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refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
//+------------------------------------------------------------------+
//| Warrior_EA |
//| AnimateDread |
//| |
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
//| Triple-barrier labelling and the async label-cache prebuild. |
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
//| |
//| PARTIAL IMPLEMENTATION FILE - not standalone. |
//| This holds CExpertSignalAIBase method BODIES only. The class |
//| declaration lives in Expert\ExpertSignalAIBase.mqh, which |
//| #includes this file at the bottom, after the declaration. Do not |
//| include it anywhere else and do not compile it on its own. |
//| |
//| Split out purely to make the 8216-line original navigable; the |
//| code inside was moved verbatim, not rewritten. |
//+------------------------------------------------------------------+
#ifndef WARRIOR_AIBASE_LABELS_MQH
#define WARRIOR_AIBASE_LABELS_MQH
//+------------------------------------------------------------------+
//| (Re)sizes the label AND feature caches and clears them if `bars` |
//| (or the now-relative index frame) has changed since the last |
//| build - see the member declaration comments for why this is the |
//| correct invalidation trigger. Returns true if a rebuild happened. |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::EnsureBarCachesCapacity(int bars)
{
if(bars == m_labelCacheBars && m_Time.GetData(0) == m_labelCacheAnchorTime)
return false;
ArrayResize(m_labelCacheBuy, bars);
ArrayResize(m_labelCacheSell, bars);
diag: is "optimal SL/TP" learnable? Score the features against excursions Proposed direction: train the net to predict entry/SL/TP that maximise return and minimise drawdown, rather than to classify direction. Before rebuilding a head, measure whether the target is learnable at all. That question splits into two that behave nothing alike: HOW FAR price travels (MFE/MAE) - essentially volatility, and volatility clustering is about the most robust regularity in markets. WHICH WAY it goes first (the asymmetry) - direction, which is what every noise-floor verdict in this project has been about. Expectancy comes ONLY from the second. The first buys position sizing and drawdown control - worth having under prop-firm limits, but not an edge: exit management on RANDOM entries already moved the payoff ratio 0.92 -> 5.72 with expectancy FLAT. Crucially this is NOT already answered. Every MI figure here scored the triple-barrier label, i.e. one specific question at one fixed geometry. A noise-floor result there says nothing about whether excursion MAGNITUDE is learnable - different target, different answer. Four targets, and the verdict is the CONTRAST, printed explicitly because the dangerous misreading of "UP clears" is "we can predict profitable trades": RANGE (up+dn) - realised volatility, included as a POSITIVE CONTROL that SHOULD clear. Every prior verdict here lacked a control expected to pass; a range target at the floor indicts the measurement, not the market. UP / DOWN - MFE / MAE. ASYMMETRY - up-dn, the only one that can pay. Collected inside the walk the label already does (one max, one min per bar). The early-out when both barriers resolved is GONE: it would have truncated the excursions at whichever bar tripped the last barrier, making the measurement a function of the CURRENT SL/TP - the circularity this is trying to escape. The loop was already bounded by the horizon, so only the average cost moves. Discretised into 3 EQUAL-FREQUENCY bins, so every downstream piece (block permutation, null, p-value) is reused unchanged. Equal-frequency because MFE is fat-tailed and fixed-width bins would put nearly every row in bin 0; it also pins H(Y) at ln(3)=1.099 for all four, making them comparable to each other and to the barrier label's ~1.02 instead of confounded by class balance. Two bugs fixed in this code before it ever ran, both of which would have produced a plausible quiet wrong answer rather than an error: - TripleBarrierLabel early-returns on invalid ATR/close BEFORE the point the accumulators were reset, so one bar's excursions would be cached under another bar's index. Cleared at the top now, ahead of every return. - An unresolvable bar is still flagged as labelled but carries excursions of exactly 0. Under equal-frequency binning a block of identical zeros drags the lowest cut onto zero and a third of the sample lands in one uninformative bin - a depressed score that reads as "not predictable", a false negative in the direction that would wrongly kill the idea. Rows where both excursions are zero are dropped; price cannot travel zero both ways over a whole horizon. Read-only diagnostic. No topology or label change: no retrain of its own. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 10:22:41 -04:00
//--- Sized with the label caches they share a validity flag with, so the three can never disagree
//--- about how many bars they cover.
ArrayResize(m_excUpCache, bars);
ArrayResize(m_excDownCache, bars);
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
ArrayResize(m_labelCacheHasValue, bars);
ArrayInitialize(m_labelCacheHasValue, false);
ArrayResize(m_featureCache, bars * m_neuronsCount);
ArrayResize(m_featureCacheHasValue, bars);
ArrayResize(m_featureCacheValid, bars);
ArrayInitialize(m_featureCacheHasValue, false);
m_labelCacheBars = bars;
m_labelCacheAnchorTime = m_Time.GetData(0);
return true;
}
//+------------------------------------------------------------------+
//| Lazy cache-miss fallback for a bar the eager prebuild pass (see |
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
//| AdvanceBarrierLabelState()) didn't cover - e.g. a new candle that |
//| closed after prebuild already completed. Such a bar sits inside |
//| the unresolved horizon: its triple-barrier outcome needs |
//| m_barrierHorizonBars more closes before it is knowable at all. |
//| Rather than guess, this always labels Neutral; the sequential |
//| prebuild scan is what assigns Buy/Sell once the forward window |
//| this bar's verdict depends on has actually closed. |
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
//+------------------------------------------------------------------+
void CExpertSignalAIBase::ComputeLabelForBar(int i, int bars, bool &buy, bool &sell)
{
buy = false;
sell = false;
}
//+------------------------------------------------------------------+
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
//| SL/TP ATR multiples for the triple-barrier label, taken from the |
//| EA's own SL_Mode/TP_Mode (m_sl_mode/m_tp_mode, protected members |
//| of CExpertSignalCustom, set in Warrior_EA.mq5's per-topology |
//| setup block). Using the traded values is the entire point: it is |
//| what makes the era line's dir-precision a real win rate instead |
//| of a proxy for one. |
//| |
//| The INTELLIGENT modes scale with AI confidence, which does not |
//| exist when a label is computed - and must not, or the target |
//| would depend on the model's own output and the whole thing would |
//| be circular. Both therefore fall back to their ZERO-CONFIDENCE |
//| base (the trade the EA would place knowing nothing), which is |
//| also the widest stop and tightest target either mode can pick, so |
//| the label is the conservative member of the family it stands for. |
//| TP_INTELLIGENT is risk-relative by design, so its multiple is |
//| expressed against the resolved stop rather than against ATR. |
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
//+------------------------------------------------------------------+
feat: derive the ATR multiples from measured excursions - no hardcoded geometry The barrier was still two constants. SL_Mode/TP_Mode left the Inputs tab in 3482b6c, but the fallback was a hardcoded 2:6 and the geometry scan only ever chose from a hardcoded grid {2,3} x {2,3,4,6,8,10}. Picking the least-bad of eleven guesses is not deriving anything. WHY THE SCAN WAS THE WRONG INSTRUMENT, now measurable rather than argued. It ranks pairings by how predictable their OUTCOME is - a question about direction. The excursion test (2c78f3b) ran on SP500 H1 and direction is the one thing absent: ASYMMETRY p=0.0846, against RANGE/UP/DOWN all at p=0.0050, with RANGE scoring 0.01345 vs a 0.00343 null - 4x, where the barrier label sits at 1.01x. Hence the scan failing its own gate on every run, and its "winner" wandering 2:8 -> 3:8 -> 2:8 -> 2:4 across four runs of the same data. Excursion SIZE is strongly measurable, so derive the geometry from that instead. stop = q25 of measured ADVERSE travel (ordinary noise does not reach it) target = q50 of measured FAVOURABLE travel (reached ~half the time, by construction, inside the horizon) Continuous, in ATR units, superseding the enum multiples. Reachability ("target on X% of bars, stop on Y%") and the implied break-even are printed so the choice is auditable rather than trusted. FIXED-POINT ITERATION, not one-shot. ComputeBarrierHorizonBars scales the horizon with the target (first-passage time grows with the band) and the excursions are measured OVER the horizon, so target -> horizon -> excursions -> target is a real loop - deriving once sizes the target from travel measured under the PREVIOUS horizon. Re-measures until the multiples move <5%, capped at 3 passes, and says so if it does not settle. Does NOT create expectancy, and the log says as much: chance precision equals break-even at every geometry (m/(m+k) on both sides). It buys a target the market reaches and a stop that survives noise. Where Min_Risk_Reward_Ratio forces a target the market rarely reaches, it WARNS rather than overriding - the ratio is the user's risk policy, so the honest move is to state its cost. That is the collision that once rejected 100% of setups. Pinned in the .cfg as doubles appended AFTER this morning's two ints, so .cfg files written earlier today still load (their length guard finds no doubles) and a model that carries them was trained on them and never re-derives. Also fixes a message from e5ceed6 that claimed "this model resumed from disk" unconditionally - it printed above a "seeding era 0" line on a brand-new model, because the branch fires whenever the cache is not built, which is equally true before a fresh model's first prebuild. A diagnostic that misreports its own trigger is worse than one that says nothing: it gets quoted back as evidence. FORCES A FULL RETRAIN (labels change). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 12:06:25 -04:00
//+------------------------------------------------------------------+
//| DERIVE THE BARRIER FROM WHAT PRICE ACTUALLY DOES, not from an |
//| enum. Reads the measured MFE/MAE distribution collected by the |
//| label prebuild and sets the ATR multiples from its quantiles. |
//| |
//| WHY THIS AND NOT THE GEOMETRY SCAN. The scan ranks candidate SL:TP |
//| pairings by how predictable their OUTCOME is, which is a question |
//| about direction - and direction is the one thing measured absent |
//| here (ASYMMETRY p=0.0846 on SP500 H1, against RANGE/UP/DOWN all at |
//| p=0.0050). That is why its winner fails its own gate on every run |
//| and why its "best" wanders 2:8 -> 3:8 -> 2:8 -> 2:4. Excursion |
//| SIZE, by contrast, clears at 4x its null. So derive the geometry |
//| from the quantity that is actually measurable. |
//| |
//| WHAT THIS DOES NOT DO: create expectancy. Under a driftless walk |
//| the probability of touching +k*ATR before -m*ATR is m/(m+k), which |
//| is ALSO the break-even win rate for that payoff - so no choice of |
//| geometry has an edge, and this one does not either. What it buys |
//| is a target that is actually reachable inside the horizon and a |
//| stop wide enough to survive ordinary noise, both read off the |
//| data instead of guessed. The reachability figures are printed so |
//| the choice can be audited rather than trusted. |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::DeriveBarrierGeometry(void)
{
int bars = m_labelCacheBars;
double up[], dn[];
ArrayResize(up, bars);
ArrayResize(dn, bars);
int n = 0;
//--- IS region only, matching BuildMiSample: a geometry chosen with the holdout in view has used the
//--- holdout for selection, and it stops being a holdout.
