Add recent strategy activity diagnostics

reports/eth-exploration/current-strategy-recent-activity-report.md

@@ -0,0 +1,15 @@
+# Current Strategy Recent Activity
+
+Scope: current live BB squeeze, selected T-gated BB squeeze observer, and crash-follow short observer.
+
+| strategy | window_days | total_return | trades | last_candle | reentry_entries_full | max_drawdown | profit_factor | win_rate |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| live_bb_squeeze_mxbuf0.0005 | 30 | 4.60% | 21 | 2026-05-19T07:45:00+00:00 |  |  |  |  |
+| live_bb_squeeze_mxbuf0.0005 | 14 | -4.04% | 11 | 2026-05-19T07:45:00+00:00 |  |  |  |  |
+| live_bb_squeeze_mxbuf0.0005 | 7 | 3.36% | 5 | 2026-05-19T07:45:00+00:00 |  |  |  |  |
+| bb_squeeze_t_gated_tre96 | 30 | -10.22% | 0 | 2026-05-19T07:45:00+00:00 | 32.0 |  |  |  |
+| bb_squeeze_t_gated_tre96 | 14 | -9.32% | 0 | 2026-05-19T07:45:00+00:00 | 32.0 |  |  |  |
+| bb_squeeze_t_gated_tre96 | 7 | -9.32% | 0 | 2026-05-19T07:45:00+00:00 | 32.0 |  |  |  |
+| crash_follow_short | 30 | 0.00% | 0 | 2026-05-19T00:00:00+00:00 |  | 0.00% | 0.000 | 0.00% |
+| crash_follow_short | 14 | 0.00% | 0 | 2026-05-19T00:00:00+00:00 |  | 0.00% | 0.000 | 0.00% |
+| crash_follow_short | 7 | 0.00% | 0 | 2026-05-19T00:00:00+00:00 |  | 0.00% | 0.000 | 0.00% |

reports/eth-exploration/current-strategy-recent-activity.csv

@@ -0,0 +1,10 @@
+strategy,window_days,total_return,trades,last_candle,reentry_entries_full,max_drawdown,profit_factor,win_rate
+live_bb_squeeze_mxbuf0.0005,30,0.04599530586268963,21,2026-05-19T07:45:00+00:00,,,,
+live_bb_squeeze_mxbuf0.0005,14,-0.04043563931842753,11,2026-05-19T07:45:00+00:00,,,,
+live_bb_squeeze_mxbuf0.0005,7,0.03362544576375481,5,2026-05-19T07:45:00+00:00,,,,
+bb_squeeze_t_gated_tre96,30,-0.10223785436994537,0,2026-05-19T07:45:00+00:00,32.0,,,
+bb_squeeze_t_gated_tre96,14,-0.09324184616099995,0,2026-05-19T07:45:00+00:00,32.0,,,
+bb_squeeze_t_gated_tre96,7,-0.09324184616099995,0,2026-05-19T07:45:00+00:00,32.0,,,
+crash_follow_short,30,0.0,0,2026-05-19T00:00:00+00:00,,0.0,0.0,0.0
+crash_follow_short,14,0.0,0,2026-05-19T00:00:00+00:00,,0.0,0.0,0.0
+crash_follow_short,7,0.0,0,2026-05-19T00:00:00+00:00,,0.0,0.0,0.0

reports/eth-exploration/eth-filtered-recent-short-bidir-candidates.csv

@@ -0,0 +1,49 @@
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+ETH-USDT-SWAP,1H,failed_rebound_short,failed_rebound_short-1H-fast32-slow128-stop0.012-take0.024-hold24-gatebtc_down,"{""fast"":32,""slow"":128,""stop"":0.012,""take"":0.024,""hold"":24,""gate"":""btc_down""}",2023-06-10 23:00,2026-05-19 07:00,-0.7212738988967229,-0.3523715236426809,0.7838575077187531,-0.4495351772137536,236,236,0,0.8142901217928814,0.3347457627118644,-0.7212738988967229,-0.3523715236426809,0.7838575077187531,-0.4495351772137536,236,236,0,0.8142901217928814,0.3347457627118644,-0.2378941345508072,-0.2378941345508072,0.4037132144415617,-0.5892651665610585,79,79,0,0.9163572187137603,0.34177215189873417,-0.27834596272944034,-0.7375842447796426,0.28147864981670984,-2.6203914409136697,11,11,0,0.11487871977096797,0.18181818181818182,-0.19944895399743123,-0.9332322835084876,0.19944895399743126,-4.679053285586582,7,7,0,0.11744615856175482,0.14285714285714285,-0.028404477890228597,-0.5282311888937412,0.07491596304639149,-7.050983093771825,2,2,0,0.7046769513705278,0.5,33,no
+ETH-USDT-SWAP,1H,filtered_breakdown_short,filtered_breakdown_short-1H-lookback96-stop0.012-take0.024-hold24-gatenone,"{""lookback"":96,""stop"":0.012,""take"":0.024,""hold"":24,""gate"":""none""}",2023-06-10 23:00,2026-05-19 07:00,0.12590617074365174,0.041151542023509124,0.4255948904977289,0.09669181407554685,179,179,0,1.084258707079674,0.3687150837988827,0.12590617074365174,0.041151542023509124,0.4255948904977289,0.09669181407554685,179,179,0,1.084258707079674,0.3687150837988827,-0.24471654805063436,-0.24471654805063436,0.34241158916592057,-0.7146853546830547,64,64,0,0.8741952168566461,0.328125,-0.041027778531412706,-0.15786005298160122,0.0842216737902042,-1.8743400110383686,5,5,0,0.5550551598960721,0.4,-0.0288574781590748,-0.29971288032306675,0.08258203067869027,-3.6292747690982323,4,4,0,0.6466104630029604,0.5,-0.0288574781590748,-0.5339324478741027,0.08258203067869027,-6.465479759773942,4,4,0,0.6466104630029604,0.5,29,no
+ETH-USDT-SWAP,1H,filtered_breakdown_short,filtered_breakdown_short-1H-lookback96-stop0.012-take0.024-hold24-gatebtc_down,"{""lookback"":96,""stop"":0.012,""take"":0.024,""hold"":24,""gate"":""btc_down""}",2023-06-10 23:00,2026-05-19 07:00,0.09237094310278393,0.030500556515579147,0.4427036039995951,0.0688961107161148,177,177,0,1.0770145768219808,0.3672316384180791,0.09237094310278393,0.030500556515579147,0.4427036039995951,0.0688961107161148,177,177,0,1.0770145768219808,0.3672316384180791,-0.24471654805063403,-0.24471654805063403,0.34241158916592046,-0.7146853546830539,64,64,0,0.8741952168566461,0.328125,-0.041027778531412706,-0.15786005298160122,0.08422167379020422,-1.8743400110383681,5,5,0,0.5550551598960721,0.4,-0.028857478159074912,-0.29971288032306775,0.08258203067869023,-3.629274769098246,4,4,0,0.6466104630029604,0.5,-0.028857478159074912,-0.5339324478741041,0.08258203067869023,-6.465479759773962,4,4,0,0.6466104630029604,0.5,29,no
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reports/eth-exploration/eth-filtered-recent-short-bidir-report.md

