day Hour Count Duration Avg duration 09 37,958 748ms 0ms -
select 1;
Date: 2026-03-06 09:12:17 Duration: 6ms Database: postgres
-
select 1;
Date: 2026-03-06 09:21:04 Duration: 5ms Database: postgres
-
select 1;
Date: 2026-03-06 09:35:33 Duration: 5ms Database: postgres
6 747ms 24 0ms 77ms 31ms with wh_patitioned as ( ;Times Reported Time consuming bind #6
Day Hour Count Duration Avg duration 09 24 747ms 31ms -
with wh_patitioned as ( ;
Date: 2026-03-06 09:15:44 Duration: 77ms 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: 2026-03-06 09:02:30 Duration: 54ms 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: 2026-03-06 09:08:45 Duration: 45ms Database: postgres parameters: $1 = '558', $2 = '558', $3 = '558', $4 = '558', $5 = '558', $6 = '558', $7 = '558', $8 = '558', $9 = '558'
7 723ms 7,997 0ms 1ms 0ms select category, ;Times Reported Time consuming bind #7
Day Hour Count Duration Avg duration 09 7,997 723ms 0ms -
select category, ;
Date: 2026-03-06 09:01:24 Duration: 1ms Database: postgres parameters: $1 = '605717914811078307', $2 = 'symbol', $3 = 'US30', $4 = 'UK100', $5 = 'USTEC', $6 = 'US500', $7 = 'US2000', $8 = 'JP225', $9 = 'DE30', $10 = 'AUS200', $11 = 'CHINA50', $12 = 'F40', $13 = 'STOXX50', $14 = 'HK50', $15 = 'IT40', $16 = 'US2000', $17 = 'US30', $18 = 'USTEC', $19 = 'UK100', $20 = 'AUS200', $21 = 'ES35', $22 = 'US500', $23 = 'JP225', $24 = 'CHINA50', $25 = 'DE30', $26 = 'STOXX50', $27 = 'F40', $28 = 'HK50', $29 = 'ES35', $30 = 'IT40', $31 = '605717914811078307', $32 = 'symbol', $33 = 'US30', $34 = 'UK100', $35 = 'USTEC', $36 = 'US500', $37 = 'US2000', $38 = 'JP225', $39 = 'DE30', $40 = 'AUS200', $41 = 'CHINA50', $42 = 'F40', $43 = 'STOXX50', $44 = 'HK50', $45 = 'IT40', $46 = 'US2000', $47 = 'US30', $48 = 'USTEC', $49 = 'UK100', $50 = 'AUS200', $51 = 'ES35', $52 = 'US500', $53 = 'JP225', $54 = 'CHINA50', $55 = 'DE30', $56 = 'STOXX50', $57 = 'F40', $58 = 'HK50', $59 = 'ES35', $60 = 'IT40'
-
select category, ;
Date: 2026-03-06 09:55:42 Duration: 1ms Database: postgres parameters: $1 = '515852059312800307', $2 = 'symbol', $3 = 'XMRUSD', $4 = 'BTCGBP', $5 = 'LTCUSD', $6 = 'ETHGBP', $7 = 'BTCEUR', $8 = 'LTCEUR', $9 = 'NEOUSD', $10 = 'BTCUSD', $11 = 'ETHEUR', $12 = 'DASHUSD', $13 = 'ETHUSD', $14 = 'XRPUSD', $15 = 'ZECUSD', $16 = 'EOSUSD', $17 = 'USOIL', $18 = 'NEOUSD', $19 = 'XAGUSD', $20 = 'XAUUSD', $21 = 'US30', $22 = 'XAUEUR', $23 = 'NAS100', $24 = 'SPX500', $25 = 'LTCUSD', $26 = 'LTCEUR', $27 = 'IOTAUSD', $28 = 'IOTAUSD', $29 = 'DASHUSD', $30 = 'EOSUSD', $31 = 'XPTUSD', $32 = 'CL_BRENT', $33 = 'AUS_200', $34 = 'ETHEUR', $35 = 'XRPUSD', $36 = 'ETHGBP', $37 = 'XMRUSD', $38 = 'ZECUSD', $39 = 'ETHUSD', $40 = 'BTCUSD', $41 = 'TRXUSD', $42 = 'BTCEUR', $43 = 'BTCGBP', $44 = 'TRXUSD', $45 = 'EUR_50', $46 = 'FRA_40', $47 = 'GBR_100', $48 = 'US30', $49 = 'NAS100', $50 = 'XAGUSD', $51 = 'XAUUSD', $52 = 'XPTUSD', $53 = '515852059312800307', $54 = 'symbol', $55 = 'XMRUSD', $56 = 'BTCGBP', $57 = 'LTCUSD', $58 = 'ETHGBP', $59 = 'BTCEUR', $60 = 'LTCEUR', $61 = 'NEOUSD', $62 = 'BTCUSD', $63 = 'ETHEUR', $64 = 'DASHUSD', $65 = 'ETHUSD', $66 = 'XRPUSD', $67 = 'ZECUSD', $68 = 'EOSUSD', $69 = 'USOIL', $70 = 'NEOUSD', $71 = 'XAGUSD', $72 = 'XAUUSD', $73 = 'US30', $74 = 'XAUEUR', $75 = 'NAS100', $76 = 'SPX500', $77 = 'LTCUSD', $78 = 'LTCEUR', $79 = 'IOTAUSD', $80 = 'IOTAUSD', $81 = 'DASHUSD', $82 = 'EOSUSD', $83 = 'XPTUSD', $84 = 'CL_BRENT', $85 = 'AUS_200', $86 = 'ETHEUR', $87 = 'XRPUSD', $88 = 'ETHGBP', $89 = 'XMRUSD', $90 = 'ZECUSD', $91 = 'ETHUSD', $92 = 'BTCUSD', $93 = 'TRXUSD', $94 = 'BTCEUR', $95 = 'BTCGBP', $96 = 'TRXUSD', $97 = 'EUR_50', $98 = 'FRA_40', $99 = 'GBR_100', $100 = 'US30', $101 = 'NAS100', $102 = 'XAGUSD', $103 = 'XAUUSD', $104 = 'XPTUSD'