int oosCutoff = (int)(MathMax(0, MathMin(100, m_oosSplitPct)) / 100.0
* MathMax(bars - MathMax(m_historyBars, 0), 0));
for(int i = MathMax(oosCutoff, 0); i < bars; i++)
{
if(i >= ArraySize(m_labelCacheHasValue) || !m_labelCacheHasValue[i])
continue;
if(i >= ArraySize(m_excUpCache))
continue;
double u = m_excUpCache[i], d = m_excDownCache[i];
if(!MathIsValidNumber(u) || !MathIsValidNumber(d) || (u <= 0.0 && d <= 0.0))
continue; // unresolvable bar - see the same guard in BuildMiSample
up[n] = u;
dn[n] = d;
n++;
}
if(n < BARRIER_DERIVE_MIN_SAMPLES)
{
Print(ID + StringFormat(": barrier geometry NOT derived - only %d usable excursion samples "
"(need %d). Falling back to the configured %d:%d.", n,
BARRIER_DERIVE_MIN_SAMPLES, m_sl_mode, m_tp_mode));
return false;
}
ArrayResize(up, n);
ArrayResize(dn, n);
ArraySort(up);
ArraySort(dn);
//--- STOP from the ADVERSE distribution, TARGET from the FAVOURABLE one - each leg sized by the thing
fix: normalise the asymmetry target - the raw one is confounded by volatility Three symbols ran the excursion test. RANGE/UP/DOWN cleared on all three; raw ASYMMETRY cleared on EURUSD and USDCAD at p=0.0050 and not on SP500 (p=0.1045). That looked like the first directional signal this project has found. It probably is not, and the test as built could not tell. (up-dn) IS NOT SCALE-FREE. If sigma is predictable - and RANGE clears at ~4x its null on every instrument - and the directional part is symmetric noise eps, then up-dn ~ sigma*eps, so a large sigma pushes the value into BOTH outer terciles. A pure volatility predictor scores positive MI against a 3-bin (up-dn) while carrying no directional information at all. Crucially that confound REPLICATES, so reproducing on two instruments is not evidence against it - and the effect sizes fit it: asymmetry runs 1.3-1.6x its null where RANGE runs ~4x, and carries ~0.1% of the target's entropy against RANGE's ~0.9%. That is the shape of a leaked fraction of the volatility signal, not an independent one. So add (up-dn)/(up+dn): bounded in [-1,+1], volatility divided out, and the only target a directional claim may rest on. The verdict now separates the cases and NAMES the confound when raw clears while normalised does not, instead of reporting the raw line as a finding. Two bugs of mine in the same block, both caught by output rather than review: - The derived-geometry line had a MISORDERED argument list: it printed "stop 25.00*ATR (q3 of adverse travel)" - the quantile percentage as the multiple and the multiple as the quantile. Real values were 2.61 stop / 8.03 target. A 25*ATR stop is absurd on its face, which is why it was seen. - THE STOP QUANTILE WAS BACKWARDS, and this one changes labels. It was 0.25 "so ordinary noise does not reach it", but q25 means 75% of bars EXCEED the stop - hit three times in four. The printed reachability said exactly that ("stop on 75.0% of bars"). Now 0.75. A quantile is a threshold, not a rate. This is the entire reason reachability is measured and printed rather than assumed. Also raises BARRIER_DERIVE_MAX_PASSES 3 -> 5: SP500 did not settle in 3 (stop still moving ~14% per pass) while EURUSD and USDCAD converged on pass 2. And bounds both quantile indices with MathMin(..., n-1) so q=1.0 cannot run off the end of the sorted array. The geometry from the previous run is NOT usable and the asymmetry result is unresolved, not established. Both are decided by the next run. FORCES A FULL RETRAIN (the stop quantile changes every label). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 13:04:13 -04:00
//--- it actually has to survive or reach. The stop sits at a HIGH quantile of MAE so only the minority
//--- of bars whose adverse travel exceeds it ever reach it; the target at the MEDIAN of MFE so it is
//--- reached about half the time within the horizon. See BARRIER_SL_QUANTILE for why that quantile is
//--- 0.75 and not 0.25 - the first version had it backwards and the printed reachability caught it.
double slRaw = dn[(int)MathMin(BARRIER_SL_QUANTILE * n, n - 1)];
double tpRaw = up[(int)MathMin(BARRIER_TP_QUANTILE * n, n - 1)];
feat: derive the ATR multiples from measured excursions - no hardcoded geometry The barrier was still two constants. SL_Mode/TP_Mode left the Inputs tab in 3482b6c, but the fallback was a hardcoded 2:6 and the geometry scan only ever chose from a hardcoded grid {2,3} x {2,3,4,6,8,10}. Picking the least-bad of eleven guesses is not deriving anything. WHY THE SCAN WAS THE WRONG INSTRUMENT, now measurable rather than argued. It ranks pairings by how predictable their OUTCOME is - a question about direction. The excursion test (2c78f3b) ran on SP500 H1 and direction is the one thing absent: ASYMMETRY p=0.0846, against RANGE/UP/DOWN all at p=0.0050, with RANGE scoring 0.01345 vs a 0.00343 null - 4x, where the barrier label sits at 1.01x. Hence the scan failing its own gate on every run, and its "winner" wandering 2:8 -> 3:8 -> 2:8 -> 2:4 across four runs of the same data. Excursion SIZE is strongly measurable, so derive the geometry from that instead. stop = q25 of measured ADVERSE travel (ordinary noise does not reach it) target = q50 of measured FAVOURABLE travel (reached ~half the time, by construction, inside the horizon) Continuous, in ATR units, superseding the enum multiples. Reachability ("target on X% of bars, stop on Y%") and the implied break-even are printed so the choice is auditable rather than trusted. FIXED-POINT ITERATION, not one-shot. ComputeBarrierHorizonBars scales the horizon with the target (first-passage time grows with the band) and the excursions are measured OVER the horizon, so target -> horizon -> excursions -> target is a real loop - deriving once sizes the target from travel measured under the PREVIOUS horizon. Re-measures until the multiples move <5%, capped at 3 passes, and says so if it does not settle. Does NOT create expectancy, and the log says as much: chance precision equals break-even at every geometry (m/(m+k) on both sides). It buys a target the market reaches and a stop that survives noise. Where Min_Risk_Reward_Ratio forces a target the market rarely reaches, it WARNS rather than overriding - the ratio is the user's risk policy, so the honest move is to state its cost. That is the collision that once rejected 100% of setups. Pinned in the .cfg as doubles appended AFTER this morning's two ints, so .cfg files written earlier today still load (their length guard finds no doubles) and a model that carries them was trained on them and never re-derives. Also fixes a message from e5ceed6 that claimed "this model resumed from disk" unconditionally - it printed above a "seeding era 0" line on a brand-new model, because the branch fires whenever the cache is not built, which is equally true before a fresh model's first prebuild. A diagnostic that misreports its own trigger is worse than one that says nothing: it gets quoted back as evidence. FORCES A FULL RETRAIN (labels change). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 12:06:25 -04:00
//--- Same floor a real order gets, applied before the ratio so the ratio is computed on the stop that
//--- will actually be used (the ordering bug that once let TP sit under minRR - see 168422f's note).
if(slRaw < MIN_SL_ATR_MULTIPLIER)
slRaw = MIN_SL_ATR_MULTIPLIER;
double minRR = (double)Min_Risk_Reward_Ratio;
bool rrForced = false;
if(minRR > 0.0 && tpRaw < minRR * slRaw)
{
tpRaw = minRR * slRaw;
rrForced = true;
}
//--- REACHABILITY, measured not assumed: what share of bars actually saw an excursion this big. This
//--- is the number that catches a target the horizon cannot deliver - the failure that shipped once
//--- already, where a clamped horizon quietly made every label "target within 128 bars".
int reachTp = 0, reachSl = 0;
for(int i = 0; i < n; i++)
{
if(up[i] >= tpRaw)
reachTp++;
if(dn[i] >= slRaw)
reachSl++;
}
double tpReach = 100.0 * reachTp / n;
double slReach = 100.0 * reachSl / n;
double breakeven = 100.0 * slRaw / (slRaw + tpRaw);
m_derivedSlMult = slRaw;
m_derivedTpMult = tpRaw;
m_geometryDerived = true;
Print(ID + StringFormat(": barrier geometry DERIVED from %d measured excursions - stop %.2f*ATR "
"(q%.0f of adverse travel), target %.2f*ATR (q%.0f of favourable)%s | reached "
"within the horizon: target on %.1f%% of bars, stop on %.1f%% | implied "
"break-even %.1f%%. Replaces the enum multiples; the grid those came from was "
"a set of guesses. This does NOT create expectancy - chance precision equals "
"break-even at every geometry - it makes the target reachable and the stop "
"survivable, both read off the data.",
fix: normalise the asymmetry target - the raw one is confounded by volatility Three symbols ran the excursion test. RANGE/UP/DOWN cleared on all three; raw ASYMMETRY cleared on EURUSD and USDCAD at p=0.0050 and not on SP500 (p=0.1045). That looked like the first directional signal this project has found. It probably is not, and the test as built could not tell. (up-dn) IS NOT SCALE-FREE. If sigma is predictable - and RANGE clears at ~4x its null on every instrument - and the directional part is symmetric noise eps, then up-dn ~ sigma*eps, so a large sigma pushes the value into BOTH outer terciles. A pure volatility predictor scores positive MI against a 3-bin (up-dn) while carrying no directional information at all. Crucially that confound REPLICATES, so reproducing on two instruments is not evidence against it - and the effect sizes fit it: asymmetry runs 1.3-1.6x its null where RANGE runs ~4x, and carries ~0.1% of the target's entropy against RANGE's ~0.9%. That is the shape of a leaked fraction of the volatility signal, not an independent one. So add (up-dn)/(up+dn): bounded in [-1,+1], volatility divided out, and the only target a directional claim may rest on. The verdict now separates the cases and NAMES the confound when raw clears while normalised does not, instead of reporting the raw line as a finding. Two bugs of mine in the same block, both caught by output rather than review: - The derived-geometry line had a MISORDERED argument list: it printed "stop 25.00*ATR (q3 of adverse travel)" - the quantile percentage as the multiple and the multiple as the quantile. Real values were 2.61 stop / 8.03 target. A 25*ATR stop is absurd on its face, which is why it was seen. - THE STOP QUANTILE WAS BACKWARDS, and this one changes labels. It was 0.25 "so ordinary noise does not reach it", but q25 means 75% of bars EXCEED the stop - hit three times in four. The printed reachability said exactly that ("stop on 75.0% of bars"). Now 0.75. A quantile is a threshold, not a rate. This is the entire reason reachability is measured and printed rather than assumed. Also raises BARRIER_DERIVE_MAX_PASSES 3 -> 5: SP500 did not settle in 3 (stop still moving ~14% per pass) while EURUSD and USDCAD converged on pass 2. And bounds both quantile indices with MathMin(..., n-1) so q=1.0 cannot run off the end of the sorted array. The geometry from the previous run is NOT usable and the asymmetry result is unresolved, not established. Both are decided by the next run. FORCES A FULL RETRAIN (the stop quantile changes every label). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 13:04:13 -04:00
//--- ORDER MATTERS AND WAS WRONG ONCE: the multiples and the quantile labels
//--- were swapped, so the log read "stop 25.00*ATR (q3 ...)" - printing the
//--- quantile percentage as the multiple and the multiple as the quantile.
//--- 25*ATR is absurd on its face, which is the only reason it was caught.