@@ -0,0 +1,32 @@
+# ETH filtered recent short/bidirectional search
+
+Run command: `rtk .venv/bin/python /home/lxy/okx-codex-trader/scripts/search_eth_filtered_recent_short_bidir_candidates.py --bars 15m 1H --years 3`
+Scope: 15m/1H only, local ETH/BTC candle cache, taker-fee model, no live path touched.
+
+Output files:
+- `reports/eth-exploration/eth-filtered-recent-short-bidir-candidates.csv`
+- `reports/eth-exploration/eth-filtered-recent-short-bidir-top.csv`
+- `reports/eth-exploration/eth-filtered-recent-short-bidir-report.md`
+
+Selection rule: recent activity plus positive 3y/1y return, bounded 3y/1y drawdown, and 1y profit factor >= 1.05.
+
+## Read-only observation candidates
+
+No candidates passed the read-only observation rule.
+
+## Top recent filtered candidates
+
+| family | bar | name | 3y_total_return | 3y_max_drawdown | 3y_trades | 1y_total_return | 1y_max_drawdown | 1y_profit_factor | 3m_total_return | 3m_trades | 1m_trades | 2w_trades | readonly_observe |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| range_breakout_bidir | 15m | range_breakout_bidir-15m-lookback48-stop0.01-take0.018-hold16-gatebtc_trend | -0.9695 | 0.9771 | 1585 | -0.6195 | 0.6611 | 0.9276 | -0.1069 | 116 | 32 | 15 | no |
+| failed_rebound_short | 15m | failed_rebound_short-15m-fast32-slow128-stop0.012-take0.024-hold24-gatenone | -0.9646 | 0.9673 | 971 | -0.7132 | 0.7614 | 0.8075 | -0.4749 | 78 | 38 | 17 | no |
+| range_breakout_bidir | 15m | range_breakout_bidir-15m-lookback96-stop0.01-take0.018-hold16-gatebtc_trend | -0.8436 | 0.8995 | 1245 | -0.1867 | 0.4141 | 1.0093 | 0.0278 | 94 | 24 | 13 | no |
+| filtered_breakdown_short | 15m | filtered_breakdown_short-15m-lookback24-stop0.01-take0.018-hold16-gatenone | -0.8219 | 0.8728 | 1111 | -0.2995 | 0.5689 | 0.9801 | -0.0981 | 71 | 29 | 15 | no |
+| filtered_breakdown_short | 15m | filtered_breakdown_short-15m-lookback24-stop0.01-take0.018-hold16-gatebtc_down | -0.8024 | 0.8534 | 1028 | -0.1908 | 0.5455 | 1.0023 | -0.1950 | 68 | 28 | 15 | no |
+| filtered_breakdown_short | 15m | filtered_breakdown_short-15m-lookback24-stop0.012-take0.024-hold24-gatenone | -0.7689 | 0.8291 | 992 | -0.1990 | 0.5420 | 1.0154 | -0.2244 | 67 | 28 | 14 | no |
+| failed_rebound_short | 15m | failed_rebound_short-15m-fast32-slow128-stop0.012-take0.024-hold24-gatebtc_down | -0.8936 | 0.9050 | 776 | -0.5808 | 0.6587 | 0.8432 | -0.3667 | 61 | 29 | 15 | no |
+| filtered_breakdown_short | 15m | filtered_breakdown_short-15m-lookback24-stop0.012-take0.024-hold24-gatebtc_down | -0.7570 | 0.8262 | 919 | -0.1519 | 0.4972 | 1.0225 | -0.2552 | 64 | 27 | 14 | no |
+| failed_rebound_short | 15m | failed_rebound_short-15m-fast48-slow192-stop0.012-take0.024-hold24-gatenone | -0.9179 | 0.9228 | 758 | -0.4874 | 0.5726 | 0.8803 | -0.3391 | 54 | 27 | 10 | no |
+| filtered_breakdown_short | 15m | filtered_breakdown_short-15m-lookback48-stop0.01-take0.018-hold16-gatenone | -0.8081 | 0.8403 | 845 | -0.1370 | 0.5300 | 1.0087 | -0.1840 | 53 | 21 | 11 | no |
+| filtered_breakdown_short | 15m | filtered_breakdown_short-15m-lookback48-stop0.01-take0.018-hold16-gatebtc_down | -0.8128 | 0.8466 | 819 | -0.0606 | 0.5117 | 1.0265 | -0.1796 | 52 | 21 | 11 | no |
+| filtered_breakdown_short | 15m | filtered_breakdown_short-15m-lookback48-stop0.012-take0.024-hold24-gatenone | -0.5529 | 0.7428 | 758 | 0.0799 | 0.4960 | 1.0674 | -0.2650 | 51 | 21 | 11 | no |

reports/eth-exploration/eth-filtered-recent-short-bidir-top.csv

@@ -0,0 +1,49 @@
+symbol,bar,family,name,params_json,first_time,last_time,full_total_return,full_annualized_return,full_max_drawdown,full_calmar,full_trades,full_short_trades,full_long_trades,full_profit_factor,full_win_rate,3y_total_return,3y_annualized_return,3y_max_drawdown,3y_calmar,3y_trades,3y_short_trades,3y_long_trades,3y_profit_factor,3y_win_rate,1y_total_return,1y_annualized_return,1y_max_drawdown,1y_calmar,1y_trades,1y_short_trades,1y_long_trades,1y_profit_factor,1y_win_rate,3m_total_return,3m_annualized_return,3m_max_drawdown,3m_calmar,3m_trades,3m_short_trades,3m_long_trades,3m_profit_factor,3m_win_rate,1m_total_return,1m_annualized_return,1m_max_drawdown,1m_calmar,1m_trades,1m_short_trades,1m_long_trades,1m_profit_factor,1m_win_rate,2w_total_return,2w_annualized_return,2w_max_drawdown,2w_calmar,2w_trades,2w_short_trades,2w_long_trades,2w_profit_factor,2w_win_rate,recent_trigger_score,readonly_observe
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reports/ultrashort/eth-highfreq-short-bidir-candidates.csv

@@ -0,0 +1,31 @@
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+ETH-USDT-SWAP,3m,breakdown_short,breakdown_short-3m-lookback96-stop0.006-take0.012-hold12,"{""lookback"":96,""stop"":0.006,""take"":0.012,""hold"":12}",2023-05-18 14:09,2026-05-17 09:15,-0.99999763502408,-0.9866875138446809,0.9999978599049332,-0.9866896254542808,4220,4220,0,0.6670005235675207,0.32630331753554503,-0.99999763502408,-0.9866875138446809,0.9999978599049332,-0.9866896254542808,4220,4220,0,0.6670005235675207,0.32630331753554503,-0.9924977597293435,-0.9924977597293435,0.9937485198671223,-0.9987413715716064,1432,1432,0,0.653382474826234,0.34217877094972066,-0.6417004589100459,-0.9851437641312131,0.6617704856104312,-1.4886486864437531,317,317,0,0.6277445792825704,0.3501577287066246,-0.30541621693165133,-0.9881336693654661,0.33732599623774273,-2.9293137214039175,104,104,0,0.5071331078186634,0.3076923076923077,-0.15232535155033067,-0.986546583748685,0.191268257524765,-5.157921113078311,52,52,0,0.49215228170089453,0.28846153846153844,733,no
+ETH-USDT-SWAP,5m,breakdown_short,breakdown_short-5m-lookback48-stop0.006-take0.012-hold12,"{""lookback"":48,""stop"":0.006,""take"":0.012,""hold"":12}",2023-05-18 13:20,2026-05-17 09:10,-0.9998942646372599,-0.9527360586612449,0.9999016777814035,-0.9528297429955209,4086,4086,0,0.7773266449319676,0.34924131179637785,-0.9998942646372599,-0.9527360586612449,0.9999016777814035,-0.9528297429955209,4086,4086,0,0.7773266449319676,0.34924131179637785,-0.9449488836455275,-0.9449488836455275,0.9473549802760262,-0.997460195301029,1398,1398,0,0.804381964006834,0.3562231759656652,-0.44218747763474664,-0.9087323182251456,0.49509717891673205,-1.8354625251823153,311,311,0,0.7931508883168624,0.3762057877813505,-0.23038282058473536,-0.9586626307837308,0.26196874691955924,-3.659454198473912,103,103,0,0.6835829535455828,0.34951456310679613,-0.1233088780025573,-0.9676458914319952,0.15709373081387062,-6.159672231468557,52,52,0,0.6745368744125424,0.38461538461538464,725,no
+ETH-USDT-SWAP,15m,vwap_short,vwap_short-15m-window96-entry_z1.5-exit_z0.2-stop0.006-take0.009-hold12,"{""window"":96,""entry_z"":1.5,""exit_z"":0.2,""stop"":0.006,""take"":0.009,""hold"":12}",2023-05-21 08:15,2026-05-19 07:45,-0.9999979393158124,-0.9873263451395025,0.9999980728918325,-0.9873282478278328,3818,3818,0,0.7129711113582604,0.4036144578313253,-0.9999979393158124,-0.9873263451395025,0.9999980728918325,-0.9873282478278328,3818,3818,0,0.7129711113582604,0.4036144578313253,-0.9905150391252561,-0.9905150391252561,0.9910600405328068,-0.9994500823509564,1302,1302,0,0.7064063281114069,0.39400921658986177,-0.7961906301014701,-0.9985309287918075,0.8111353858495869,-1.2310286867166345,310,310,0,0.5848903015610059,0.3580645161290323,-0.2812242700872124,-0.9820021862600922,0.3224826050681707,-3.045132267064462,67,67,0,0.5020329315249633,0.3880597014925373,-0.062187467195504276,-0.8124897003982224,0.11785159811478654,-6.894176348859213,24,24,0,0.6707294239214756,0.4583333333333333,540,no
+ETH-USDT-SWAP,5m,breakdown_short,breakdown_short-5m-lookback96-stop0.006-take0.012-hold12,"{""lookback"":96,""stop"":0.006,""take"":0.012,""hold"":12}",2023-05-18 17:20,2026-05-17 09:10,-0.9972500263998224,-0.8599840500971031,0.9975103147713651,-0.862130483627346,2636,2636,0,0.7929182760842965,0.3516691957511381,-0.9972500263998224,-0.8599840500971031,0.9975103147713651,-0.862130483627346,2636,2636,0,0.7929182760842965,0.3516691957511381,-0.7133493464210348,-0.7133493464210348,0.7400793372203669,-0.9638822630831362,904,904,0,0.8819513479339048,0.375,-0.31485945956716965,-0.7879139454412698,0.360956843035073,-2.182848062433632,203,203,0,0.8051181494246616,0.3645320197044335,-0.1837931719588105,-0.915491047192427,0.2142151566347554,-4.273698750240032,63,63,0,0.5914547180922322,0.3492063492063492,-0.0897178492111067,-0.91376953990212,0.12342164248410181,-7.403641059304688,30,30,0,0.5525093813290584,0.36666666666666664,449,no
+ETH-USDT-SWAP,15m,breakdown_short,breakdown_short-15m-lookback48-stop0.006-take0.012-hold12,"{""lookback"":48,""stop"":0.006,""take"":0.012,""hold"":12}",2023-05-20 20:15,2026-05-19 07:45,-0.9816899876158605,-0.7365970511358882,0.9849341433517138,-0.7478642669745017,1552,1552,0,0.8106167790762198,0.3382731958762887,-0.9816899876158605,-0.7365970511358882,0.9849341433517138,-0.7478642669745017,1552,1552,0,0.8106167790762198,0.3382731958762887,-0.6627415579627207,-0.6627415579627207,0.7142418359316473,-0.9278951814664422,552,552,0,0.8622058700404764,0.3496376811594203,0.02106190284900311,0.08924021624394696,0.1866309269258001,0.47816413771243116,113,113,0,1.0439709218864883,0.3805309734513274,-0.09001741156497833,-0.6826275883378357,0.14477129085068074,-4.715213799135824,32,32,0,0.7314776162042558,0.28125,-0.07415385348235937,-0.8658411513928107,0.08658006523631612,-10.000467763907766,16,16,0,0.5059189899174716,0.25,241,no
+ETH-USDT-SWAP,15m,breakdown_short,breakdown_short-15m-lookback96-stop0.006-take0.012-hold12,"{""lookback"":96,""stop"":0.006,""take"":0.012,""hold"":12}",2023-05-21 08:15,2026-05-19 07:45,-0.8812095554252265,-0.5087460556171569,0.9174139041792305,-0.5545436506898263,970,970,0,0.8453401346267548,0.3402061855670103,-0.8812095554252265,-0.5087460556171569,0.9174139041792305,-0.5545436506898263,970,970,0,0.8453401346267548,0.3402061855670103,-0.20162951537149532,-0.20162951537149532,0.39400647145970125,-0.5117416336450146,352,352,0,0.971720750593225,0.375,0.1676396050458766,0.8881729822136988,0.17199814287556303,5.163852163545004,74,74,0,1.252497498607885,0.43243243243243246,-0.06435643830011151,-0.5548460061279052,0.12543308840504222,-4.423442117092935,19,19,0,0.635515132675228,0.2631578947368421,-0.007956520453661908,-0.18801019970231503,0.056503126348991524,-3.327430035305837,10,10,0,0.8566905863671753,0.3,152,no