-
select category, ;
Date: 2026-03-06 09:56:43 Duration: 1ms Database: postgres parameters: $1 = '601729875362372307', $2 = 'overall', $3 = '601729875362372307', $4 = 'overall'
8 642ms 377 1ms 2ms 1ms SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;Times Reported Time consuming bind #8
Day Hour Count Duration Avg duration 09 377 642ms 1ms -
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-06 09:15:39 Duration: 2ms Database: postgres parameters: $1 = 'ICMARKETS'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-06 09:30:44 Duration: 2ms Database: postgres parameters: $1 = 'AXIORY'
-
SELECT s.symbolid, dss.downloadfrequency, dss.downloadersymbol;
Date: 2026-03-06 09:00:38 Duration: 2ms Database: postgres parameters: $1 = 'BDSWISS'
9 489ms 66 4ms 16ms 7ms WITH last_candle AS ( ;Times Reported Time consuming bind #9
Day Hour Count Duration Avg duration 09 66 489ms 7ms -
WITH last_candle AS ( ;
Date: 2026-03-06 09:16:00 Duration: 16ms Database: postgres parameters: $1 = '558', $2 = '558'
-
WITH last_candle AS ( ;
Date: 2026-03-06 09:06:04 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '558'
-
WITH last_candle AS ( ;
Date: 2026-03-06 09:16:00 Duration: 13ms Database: postgres parameters: $1 = '558', $2 = '558'
10 334ms 421 0ms 3ms 0ms SELECT absolutetimezoneoffset;Times Reported Time consuming bind #10
Day Hour Count Duration Avg duration 09 421 334ms 0ms -
SELECT absolutetimezoneoffset;
Date: 2026-03-06 09:01:14 Duration: 3ms Database: postgres parameters: $1 = '914', $2 = 'Crypto'
-
SELECT absolutetimezoneoffset;
Date: 2026-03-06 09:45:47 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'BROKER'
-
SELECT absolutetimezoneoffset;
Date: 2026-03-06 09:10:56 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'BROKER'
11 245ms 8 28ms 44ms 30ms with sym_info as ( ;Times Reported Time consuming bind #11
Day Hour Count Duration Avg duration 09 8 245ms 30ms -
with sym_info as ( ;
Date: 2026-03-06 09:36:54 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: 2026-03-06 09:36:45 Duration: 28ms Database: postgres parameters: $1 = '617', $2 = 'Forex', $3 = 'Forex', $4 = '617', $5 = 'Forex', $6 = '617', $7 = '617', $8 = 'Forex', $9 = '617'
-
with sym_info as ( ;
Date: 2026-03-06 09:06:43 Duration: 28ms Database: postgres parameters: $1 = '620', $2 = 'Forex', $3 = 'Forex', $4 = '620', $5 = 'Forex', $6 = '620', $7 = '620', $8 = 'Forex', $9 = '620'
12 219ms 4,944 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 #12
Day Hour Count Duration Avg duration 09 4,944 219ms 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: 2026-03-06 09:56:49 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 09:30:00', $2 = '48053.75', $3 = '48066.25', $4 = '48026.75', $5 = '48031.75', $6 = '831', $7 = '515840248000537300', $8 = '0', $9 = '2026-03-06 09:56:49.729', $10 = '2026-03-06 09:56:49.66', $11 = '48053.75', $12 = '48066.25', $13 = '48026.75', $14 = '48031.75', $15 = '831', $16 = '0', $17 = '2026-03-06 09:56:49.729', $18 = '2026-03-06 09:56:49.66'
-
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: 2026-03-06 09:10:37 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 08:30:00', $2 = '8839.4', $3 = '8842.9', $4 = '8837.1', $5 = '8839.6', $6 = '488', $7 = '515840248015086300', $8 = '0', $9 = '2026-03-06 09:10:37.342', $10 = '2026-03-06 09:10:37.249', $11 = '8839.4', $12 = '8842.9', $13 = '8837.1', $14 = '8839.6', $15 = '488', $16 = '0', $17 = '2026-03-06 09:10:37.342', $18 = '2026-03-06 09:10:37.249'
-