n, m_derivedSlMult, 100.0 * BARRIER_SL_QUANTILE, m_derivedTpMult,
100.0 * BARRIER_TP_QUANTILE,
(rrForced ? " [target RAISED to meet Min_Risk_Reward_Ratio]" : ""),
feat: derive the ATR multiples from measured excursions - no hardcoded geometry The barrier was still two constants. SL_Mode/TP_Mode left the Inputs tab in 3482b6c, but the fallback was a hardcoded 2:6 and the geometry scan only ever chose from a hardcoded grid {2,3} x {2,3,4,6,8,10}. Picking the least-bad of eleven guesses is not deriving anything. WHY THE SCAN WAS THE WRONG INSTRUMENT, now measurable rather than argued. It ranks pairings by how predictable their OUTCOME is - a question about direction. The excursion test (2c78f3b) ran on SP500 H1 and direction is the one thing absent: ASYMMETRY p=0.0846, against RANGE/UP/DOWN all at p=0.0050, with RANGE scoring 0.01345 vs a 0.00343 null - 4x, where the barrier label sits at 1.01x. Hence the scan failing its own gate on every run, and its "winner" wandering 2:8 -> 3:8 -> 2:8 -> 2:4 across four runs of the same data. Excursion SIZE is strongly measurable, so derive the geometry from that instead. stop = q25 of measured ADVERSE travel (ordinary noise does not reach it) target = q50 of measured FAVOURABLE travel (reached ~half the time, by construction, inside the horizon) Continuous, in ATR units, superseding the enum multiples. Reachability ("target on X% of bars, stop on Y%") and the implied break-even are printed so the choice is auditable rather than trusted. FIXED-POINT ITERATION, not one-shot. ComputeBarrierHorizonBars scales the horizon with the target (first-passage time grows with the band) and the excursions are measured OVER the horizon, so target -> horizon -> excursions -> target is a real loop - deriving once sizes the target from travel measured under the PREVIOUS horizon. Re-measures until the multiples move <5%, capped at 3 passes, and says so if it does not settle. Does NOT create expectancy, and the log says as much: chance precision equals break-even at every geometry (m/(m+k) on both sides). It buys a target the market reaches and a stop that survives noise. Where Min_Risk_Reward_Ratio forces a target the market rarely reaches, it WARNS rather than overriding - the ratio is the user's risk policy, so the honest move is to state its cost. That is the collision that once rejected 100% of setups. Pinned in the .cfg as doubles appended AFTER this morning's two ints, so .cfg files written earlier today still load (their length guard finds no doubles) and a model that carries them was trained on them and never re-derives. Also fixes a message from e5ceed6 that claimed "this model resumed from disk" unconditionally - it printed above a "seeding era 0" line on a brand-new model, because the branch fires whenever the cache is not built, which is equally true before a fresh model's first prebuild. A diagnostic that misreports its own trigger is worse than one that says nothing: it gets quoted back as evidence. FORCES A FULL RETRAIN (labels change). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 12:06:25 -04:00
tpReach, slReach, breakeven));
if(rrForced && tpReach < BARRIER_MIN_TP_REACH_PCT)
Print(ID + StringFormat(": WARNING - Min_Risk_Reward_Ratio forced the target to %.2f*ATR, which only "
"%.1f%% of bars ever reach inside the horizon. The reward:risk floor is "
"asking for a move this market rarely makes, so most trades will resolve at "
"the stop or time out. Lower the ratio or accept the hit rate - this is the "
"same collision that once rejected 100%% of setups.",
m_derivedTpMult, tpReach));
return true;
}
//+------------------------------------------------------------------+
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
void CExpertSignalAIBase::BarrierMultiples(double &slMult, double &tpMult)
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
{
feat(labels): measure which barrier is predictable at entry, don't guess The alignment scan settled the shape of the problem: 4.7x more is knowable 5 bars into a 128-bar window than at the entry the model actually trades. A 6xATR target reached over 128 bars is decided overwhelmingly by what happens DURING the window, so whatever the entry state knows is buried under 128 bars of later noise. That is a property of the TARGET, and it is why four different architectures all landed on precision exactly equal to the base rate - no topology can undo it. So measure the target. For each SL/TP pairing a user can actually select, relabel the same sampled bars and score how much the SAME features say about THAT outcome at entry. Seconds, no training, no topology, and it runs on the diagnostic path that already exists. Ranked on excess over its OWN null as a share of its OWN H(Y), never on raw nats: each geometry has a different class balance, hence a different finite-sample bias and a different amount of information there to find, so raw MI would rank the most BALANCED label rather than the most PREDICTABLE one. The break-even win rate m/(m+k) is printed beside each so the ranking is read next to the bar the model must clear. Stated in the output because it is the easy thing to get wrong: chance precision EQUALS break-even at every geometry, so a tighter target does not hand you expectancy. It buys predictability - less noise piled on top of what the entry state knows - which is the one thing changing topology cannot do. Read-only by construction: it relabels a sampled copy via TripleBarrierLabel(), never writes the label cache (which belongs to the configured geometry), and restores the horizon and overrides it borrowed. The overrides apply only when BOTH are positive, so a half-set pair can never silently relabel a live run. Compiles 0 errors / 0 warnings, standard and Market. Build tag geometry-scan-v1. Redeploy only - no retrain to READ the ranking. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 14:32:04 -04:00
//--- Scan override (ReportBarrierGeometryScan). Both must be positive or neither applies, so a half-set
//--- pair can never silently relabel a live run. Restored to 0 by the scan before it returns; nothing
//--- else writes these, and no persisted state is keyed on them.
if(m_barrierScanSlMult > 0.0 && m_barrierScanTpMult > 0.0)
{
slMult = m_barrierScanSlMult;
tpMult = m_barrierScanTpMult;
return;
}
feat: derive the ATR multiples from measured excursions - no hardcoded geometry The barrier was still two constants. SL_Mode/TP_Mode left the Inputs tab in 3482b6c, but the fallback was a hardcoded 2:6 and the geometry scan only ever chose from a hardcoded grid {2,3} x {2,3,4,6,8,10}. Picking the least-bad of eleven guesses is not deriving anything. WHY THE SCAN WAS THE WRONG INSTRUMENT, now measurable rather than argued. It ranks pairings by how predictable their OUTCOME is - a question about direction. The excursion test (2c78f3b) ran on SP500 H1 and direction is the one thing absent: ASYMMETRY p=0.0846, against RANGE/UP/DOWN all at p=0.0050, with RANGE scoring 0.01345 vs a 0.00343 null - 4x, where the barrier label sits at 1.01x. Hence the scan failing its own gate on every run, and its "winner" wandering 2:8 -> 3:8 -> 2:8 -> 2:4 across four runs of the same data. Excursion SIZE is strongly measurable, so derive the geometry from that instead. stop = q25 of measured ADVERSE travel (ordinary noise does not reach it) target = q50 of measured FAVOURABLE travel (reached ~half the time, by construction, inside the horizon) Continuous, in ATR units, superseding the enum multiples. Reachability ("target on X% of bars, stop on Y%") and the implied break-even are printed so the choice is auditable rather than trusted. FIXED-POINT ITERATION, not one-shot. ComputeBarrierHorizonBars scales the horizon with the target (first-passage time grows with the band) and the excursions are measured OVER the horizon, so target -> horizon -> excursions -> target is a real loop - deriving once sizes the target from travel measured under the PREVIOUS horizon. Re-measures until the multiples move <5%, capped at 3 passes, and says so if it does not settle. Does NOT create expectancy, and the log says as much: chance precision equals break-even at every geometry (m/(m+k) on both sides). It buys a target the market reaches and a stop that survives noise. Where Min_Risk_Reward_Ratio forces a target the market rarely reaches, it WARNS rather than overriding - the ratio is the user's risk policy, so the honest move is to state its cost. That is the collision that once rejected 100% of setups. Pinned in the .cfg as doubles appended AFTER this morning's two ints, so .cfg files written earlier today still load (their length guard finds no doubles) and a model that carries them was trained on them and never re-derives. Also fixes a message from e5ceed6 that claimed "this model resumed from disk" unconditionally - it printed above a "seeding era 0" line on a brand-new model, because the branch fires whenever the cache is not built, which is equally true before a fresh model's first prebuild. A diagnostic that misreports its own trigger is worse than one that says nothing: it gets quoted back as evidence. FORCES A FULL RETRAIN (labels change). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 12:06:25 -04:00
//--- DERIVED geometry wins over the mode constants. Set once from the measured excursion distribution
//--- (DeriveBarrierGeometry) and then pinned in the .cfg, so a trained model keeps the barriers it
//--- learned. Below the scan override deliberately: the scan is exploring hypothetical geometries and
//--- must still be able to impose one.
if(m_geometryDerived && m_derivedSlMult > 0.0 && m_derivedTpMult > 0.0)
{
slMult = m_derivedSlMult;
tpMult = m_derivedTpMult;
return;
}
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
slMult = (m_sl_mode == SL_INTELLIGENT_MODE) ? SL_INTELLIGENT_BASE_MULT : (double)m_sl_mode;
//--- Same floor OpenLongParams/OpenShortParams apply before sizing anything off the stop, reproduced
//--- here so the label's risk leg cannot be tighter than the one a real order would receive.
if(slMult < MIN_SL_ATR_MULTIPLIER)
slMult = MIN_SL_ATR_MULTIPLIER;
tpMult = (m_tp_mode == TP_INTELLIGENT_MODE) ? (TP_INTELLIGENT_BASE_RR * slMult) : (double)m_tp_mode;
if(tpMult <= 0.0)
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
{
//--- UNREACHABLE via the Inputs tab: ValidateBarrierInputs() (Warrior_EA.mq5) refuses to start on
//--- any value that is not an enum member. It is kept, and made LOUD, because the silent version of
//--- this line is what let a stale TP_PREV_SWING (-101) train four topologies for ~250 eras on a
//--- 1:1 barrier while the log cheerfully reported "target 1.00*ATR" as if that were configured.
//--- A fallback that cannot announce itself is indistinguishable from correct behaviour.
if(!m_barrierFallbackWarned)
{
m_barrierFallbackWarned = true;
Print(ID + ": ERROR - take-profit mode " + IntegerToString(m_tp_mode) + " is not a valid ATR "
"multiple; the barrier label is falling back to " + DoubleToString(slMult, 2) + "*ATR (1:1). "
"This should have been caught at init - the model being trained does NOT match the "
"configured strategy.");
}
tpMult = slMult;
}
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
}
//+------------------------------------------------------------------+
//| TRIPLE-BARRIER LABEL for one bar (Lopez de Prado ch. 3). See the |
//| BARRIER_TIE_GOES_TO_STOP block in Expert\ExpertSignalAIBase.mqh |
//| for why this replaced the exact-pivot ZigZag target. |
//| |
//| Hypothetical entry at bar `idx`'s CLOSE - the same instant the |
//| feature window ends, so the label answers exactly the question |
//| the deployed model is asked live: "from what I can see right now, |
//| does a trade placed here reach its target before its stop?" |
//| |
//| Costs are charged. MT5 bar series are BID, so a long fills at ask |
//| (close + spread) and exits at bid, while a short fills at bid and |
//| buys back at ask - both legs shifted so the returned outcome is a |
//| NET result. Spread is taken as the symbol's current value, held |
//| constant across history: MT5's standard timeseries carries no |
//| per-bar spread, and a label that ignored the cost entirely would |
//| report a win rate the account cannot reproduce. |
//| |
//| Walks forward in time (toward index 0) for m_barrierHorizonBars. |
//| Ties inside one bar resolve to the STOP - OHLC cannot order two |
//| touches within a bar, and the optimistic reading is how a |
//| backtested edge becomes a live loss. |
//+------------------------------------------------------------------+
ENUM_SIGNAL CExpertSignalAIBase::TripleBarrierLabel(int idx)
{
diag: is "optimal SL/TP" learnable? Score the features against excursions Proposed direction: train the net to predict entry/SL/TP that maximise return and minimise drawdown, rather than to classify direction. Before rebuilding a head, measure whether the target is learnable at all. That question splits into two that behave nothing alike: HOW FAR price travels (MFE/MAE) - essentially volatility, and volatility clustering is about the most robust regularity in markets. WHICH WAY it goes first (the asymmetry) - direction, which is what every noise-floor verdict in this project has been about. Expectancy comes ONLY from the second. The first buys position sizing and drawdown control - worth having under prop-firm limits, but not an edge: exit management on RANDOM entries already moved the payoff ratio 0.92 -> 5.72 with expectancy FLAT. Crucially this is NOT already answered. Every MI figure here scored the triple-barrier label, i.e. one specific question at one fixed geometry. A noise-floor result there says nothing about whether excursion MAGNITUDE is learnable - different target, different answer. Four targets, and the verdict is the CONTRAST, printed explicitly because the dangerous misreading of "UP clears" is "we can predict profitable trades": RANGE (up+dn) - realised volatility, included as a POSITIVE CONTROL that SHOULD clear. Every prior verdict here lacked a control expected to pass; a range target at the floor indicts the measurement, not the market. UP / DOWN - MFE / MAE. ASYMMETRY - up-dn, the only one that can pay. Collected inside the walk the label already does (one max, one min per bar). The early-out when both barriers resolved is GONE: it would have truncated the excursions at whichever bar tripped the last barrier, making the measurement a function of the CURRENT SL/TP - the circularity this is trying to escape. The loop was already bounded by the horizon, so only the average cost moves. Discretised into 3 EQUAL-FREQUENCY bins, so every downstream piece (block permutation, null, p-value) is reused unchanged. Equal-frequency because MFE is fat-tailed and fixed-width bins would put nearly every row in bin 0; it also pins H(Y) at ln(3)=1.099 for all four, making them comparable to each other and to the barrier label's ~1.02 instead of confounded by class balance. Two bugs fixed in this code before it ever ran, both of which would have produced a plausible quiet wrong answer rather than an error: - TripleBarrierLabel early-returns on invalid ATR/close BEFORE the point the accumulators were reset, so one bar's excursions would be cached under another bar's index. Cleared at the top now, ahead of every return. - An unresolvable bar is still flagged as labelled but carries excursions of exactly 0. Under equal-frequency binning a block of identical zeros drags the lowest cut onto zero and a third of the sample lands in one uninformative bin - a depressed score that reads as "not predictable", a false negative in the direction that would wrongly kill the idea. Rows where both excursions are zero are dropped; price cannot travel zero both ways over a whole horizon. Read-only diagnostic. No topology or label change: no retrain of its own. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 10:22:41 -04:00
//--- CLEARED FIRST, ahead of every early return below. These are published to the caller the way
//--- m_lastBarrierTimedOut is, and an unresolvable bar that returned before touching them would leave
//--- the PREVIOUS bar's excursions in place for AdvanceBarrierLabelState to cache against this index -
//--- one bar's outcome filed under another's, which is exactly the kind of silent contamination the
//--- excursion measurement is being built to avoid.