reports/ultrashort/eth-highfreq-short-bidir-report.md

@@ -0,0 +1,50 @@
+# ETH high-frequency short/bidirectional candidate search
+
+Run command: `rtk .venv/bin/python /home/lxy/okx-codex-trader/scripts/search_eth_high_freq_short_bidir_candidates.py --bars 3m 5m 15m --years 3`
+Scope: local OKX ETH candle CSV only; no live executor, deployment, private API, or order path touched.
+Cost model: taker fee `0.0004` each side on `3x` notional; entries execute on next open.
+
+Output files:
+- `reports/ultrashort/eth-highfreq-short-bidir-candidates.csv`
+- `reports/ultrashort/eth-highfreq-short-bidir-top.csv`
+- `reports/ultrashort/eth-highfreq-short-bidir-report.md`
+
+Selection rule for `readonly_observe`: 3m >= 12 trades, 1m >= 4 trades, 2w >= 1 trade, positive 3y/1y return, 3y MDD <= 35%, 1y MDD <= 25%.
+
+## Read-only observation candidates
+
+No candidates passed the read-only observation rule.
+
+## Least-bad risk rows
+
+| family | bar | name | full_total_return | full_max_drawdown | full_trades | 3y_total_return | 3y_max_drawdown | 3y_trades | 1y_total_return | 1y_max_drawdown | 1y_trades | 3m_trades | 1m_trades | 2w_trades | readonly_observe |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| breakdown_short | 15m | breakdown_short-15m-lookback96-stop0.006-take0.012-hold12 | -0.8812 | 0.9174 | 970 | -0.8812 | 0.9174 | 970 | -0.2016 | 0.3940 | 352 | 74 | 19 | 10 | no |
+| breakdown_short | 15m | breakdown_short-15m-lookback48-stop0.006-take0.012-hold12 | -0.9817 | 0.9849 | 1552 | -0.9817 | 0.9849 | 1552 | -0.6627 | 0.7142 | 552 | 113 | 32 | 16 | no |
+| breakdown_short | 5m | breakdown_short-5m-lookback96-stop0.006-take0.012-hold12 | -0.9973 | 0.9975 | 2636 | -0.9973 | 0.9975 | 2636 | -0.7133 | 0.7401 | 904 | 203 | 63 | 30 | no |
+| breakdown_short | 5m | breakdown_short-5m-lookback48-stop0.006-take0.012-hold12 | -0.9999 | 0.9999 | 4086 | -0.9999 | 0.9999 | 4086 | -0.9449 | 0.9474 | 1398 | 311 | 103 | 52 | no |
+| breakdown_short | 3m | breakdown_short-3m-lookback96-stop0.006-take0.012-hold12 | -1.0000 | 1.0000 | 4220 | -1.0000 | 1.0000 | 4220 | -0.9925 | 0.9937 | 1432 | 317 | 104 | 52 | no |
+| vwap_short | 15m | vwap_short-15m-window96-entry_z1.5-exit_z0.2-stop0.006-take0.009-hold12 | -1.0000 | 1.0000 | 3818 | -1.0000 | 1.0000 | 3818 | -0.9905 | 0.9911 | 1302 | 310 | 67 | 24 | no |
+| vwap_short | 15m | vwap_short-15m-window48-entry_z1.5-exit_z0.2-stop0.006-take0.009-hold12 | -1.0000 | 1.0000 | 4398 | -1.0000 | 1.0000 | 4398 | -0.9937 | 0.9941 | 1510 | 342 | 81 | 34 | no |
+| rsi_short | 15m | rsi_short-15m-trend96-entry90-exit45-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 8619 | -1.0000 | 1.0000 | 8619 | -0.9977 | 0.9977 | 2853 | 732 | 246 | 132 | no |
+| rsi_short | 15m | rsi_short-15m-trend192-entry90-exit45-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 8940 | -1.0000 | 1.0000 | 8940 | -0.9962 | 0.9962 | 3009 | 729 | 293 | 152 | no |
+| breakdown_short | 3m | breakdown_short-3m-lookback48-stop0.006-take0.012-hold12 | -1.0000 | 1.0000 | 6679 | -1.0000 | 1.0000 | 6679 | -0.9994 | 0.9995 | 2269 | 497 | 159 | 78 | no |
+| vwap_short | 5m | vwap_short-5m-window96-entry_z1.5-exit_z0.2-stop0.006-take0.009-hold12 | -1.0000 | 1.0000 | 7725 | -1.0000 | 1.0000 | 7725 | -0.9994 | 0.9994 | 2609 | 589 | 165 | 73 | no |
+| vwap_short | 5m | vwap_short-5m-window48-entry_z1.5-exit_z0.2-stop0.006-take0.009-hold12 | -1.0000 | 1.0000 | 9430 | -1.0000 | 1.0000 | 9430 | -0.9996 | 0.9996 | 3128 | 693 | 201 | 86 | no |
+
+## Most recently active candidates
+
+| family | bar | name | full_total_return | full_max_drawdown | full_trades | 3y_total_return | 3y_max_drawdown | 3y_trades | 1y_total_return | 1y_max_drawdown | 1y_trades | 3m_trades | 1m_trades | 2w_trades | readonly_observe |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
+| rsi_bidir | 3m | rsi_bidir-3m-trend192-entry10-exit55-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 142465 | -1.0000 | 1.0000 | 142465 | -1.0000 | 1.0000 | 47426 | 11813 | 4023 | 1898 | no |
+| rsi_bidir | 3m | rsi_bidir-3m-trend96-entry10-exit55-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 139028 | -1.0000 | 1.0000 | 139028 | -1.0000 | 1.0000 | 46445 | 11563 | 3937 | 1861 | no |
+| rsi_bidir | 5m | rsi_bidir-5m-trend192-entry10-exit55-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 86165 | -1.0000 | 1.0000 | 86165 | -1.0000 | 1.0000 | 28654 | 7213 | 2442 | 1157 | no |
+| rsi_bidir | 5m | rsi_bidir-5m-trend96-entry10-exit55-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 84265 | -1.0000 | 1.0000 | 84265 | -1.0000 | 1.0000 | 28061 | 6994 | 2351 | 1086 | no |
+| rsi_short | 3m | rsi_short-3m-trend192-entry90-exit45-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 43897 | -1.0000 | 1.0000 | 43897 | -1.0000 | 1.0000 | 14495 | 3631 | 1223 | 556 | no |
+| rsi_short | 3m | rsi_short-3m-trend96-entry90-exit45-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 41504 | -1.0000 | 1.0000 | 41504 | -1.0000 | 1.0000 | 13829 | 3424 | 1165 | 531 | no |
+| rsi_bidir | 15m | rsi_bidir-15m-trend192-entry10-exit55-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 29354 | -1.0000 | 1.0000 | 29354 | -1.0000 | 1.0000 | 9872 | 2403 | 845 | 412 | no |
+| rsi_bidir | 15m | rsi_bidir-15m-trend96-entry10-exit55-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 28943 | -1.0000 | 1.0000 | 28943 | -1.0000 | 1.0000 | 9659 | 2371 | 788 | 383 | no |
+| rsi_short | 5m | rsi_short-5m-trend192-entry90-exit45-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 26763 | -1.0000 | 1.0000 | 26763 | -1.0000 | 1.0000 | 8751 | 2301 | 788 | 380 | no |
+| vwap_bidir | 3m | vwap_bidir-3m-window48-entry_z1.5-exit_z0.2-stop0.006-take0.009-hold12 | -1.0000 | 1.0000 | 27881 | -1.0000 | 1.0000 | 27881 | -1.0000 | 1.0000 | 9353 | 2119 | 688 | 333 | no |
+| rsi_short | 5m | rsi_short-5m-trend96-entry90-exit45-stop0.0075-take0.01-hold12 | -1.0000 | 1.0000 | 25205 | -1.0000 | 1.0000 | 25205 | -1.0000 | 1.0000 | 8223 | 2076 | 705 | 329 | no |
+| vwap_bidir | 3m | vwap_bidir-3m-window96-entry_z1.5-exit_z0.2-stop0.006-take0.009-hold12 | -1.0000 | 1.0000 | 22728 | -1.0000 | 1.0000 | 22728 | -1.0000 | 1.0000 | 7568 | 1673 | 518 | 240 | no |