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: 2026-03-06 09:32:07 Duration: 0ms Database: postgres parameters: $1 = '2026-03-05 21:00:00', $2 = '44525.01', $3 = '44565.01', $4 = '44507.51', $5 = '44562.51', $6 = '1488', $7 = '500991628285199200', $8 = '0', $9 = '2026-03-06 09:32:07.122', $10 = '2026-03-06 09:32:06.948', $11 = '44525.01', $12 = '44565.01', $13 = '44507.51', $14 = '44562.51', $15 = '1488', $16 = '0', $17 = '2026-03-06 09:32:07.122', $18 = '2026-03-06 09:32:06.948'
13 209ms 2,904 0ms 0ms 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 #13
Day Hour Count Duration Avg duration 09 2,904 209ms 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: 2026-03-06 09:00:51 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 06:30:00', $2 = '0.54922', $3 = '0.549615', $4 = '0.549195', $5 = '0.54942', $6 = '8829000000', $7 = '515840249717515300', $8 = '0', $9 = '2026-03-06 09:00:51.254', $10 = '2026-03-06 09:00:51.243', $11 = '0.54922', $12 = '0.549615', $13 = '0.549195', $14 = '0.54942', $15 = '8829000000', $16 = '0', $17 = '2026-03-06 09:00:51.254', $18 = '2026-03-06 09:00:51.243'
-
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: 2026-03-06 09:01:36 Duration: 0ms Database: postgres parameters: $1 = '2026-03-05 22:30:00', $2 = '8051.2', $3 = '8058', $4 = '8042.2', $5 = '8054.5', $6 = '1148', $7 = '515840233915106300', $8 = '0', $9 = '2026-03-06 09:01:36.378', $10 = '2026-03-06 09:01:36.369', $11 = '8051.2', $12 = '8058', $13 = '8042.2', $14 = '8054.5', $15 = '1148', $16 = '0', $17 = '2026-03-06 09:01:36.378', $18 = '2026-03-06 09:01:36.369'
-
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: 2026-03-06 09:10:47 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 08:00:00', $2 = '25099.1', $3 = '25104.1', $4 = '25072.1', $5 = '25076.9', $6 = '6909', $7 = '515840248039147300', $8 = '0', $9 = '2026-03-06 09:10:47.519', $10 = '2026-03-06 09:10:47.418', $11 = '25099.1', $12 = '25104.1', $13 = '25072.1', $14 = '25076.9', $15 = '6909', $16 = '0', $17 = '2026-03-06 09:10:47.519', $18 = '2026-03-06 09:10:47.418'
14 159ms 1,986 0ms 1ms 0ms INSERT INTO T60 (pricedatetime, open, high, low, close, volume, symbolid, bsf, sastdatetimewritten, sastdatetimereceived) VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9, $10) ON CONFLICT (pricedatetime, symbolid) DO UPDATE SET open = $11, high = $12, low = $13, close = $14, volume = $15, bsf = $16, sastdatetimewritten = $17, sastdatetimereceived = $18;Times Reported Time consuming bind #14
Day Hour Count Duration Avg duration 09 1,986 159ms 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: 2026-03-06 09:01:36 Duration: 1ms Database: postgres parameters: $1 = '2026-03-05 22:00:00', $2 = '8015', $3 = '8062.2', $4 = '8008.2', $5 = '8054.5', $6 = '2837', $7 = '515840233915368300', $8 = '0', $9 = '2026-03-06 09:01:36.374', $10 = '2026-03-06 09:01:36.373', $11 = '8015', $12 = '8062.2', $13 = '8008.2', $14 = '8054.5', $15 = '2837', $16 = '0', $17 = '2026-03-06 09:01:36.374', $18 = '2026-03-06 09:01:36.373'
-
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: 2026-03-06 09:11:25 Duration: 0ms Database: postgres parameters: $1 = '2026-03-05 21:00:00', $2 = '257.8', $3 = '259.45', $4 = '257.18', $5 = '258.63', $6 = '11680', $7 = '515840247917405300', $8 = '0', $9 = '2026-03-06 09:11:25.783', $10 = '2026-03-06 09:11:25.717', $11 = '257.8', $12 = '259.45', $13 = '257.18', $14 = '258.63', $15 = '11680', $16 = '0', $17 = '2026-03-06 09:11:25.783', $18 = '2026-03-06 09:11:25.717'
-