m_lastExcUp = 0.0;
m_lastExcDown = 0.0;
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
double atr = m_ATR.Main(idx);
if(!MathIsValidNumber(atr) || atr <= 0.0)
return Neutral; // no volatility scale yet - unresolvable, same practical answer as "no setup"
double entry = m_Close.GetData(idx);
if(!MathIsValidNumber(entry) || entry <= 0.0)
return Neutral;
double slMult, tpMult;
BarrierMultiples(slMult, tpMult);
double risk = slMult * atr;
double reward = tpMult * atr;
//--- Round-trip cost, in price. Both sides pay it once.
double spread = (double)m_symbol.Spread() * m_symbol.Point();
if(!MathIsValidNumber(spread) || spread < 0.0)
spread = 0.0;
//--- Barrier levels expressed in BID terms, which is what m_High/m_Low carry.
//--- Long fills at close+spread: target needs bid >= fill+reward, stop trips at bid <= fill-risk.
//--- Short fills at close: target needs bid <= close-reward-spread (it buys back at ask),
//--- stop trips at bid >= close+risk-spread.
double longTp = entry + spread + reward;
double longSl = entry + spread - risk;
double shortTp = entry - reward - spread;
double shortSl = entry + risk - spread;
bool longWon = false, longLost = false, shortWon = false, shortLost = false;
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
m_lastBarrierTimedOut = false;
diag: is "optimal SL/TP" learnable? Score the features against excursions Proposed direction: train the net to predict entry/SL/TP that maximise return and minimise drawdown, rather than to classify direction. Before rebuilding a head, measure whether the target is learnable at all. That question splits into two that behave nothing alike: HOW FAR price travels (MFE/MAE) - essentially volatility, and volatility clustering is about the most robust regularity in markets. WHICH WAY it goes first (the asymmetry) - direction, which is what every noise-floor verdict in this project has been about. Expectancy comes ONLY from the second. The first buys position sizing and drawdown control - worth having under prop-firm limits, but not an edge: exit management on RANDOM entries already moved the payoff ratio 0.92 -> 5.72 with expectancy FLAT. Crucially this is NOT already answered. Every MI figure here scored the triple-barrier label, i.e. one specific question at one fixed geometry. A noise-floor result there says nothing about whether excursion MAGNITUDE is learnable - different target, different answer. Four targets, and the verdict is the CONTRAST, printed explicitly because the dangerous misreading of "UP clears" is "we can predict profitable trades": RANGE (up+dn) - realised volatility, included as a POSITIVE CONTROL that SHOULD clear. Every prior verdict here lacked a control expected to pass; a range target at the floor indicts the measurement, not the market. UP / DOWN - MFE / MAE. ASYMMETRY - up-dn, the only one that can pay. Collected inside the walk the label already does (one max, one min per bar). The early-out when both barriers resolved is GONE: it would have truncated the excursions at whichever bar tripped the last barrier, making the measurement a function of the CURRENT SL/TP - the circularity this is trying to escape. The loop was already bounded by the horizon, so only the average cost moves. Discretised into 3 EQUAL-FREQUENCY bins, so every downstream piece (block permutation, null, p-value) is reused unchanged. Equal-frequency because MFE is fat-tailed and fixed-width bins would put nearly every row in bin 0; it also pins H(Y) at ln(3)=1.099 for all four, making them comparable to each other and to the barrier label's ~1.02 instead of confounded by class balance. Two bugs fixed in this code before it ever ran, both of which would have produced a plausible quiet wrong answer rather than an error: - TripleBarrierLabel early-returns on invalid ATR/close BEFORE the point the accumulators were reset, so one bar's excursions would be cached under another bar's index. Cleared at the top now, ahead of every return. - An unresolvable bar is still flagged as labelled but carries excursions of exactly 0. Under equal-frequency binning a block of identical zeros drags the lowest cut onto zero and a third of the sample lands in one uninformative bin - a depressed score that reads as "not predictable", a false negative in the direction that would wrongly kill the idea. Rows where both excursions are zero are dropped; price cannot travel zero both ways over a whole horizon. Read-only diagnostic. No topology or label change: no retrain of its own. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 10:22:41 -04:00
//--- Excursion accumulators. Deliberately NOT stopped when a barrier trips: they describe how far
//--- price travelled over the whole horizon, which is the question a predicted SL/TP needs answered,
//--- whereas the barriers describe what a trade with THIS geometry would have collected. Truncating
//--- them at the first touch would bake the current SL/TP back into the measurement of whether a
//--- different SL/TP is learnable - the circularity the whole exercise is trying to escape.
double maxHigh = -DBL_MAX, minLow = DBL_MAX; // published values already cleared at the top
fix: excursion window must not depend on the barrier it sizes DIRECTION IS NOT THERE, and this run is what establishes it. Three symbols: raw ASYMMETRY clears on all three (p=0.0199 / 0.0050 / 0.0050) norm ASYMMETRY collapses on all three (p=0.3433 / 0.5075 / 0.2736), USDCAD landing BELOW its own null RANGE control strengthens to 3-5x its null everywhere Divide sigma out and the apparent directional signal vanishes entirely. What cleared was volatility leaking through an unnormalised difference. Note this would have passed any replication test: three instruments at p=0.005 is exactly the evidence one would accept before committing to a rebuild, and the confound reproduces perfectly. Replication was never going to catch it - only the normalisation could. Two defects of mine, both surfaced by the same run. 1. THE GEOMETRY DERIVATION WAS DIVERGING, NOT CONVERGING. It produced a 14.57*ATR stop and a 29.14*ATR target that only 5.7% of bars ever reach. Excursions were measured over the barrier horizon; the horizon scales with the target; the target is a quantile of the excursions - so target -> horizon -> excursions -> target ran away, and "settled" only because the horizon ladder caps at 384 bars. A saturated runaway, which the iteration guard could not catch because it watches for OSCILLATION. Fixed at the root: excursions now accumulate only over m_swingMedianBars - the UNSCALED median ZigZag leg, a property of the instrument that owes nothing to the barrier. The barrier walk still runs the full horizon, because that is how long the trade is held; only the MEASUREMENT used to size the barrier is confined to a geometry-independent window. (The Min_Risk_Reward_Ratio warning fired correctly and is what flagged it - the diagnostic worked while the derivation behind it did not.) 2. THE CONFOUND VERDICT WAS UNREACHABLE. `sizeCleared && !asymCleared` was tested first and is true whenever size clears - i.e. always - so the branch that NAMES the volatility confound never printed; all three symbols showed the generic size-not-direction message instead. Verdict chain rewritten with the specific case first, and the dangling elses my first patch introduced removed. FORCES A FULL RETRAIN (the excursion window changes every derived barrier). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 13:57:23 -04:00
//--- Never longer than the horizon actually walked, so the window cannot claim bars the loop below
//--- does not visit; falls back to the horizon before the swing median has been measured.
int excWindow = (m_swingMedianBars > 0)
? (int)MathMin(m_swingMedianBars, MathMax(m_barrierHorizonBars, 1))
: MathMax(m_barrierHorizonBars, 1);
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
int last = idx - MathMax(m_barrierHorizonBars, 1);
if(last < 0)
last = 0;
for(int t = idx - 1; t >= last; t--)
{
double hi = m_High.GetData(t);
double lo = m_Low.GetData(t);
if(!MathIsValidNumber(hi) || !MathIsValidNumber(lo) || hi == EMPTY_VALUE || lo == EMPTY_VALUE)
break; // ran off loaded history - whatever resolved so far stands, the rest times out
fix: excursion window must not depend on the barrier it sizes DIRECTION IS NOT THERE, and this run is what establishes it. Three symbols: raw ASYMMETRY clears on all three (p=0.0199 / 0.0050 / 0.0050) norm ASYMMETRY collapses on all three (p=0.3433 / 0.5075 / 0.2736), USDCAD landing BELOW its own null RANGE control strengthens to 3-5x its null everywhere Divide sigma out and the apparent directional signal vanishes entirely. What cleared was volatility leaking through an unnormalised difference. Note this would have passed any replication test: three instruments at p=0.005 is exactly the evidence one would accept before committing to a rebuild, and the confound reproduces perfectly. Replication was never going to catch it - only the normalisation could. Two defects of mine, both surfaced by the same run. 1. THE GEOMETRY DERIVATION WAS DIVERGING, NOT CONVERGING. It produced a 14.57*ATR stop and a 29.14*ATR target that only 5.7% of bars ever reach. Excursions were measured over the barrier horizon; the horizon scales with the target; the target is a quantile of the excursions - so target -> horizon -> excursions -> target ran away, and "settled" only because the horizon ladder caps at 384 bars. A saturated runaway, which the iteration guard could not catch because it watches for OSCILLATION. Fixed at the root: excursions now accumulate only over m_swingMedianBars - the UNSCALED median ZigZag leg, a property of the instrument that owes nothing to the barrier. The barrier walk still runs the full horizon, because that is how long the trade is held; only the MEASUREMENT used to size the barrier is confined to a geometry-independent window. (The Min_Risk_Reward_Ratio warning fired correctly and is what flagged it - the diagnostic worked while the derivation behind it did not.) 2. THE CONFOUND VERDICT WAS UNREACHABLE. `sizeCleared && !asymCleared` was tested first and is true whenever size clears - i.e. always - so the branch that NAMES the volatility confound never printed; all three symbols showed the generic size-not-direction message instead. Verdict chain rewritten with the specific case first, and the dangling elses my first patch introduced removed. FORCES A FULL RETRAIN (the excursion window changes every derived barrier). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 13:57:23 -04:00
//--- Excursions accumulate only over the REFERENCE WINDOW, not the whole barrier horizon - see
//--- m_swingMedianBars. The barrier walk below still runs the full horizon, because that is how
//--- long the trade is actually held; only the MEASUREMENT used to size the barrier is confined to
//--- a window that does not depend on the barrier.
if(idx - t <= excWindow)
{
if(hi > maxHigh)
maxHigh = hi;
if(lo < minLow)
minLow = lo;
}
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
//--- Stop tested FIRST on each side, so a bar that spans both barriers is scored as the loss.