reports/ultrashort/eth-highfreq-short-bidir-top.csv

@@ -0,0 +1,31 @@
+symbol,bar,family,name,params_json,first_time,last_time,full_total_return,full_annualized_return,full_max_drawdown,full_calmar,full_trades,full_short_trades,full_long_trades,full_profit_factor,full_win_rate,3y_total_return,3y_annualized_return,3y_max_drawdown,3y_calmar,3y_trades,3y_short_trades,3y_long_trades,3y_profit_factor,3y_win_rate,1y_total_return,1y_annualized_return,1y_max_drawdown,1y_calmar,1y_trades,1y_short_trades,1y_long_trades,1y_profit_factor,1y_win_rate,3m_total_return,3m_annualized_return,3m_max_drawdown,3m_calmar,3m_trades,3m_short_trades,3m_long_trades,3m_profit_factor,3m_win_rate,1m_total_return,1m_annualized_return,1m_max_drawdown,1m_calmar,1m_trades,1m_short_trades,1m_long_trades,1m_profit_factor,1m_win_rate,2w_total_return,2w_annualized_return,2w_max_drawdown,2w_calmar,2w_trades,2w_short_trades,2w_long_trades,2w_profit_factor,2w_win_rate,recent_trigger_score,readonly_observe
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scripts/report_candle_cache_freshness.py

@@ -0,0 +1,90 @@
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import sys
+from datetime import UTC, datetime
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from okx_codex_trader.okx_client import OkxClient
+from scripts import explore_ultrashort as explore
+
+
+SYMBOLS = ("BTC-USDT-SWAP", "ETH-USDT-SWAP")
+BARS = ("15m", "5m", "3m")
+
+
+def iso_text(ts: int | None) -> str | None:
+    if ts is None:
+        return None
+    return datetime.fromtimestamp(ts / 1000, UTC).isoformat().replace("+00:00", "Z")
+
+
+def local_last_ts(cache_dir: Path, symbol: str, bar: str) -> tuple[int, int | None]:
+    path = cache_dir / symbol / f"{bar}.csv"
+    rows = 0
+    last_ts: int | None = None
+    if not path.exists():
+        return rows, last_ts
+    with path.open("r", encoding="utf-8", newline="") as handle:
+        for row in csv.DictReader(handle):
+            rows += 1
+            last_ts = int(row["ts"])
+    return rows, last_ts
+
+
+def freshness_rows(
+    *,
+    client: OkxClient,
+    cache_dir: Path,
+    symbols: tuple[str, ...] = SYMBOLS,
+    bars: tuple[str, ...] = BARS,
+) -> list[dict[str, object]]:
+    rows = []
+    for symbol in symbols:
+        for bar in bars:
+            local_rows, local_ts = local_last_ts(cache_dir, symbol, bar)
+            recent = client.get_recent_candles(symbol, bar, 2)
+            remote_ts = recent[-1].ts if recent else None
+            interval = explore.CANDLE_BAR_MS[bar]
+            lag_bars = None if local_ts is None or remote_ts is None else max(0, (remote_ts - local_ts) // interval)
+            rows.append(
+                {
+                    "symbol": symbol,
+                    "bar": bar,
+                    "local_rows": local_rows,
+                    "local_last_ts": local_ts,
+                    "local_last_time": iso_text(local_ts),
+                    "remote_last_ts": remote_ts,
+                    "remote_last_time": iso_text(remote_ts),
+                    "lag_bars": lag_bars,
+                    "lag_ms": None if local_ts is None or remote_ts is None else max(0, remote_ts - local_ts),
+                }
+            )
+    return rows
+
+
+def build_report(cache_dir: Path, client: OkxClient) -> dict[str, object]:
+    rows = freshness_rows(client=client, cache_dir=cache_dir)
+    max_lag_bars = max((int(row["lag_bars"]) for row in rows if row["lag_bars"] is not None), default=None)
+    return {
+        "created_at": datetime.now(UTC).isoformat(timespec="seconds").replace("+00:00", "Z"),
+        "cache_dir": str(cache_dir),
+        "max_lag_bars": max_lag_bars,
+        "rows": rows,
+    }
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Report local OKX candle cache freshness against recent OKX candles.")
+    parser.add_argument("--cache-dir", type=Path, default=explore.CANDLE_CACHE_DIR)
+    args = parser.parse_args()
+    print(json.dumps(build_report(args.cache_dir, OkxClient()), indent=2, sort_keys=True))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/search_eth_filtered_recent_short_bidir_candidates.py