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: 2026-03-06 09:01:36 Duration: 0ms Database: postgres parameters: $1 = '2026-03-06 09:00:00', $2 = '887.039', $3 = '890.489', $4 = '880.919', $5 = '885.3525', $6 = '7130', $7 = '515840249473320300', $8 = '0', $9 = '2026-03-06 09:01:36.375', $10 = '2026-03-06 09:01:36.375', $11 = '887.039', $12 = '890.489', $13 = '880.919', $14 = '885.3525', $15 = '7130', $16 = '0', $17 = '2026-03-06 09:01:36.375', $18 = '2026-03-06 09:01:36.375'
15 89ms 9 0ms 12ms 9ms WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;Times Reported Time consuming bind #15
Day Hour Count Duration Avg duration 09 9 89ms 9ms -
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-03-06 09:08:52 Duration: 12ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
-
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-03-06 09:08:18 Duration: 12ms Database: postgres parameters: $1 = '489', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
-
WITH /*Latest.JapSticks*/ all_results AS ( SELECT ;
Date: 2026-03-06 09:02:46 Duration: 11ms Database: postgres parameters: $1 = '689', $2 = '0', $3 = '0', $4 = '0', $5 = '', $6 = '0', $7 = '', $8 = '0', $9 = '', $10 = '0', $11 = '0'
16 89ms 231 0ms 0ms 0ms SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;Times Reported Time consuming bind #16
Day Hour Count Duration Avg duration 09 231 89ms 0ms -
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-03-06 09:12:50 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-03-06 09:12:50 Duration: 0ms Database: postgres
-
SELECT NULL AS TABLE_CAT, n.nspname AS TABLE_SCHEM, c.relname AS TABLE_NAME, CASE n.nspname ~ '^pg_' OR n.nspname = 'information_schema' WHEN true THEN CASE WHEN n.nspname = 'pg_catalog' OR n.nspname = 'information_schema' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TABLE' WHEN 'v' THEN 'SYSTEM VIEW' WHEN 'i' THEN 'SYSTEM INDEX' ELSE NULL END WHEN n.nspname = 'pg_toast' THEN CASE c.relkind WHEN 'r' THEN 'SYSTEM TOAST TABLE' WHEN 'i' THEN 'SYSTEM TOAST INDEX' ELSE NULL END ELSE CASE c.relkind WHEN 'r' THEN 'TEMPORARY TABLE' WHEN 'p' THEN 'TEMPORARY TABLE' WHEN 'i' THEN 'TEMPORARY INDEX' WHEN 'S' THEN 'TEMPORARY SEQUENCE' WHEN 'v' THEN 'TEMPORARY VIEW' ELSE NULL END END WHEN false THEN CASE c.relkind WHEN 'r' THEN 'TABLE' WHEN 'p' THEN 'PARTITIONED TABLE' WHEN 'i' THEN 'INDEX' WHEN 'S' THEN 'SEQUENCE' WHEN 'v' THEN 'VIEW' WHEN 'c' THEN 'TYPE' WHEN 'f' THEN 'FOREIGN TABLE' WHEN 'm' THEN 'MATERIALIZED VIEW' ELSE NULL END ELSE NULL END AS TABLE_TYPE, d.description AS REMARKS, '' as TYPE_CAT, '' as TYPE_SCHEM, '' as TYPE_NAME, '' AS SELF_REFERENCING_COL_NAME, '' AS REF_GENERATION FROM pg_catalog.pg_namespace n, pg_catalog.pg_class c LEFT JOIN pg_catalog.pg_description d ON (c.oid = d.objoid AND d.objsubid = 0) LEFT JOIN pg_catalog.pg_class dc ON (d.classoid = dc.oid AND dc.relname = 'pg_class') LEFT JOIN pg_catalog.pg_namespace dn ON (dn.oid = dc.relnamespace AND dn.nspname = 'pg_catalog') WHERE c.relnamespace = n.oid AND c.relname LIKE 'PROBABLYNOT' AND (false OR (c.relkind = 'r' AND n.nspname !~ '^pg_' AND n.nspname <> 'information_schema')) ORDER BY TABLE_TYPE, TABLE_SCHEM, TABLE_NAME;
Date: 2026-03-06 09:12:50 Duration: 0ms Database: postgres
17 80ms 14 3ms 9ms 5ms SELECT DISTINCT ON (basegroupname, symbol) ;Times Reported Time consuming bind #17
Day Hour Count Duration Avg duration 09 14 80ms 5ms -
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-03-06 09:11:06 Duration: 9ms Database: postgres parameters: $1 = '627', $2 = '627'
-
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-03-06 09:10:53 Duration: 9ms Database: postgres parameters: $1 = '627', $2 = '627'
-
SELECT DISTINCT ON (basegroupname, symbol) ;