if(!longWon && !longLost)
{
if(lo <= longSl)
longLost = true;
else if(hi >= longTp)
longWon = true;
}
if(!shortWon && !shortLost)
{
if(hi >= shortSl)
shortLost = true;
else if(lo <= shortTp)
shortWon = true;
}
diag: is "optimal SL/TP" learnable? Score the features against excursions Proposed direction: train the net to predict entry/SL/TP that maximise return and minimise drawdown, rather than to classify direction. Before rebuilding a head, measure whether the target is learnable at all. That question splits into two that behave nothing alike: HOW FAR price travels (MFE/MAE) - essentially volatility, and volatility clustering is about the most robust regularity in markets. WHICH WAY it goes first (the asymmetry) - direction, which is what every noise-floor verdict in this project has been about. Expectancy comes ONLY from the second. The first buys position sizing and drawdown control - worth having under prop-firm limits, but not an edge: exit management on RANDOM entries already moved the payoff ratio 0.92 -> 5.72 with expectancy FLAT. Crucially this is NOT already answered. Every MI figure here scored the triple-barrier label, i.e. one specific question at one fixed geometry. A noise-floor result there says nothing about whether excursion MAGNITUDE is learnable - different target, different answer. Four targets, and the verdict is the CONTRAST, printed explicitly because the dangerous misreading of "UP clears" is "we can predict profitable trades": RANGE (up+dn) - realised volatility, included as a POSITIVE CONTROL that SHOULD clear. Every prior verdict here lacked a control expected to pass; a range target at the floor indicts the measurement, not the market. UP / DOWN - MFE / MAE. ASYMMETRY - up-dn, the only one that can pay. Collected inside the walk the label already does (one max, one min per bar). The early-out when both barriers resolved is GONE: it would have truncated the excursions at whichever bar tripped the last barrier, making the measurement a function of the CURRENT SL/TP - the circularity this is trying to escape. The loop was already bounded by the horizon, so only the average cost moves. Discretised into 3 EQUAL-FREQUENCY bins, so every downstream piece (block permutation, null, p-value) is reused unchanged. Equal-frequency because MFE is fat-tailed and fixed-width bins would put nearly every row in bin 0; it also pins H(Y) at ln(3)=1.099 for all four, making them comparable to each other and to the barrier label's ~1.02 instead of confounded by class balance. Two bugs fixed in this code before it ever ran, both of which would have produced a plausible quiet wrong answer rather than an error: - TripleBarrierLabel early-returns on invalid ATR/close BEFORE the point the accumulators were reset, so one bar's excursions would be cached under another bar's index. Cleared at the top now, ahead of every return. - An unresolvable bar is still flagged as labelled but carries excursions of exactly 0. Under equal-frequency binning a block of identical zeros drags the lowest cut onto zero and a third of the sample lands in one uninformative bin - a depressed score that reads as "not predictable", a false negative in the direction that would wrongly kill the idea. Rows where both excursions are zero are dropped; price cannot travel zero both ways over a whole horizon. Read-only diagnostic. No topology or label change: no retrain of its own. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 10:22:41 -04:00
//--- The early-out that used to sit here (both sides resolved -> break) is GONE, because the
//--- excursion accumulators above must see the whole horizon and it would have truncated them at
//--- whichever bar happened to trip the last barrier - making the measured excursion a function of
//--- the current SL/TP, which is exactly the circularity being escaped. The loop was already
//--- bounded by m_barrierHorizonBars, so the worst case is unchanged and only the average moves.
}
if(maxHigh > -DBL_MAX && minLow < DBL_MAX)
{
//--- Same spread convention as the barriers: a long fills at close+spread, so its favourable
//--- excursion is measured from that fill and its adverse excursion likewise. Clamped at zero -
//--- a horizon whose every high sits below the fill has no favourable excursion, not a negative one.
m_lastExcUp = MathMax((maxHigh - (entry + spread)) / atr, 0.0);
m_lastExcDown = MathMax(((entry + spread) - minLow) / atr, 0.0);
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
}
//--- Mutually exclusive whenever reward >= risk, which every shipped SL/TP pairing satisfies. The
//--- both-won branch is unreachable there but costs one comparison and keeps a degenerate custom
//--- configuration (TP tighter than SL) from silently producing two contradictory positives.
if(longWon && !shortWon)
return Buy;
if(shortWon && !longWon)
return Sell;
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
//--- Neither side resolved AT ALL = the vertical barrier is what ended it. Recorded separately from a
//--- stop-out because only this outcome says the horizon is too short - see m_lastBarrierTimedOut.
m_lastBarrierTimedOut = (!longWon && !longLost && !shortWon && !shortLost);
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
return Neutral; // timed out, stopped out, or an ambiguous config - no tradeable edge at this bar
}
//+------------------------------------------------------------------+
//| Median distance in bars between consecutive confirmed ZigZag |
//| pivots - this symbol/timeframe's own swing horizon, and what the |
//| vertical barrier is set to. Snapped to a coarse ladder so the |
//| estimate has to move ~30% to change the answer; see the |
//| BARRIER_HORIZON_* constants for why the quantization matters more |
//| than the precision (an unquantized horizon that drifted as |
//| history downloaded would relabel a partly-trained model's |
//| targets mid-run). |
//| |
//| Reads only pivots old enough to be non-repainting, for the same |
//| reason every other ZigZag read in this class does. |
//+------------------------------------------------------------------+
int CExpertSignalAIBase::ComputeBarrierHorizonBars(int bars)
{
fix(labels): the 128-bar horizon ceiling was truncating the shipped label The corrected geometry scan exposed something bigger than the geometry question it was asked. Every pairing from 2:6 upward came back CLAMPED - including 2:6, the SHIPPED configuration. First-passage time for a driftless walk leaving [-m,+k] goes as m*k, and the measured swing median here is ~12 bars at m*k=1, so 2:6 wants ~144 bars and 3:10 wants ~360. The ladder stopped at 128. A clamped label stops meaning "does the target come before the stop" and quietly becomes "...within 128 bars", while the deployed EA holds until SL or TP with no bar limit. So the target the models have been trained on all along was not the strategy the EA executes, and the trades it silently reclassified as Neutral were the SLOW WINNERS - precisely the ones a 1:3 barrier exists to capture. Timeout share stayed ~0% throughout, which is why this never showed up: the truncation lands in Neutral, not in the timeout counter that was watching for it. Ladder extended to 384 (12..128, 192, 256, 384) so every selectable geometry gets an honest horizon. Cost is one embargo of at most 384 bars out of ~38k. Second fix, same class of error as the H(Y) one: the scan's "best eligible" was 2:2, a 1:1 barrier, against a shipped Min_Risk_Reward_Ratio of 1:2. Training four topologies on that target would have produced a model whose every setup is rejected at the door - the exact failure behind four consecutive Market rejections for "no trading operations". Sub-minRR geometries are now ineligible and marked [<minRR], printed rather than hidden. Also drops the dense-depth tag from the display name ("Perceptron 3L" -> "Perceptron"). Depth is derived, so it names nothing a user chose; the config tag [PAI-0be2] already disambiguates concurrent charts and does it for every input rather than one. Full topology still logged by "config -". Compiles 0 errors / 0 warnings, standard and Market. Build tag horizon-384-v1. Changes the LABEL for every geometry, so the next scan supersedes the previous numbers - and a retrain is required before any model trained under the truncated target means anything. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 14:54:45 -04:00
int ladder[BARRIER_HORIZON_LADDER_COUNT] = { 12, 16, 24, 32, 48, 64, 96, 128, 192, 256, 384 };
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
int gaps[];
ArrayResize(gaps, 0);
int prevPivot = -1;
int scanned = 0;
//--- Oldest-to-newest is irrelevant here (a median has no order dependence), so scan newest-first from
//--- the first non-repainting bar and stop at the history edge.
for(int p = MathMax(m_swingConfirmationBars, 1); p < bars && scanned < SWING_SCAN_CAP_BARS * 4; p++, scanned++)
{
if(m_Open.GetData(p) == EMPTY_VALUE)
break;
if(m_ADZigZag.GetData(0, p) == 0.0)
continue;
if(prevPivot >= 0)
{
int gap = p - prevPivot;
if(gap > 0)
{
int n = ArraySize(gaps);
ArrayResize(gaps, n + 1);
gaps[n] = gap;
}
}
prevPivot = p;
}
int count = ArraySize(gaps);
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
double swingMedian = BARRIER_HORIZON_FALLBACK;
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
if(count >= BARRIER_HORIZON_MIN_SAMPLES)
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
{
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
ArraySort(gaps);
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
swingMedian = gaps[count / 2];
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
}
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
else
Print(ID + ": barrier horizon - only " + IntegerToString(count) + " confirmed ZigZag legs available (need " +
IntegerToString(BARRIER_HORIZON_MIN_SAMPLES) + "), falling back to " +
IntegerToString(BARRIER_HORIZON_FALLBACK) + " bars");
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
//--- SCALE BY THE BARRIER GEOMETRY. The swing median alone measures how long a ~1 ATR move takes on
//--- this instrument; it says nothing about how long the CONFIGURED barrier takes to resolve, and the
//--- first version of this function ignored that entirely.
//--- For a driftless random walk leaving the band [-m*ATR, +k*ATR], the expected first-passage time is
//--- proportional to m*k. So a 1:3 barrier takes ~3x as long to resolve as a 1:1 one, and a horizon
//--- tuned for 1:1 applied to 1:3 would time out most trades - pushing Neutral straight back up and
//--- re-creating the imbalance the relabel exists to remove.
//--- Calibrated against a real measurement rather than assumed: the 2026-08-01 run resolved at m=k=1
//--- with a 12-bar horizon and only 16.7% timeouts, so the swing median IS the right scale at m*k=1.
//--- Multiplying by m*k carries that calibration to every other barrier (1:3 -> 36, snapping to 32).
double slMult, tpMult;
BarrierMultiples(slMult, tpMult);
fix: excursion window must not depend on the barrier it sizes DIRECTION IS NOT THERE, and this run is what establishes it. Three symbols: raw ASYMMETRY clears on all three (p=0.0199 / 0.0050 / 0.0050) norm ASYMMETRY collapses on all three (p=0.3433 / 0.5075 / 0.2736), USDCAD landing BELOW its own null RANGE control strengthens to 3-5x its null everywhere Divide sigma out and the apparent directional signal vanishes entirely. What cleared was volatility leaking through an unnormalised difference. Note this would have passed any replication test: three instruments at p=0.005 is exactly the evidence one would accept before committing to a rebuild, and the confound reproduces perfectly. Replication was never going to catch it - only the normalisation could. Two defects of mine, both surfaced by the same run. 1. THE GEOMETRY DERIVATION WAS DIVERGING, NOT CONVERGING. It produced a 14.57*ATR stop and a 29.14*ATR target that only 5.7% of bars ever reach. Excursions were measured over the barrier horizon; the horizon scales with the target; the target is a quantile of the excursions - so target -> horizon -> excursions -> target ran away, and "settled" only because the horizon ladder caps at 384 bars. A saturated runaway, which the iteration guard could not catch because it watches for OSCILLATION. Fixed at the root: excursions now accumulate only over m_swingMedianBars - the UNSCALED median ZigZag leg, a property of the instrument that owes nothing to the barrier. The barrier walk still runs the full horizon, because that is how long the trade is held; only the MEASUREMENT used to size the barrier is confined to a geometry-independent window. (The Min_Risk_Reward_Ratio warning fired correctly and is what flagged it - the diagnostic worked while the derivation behind it did not.) 2. THE CONFOUND VERDICT WAS UNREACHABLE. `sizeCleared && !asymCleared` was tested first and is true whenever size clears - i.e. always - so the branch that NAMES the volatility confound never printed; all three symbols showed the generic size-not-direction message instead. Verdict chain rewritten with the specific case first, and the dangling elses my first patch introduced removed. FORCES A FULL RETRAIN (the excursion window changes every derived barrier). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 13:57:23 -04:00
//--- THE EXCURSION REFERENCE WINDOW, published UNSCALED. This is a property of the instrument (how
//--- long its typical swing leg lasts) and owes nothing to the barrier, which is exactly what makes it
//--- usable for sizing the barrier. Sizing a stop off travel measured over the SCALED horizon below
//--- is circular: horizon grows with the target, excursions grow with the horizon, the target is a
//--- quantile of the excursions - so target -> horizon -> excursions -> target diverges. Measured
//--- 2026-08-07 on EURUSD/USDCAD: it ran away to a 14-15*ATR stop and a 29-31*ATR target that only
//--- 5.7-7.2% of bars ever reached, and "converged" solely because the ladder caps at 384 bars. A
//--- saturated runaway, not a fixed point - which is why the iteration guard, watching for
//--- oscillation, did not catch it.