@@ -0,0 +1,375 @@
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+from dataclasses import dataclass
+from pathlib import Path
+
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+
+DATA_DIR = Path("data/okx-candles")
+OUT_DIR = Path("reports/eth-exploration")
+SYMBOL = "ETH-USDT-SWAP"
+BTC_SYMBOL = "BTC-USDT-SWAP"
+INITIAL_EQUITY = 10_000.0
+LEVERAGE = 3.0
+TAKER_FEE = 0.0004
+HORIZONS = (
+    ("full", None),
+    ("3y", pd.DateOffset(years=3)),
+    ("1y", pd.DateOffset(years=1)),
+    ("3m", pd.DateOffset(months=3)),
+    ("1m", pd.DateOffset(months=1)),
+    ("2w", pd.DateOffset(weeks=2)),
+)
+
+
+@dataclass(frozen=True)
+class Candidate:
+    family: str
+    bar: str
+    params: dict[str, float | int | str]
+
+    @property
+    def name(self) -> str:
+        body = "-".join(f"{key}{value:g}" if isinstance(value, float) else f"{key}{value}" for key, value in self.params.items())
+        return f"{self.family}-{self.bar}-{body}"
+
+
+def load_15m(symbol: str) -> pd.DataFrame:
+    frame = pd.read_csv(DATA_DIR / symbol / "15m.csv")
+    frame["dt"] = pd.to_datetime(frame["ts"], unit="ms", utc=True)
+    return frame.sort_values("ts").drop_duplicates("ts", keep="last").set_index("dt")
+
+
+def resample(frame: pd.DataFrame, bar: str) -> pd.DataFrame:
+    if bar == "15m":
+        return frame.reset_index()
+    rule = {"1H": "1h"}[bar]
+    return (
+        frame.resample(rule, label="left", closed="left")
+        .agg(ts=("ts", "first"), open=("open", "first"), high=("high", "max"), low=("low", "min"), close=("close", "last"), volume=("volume", "sum"))
+        .dropna()
+        .reset_index()
+    )
+
+
+def joined_frame(bar: str, years: float) -> pd.DataFrame:
+    eth = resample(load_15m(SYMBOL), bar)
+    btc = resample(load_15m(BTC_SYMBOL), bar)[["dt", "close"]].rename(columns={"close": "btc_close"})
+    frame = eth.merge(btc, on="dt", how="inner").sort_values("dt").reset_index(drop=True)
+    cutoff = frame["dt"].iloc[-1] - pd.DateOffset(days=int(years * 365))
+    return frame[frame["dt"] >= cutoff].reset_index(drop=True)
+
+
+def rsi(close: pd.Series, length: int) -> pd.Series:
+    diff = close.diff()
+    gain = diff.clip(lower=0).ewm(alpha=1 / length, adjust=False).mean()
+    loss = (-diff.clip(upper=0)).ewm(alpha=1 / length, adjust=False).mean()
+    return 100.0 - 100.0 / (1.0 + gain / loss)
+
+
+def atr_pct(frame: pd.DataFrame, length: int) -> pd.Series:
+    close = frame["close"].astype(float)
+    prev_close = close.shift(1)
+    true_range = pd.concat(
+        [
+            (frame["high"] - frame["low"]).abs(),
+            (frame["high"] - prev_close).abs(),
+            (frame["low"] - prev_close).abs(),
+        ],
+        axis=1,
+    ).max(axis=1)
+    return true_range.rolling(length).mean() / close
+
+
+def build_candidates(bars: list[str]) -> list[Candidate]:
+    candidates: list[Candidate] = []
+    for bar in bars:
+        for lookback in (24, 48, 96):
+            for gate in ("btc_down", "btc_riskoff", "none"):
+                candidates.append(Candidate("filtered_breakdown_short", bar, {"lookback": lookback, "stop": 0.010, "take": 0.018, "hold": 16, "gate": gate}))
+                candidates.append(Candidate("filtered_breakdown_short", bar, {"lookback": lookback, "stop": 0.012, "take": 0.024, "hold": 24, "gate": gate}))
+        for fast, slow in ((32, 128), (48, 192)):
+            for gate in ("btc_down", "none"):
+                candidates.append(Candidate("failed_rebound_short", bar, {"fast": fast, "slow": slow, "stop": 0.012, "take": 0.024, "hold": 24, "gate": gate}))
+        for lookback in (48, 96):
+            candidates.append(Candidate("range_breakout_bidir", bar, {"lookback": lookback, "stop": 0.010, "take": 0.018, "hold": 16, "gate": "btc_trend"}))
+    return candidates
+
+
+def gate_series(frame: pd.DataFrame, mode: str) -> pd.Series:
+    btc = frame["btc_close"].astype(float)
+    if mode == "none":
+        return pd.Series(True, index=frame.index)
+    btc_sma = btc.rolling(96).mean()
+    btc_return = btc / btc.shift(24) - 1.0
+    if mode == "btc_down":
+        return btc < btc_sma
+    if mode == "btc_riskoff":
+        return (btc < btc_sma) & (btc_return < -0.01)
+    if mode == "btc_trend":
+        return pd.Series(True, index=frame.index)
+    raise ValueError(mode)
+
+
+def signals(frame: pd.DataFrame, candidate: Candidate) -> tuple[pd.Series, pd.Series]:
+    close = frame["close"].astype(float)
+    high = frame["high"].astype(float)
+    low = frame["low"].astype(float)
+    open_ = frame["open"].astype(float)
+    params = candidate.params
+    entry = pd.Series("", index=frame.index, dtype=object)
+    gate = gate_series(frame, str(params["gate"]))
+    atr = atr_pct(frame, 48)
+    atr_ok = atr >= atr.rolling(480).quantile(0.35)
+
+    if candidate.family == "filtered_breakdown_short":
+        lookback = int(params["lookback"])
+        prior_low = low.shift(1).rolling(lookback).min()
+        downtrend = close < close.rolling(192).mean()
+        entry.loc[gate & atr_ok & downtrend & (close < prior_low)] = "short"
+        exit_ = close > close.rolling(32).mean()
+        return entry, exit_.fillna(False)
+
+    if candidate.family == "failed_rebound_short":
+        fast = close.ewm(span=int(params["fast"]), adjust=False).mean()
+        slow = close.ewm(span=int(params["slow"]), adjust=False).mean()
+        value = rsi(close, 14)
+        downtrend = (fast < slow) & (slow < slow.shift(8))
+        rejection = (high >= fast) & (close < fast) & (close < open_) & (value > 42)
+        entry.loc[gate & atr_ok & downtrend & rejection] = "short"
+        exit_ = (close > slow) | (value < 35)
+        return entry, exit_.fillna(False)
+
+    lookback = int(params["lookback"])
+    btc = frame["btc_close"].astype(float)
+    btc_sma = btc.rolling(192).mean()
+    prior_high = high.shift(1).rolling(lookback).max()
+    prior_low = low.shift(1).rolling(lookback).min()
+    entry.loc[atr_ok & (btc > btc_sma) & (close > prior_high)] = "long"
+    entry.loc[atr_ok & (btc < btc_sma) & (close < prior_low)] = "short"
+    exit_ = close.rolling(16).mean().diff().abs() < close * 0.0002
+    return entry, exit_.fillna(False)
+
+
+def close_return(side: str, entry: float, exit_price: float) -> float:
+    price_return = exit_price / entry - 1.0 if side == "long" else entry / exit_price - 1.0
+    return LEVERAGE * price_return - LEVERAGE * TAKER_FEE * (1.0 + exit_price / entry)
+
+
+def mark_return(side: str, entry: float, close: float) -> float:
+    price_return = close / entry - 1.0 if side == "long" else entry / close - 1.0
+    return LEVERAGE * price_return - LEVERAGE * TAKER_FEE
+
+
+def backtest(frame: pd.DataFrame, candidate: Candidate) -> tuple[pd.Series, pd.DataFrame]:
+    entry_signal, exit_signal = signals(frame, candidate)
+    warmup = 520
+    equity = INITIAL_EQUITY
+    position: dict[str, object] | None = None
+    pending_entry = ""
+    pending_exit = False
+    curve: list[tuple[pd.Timestamp, float]] = []
+    trades: list[dict[str, object]] = []
+
+    rows = list(frame.itertuples(index=False))
+    for index in range(warmup, len(rows)):
+        candle = rows[index]
+        if pending_exit and position is not None:
+            net = close_return(str(position["side"]), float(position["entry"]), float(candle.open))
+            equity *= 1.0 + net
+            trades.append({"entry_time": position["entry_time"], "exit_time": candle.dt, "side": position["side"], "return": net})
+            position = None
+            pending_exit = False
+        if pending_entry and position is None and equity > 0.0:
+            position = {"side": pending_entry, "entry": float(candle.open), "entry_index": index, "entry_time": candle.dt}
+            pending_entry = ""
+
+        mark = equity
+        if position is not None:
+            side = str(position["side"])
+            entry = float(position["entry"])
+            stop = float(candidate.params["stop"])
+            take = float(candidate.params["take"])
+            stop_price = entry * (1.0 - stop if side == "long" else 1.0 + stop)
+            take_price = entry * (1.0 + take if side == "long" else 1.0 - take)
+            stop_hit = candle.low <= stop_price if side == "long" else candle.high >= stop_price
+            take_hit = candle.high >= take_price if side == "long" else candle.low <= take_price
+            if stop_hit or take_hit:
+                exit_price = stop_price if stop_hit else take_price
+                net = close_return(side, entry, exit_price)
+                equity *= 1.0 + net
+                trades.append({"entry_time": position["entry_time"], "exit_time": candle.dt, "side": side, "return": net})
+                position = None
+                mark = equity
+            else:
+                mark = equity * (1.0 + mark_return(side, entry, float(candle.close)))
+
+        curve.append((candle.dt, mark))
+        if index == len(rows) - 1 or equity <= 0.0:
+            continue
+        next_entry = str(entry_signal.iloc[index])
+        if position is not None:
+            reverse = bool(next_entry) and next_entry != position["side"]
+            stale = index - int(position["entry_index"]) >= int(candidate.params["hold"])
+            if bool(exit_signal.iloc[index]) or reverse or stale:
+                pending_exit = True
+                pending_entry = next_entry if reverse else ""
+        elif next_entry:
+            pending_entry = next_entry
+
+    return pd.Series(dict(curve)).sort_index(), pd.DataFrame(trades)
+
+
+def scoped(equity: pd.Series, trades: pd.DataFrame, offset: pd.DateOffset | None) -> tuple[pd.Series, pd.DataFrame]:
+    if offset is None:
+        return equity, trades
+    start = equity.index[-1] - offset
+    scoped_equity = equity[equity.index >= start]
+    if len(scoped_equity) < 2:
+        scoped_equity = equity
+    scoped_trades = trades[trades["entry_time"] >= scoped_equity.index[0]] if len(trades) else trades
+    return scoped_equity, scoped_trades
+
+
+def metrics(equity: pd.Series, trades: pd.DataFrame) -> dict[str, float | int]:
+    total = float(equity.iloc[-1] / equity.iloc[0] - 1.0)
+    years = (equity.index[-1] - equity.index[0]).total_seconds() / 31_536_000
+    annual = (1.0 + total) ** (1.0 / years) - 1.0 if total > -1.0 and years > 0 else 0.0
+    drawdown = float(((equity.cummax() - equity) / equity.cummax()).max())
+    returns = trades["return"] if len(trades) else pd.Series(dtype=float)
+    wins = returns[returns > 0.0]
+    losses = returns[returns < 0.0]
+    return {
+        "total_return": total,
+        "annualized_return": annual,
+        "max_drawdown": drawdown,
+        "calmar": annual / drawdown if drawdown else 0.0,
+        "trades": int(len(trades)),
+        "short_trades": int((trades["side"] == "short").sum()) if len(trades) else 0,
+        "long_trades": int((trades["side"] == "long").sum()) if len(trades) else 0,
+        "profit_factor": float(wins.sum() / abs(losses.sum())) if len(losses) else (999.0 if len(wins) else 0.0),
+        "win_rate": float(len(wins) / len(returns)) if len(returns) else 0.0,
+    }
+
+
+def summarize(candidate: Candidate, equity: pd.Series, trades: pd.DataFrame) -> dict[str, object]:
+    row: dict[str, object] = {
+        "symbol": SYMBOL,
+        "bar": candidate.bar,
+        "family": candidate.family,
+        "name": candidate.name,
+        "params_json": json.dumps(candidate.params, separators=(",", ":")),
+        "first_time": equity.index[0].strftime("%Y-%m-%d %H:%M"),
+        "last_time": equity.index[-1].strftime("%Y-%m-%d %H:%M"),
+    }
+    for label, offset in HORIZONS:
+        part_equity, part_trades = scoped(equity, trades, offset)
+        for key, value in metrics(part_equity, part_trades).items():
+            row[f"{label}_{key}"] = value
+    row["recent_trigger_score"] = int(row["3m_trades"]) + int(row["1m_trades"]) * 2 + int(row["2w_trades"]) * 4
+    observe = (
+        int(row["3m_trades"]) >= 8
+        and int(row["1m_trades"]) >= 2
+        and int(row["2w_trades"]) >= 1
+        and float(row["3y_total_return"]) > 0.0
+        and float(row["1y_total_return"]) > 0.0
+        and float(row["3y_max_drawdown"]) <= 0.45
+        and float(row["1y_max_drawdown"]) <= 0.30
+        and float(row["1y_profit_factor"]) >= 1.05
+    )
+    row["readonly_observe"] = "yes" if observe else "no"
+    return row
+
+
+def markdown_table(frame: pd.DataFrame) -> str:
+    def cell(value: object) -> str:
+        if isinstance(value, float):
+            return f"{value:.4f}"
+        return str(value).replace("|", "\\|")
+
+    rows = [list(frame.columns), ["---" for _ in frame.columns]]
+    rows.extend(frame.astype(object).where(pd.notna(frame), "").values.tolist())
+    return "\n".join("| " + " | ".join(cell(value) for value in row) + " |" for row in rows)
+
+
+def write_report(totals: pd.DataFrame, paths: list[Path], command: str) -> str:
+    selected = totals[totals["readonly_observe"] == "yes"].head(12)
+    recent = totals.sort_values(["readonly_observe", "recent_trigger_score", "3y_calmar"], ascending=[False, False, False]).head(12)
+    cols = [
+        "family",
+        "bar",
+        "name",
+        "3y_total_return",
+        "3y_max_drawdown",
+        "3y_trades",
+        "1y_total_return",
+        "1y_max_drawdown",
+        "1y_profit_factor",
+        "3m_total_return",
+        "3m_trades",
+        "1m_trades",
+        "2w_trades",
+        "readonly_observe",
+    ]
+    return "\n".join(
+        [
+            "# ETH filtered recent short/bidirectional search",
+            "",
+            f"Run command: `{command}`",
+            "Scope: 15m/1H only, local ETH/BTC candle cache, taker-fee model, no live path touched.",
+            "",
+            "Output files:",
+            *[f"- `{path}`" for path in paths],
+            "",
+            "Selection rule: recent activity plus positive 3y/1y return, bounded 3y/1y drawdown, and 1y profit factor >= 1.05.",
+            "",
+            "## Read-only observation candidates",
+            "",
+            markdown_table(selected[cols]) if len(selected) else "No candidates passed the read-only observation rule.",
+            "",
+            "## Top recent filtered candidates",
+            "",
+            markdown_table(recent[cols]),
+        ]
+    ) + "\n"
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--bars", nargs="+", default=["15m", "1H"])
+    parser.add_argument("--output-dir", type=Path, default=OUT_DIR)
+    parser.add_argument("--years", type=float, default=3.0)
+    args = parser.parse_args()
+
+    frames = {bar: joined_frame(bar, args.years) for bar in args.bars}
+    rows = []
+    for candidate in build_candidates(args.bars):
+        equity, trades = backtest(frames[candidate.bar], candidate)
+        if len(equity) >= 2:
+            rows.append(summarize(candidate, equity, trades))
+
+    totals = pd.DataFrame(rows).sort_values(
+        ["readonly_observe", "recent_trigger_score", "3y_calmar", "1y_calmar"],
+        ascending=[False, False, False, False],
+    )
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    totals_path = args.output_dir / "eth-filtered-recent-short-bidir-candidates.csv"
+    top_path = args.output_dir / "eth-filtered-recent-short-bidir-top.csv"
+    report_path = args.output_dir / "eth-filtered-recent-short-bidir-report.md"
+    paths = [totals_path, top_path, report_path]
+    totals.to_csv(totals_path, index=False)
+    totals.head(50).to_csv(top_path, index=False)
+    command = f"rtk .venv/bin/python {Path(__file__).as_posix()} --bars {' '.join(args.bars)} --years {args.years:g}"
+    report_path.write_text(write_report(totals, paths, command), encoding="utf-8")
+    print(totals.head(20).to_string(index=False))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/search_eth_high_freq_short_bidir_candidates.py