Date: 2026-03-06 09:01:24 Duration: 9ms Database: postgres parameters: $1 = '627', $2 = '627'
18 56ms 61 0ms 1ms 0ms 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 #18
Day Hour Count Duration Avg duration 09 61 56ms 0ms -
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: 2026-03-06 09:06:08 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'XNGUSD', $3 = '558'
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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: 2026-03-06 09:51:06 Duration: 1ms Database: postgres parameters: $1 = '558', $2 = 'AUDUSD', $3 = '558'
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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: 2026-03-06 09:50:49 Duration: 1ms Database: postgres parameters: $1 = '632', $2 = 'GBPUSD', $3 = '632'
19 52ms 32 1ms 2ms 1ms WITH rcr_max as ( ;Times Reported Time consuming bind #19
Day Hour Count Duration Avg duration 09 32 52ms 1ms -
WITH rcr_max as ( ;
Date: 2026-03-06 09:11:09 Duration: 2ms Database: postgres parameters: $1 = '607788096604987305', $2 = '607788096604987305', $3 = '607788096604987305'
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WITH rcr_max as ( ;
Date: 2026-03-06 09:47:10 Duration: 2ms Database: postgres parameters: $1 = '607788096604987305', $2 = '607788096604987305', $3 = '607788096604987305'
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WITH rcr_max as ( ;
Date: 2026-03-06 09:32:13 Duration: 2ms Database: postgres parameters: $1 = '607788096604987305', $2 = '607788096604987305', $3 = '607788096604987305'
20 49ms 842 0ms 0ms 0ms select distinct category;Times Reported Time consuming bind #20
Day Hour Count Duration Avg duration 09 842 49ms 0ms -
select distinct category;
Date: 2026-03-06 09:01:14 Duration: 0ms Database: postgres parameters: $1 = '604104683406582307'
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select distinct category;
Date: 2026-03-06 09:10:56 Duration: 0ms Database: postgres parameters: $1 = '515852059305993307', $2 = '515852059305993307'
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select distinct category;
Date: 2026-03-06 09:45:48 Duration: 0ms Database: postgres parameters: $1 = '515852059305993307', $2 = '515852059305993307'
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Events
Log levels
Key values
- 520,893 Log entries
Events distribution
Key values
- 0 PANIC entries
- 1 FATAL entries
- 411 ERROR entries
- 0 WARNING entries
Most Frequent Errors/Events
Key values
- 360 Max number of times the same event was reported
- 412 Total events found
Rank Times reported Error 1 360 ERROR: pg_stat_statements must be loaded via shared_preload_libraries
Times Reported Most Frequent Error / Event #1
Day Hour Count Mar 06 09 360 - ERROR: pg_stat_statements must be loaded via shared_preload_libraries
Statement: /* service='datadog-agent' */ SELECT COUNT(*) FROM pg_stat_statements(false)
Date: 2026-03-06 09:00:06
2 50 ERROR: schema "..." does not exist
Times Reported Most Frequent Error / Event #2
Day Hour Count Mar 06 09 50 - ERROR: schema "datadog" does not exist at character 38
Statement: /* service='datadog-agent' */ SELECT datadog.explain_statement($stmt$SELECT * FROM pg_stat_activity$stmt$)
Date: 2026-03-06 09:00:51
3 1 ERROR: relation "..." does not exist
Times Reported Most Frequent Error / Event #3
Day Hour Count Mar 06 09 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: 2026-03-06 09:14:24
4 1 FATAL: connection to client lost
Times Reported Most Frequent Error / Event #4
Day Hour Count Mar 06 09 1 - FATAL: connection to client lost
Date: 2026-03-06 09:10:25