m_swingMedianBars = (int)MathMax(MathRound(swingMedian), 1);
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
int raw = (int)MathRound(swingMedian * slMult * tpMult);
fix(labels): the geometry scan rewarded the labels it should reject First run named 3:10 on all four charts, at 2.3x the configured 2:6. That answer was wrong and the fault was the ranking statistic. 3:10 wants a horizon of ~swingMedian*30 (~320 bars) and gets BARRIER_HORIZON_MAX. Clamped, most trades never resolve, the unresolved remainder all lands in Neutral, and H(Y) collapses. The old statistic divided the excess BY H(Y) - so a collapsing denominator made the most degenerate label look like the most predictable one. Every geometry from 2:6 upward was already showing the clamped h128, and the two widest scored highest, which is the fingerprint of the artefact rather than of signal. Two fixes: Rank on the raw excess in nats. Subtracting each geometry's OWN measured null already removes the class-balance bias, which is the only thing the normalisation was ever needed for. Disqualify clamped geometries outright rather than ranking them down. The deployed EA holds until SL or TP with no bar limit, so a truncated label trains the model on a question the strategy never asks. They are still printed, marked '!', so the disqualification is visible instead of a silent omission - and the scan now says so explicitly when nothing eligible is left, because "the limit is the feature set, not the target" is itself the finding in that case. The scan also reports each geometry's directional share and timeout share now. A label nobody can trade is not a candidate however well it scores, and that has to be visible in the same line as the score. Compiles 0 errors / 0 warnings. Build tag geometry-scan-v2. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 14:46:25 -04:00
//--- CLAMPED means the barrier this geometry describes needs MORE time than the ceiling allows, so the
//--- label stops being "does the target come before the stop" and quietly becomes "does the target come
//--- within BARRIER_HORIZON_MAX bars". The deployed EA has no such bar limit - it holds until SL or TP -
//--- so a clamped label trains the model on a question the strategy never asks, and the unresolved
//--- remainder all lands in Neutral. Recorded rather than merely clamped because the geometry scan must
//--- be able to disqualify these: they LOOK informative precisely because a Neutral-dominated label has
//--- little entropy left to explain.
m_barrierHorizonClamped = (raw > BARRIER_HORIZON_MAX);
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
if(raw < BARRIER_HORIZON_MIN)
raw = BARRIER_HORIZON_MIN;
if(raw > BARRIER_HORIZON_MAX)
raw = BARRIER_HORIZON_MAX;
//--- Snap DOWN to the ladder, matching ComputeFirstLayerWidth()'s direction: a horizon shorter than
//--- measured makes the label stricter (more Neutral), never more permissive.
int snapped = ladder[0];
for(int k = 0; k < BARRIER_HORIZON_LADDER_COUNT; k++)
if(ladder[k] <= raw)
snapped = ladder[k];
return snapped;
}
//+------------------------------------------------------------------+
//| Resolves m_barrierHorizonBars once per process and logs the whole |
//| label definition. Called from BOTH the training prebuild and the |
//| deployed inference path - see the declaration for why a deployed |
//| model that skipped this would silently learn online from bars |
//| whose barriers had not resolved. |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::EnsureBarrierHorizon(int bars)
{
if(m_barrierHorizonResolved)
return;
m_barrierHorizonBars = ComputeBarrierHorizonBars(bars);
m_barrierHorizonResolved = true;
double slMultLog, tpMultLog;
BarrierMultiples(slMultLog, tpMultLog);
Print(ID + ": triple-barrier labels - stop " + DoubleToString(slMultLog, 2) + "*ATR, target " +
DoubleToString(tpMultLog, 2) + "*ATR, horizon " + IntegerToString(m_barrierHorizonBars) +
" bars (median confirmed ZigZag leg, snapped) | spread charged " +
IntegerToString(m_symbol.Spread()) + " points | intrabar ties score as the STOP");
}
//+------------------------------------------------------------------+
//| Resolves the triple-barrier label for whichever candidate bar is |
//| exactly m_barrierHorizonBars behind the one being visited - i.e. |
//| the newest bar whose outcome is now fully knowable. Mirrors the |
//| shape of the ZigZag-confirmation scan this replaced, with the |
//| lookahead depth changed from "how long until a pivot stops |
//| repainting" to "how long until the trade resolves". |
//| |
//| Unlike the ZigZag version, a bar's verdict here is FINAL the |
//| moment it is computed: the barrier outcome depends only on price |
//| within a fixed forward window, so nothing later can revise it. |
//| That is what lets the widening/re-spreading pass this file used |
//| to need disappear entirely. |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::AdvanceBarrierLabelState(int i, int bars)
{
int idx = i + MathMax(m_barrierHorizonBars, 1);
if(idx >= bars || m_labelCacheHasValue[idx])
return;
ENUM_SIGNAL verdict = TripleBarrierLabel(idx);
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
if(verdict == Neutral && m_lastBarrierTimedOut)
m_labelPrebuildTimeoutCount++;
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
m_labelCacheBuy[idx] = (verdict == Buy);
m_labelCacheSell[idx] = (verdict == Sell);
diag: is "optimal SL/TP" learnable? Score the features against excursions Proposed direction: train the net to predict entry/SL/TP that maximise return and minimise drawdown, rather than to classify direction. Before rebuilding a head, measure whether the target is learnable at all. That question splits into two that behave nothing alike: HOW FAR price travels (MFE/MAE) - essentially volatility, and volatility clustering is about the most robust regularity in markets. WHICH WAY it goes first (the asymmetry) - direction, which is what every noise-floor verdict in this project has been about. Expectancy comes ONLY from the second. The first buys position sizing and drawdown control - worth having under prop-firm limits, but not an edge: exit management on RANDOM entries already moved the payoff ratio 0.92 -> 5.72 with expectancy FLAT. Crucially this is NOT already answered. Every MI figure here scored the triple-barrier label, i.e. one specific question at one fixed geometry. A noise-floor result there says nothing about whether excursion MAGNITUDE is learnable - different target, different answer. Four targets, and the verdict is the CONTRAST, printed explicitly because the dangerous misreading of "UP clears" is "we can predict profitable trades": RANGE (up+dn) - realised volatility, included as a POSITIVE CONTROL that SHOULD clear. Every prior verdict here lacked a control expected to pass; a range target at the floor indicts the measurement, not the market. UP / DOWN - MFE / MAE. ASYMMETRY - up-dn, the only one that can pay. Collected inside the walk the label already does (one max, one min per bar). The early-out when both barriers resolved is GONE: it would have truncated the excursions at whichever bar tripped the last barrier, making the measurement a function of the CURRENT SL/TP - the circularity this is trying to escape. The loop was already bounded by the horizon, so only the average cost moves. Discretised into 3 EQUAL-FREQUENCY bins, so every downstream piece (block permutation, null, p-value) is reused unchanged. Equal-frequency because MFE is fat-tailed and fixed-width bins would put nearly every row in bin 0; it also pins H(Y) at ln(3)=1.099 for all four, making them comparable to each other and to the barrier label's ~1.02 instead of confounded by class balance. Two bugs fixed in this code before it ever ran, both of which would have produced a plausible quiet wrong answer rather than an error: - TripleBarrierLabel early-returns on invalid ATR/close BEFORE the point the accumulators were reset, so one bar's excursions would be cached under another bar's index. Cleared at the top now, ahead of every return. - An unresolvable bar is still flagged as labelled but carries excursions of exactly 0. Under equal-frequency binning a block of identical zeros drags the lowest cut onto zero and a third of the sample lands in one uninformative bin - a depressed score that reads as "not predictable", a false negative in the direction that would wrongly kill the idea. Rows where both excursions are zero are dropped; price cannot travel zero both ways over a whole horizon. Read-only diagnostic. No topology or label change: no retrain of its own. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 10:22:41 -04:00
//--- Stored under the SAME validity flag as the label, set last so no reader can see one without the
//--- other. TripleBarrierLabel() publishes these for the bar it just walked.
if(idx < ArraySize(m_excUpCache))
{
m_excUpCache[idx] = m_lastExcUp;
m_excDownCache[idx] = m_lastExcDown;
}
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
m_labelCacheHasValue[idx] = true;
}
//+------------------------------------------------------------------+
//| Nearest confirmed ZigZag pivot at fromIdx or older (now-relative |
//| index, so "older" means scanning with INCREASING p - see this |
//| file's now-relative-index convention, same as AdvanceZigZagLabel- |
//| State() above). Capped at SWING_SCAN_CAP_BARS so a long quiet |
//| stretch with no qualifying pivot can't turn this into an unbounded |
//| scan; returns false (no pivot found) rather than looping forever |
//| if the cap is hit or history runs out first. |
//| |
//| Caller's responsibility, not this method's: applying the |
//| m_swingConfirmationBars repainting embargo to fromIdx before |
//| calling. This method itself just finds the nearest nonzero |
//| ADZigZag buffer entry at/after whatever index it's given - it has |
//| no opinion on whether that index is safe to read yet. The ONE |
//| caller that needs the embargo (BufferTempDataCompute()'s |
//| m_useSwingContext block, looking up "the pivot as of THIS bar") |
//| applies it before the first call; the second call in that same |
//| block (finding the PRIOR completed leg, starting from pivotIdx+1) |
//| doesn't need to re-apply it - anything at or before an already- |
//| confirmed pivot is necessarily even older, hence already confirmed |
//| too. |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::FindConfirmedZigZagPivot(int fromIdx, int &pivotIdx, double &pivotPrice, bool &pivotIsLow)
{
for(int p = MathMax(fromIdx, 0); p < fromIdx + SWING_SCAN_CAP_BARS; p++)
{
if(m_Open.GetData(p) == EMPTY_VALUE)
return false; // ran off the end of available history
double zz = m_ADZigZag.GetData(0, p);
if(zz == 0.0)
continue;
pivotIdx = p;
pivotPrice = zz;
pivotIsLow = (zz <= m_Low.GetData(p) + _Point);
return true;
}
return false;
}
//+------------------------------------------------------------------+
//| Kicks off the one-time eager label-cache pre-build for a fresh |
//| start (see m_labelCachePrebuilt's declaration comment). Computes |
//| the bar count/OOS split exactly as Train()'s era-start block |
//| would, then arms AdvanceLabelCachePrebuild() to do the actual |
//| chunked scan on this and subsequent Train() calls. |
//+------------------------------------------------------------------+
void CExpertSignalAIBase::StartLabelCachePrebuild(void)
{
int barsNow = (int)MathMin(Bars(m_symbol.Name(), PERIOD_CURRENT, dtStudied, TimeCurrent()) + m_historyBars, Bars(m_symbol.Name(), PERIOD_CURRENT));
if(!ResizeBuffers(barsNow) || !RefreshData())
return; // couldn't prep buffers yet - m_labelCachePrebuilt stays false, retried next call
EnsureBarCachesCapacity(barsNow);
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
//--- Settle the vertical barrier BEFORE the first label is computed. Derived once per process and then
//--- held: AdvanceBarrierLabelState() indexes off it, so a value that moved mid-scan would leave the
//--- cache holding labels from two different rules.