@@ -0,0 +1,361 @@
+from __future__ import annotations
+
+import argparse
+import json
+from dataclasses import dataclass
+from pathlib import Path
+
+import pandas as pd
+
+
+DATA_DIR = Path("data/okx-candles")
+OUT_DIR = Path("reports/ultrashort")
+SYMBOL = "ETH-USDT-SWAP"
+INITIAL_EQUITY = 10_000.0
+LEVERAGE = 3.0
+TAKER_FEE = 0.0004
+HORIZONS = (
+    ("full", None),
+    ("3y", pd.DateOffset(years=3)),
+    ("1y", pd.DateOffset(years=1)),
+    ("3m", pd.DateOffset(months=3)),
+    ("1m", pd.DateOffset(months=1)),
+    ("2w", pd.DateOffset(weeks=2)),
+)
+
+
+@dataclass(frozen=True)
+class Candidate:
+    family: str
+    bar: str
+    params: dict[str, float | int | str]
+
+    @property
+    def name(self) -> str:
+        body = "-".join(f"{key}{value:g}" if isinstance(value, float) else f"{key}{value}" for key, value in self.params.items())
+        return f"{self.family}-{self.bar}-{body}"
+
+
+def load_frame(bar: str, years: float) -> pd.DataFrame:
+    frame = pd.read_csv(DATA_DIR / SYMBOL / f"{bar}.csv")
+    frame["dt"] = pd.to_datetime(frame["ts"], unit="ms", utc=True)
+    frame = frame.sort_values("ts").drop_duplicates("ts", keep="last")
+    cutoff = frame["dt"].iloc[-1] - pd.DateOffset(days=int(years * 365))
+    return frame[frame["dt"] >= cutoff].reset_index(drop=True)
+
+
+def rsi(close: pd.Series, length: int) -> pd.Series:
+    delta = close.diff()
+    gain = delta.clip(lower=0.0).rolling(length).mean()
+    loss = (-delta.clip(upper=0.0)).rolling(length).mean()
+    return 100.0 - 100.0 / (1.0 + gain / loss)
+
+
+def build_candidates(bars: list[str]) -> list[Candidate]:
+    candidates: list[Candidate] = []
+    for bar in bars:
+        for window in (48, 96):
+            for entry_z in (1.5,):
+                for hold in (12,):
+                    base = {"window": window, "entry_z": entry_z, "exit_z": 0.20, "stop": 0.006, "take": 0.009, "hold": hold}
+                    candidates.append(Candidate("vwap_bidir", bar, base))
+                    candidates.append(Candidate("vwap_short", bar, base))
+        for trend in (96, 192):
+            for entry in (90,):
+                for hold in (12,):
+                    candidates.append(
+                        Candidate(
+                            "rsi_short",
+                            bar,
+                            {"trend": trend, "entry": entry, "exit": 45, "stop": 0.0075, "take": 0.010, "hold": hold},
+                        )
+                    )
+            for entry in (10,):
+                for hold in (12,):
+                    candidates.append(
+                        Candidate(
+                            "rsi_bidir",
+                            bar,
+                            {"trend": trend, "entry": entry, "exit": 55, "stop": 0.0075, "take": 0.010, "hold": hold},
+                        )
+                    )
+        for lookback in (48, 96):
+            for hold in (12,):
+                candidates.append(
+                    Candidate(
+                        "breakdown_short",
+                        bar,
+                        {"lookback": lookback, "stop": 0.006, "take": 0.012, "hold": hold},
+                    )
+                )
+    return candidates
+
+
+def signal_columns(frame: pd.DataFrame, candidate: Candidate) -> tuple[pd.Series, pd.Series]:
+    close = frame["close"]
+    false = pd.Series(False, index=frame.index)
+    params = candidate.params
+    if candidate.family in ("vwap_bidir", "vwap_short"):
+        window = int(params["window"])
+        volume = frame["volume"]
+        vwap = (close * volume).rolling(window).sum() / volume.rolling(window).sum()
+        stdev = close.rolling(window).std(ddof=0)
+        zscore = (close - vwap) / stdev
+        short_entry = zscore >= float(params["entry_z"])
+        long_entry = zscore <= -float(params["entry_z"])
+        entry = pd.Series("", index=frame.index, dtype=object)
+        entry.loc[short_entry] = "short"
+        if candidate.family == "vwap_bidir":
+            entry.loc[long_entry] = "long"
+        exit_ = (zscore.abs() <= float(params["exit_z"]))
+        return entry, exit_
+    if candidate.family in ("rsi_short", "rsi_bidir"):
+        trend = close.rolling(int(params["trend"])).mean()
+        value = rsi(close, 2)
+        entry = pd.Series("", index=frame.index, dtype=object)
+        entry.loc[(close < trend) & (value >= float(params["entry"]))] = "short"
+        if candidate.family == "rsi_bidir":
+            entry.loc[(close > trend) & (value <= float(params["entry"]))] = "long"
+        exit_ = (value <= 100.0 - float(params["exit"])) | (value >= float(params["exit"]))
+        return entry, exit_
+    lookback = int(params["lookback"])
+    prior_low = frame["low"].shift(1).rolling(lookback).min()
+    entry = pd.Series("", index=frame.index, dtype=object)
+    entry.loc[close < prior_low] = "short"
+    return entry, false
+
+
+def close_return(side: str, entry: float, exit_price: float) -> float:
+    price_return = exit_price / entry - 1.0 if side == "long" else entry / exit_price - 1.0
+    return LEVERAGE * price_return - LEVERAGE * TAKER_FEE * (1.0 + exit_price / entry)
+
+
+def mark_return(side: str, entry: float, close: float) -> float:
+    price_return = close / entry - 1.0 if side == "long" else entry / close - 1.0
+    return LEVERAGE * price_return - LEVERAGE * TAKER_FEE
+
+
+def backtest(frame: pd.DataFrame, candidate: Candidate) -> tuple[pd.Series, pd.DataFrame]:
+    entry_signal, exit_signal = signal_columns(frame, candidate)
+    warmup = max(int(value) for key, value in candidate.params.items() if key in {"window", "trend", "lookback"}) + 2
+    equity = INITIAL_EQUITY
+    position: dict[str, object] | None = None
+    pending_entry = ""
+    pending_exit = False
+    curve: list[tuple[pd.Timestamp, float]] = []
+    trades: list[dict[str, object]] = []
+
+    rows = list(frame.itertuples(index=False))
+    for index in range(warmup, len(rows)):
+        candle = rows[index]
+        if pending_exit and position is not None:
+            net = close_return(str(position["side"]), float(position["entry"]), float(candle.open))
+            equity *= 1.0 + net
+            trades.append({"entry_time": position["entry_time"], "exit_time": candle.dt, "side": position["side"], "return": net})
+            position = None
+            pending_exit = False
+        if pending_entry and position is None and equity > 0.0:
+            position = {"side": pending_entry, "entry": float(candle.open), "entry_index": index, "entry_time": candle.dt}
+            pending_entry = ""
+
+        mark = equity
+        if position is not None:
+            side = str(position["side"])
+            entry = float(position["entry"])
+            stop = float(candidate.params["stop"])
+            take = float(candidate.params["take"])
+            stop_price = entry * (1.0 - stop if side == "long" else 1.0 + stop)
+            take_price = entry * (1.0 + take if side == "long" else 1.0 - take)
+            stop_hit = candle.low <= stop_price if side == "long" else candle.high >= stop_price
+            take_hit = candle.high >= take_price if side == "long" else candle.low <= take_price
+            if stop_hit or take_hit:
+                exit_price = stop_price if stop_hit else take_price
+                net = close_return(side, entry, exit_price)
+                equity *= 1.0 + net
+                trades.append({"entry_time": position["entry_time"], "exit_time": candle.dt, "side": side, "return": net})
+                position = None
+                mark = equity
+            else:
+                mark = equity * (1.0 + mark_return(side, entry, float(candle.close)))
+
+        curve.append((candle.dt, mark))
+        if index == len(rows) - 1 or equity <= 0.0:
+            continue
+        next_entry = str(entry_signal.iloc[index])
+        if position is not None:
+            reverse = bool(next_entry) and next_entry != position["side"]
+            stale = index - int(position["entry_index"]) >= int(candidate.params["hold"])
+            if bool(exit_signal.iloc[index]) or reverse or stale:
+                pending_exit = True
+                pending_entry = next_entry if reverse else ""
+        elif next_entry:
+            pending_entry = next_entry
+
+    if position is not None:
+        final = rows[-1]
+        net = close_return(str(position["side"]), float(position["entry"]), float(final.close))
+        equity *= 1.0 + net
+        trades.append({"entry_time": position["entry_time"], "exit_time": final.dt, "side": position["side"], "return": net})
+        curve.append((final.dt, equity))
+
+    return pd.Series(dict(curve)).sort_index(), pd.DataFrame(trades)
+
+
+def scoped(equity: pd.Series, trades: pd.DataFrame, offset: pd.DateOffset | None) -> tuple[pd.Series, pd.DataFrame]:
+    if offset is None:
+        return equity, trades
+    start = equity.index[-1] - offset
+    scoped_equity = equity[equity.index >= start]
+    if len(scoped_equity) < 2:
+        scoped_equity = equity
+    scoped_trades = trades[trades["entry_time"] >= scoped_equity.index[0]] if len(trades) else trades
+    return scoped_equity, scoped_trades
+
+
+def metrics(equity: pd.Series, trades: pd.DataFrame) -> dict[str, float | int]:
+    total = float(equity.iloc[-1] / equity.iloc[0] - 1.0)
+    years = (equity.index[-1] - equity.index[0]).total_seconds() / 31_536_000
+    annual = (1.0 + total) ** (1.0 / years) - 1.0 if total > -1.0 and years > 0 else 0.0
+    drawdown = float(((equity.cummax() - equity) / equity.cummax()).max())
+    returns = trades["return"] if len(trades) else pd.Series(dtype=float)
+    wins = returns[returns > 0.0]
+    losses = returns[returns < 0.0]
+    return {
+        "total_return": total,
+        "annualized_return": annual,
+        "max_drawdown": drawdown,
+        "calmar": annual / drawdown if drawdown else 0.0,
+        "trades": int(len(trades)),
+        "short_trades": int((trades["side"] == "short").sum()) if len(trades) else 0,
+        "long_trades": int((trades["side"] == "long").sum()) if len(trades) else 0,
+        "profit_factor": float(wins.sum() / abs(losses.sum())) if len(losses) else (999.0 if len(wins) else 0.0),
+        "win_rate": float(len(wins) / len(returns)) if len(returns) else 0.0,
+    }
+
+
+def summarize(candidate: Candidate, equity: pd.Series, trades: pd.DataFrame) -> dict[str, object]:
+    row: dict[str, object] = {
+        "symbol": SYMBOL,
+        "bar": candidate.bar,
+        "family": candidate.family,
+        "name": candidate.name,
+        "params_json": json.dumps(candidate.params, separators=(",", ":")),
+        "first_time": equity.index[0].strftime("%Y-%m-%d %H:%M"),
+        "last_time": equity.index[-1].strftime("%Y-%m-%d %H:%M"),
+    }
+    for label, offset in HORIZONS:
+        part_equity, part_trades = scoped(equity, trades, offset)
+        for key, value in metrics(part_equity, part_trades).items():
+            row[f"{label}_{key}"] = value
+    row["recent_trigger_score"] = int(row["3m_trades"]) + int(row["1m_trades"]) * 2 + int(row["2w_trades"]) * 4
+    observe = (
+        int(row["3m_trades"]) >= 12
+        and int(row["1m_trades"]) >= 4
+        and int(row["2w_trades"]) >= 1
+        and float(row["3y_total_return"]) > 0.0
+        and float(row["1y_total_return"]) > 0.0
+        and float(row["3y_max_drawdown"]) <= 0.35
+        and float(row["1y_max_drawdown"]) <= 0.25
+    )
+    row["readonly_observe"] = "yes" if observe else "no"
+    return row
+
+
+def markdown_table(frame: pd.DataFrame) -> str:
+    def cell(value: object) -> str:
+        if isinstance(value, float):
+            return f"{value:.4f}"
+        return str(value).replace("|", "\\|")
+
+    rows = [list(frame.columns), ["---" for _ in frame.columns]]
+    rows.extend(frame.astype(object).where(pd.notna(frame), "").values.tolist())
+    return "\n".join("| " + " | ".join(cell(value) for value in row) + " |" for row in rows)
+
+
+def write_report(totals: pd.DataFrame, paths: list[Path], command: str) -> str:
+    selected = totals[totals["readonly_observe"] == "yes"].head(12)
+    recent = totals.sort_values(["recent_trigger_score", "3y_calmar", "1y_calmar"], ascending=[False, False, False]).head(12)
+    least_bad = totals.sort_values(
+        ["3y_total_return", "1y_total_return", "3m_total_return", "3m_trades"],
+        ascending=[False, False, False, False],
+    ).head(12)
+    cols = [
+        "family",
+        "bar",
+        "name",
+        "full_total_return",
+        "full_max_drawdown",
+        "full_trades",
+        "3y_total_return",
+        "3y_max_drawdown",
+        "3y_trades",
+        "1y_total_return",
+        "1y_max_drawdown",
+        "1y_trades",
+        "3m_trades",
+        "1m_trades",
+        "2w_trades",
+        "readonly_observe",
+    ]
+    return "\n".join(
+        [
+            "# ETH high-frequency short/bidirectional candidate search",
+            "",
+            f"Run command: `{command}`",
+            "Scope: local OKX ETH candle CSV only; no live executor, deployment, private API, or order path touched.",
+            f"Cost model: taker fee `{TAKER_FEE}` each side on `{LEVERAGE:g}x` notional; entries execute on next open.",
+            "",
+            "Output files:",
+            *[f"- `{path}`" for path in paths],
+            "",
+            "Selection rule for `readonly_observe`: 3m >= 12 trades, 1m >= 4 trades, 2w >= 1 trade, positive 3y/1y return, 3y MDD <= 35%, 1y MDD <= 25%.",
+            "",
+            "## Read-only observation candidates",
+            "",
+            markdown_table(selected[cols]) if len(selected) else "No candidates passed the read-only observation rule.",
+            "",
+            "## Least-bad risk rows",
+            "",
+            markdown_table(least_bad[cols]),
+            "",
+            "## Most recently active candidates",
+            "",
+            markdown_table(recent[cols]),
+        ]
+    ) + "\n"
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--bars", nargs="+", default=["3m", "5m", "15m"])
+    parser.add_argument("--output-dir", type=Path, default=OUT_DIR)
+    parser.add_argument("--years", type=float, default=3.0)
+    args = parser.parse_args()
+
+    rows: list[dict[str, object]] = []
+    frames = {bar: load_frame(bar, args.years) for bar in args.bars}
+    for candidate in build_candidates(args.bars):
+        equity, trades = backtest(frames[candidate.bar], candidate)
+        if len(equity) < 2:
+            continue
+        rows.append(summarize(candidate, equity, trades))
+
+    totals = pd.DataFrame(rows).sort_values(
+        ["readonly_observe", "3m_trades", "1m_trades", "2w_trades", "3y_calmar", "1y_calmar"],
+        ascending=[False, False, False, False, False, False],
+    )
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    totals_path = args.output_dir / "eth-highfreq-short-bidir-candidates.csv"
+    top_path = args.output_dir / "eth-highfreq-short-bidir-top.csv"
+    report_path = args.output_dir / "eth-highfreq-short-bidir-report.md"
+    paths = [totals_path, top_path, report_path]
+    totals.to_csv(totals_path, index=False)
+    totals.head(50).to_csv(top_path, index=False)
+    command = f"rtk .venv/bin/python {Path(__file__).as_posix()} --bars {' '.join(args.bars)} --years {args.years:g}"
+    report_path.write_text(write_report(totals, paths, command), encoding="utf-8")
+    print(totals.head(20).to_string(index=False))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/summarize_current_strategy_recent_activity.py