EnsureBarrierHorizon(barsNow);
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
int totalIter = (int)MathMax(barsNow - MathMax(m_historyBars, 0), 0);
m_labelPrebuildBars = barsNow;
m_labelPrebuildOosCutoff = (int)(MathMax(0, MathMin(100, m_oosSplitPct)) / 100.0 * totalIter);
m_labelPrebuildIndex = (int)(barsNow - MathMax(m_historyBars, 0) - 1);
m_labelPrebuildBuyCount = 0;
m_labelPrebuildSellCount = 0;
m_labelPrebuildNeutralCount = 0;
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
m_labelPrebuildTimeoutCount = 0;
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
m_labelPrebuildActive = true;
}
//+------------------------------------------------------------------+
//| Advances the eager label-cache pre-build by up to a time budget, |
//| then yields (same chunking pattern as the era loop). Mirrors the |
//| era loop's own labeling eligibility gate (minus the dPrevSignal |
//| check, meaningless pre-first-feedForward). On completion, seeds |
//| m_prevEraTrueBuyCount/Sell/Neutral from the upfront IS-only tally |
refactor(ai): extract Layer.mqh and deduplicate AI config - Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
2026-08-01 11:27:28 -04:00
//| so era 0's class priors are measured, not empty. |
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
//+------------------------------------------------------------------+
void CExpertSignalAIBase::AdvanceLabelCachePrebuild(void)
{
const uint PREBUILD_TIME_BUDGET_MS = 80;
uint chunkStartTick = GetTickCount();
int i;
for(i = m_labelPrebuildIndex; i >= 2; i--)
{
if(GetTickCount() - chunkStartTick >= PREBUILD_TIME_BUDGET_MS)
{
m_labelPrebuildIndex = i;
return;
}
if(!(i < (int)(m_labelPrebuildBars - MathMax(m_historyBars, 0) - 1) && m_Time.GetData(i) > dtStudied))
continue;
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
// A barrier label needs m_barrierHorizonBars of FUTURE (lower-index) bars to resolve, so visiting
// bar i settles the label for bar i+horizon - see AdvanceBarrierLabelState(). Unlike the ZigZag
// scan this replaced, each verdict is final when written: the outcome depends only on price inside
// a fixed forward window, so no later iteration can revise it and there is no widening/re-spread
// pass to run afterwards. The tally still happens in one pass at the end, purely because the loop
// above is chunked across Train() calls and may resume mid-scan.
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
if(!m_labelCacheHasValue[i])
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
AdvanceBarrierLabelState(i, m_labelPrebuildBars);
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
}
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
//--- Final tally pass (IS-only, matches isOOS = (i < oosCutoff) used by the era loop).
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
for(i = m_labelPrebuildBars - MathMax(m_historyBars, 0) - 1; i >= MathMax(2, m_labelPrebuildOosCutoff); i--)
{
if(!m_labelCacheHasValue[i])
continue; // e.g. bar was outside the dtStudied/window-edge eligibility gate above
if(m_labelCacheBuy[i])
m_labelPrebuildBuyCount++;
else
if(m_labelCacheSell[i])
m_labelPrebuildSellCount++;
else
m_labelPrebuildNeutralCount++;
}
refactor(ai): extract Layer.mqh and deduplicate AI config - Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
2026-08-01 11:27:28 -04:00
//--- Prebuild complete - seed era 0's class base rates from the real upfront tally instead of leaving
//--- UpdateClassPriors() nothing to measure (see m_prevEraTrueBuyCount's declaration comment).
//--- Consumed (and cleared) by Train()'s era-start block on era 0 specifically - m_prebuildSeedPending.
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
m_prevEraTrueBuyCount = m_labelPrebuildBuyCount;
m_prevEraTrueSellCount = m_labelPrebuildSellCount;
m_prevEraTrueNeutralCount = m_labelPrebuildNeutralCount;
m_prebuildSeedPending = true;
m_labelCachePrebuilt = true;
m_labelPrebuildActive = false;
refactor(ai): nine class-imbalance inputs down to two The imbalance section offered nine controls for one job. Audited against the code, five of them did not do what their names said at the shipped defaults: AILogitPriorStrength DEAD - Inference.mqh's post-hoc prior early-returns whenever the adjusted loss is on, which is default. OversampleParity DEAD in training - Training.mqh gated the replay loop on !useLogitAdjustedLoss (correctly, citing Buda et al. 2018). Live only in the online-learning path. EnableMinorityReplay DEAD as replay. It survived ONLY as a focal-gamma damper - "replay minority bars through pass-2 oversampling" was a focal-loss switch. ConstrainReplay DEAD as a cap; it only chose damper 0.125 vs 0.25. UseStaticPrior An exact duplicate of FreezePriorCalibration - the two were OR'd together in the single place either is read. So they were not five mechanisms fighting; they were one mechanism plus eight knobs that mostly described machinery that no longer ran. That is worse than a real conflict, because the log agreed with the names: the label-cache line printed "reps up to 28x (90% parity) (seeding era 0's class-balance oversampling)" on every run, describing an oversampling pass that had been switched off. It is fixed here too - it cost this session a wrong diagnosis. The one genuine redundancy was focal loss, running at gamma*0.125 alongside the adjusted loss: two corrections on the same axis, the exact stacking failure this file already cited Buda et al. for in two other places, damped by a replay flag whose replay path was itself dead. Removed rather than re-tuned. The plateau ladder is unaffected - its escape is the learning-rate warm restart; the gamma anneal beside it only ever stepped toward zero. WHAT REMAINS is logit-adjusted loss (Menon et al. 2021) plus a prior freeze: LogitAdjustTau 0 = off; replaces the separate EnableLogitAdjusted- Loss boolean, since a strength dial where 0 already means off does not need an on/off switch beside it. FreezePriorCalibration unchanged. It is the only one of the six corrections with a consistency guarantee, and it is consistent for exactly the balanced-error metric checkpoint selection already ranks on - so the loss and the deploy decision optimize one thing. The online continual-learning path keeps its own alpha-balanced focal weight, now as constants pinned to the removed inputs' shipped defaults, so its behaviour is unchanged. It legitimately needs its own correction: ApplyLogitAdjustment() only runs inside a training run, so a deployed model that was reloaded carries no logit offsets and would otherwise stream 31:1 data into itself uncorrected. The weights-filename fingerprint is BYTE-IDENTICAL. The focal slot was a double fed to a %d conversion and had always emitted a literal 0; the |MR: segment is written as the constant its shipped defaults produced. Dropping either would have re-keyed every model and forced a from-scratch retrain of the one topology currently converged and trading. Also removed as orphans: FOCAL_GAMMA_PRESET, MAX_OVERSAMPLE_REPLICAS, OVERSAMPLE_PARITY_FRACTION, PLATEAU_GAMMA_STEP, and the now-unreachable "neutralized by prior correction" diagnostic. Both builds compile 0 errors, 0 warnings. No retrain forced. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 11:46:57 -04:00
//--- Measured-imbalance visibility. This line used to also report "reps up to Nx (M% parity)" and
//--- "(seeding era 0's class-balance oversampling)" - describing an oversampling pass that the
//--- logit-adjusted loss had already disabled, and which no longer exists at all since 2026-07-31.
//--- It was pure fiction in every shipped run, and convincing enough to send a diagnosis down the
//--- wrong path. A log line must describe what the code DID, not what some earlier version would
//--- have done: report the measured distribution, which is real and useful, and nothing else.
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
int prebuildMinDir = (int)MathMin(m_labelPrebuildBuyCount, m_labelPrebuildSellCount);
int prebuildMaxCls = (int)MathMax(m_labelPrebuildNeutralCount, MathMax(m_labelPrebuildBuyCount, m_labelPrebuildSellCount));
string prebuildRatioInfo = (prebuildMinDir > 0 && prebuildMaxCls > 0)
refactor(ai): nine class-imbalance inputs down to two The imbalance section offered nine controls for one job. Audited against the code, five of them did not do what their names said at the shipped defaults: AILogitPriorStrength DEAD - Inference.mqh's post-hoc prior early-returns whenever the adjusted loss is on, which is default. OversampleParity DEAD in training - Training.mqh gated the replay loop on !useLogitAdjustedLoss (correctly, citing Buda et al. 2018). Live only in the online-learning path. EnableMinorityReplay DEAD as replay. It survived ONLY as a focal-gamma damper - "replay minority bars through pass-2 oversampling" was a focal-loss switch. ConstrainReplay DEAD as a cap; it only chose damper 0.125 vs 0.25. UseStaticPrior An exact duplicate of FreezePriorCalibration - the two were OR'd together in the single place either is read. So they were not five mechanisms fighting; they were one mechanism plus eight knobs that mostly described machinery that no longer ran. That is worse than a real conflict, because the log agreed with the names: the label-cache line printed "reps up to 28x (90% parity) (seeding era 0's class-balance oversampling)" on every run, describing an oversampling pass that had been switched off. It is fixed here too - it cost this session a wrong diagnosis. The one genuine redundancy was focal loss, running at gamma*0.125 alongside the adjusted loss: two corrections on the same axis, the exact stacking failure this file already cited Buda et al. for in two other places, damped by a replay flag whose replay path was itself dead. Removed rather than re-tuned. The plateau ladder is unaffected - its escape is the learning-rate warm restart; the gamma anneal beside it only ever stepped toward zero. WHAT REMAINS is logit-adjusted loss (Menon et al. 2021) plus a prior freeze: LogitAdjustTau 0 = off; replaces the separate EnableLogitAdjusted- Loss boolean, since a strength dial where 0 already means off does not need an on/off switch beside it. FreezePriorCalibration unchanged. It is the only one of the six corrections with a consistency guarantee, and it is consistent for exactly the balanced-error metric checkpoint selection already ranks on - so the loss and the deploy decision optimize one thing. The online continual-learning path keeps its own alpha-balanced focal weight, now as constants pinned to the removed inputs' shipped defaults, so its behaviour is unchanged. It legitimately needs its own correction: ApplyLogitAdjustment() only runs inside a training run, so a deployed model that was reloaded carries no logit offsets and would otherwise stream 31:1 data into itself uncorrected. The weights-filename fingerprint is BYTE-IDENTICAL. The focal slot was a double fed to a %d conversion and had always emitted a literal 0; the |MR: segment is written as the constant its shipped defaults produced. Dropping either would have re-keyed every model and forced a from-scratch retrain of the one topology currently converged and trading. Also removed as orphans: FOCAL_GAMMA_PRESET, MAX_OVERSAMPLE_REPLICAS, OVERSAMPLE_PARITY_FRACTION, PLATEAU_GAMMA_STEP, and the now-unreachable "neutralized by prior correction" diagnostic. Both builds compile 0 errors, 0 warnings. No retrain forced. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 11:46:57 -04:00
? " | measured imbalance ~" + DoubleToString((double)prebuildMaxCls / prebuildMinDir, 1) + ":1"
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
: " | measured imbalance n/a (a directional class has no labeled bars in this window)";
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
//--- These three counts are now WIN / LOSS-or-timeout counts under the EA's real stop and target, not
//--- pivot-spotting counts, so the Buy+Sell share here IS the fraction of bars offering a tradeable
//--- setup - and the era line's dir-precision against it is a win rate. This is the number that
//--- decides whether LogitAdjustTau still has a job: at a near-balanced split the log-prior spread
//--- collapses and the correction (plus its range cap, and the SoftMax port behind it) is redundant.
int prebuildTotal = m_labelPrebuildBuyCount + m_labelPrebuildSellCount + m_labelPrebuildNeutralCount;
string prebuildShare = (prebuildTotal > 0)
? " | share Buy " + DoubleToString(100.0 * m_labelPrebuildBuyCount / prebuildTotal, 1) +
"% Sell " + DoubleToString(100.0 * m_labelPrebuildSellCount / prebuildTotal, 1) +
"% Neutral " + DoubleToString(100.0 * m_labelPrebuildNeutralCount / prebuildTotal, 1) + "%"
: "";
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
Print(ID + ": label cache pre-built - IS true-label distribution -> Buy: " + IntegerToString(m_labelPrebuildBuyCount) +
" | Sell: " + IntegerToString(m_labelPrebuildSellCount) + " | Neutral: " + IntegerToString(m_labelPrebuildNeutralCount) +
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
prebuildRatioInfo + prebuildShare +
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon Three defects found by reading the 2026-08-01 training logs, all of which only became visible because the relabel made the numbers mean something. 1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET. `OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101` -101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in 7eb48f5. MetaTrader does not validate a saved enum input against the enum's current members, so charts saved before that kept the old integer. BarrierMultiples()'s `if(tpMult <= 0.0) tpMult = slMult;` then quietly turned it into a 1:1 barrier, and all four topologies trained ~250 eras against a strategy nobody selected - while the log reported "target 1.00*ATR" as though it were configured. Since the relabel these two inputs ARE the label definition, so this is not a bad trade setting, it is a wrong dataset. ValidateBarrier- Inputs() now refuses to start (INIT_FAILED + Alert + an explicit fix) on any value that is not an enum member. Members are enumerated rather than range-checked because both enums are sparse and carry negative sentinels, so no min/max test can tell a legal value from a deleted one - which is the entire failure mode. The fallback survives as belt-and-braces but now announces itself: a fallback that cannot say it fired is indistinguishable from correct behaviour. 2. THE DEPLOYABILITY FLOOR BECAME MATHEMATICALLY UNREACHABLE. `tradeableOK` required `dirPrecPct >= baseRatePct`, where baseRatePct is Buy+Sell as a share of all bars. At the old exact-pivot target that was ~6%, so "beat the base rate" read as "beat chance" and the test looked sound. Triple-barrier labels put it at ~83%, so the gate now demanded 83% directional precision - impossible by construction. Observed live: all four topologies cycling "PLATEAU stage 3 ... nothing safe to deploy" at a perfectly healthy 43-45% precision, with no checkpoint able to ship however good it got. Replaced with ZERO-SKILL precision, max(Buy,Sell)/allBars: exactly the score of the degenerate always-call-one-direction model this floor exists to reject. Correct at any base rate - ~43% on the current labels, ~3% on the old rare-pivot ones. The era line now prints "(chance N%, edge +Mpp)" beside the selection score, because 44% precision is excellent against a 3% chance level and worthless against a 43% one, and reading the first as the second is what made tonight's run look better than it was. 3. THE HORIZON IGNORED THE BARRIER GEOMETRY. ComputeBarrierHorizonBars() returned the median ZigZag leg, which measures how long a ~1 ATR move takes and says nothing about how long the CONFIGURED barrier needs. First-passage time out of [-m,+k] scales with m*k, so a 1:3 barrier takes ~3x as long as 1:1; the unscaled horizon would have timed out most 1:3 trades and pushed Neutral straight back up, re-creating the imbalance the relabel removes. Now multiplied by slMult*tpMult, calibrated against a real measurement rather than assumed: the accidental 1:1 run resolved at horizon 12 with only 16.7% timeouts, so the swing median is the right scale at m*k=1. Verifiable, not just asserted: the prebuild now counts barriers that ended on the VERTICAL barrier and reports them as a share of Neutral. Neutral conflates "timed out" with "stopped out" and only the first indicts the horizon. Both builds compile 0 errors / 0 warnings. Forces a retrain - correcting TP_Mode re-keys the fingerprint (|TB:1:-101 -> |TB:1:3), which is right: no existing model was trained on the intended target. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-01 00:30:49 -04:00
" | of which timed out (horizon too short?) " + IntegerToString(m_labelPrebuildTimeoutCount) +
(m_labelPrebuildNeutralCount > 0
? " = " + DoubleToString(100.0 * m_labelPrebuildTimeoutCount / m_labelPrebuildNeutralCount, 1) + "% of Neutral"
: "") +
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
(m_eraCount == 0 ? " (seeding era 0 - triple-barrier targets, so Buy/Sell mean 'target hit before stop')"
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
: " (mid-run rebuild after new-bar cache invalidation - era " + IntegerToString(m_eraCount) + " resumes on the relabeled window)"));
feat: derive the ATR multiples from measured excursions - no hardcoded geometry The barrier was still two constants. SL_Mode/TP_Mode left the Inputs tab in 3482b6c, but the fallback was a hardcoded 2:6 and the geometry scan only ever chose from a hardcoded grid {2,3} x {2,3,4,6,8,10}. Picking the least-bad of eleven guesses is not deriving anything. WHY THE SCAN WAS THE WRONG INSTRUMENT, now measurable rather than argued. It ranks pairings by how predictable their OUTCOME is - a question about direction. The excursion test (2c78f3b) ran on SP500 H1 and direction is the one thing absent: ASYMMETRY p=0.0846, against RANGE/UP/DOWN all at p=0.0050, with RANGE scoring 0.01345 vs a 0.00343 null - 4x, where the barrier label sits at 1.01x. Hence the scan failing its own gate on every run, and its "winner" wandering 2:8 -> 3:8 -> 2:8 -> 2:4 across four runs of the same data. Excursion SIZE is strongly measurable, so derive the geometry from that instead. stop = q25 of measured ADVERSE travel (ordinary noise does not reach it) target = q50 of measured FAVOURABLE travel (reached ~half the time, by construction, inside the horizon) Continuous, in ATR units, superseding the enum multiples. Reachability ("target on X% of bars, stop on Y%") and the implied break-even are printed so the choice is auditable rather than trusted. FIXED-POINT ITERATION, not one-shot. ComputeBarrierHorizonBars scales the horizon with the target (first-passage time grows with the band) and the excursions are measured OVER the horizon, so target -> horizon -> excursions -> target is a real loop - deriving once sizes the target from travel measured under the PREVIOUS horizon. Re-measures until the multiples move <5%, capped at 3 passes, and says so if it does not settle. Does NOT create expectancy, and the log says as much: chance precision equals break-even at every geometry (m/(m+k) on both sides). It buys a target the market reaches and a stop that survives noise. Where Min_Risk_Reward_Ratio forces a target the market rarely reaches, it WARNS rather than overriding - the ratio is the user's risk policy, so the honest move is to state its cost. That is the collision that once rejected 100% of setups. Pinned in the .cfg as doubles appended AFTER this morning's two ints, so .cfg files written earlier today still load (their length guard finds no doubles) and a model that carries them was trained on them and never re-derives. Also fixes a message from e5ceed6 that claimed "this model resumed from disk" unconditionally - it printed above a "seeding era 0" line on a brand-new model, because the branch fires whenever the cache is not built, which is equally true before a fresh model's first prebuild. A diagnostic that misreports its own trigger is worse than one that says nothing: it gets quoted back as evidence. FORCES A FULL RETRAIN (labels change). Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 12:06:25 -04:00
//--- DERIVE THE GEOMETRY FROM WHAT WAS JUST MEASURED, then relabel under it. Only at era 0: changing
//--- the barrier mid-run would move the target out from under weights already fitted to the old one.
//--- Iterated because the horizon scales with the target and the excursions are measured over the
//--- horizon (see BARRIER_DERIVE_MAX_PASSES) - one pass would size the target from travel measured
//--- under the previous horizon.
if(m_eraCount == 0 && m_geometryDerivePasses < BARRIER_DERIVE_MAX_PASSES)
{
double prevSl = m_derivedSlMult, prevTp = m_derivedTpMult;
m_geometryDerivePasses++;
if(DeriveBarrierGeometry())
{
bool settled = (prevSl > 0.0 && prevTp > 0.0
&& MathAbs(m_derivedSlMult - prevSl) <= BARRIER_DERIVE_TOLERANCE * prevSl
&& MathAbs(m_derivedTpMult - prevTp) <= BARRIER_DERIVE_TOLERANCE * prevTp);
if(!settled)
{
if(m_geometryDerivePasses >= BARRIER_DERIVE_MAX_PASSES)
Print(ID + StringFormat(": barrier geometry did NOT settle within %d passes (last move "
"%.2f->%.2f stop, %.2f->%.2f target). Using the latest pair; the "
"reachability figures above are the ones to check.",
BARRIER_DERIVE_MAX_PASSES, prevSl, m_derivedSlMult, prevTp,
m_derivedTpMult));
else
{
//--- Re-derive the horizon for the NEW target and relabel the whole window under it.
//--- Train()'s !m_labelCachePrebuilt gate restarts the scan on the next call.
m_barrierHorizonResolved = false;
m_labelCachePrebuilt = false;
ArrayInitialize(m_labelCacheHasValue, false);
return;
}
}
}
}
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
//--- Cold-start fix: a freshly-initialized (random-weight) network's argmax is close to uniform
//--- noise across the 3 classes, so on this typically heavily-skewed label distribution it fires
//--- far more non-majority-class calls at the very start of era 0 than the true base rate warrants,
//--- until enough backProp steps correct it. Now that the real prior is known, push the output
//--- layer's bias toward whichever class actually dominates - only the bias term moves, the
//--- per-input weights stay randomly initialized and still carry the real learning signal. Only
//--- meaningful for the 3-output classification head, and only for a fresh net (this whole prebuild
//--- path is skipped entirely when a trained net was loaded from disk - see m_labelCachePrebuilt).
//--- m_eraCount==0 gate: the prebuild can also re-run MID-run now (new-bar cache invalidation -
//--- see Train()'s era-start wipe check); stomping a partially-trained net's output biases with
//--- +-3.0 cold-start values there would erase real learned calibration, so fresh runs only.
if(m_outputNeuronsCount == 3 && m_eraCount == 0)
{
int dominant = 2; // Neutral
int dominantCount = m_labelPrebuildNeutralCount;
if(m_labelPrebuildBuyCount > dominantCount)
{
dominant = 0;
dominantCount = m_labelPrebuildBuyCount;
}
if(m_labelPrebuildSellCount > dominantCount)
{
dominant = 1;
dominantCount = m_labelPrebuildSellCount;
}
int totalLabeled = m_labelPrebuildBuyCount + m_labelPrebuildSellCount + m_labelPrebuildNeutralCount;
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
//--- Trigger raised 0.40 -> COLD_START_SEED_MIN_DOMINANCE with the triple-barrier relabel. This seed
//--- is an antidote to an EXTREME prior: under the old exact-pivot target Neutral held ~94% of bars
//--- and a uniform-ish random argmax over-called wildly for the first few thousand steps. Barrier
//--- labels land near 25/25/50, where sigmoid(+-3) ~ 0.95/0.05 is no longer a correction but a
//--- distortion - it would start the net further from the truth than random init does. Keeping the
//--- mechanism behind a genuinely-dominant threshold means it stays available for a skewed symbol
//--- (or a tight-target configuration that pushes Neutral back up) and self-disables otherwise.
if(totalLabeled > 0 && (double)dominantCount / totalLabeled > COLD_START_SEED_MIN_DOMINANCE)
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
{
const double BIAS_MAGNITUDE = 3.0; // sigmoid(+-3) ~= 0.95/0.05 - comfortably outweighs a
// fresh network's random per-input weighted-sum noise
double biasValues[3] = { -BIAS_MAGNITUDE, -BIAS_MAGNITUDE, -BIAS_MAGNITUDE };
biasValues[dominant] = BIAS_MAGNITUDE;
if(Net.SeedOutputLayerBias(biasValues))
PrintVerbose(ID + ": seeded output layer bias toward " + EnumToString((ENUM_SIGNAL)(dominant == 0 ? Buy : dominant == 1 ? Sell : Neutral)) +
" (era 0 cold-start fix)");
}
}
}
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-31 20:39:49 -04:00
//--- ConfirmedZigZagLabel() REMOVED 2026-08-01. It was the online-learning path's copy of the exact-pivot
//--- target; that target is gone, and its one caller now asks TripleBarrierLabel() the same question
//--- training asks. Keeping a second label rule alive is how the live and trained tasks drift apart.
refactor: split CExpertSignalAIBase implementation by responsibility ExpertSignalAIBase.mqh was 8216 lines: the class declaration followed by 87 method bodies covering training, labelling, feature extraction, persistence, chart drawing, online learning, the GA auto-tuner and inference, all in one file. Train() alone is 1492 lines; a change to arrow drawing meant scrolling past the era loop. Moved the bodies into Expert\AIBase\, included at the bottom of the original after the class declaration: Training.mqh 1607 era loop, plateau ladder, checkpoint select, deploy Features.mqh 1093 indicator creation + per-bar input feature vector ChartUI.mqh 634 arrows, arrow persistence, status panel, cleanup Persistence.mqh 492 .stats/.cfg sidecars, CPU-inference validation, copy OnlineLearning.mqh 461 live continual learning, EMA shadow, OOS simulator Labels.mqh 309 ZigZag pivot labels, async label-cache prebuild AutoTune.mqh 275 genetic tuner (population, crossover, halving) Inference.mqh 235 softmax, prior calibration, class priors ExpertSignalAIBase.mqh 8216 -> 3131 (declaration + topology build only) This is a pure relocation - verified mechanically, not by eye: HEAD's file reconstructed from the eight partials plus the surviving remainder is byte-identical to HEAD, span for span (scratchpad verify_split.py). No declaration moved, no signature changed, no code rewritten, so behaviour is unchanged by construction. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 00:42:45 -04:00
#endif // WARRIOR_AIBASE_LABELS_MQH
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