@@ -0,0 +1,151 @@
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from scripts.search_eth_bearish_price_proxy import (
+    BTC_SYMBOL,
+    SYMBOL,
+    Spec,
+    joined_frames,
+    load_frame,
+    period_metrics,
+    resample,
+    run_spec,
+)
+from scripts.search_eth_bb_squeeze_t_gates import (
+    Variant as TGateVariant,
+    _align_pair,
+    _load_candles as load_tgate_candles,
+    cost_equity_frame as tgate_cost_equity_frame,
+    run_variant as run_tgate_variant,
+)
+from scripts.search_live_bb_squeeze_exit_variants import (
+    PRIMARY_COST,
+    Variant as LiveVariant,
+    _load_candles as load_live_candles,
+    cost_equity_frame as live_cost_equity_frame,
+    run_variant as run_live_variant,
+)
+
+
+OUTPUT_DIR = Path("reports/eth-exploration")
+SUMMARY_CSV = OUTPUT_DIR / "current-strategy-recent-activity.csv"
+REPORT_MD = OUTPUT_DIR / "current-strategy-recent-activity-report.md"
+WINDOWS = (30, 14, 7)
+
+
+def scoped_return(frame: pd.DataFrame, days: int) -> float:
+    cutoff = frame["ts"].iloc[-1] - pd.Timedelta(days=days)
+    scoped = frame[frame["ts"] >= cutoff]
+    if len(scoped) < 2:
+        return 0.0
+    return float(scoped["equity"].iloc[-1] / scoped["equity"].iloc[0] - 1.0)
+
+
+def count_trades(trades: list[dict[str, object]], days: int, end: pd.Timestamp) -> int:
+    cutoff = end - pd.Timedelta(days=days)
+    return sum(pd.Timestamp(trade["entry_time"], tz="UTC") >= cutoff for trade in trades)
+
+
+def live_rows() -> list[dict[str, object]]:
+    candles = load_live_candles("ETH-USDT-SWAP", "15m")
+    result = run_live_variant(candles, LiveVariant(0.0005, 1))
+    frame = live_cost_equity_frame(result, dict((PRIMARY_COST, 0.0021))[PRIMARY_COST] if False else 0.0021)
+    end = pd.to_datetime(candles[-1].ts, unit="ms", utc=True)
+    return [
+        {
+            "strategy": "live_bb_squeeze_mxbuf0.0005",
+            "window_days": days,
+            "total_return": scoped_return(frame, days),
+            "trades": count_trades(result.trades, days, end),
+            "last_candle": end.isoformat(),
+        }
+        for days in WINDOWS
+    ]
+
+
+def tgate_rows() -> list[dict[str, object]]:
+    eth = load_tgate_candles("ETH-USDT-SWAP", "15m")
+    btc = load_tgate_candles("BTC-USDT-SWAP", "15m")
+    eth, btc = _align_pair(eth, btc)
+    variant = TGateVariant(96, 960, 0.25, 0.01, 0.035, "both", "btc-up", 0.006, 0.25, 24, 96, "btc_against", 0.0, 1, 0.006, 0.25, 0.008)
+    result, gate_stats = run_tgate_variant(eth, btc, variant)
+    frame = tgate_cost_equity_frame(result, 0.0021)
+    end = pd.to_datetime(eth[-1].ts, unit="ms", utc=True)
+    return [
+        {
+            "strategy": "bb_squeeze_t_gated_tre96",
+            "window_days": days,
+            "total_return": scoped_return(frame, days),
+            "trades": count_trades(result.trades, days, end),
+            "last_candle": end.isoformat(),
+            "reentry_entries_full": gate_stats["reentry_entries"],
+        }
+        for days in WINDOWS
+    ]
+
+
+def crash_follow_rows() -> list[dict[str, object]]:
+    eth = load_frame(SYMBOL)
+    btc = load_frame(BTC_SYMBOL)
+    frame = joined_frames(resample(eth, "1H"), resample(btc, "1H"))
+    spec = Spec("crash_follow", "1H", 20, 120, 8, 0.035, 0.02, 0.06, 96, "btc_riskoff")
+    equity, trades = run_spec(spec, frame)
+    end = equity.index[-1]
+    rows = []
+    for days in WINDOWS:
+        metrics = period_metrics(equity, trades, pd.DateOffset(days=days))
+        rows.append(
+            {
+                "strategy": "crash_follow_short",
+                "window_days": days,
+                "total_return": metrics["total_return"],
+                "max_drawdown": metrics["max_drawdown"],
+                "trades": metrics["trades"],
+                "profit_factor": metrics["profit_factor"],
+                "win_rate": metrics["win_rate"],
+                "last_candle": end.isoformat(),
+            }
+        )
+    return rows
+
+
+def markdown_table(frame: pd.DataFrame) -> str:
+    rows = [list(frame.columns), ["---" for _ in frame.columns]]
+    rows.extend(frame.astype(object).where(pd.notna(frame), "").values.tolist())
+    return "\n".join("| " + " | ".join(str(value).replace("|", "\\|") for value in row) + " |" for row in rows)
+
+
+def main() -> int:
+    rows = live_rows() + tgate_rows() + crash_follow_rows()
+    summary = pd.DataFrame(rows)
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    summary.to_csv(SUMMARY_CSV, index=False)
+
+    display = summary.copy()
+    display["total_return"] = display["total_return"].map(lambda value: f"{value:.2%}")
+    if "max_drawdown" in display:
+        display["max_drawdown"] = display["max_drawdown"].map(lambda value: "" if pd.isna(value) else f"{value:.2%}")
+    if "profit_factor" in display:
+        display["profit_factor"] = display["profit_factor"].map(lambda value: "" if pd.isna(value) else f"{value:.3f}")
+    if "win_rate" in display:
+        display["win_rate"] = display["win_rate"].map(lambda value: "" if pd.isna(value) else f"{value:.2%}")
+
+    REPORT_MD.write_text(
+        "# Current Strategy Recent Activity\n\n"
+        "Scope: current live BB squeeze, selected T-gated BB squeeze observer, and crash-follow short observer.\n\n"
+        f"{markdown_table(display)}\n",
+        encoding="utf-8",
+    )
+    print(f"wrote {SUMMARY_CSV} and {REPORT_MD}")
+    print(summary.to_string(index=False))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/update_candle_cache_recent.py

@@ -0,0 +1,30 @@
+from __future__ import annotations
+
+import argparse
+import sys
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).resolve().parents[1]))
+
+from okx_codex_trader.okx_client import OkxClient
+from scripts.explore_ultrashort import CANDLE_CACHE_DIR, get_candles_cached
+
+
+def main() -> int:
+    parser = argparse.ArgumentParser(description="Update local OKX candle cache using recent candles.")
+    parser.add_argument("--cache-dir", type=Path, default=CANDLE_CACHE_DIR)
+    parser.add_argument("--bar", default="15m")
+    parser.add_argument("--limit", type=int, default=300_000)
+    parser.add_argument("--symbols", nargs="+", default=["ETH-USDT-SWAP", "BTC-USDT-SWAP"])
+    args = parser.parse_args()
+    client = OkxClient()
+    for symbol in args.symbols:
+        candles = get_candles_cached(client, symbol, args.bar, args.limit, args.cache_dir)
+        first = candles[0].ts if candles else None
+        last = candles[-1].ts if candles else None
+        print(f"{symbol} {args.bar} rows={len(candles)} first_ts={first} last_ts={last}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

tests/test_report_candle_cache_freshness.py

@@ -0,0 +1,35 @@
+from okx_codex_trader.models import Candle
+from scripts import report_candle_cache_freshness as module
+
+
+def candle(ts: int) -> Candle:
+    return Candle(symbol="ETH-USDT-SWAP", ts=ts, open=1.0, high=1.0, low=1.0, close=1.0, volume=1.0)
+
+
+def test_freshness_rows_reports_local_remote_gap(tmp_path):
+    path = tmp_path / "ETH-USDT-SWAP"
+    path.mkdir()
+    (path / "15m.csv").write_text("ts,open,high,low,close,volume\n1000,1,1,1,1,1\n901000,1,1,1,1,1\n", encoding="utf-8")
+
+    class Client:
+        def get_recent_candles(self, symbol: str, bar: str, limit: int) -> list[Candle]:
+            assert symbol == "ETH-USDT-SWAP"
+            assert bar == "15m"
+            assert limit == 2
+            return [candle(1_801_000), candle(2_701_000)]
+
+    rows = module.freshness_rows(client=Client(), cache_dir=tmp_path, symbols=("ETH-USDT-SWAP",), bars=("15m",))
+
+    assert rows == [
+        {
+            "symbol": "ETH-USDT-SWAP",
+            "bar": "15m",
+            "local_rows": 2,
+            "local_last_ts": 901000,
+            "local_last_time": "1970-01-01T00:15:01Z",
+            "remote_last_ts": 2701000,
+            "remote_last_time": "1970-01-01T00:45:01Z",
+            "lag_bars": 2,
+            "lag_ms": 1800000,
+        }
+    ]