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Warrior_EA/Expert/AIBase/Persistence.mqh

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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 |
//| |
refactor(comments): box headers to stdlib length The //| box blocks were excluded from 0b06f8e and 5efdb48 and were what remained: 160 of them ran to 10+ lines, the longest to 88. Compressed to their leading topic sentences - 5 lines for a function header, 8 for a file header - keeping the box format and the standard MQL5 name/author lines verbatim. Verified at the BYTE level this time, across every in-scope file: the list of non-comment lines is byte-identical to HEAD and braces balance. The first check compared a locale-decoded 'git show' against a UTF-8 read and flagged 25 files that had not changed at all - every BOM and every non-ASCII line mismatched. 47,696 -> 40,665 lines in scope; comment share 38% -> 26%. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:30:14 -04:00
//| Model .stats / .cfg sidecars, CPU-inference validation, share-aw |
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
//+------------------------------------------------------------------+
#ifndef WARRIOR_AIBASE_PERSISTENCE_MQH
#define WARRIOR_AIBASE_PERSISTENCE_MQH
//+------------------------------------------------------------------+
refactor(comments): box headers to stdlib length The //| box blocks were excluded from 0b06f8e and 5efdb48 and were what remained: 160 of them ran to 10+ lines, the longest to 88. Compressed to their leading topic sentences - 5 lines for a function header, 8 for a file header - keeping the box format and the standard MQL5 name/author lines verbatim. Verified at the BYTE level this time, across every in-scope file: the list of non-comment lines is byte-identical to HEAD and braces balance. The first check compared a locale-decoded 'git show' against a UTF-8 read and flagged 25 files that had not changed at all - every BOM and every non-ASCII line mismatched. 47,696 -> 40,665 lines in scope; comment share 38% -> 26%. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:30:14 -04:00
//| Re-assert the parts of a just-loaded net that the CODE owns but |
//| the FILE also stores. See OutputLayerActivation()'s declaration |
//| comment and CNet::EnforceOutputActivation() (AI\Network.mqh). |
fix: stop a .nnw from pinning a superseded architecture A .nnw persists the ARCHITECTURE, not just the weights: Save writes (int)activation per neuron and Load reads it straight back. The activation chosen in BuildFreshTopology() therefore only ever reached a brand-new topology - every reload restored the file's value and the next save wrote it back out, so a wrong value could never heal while the source read as though it were already fixed. That is how five models kept training with an unbounded NONE classification head for a full day after the 07-28 revert to SIGMOID. Confirmed by parsing the binaries: 848cb42c.nnw / 2e754b43.nnw carry `act=NONE` on the 3-neuron output layer, while a genuinely reset model of the same config carries act=SIGMOID. In the log it showed as negative "OOS raw out" values - impossible under sigmoid - escalating to a 4.14e13 logit spread with all three classes numerically identical (input-independent output) and balanced accuracy pinned on the 33.3% one-class floor. - OutputLayerActivation() is now the single source of truth, called by both BuildFreshTopology() and the new load-time repair, so the two can no longer diverge the way a duplicated literal did. - CNet::EnforceOutputActivation() re-asserts it after Load and reports the stale value; CExpertSignalAIBase::EnforceTopologyContract() logs the repair loudly, since weights learned under the old head may not be worth keeping even once the head is corrected. - Hidden layers are deliberately left alone: they legitimately differ per stage (PRELU dense/conv, NONE pool, TANH LSTM). Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 12:00:40 -04:00
//+------------------------------------------------------------------+
void CExpertSignalAIBase::EnforceTopologyContract(void)
{
if(CheckPointer(Net) == POINTER_INVALID)
return;
feat(ai): real conv receptive field + the reference's channel pool CONV's convolution used window = step = one bar, which is a per-bar projection - a 1x1 conv with a temporal receptive field of ONE BAR. It never mixed information across time, so "convolutional" described the layer type and nothing about what it computed. Same finding that sank HYBRID's LSTM. Pooling was removed on 2026-07-29 for being misconfigured against the conv output's memory layout. That removal was right; leaving the conv at a one-bar window was not. The two belong together: the NeuroNet_DNG reference (references\MQL5\Experts\EDL\Trajectory.mqh layers 2-5, kernels byte-identical to ours) pairs conv(window=2, step=1, window_out=4) with pool(window=4, step=4), and the pool only earns its place because a conv with a real receptive field sits above it. The input is bar-major (BufferTempData appends m_neuronsCount contiguous features per bar), so a flat window of k*m_neuronsCount spans exactly k bars - the receptive field needed NO kernel change. The conv output is position-major, so window == step == window_out is a clean max-over-channels, which is what the reference does and what the existing pool kernels already implement correctly. New chain at H1 defaults (420 = 20 bars x 21): conv1 w=42 s=21 out=8 -> 19 pos x 8 = 152 pool w=8 s=8 -> 19 conv2 w=2 s=1 out=8 -> 18 pos x 8 = 144 (effective field: 3 bars) We deliberately stop before the reference's SECOND pool: a channel pool emits one scalar per position, so a trailing pool would hand the dense stack 18 values and force it to fan out 18 -> 64. That is a bottleneck below every learnable layer - the same class of mistake the 2026-07-29 removal was about. Fixes a latent sizing bug this exposed: CNet's conv/pool position cursor tracked sliding POSITIONS, but a conv's real width is units_count * window_out. Any pool stacked on a conv would therefore have sized against a width window_out times too small and silently built the wrong shape. Both branches now read the built layer's actual Neurons(), which is what the batch-norm branch already did for the same reason. Also closes the architecture-pinning trap: a .nnw persists the window each conv was built with, so an existing CONV/HYBRID model would have loaded cleanly and gone on training under the OLD architecture. The conv weight tensor is (window+1)*window_out, so this cannot be repaired in place - EnforceTopologyContract now detects it, reports both shapes, and retrains. Conv chain shape is derived in one place (ConvReceptiveFieldBars / ConvFirstStagePositions / HasSecondConvStage / ConvOutputPositions / ConvOutputWidth) and consumed by AddConvStage, LstmFanIn and the startup config line, so what is built and what is logged cannot drift. Both builds compile 0 errors, 0 warnings. Forces a CONV and HYBRID retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-30 20:05:37 -04:00
//--- Stale conv receptive field. Unlike the activation below this cannot be repaired in place: the
//--- conv weight block is (window+1)*window_out, so a different window is a different tensor. Flag it
//--- and let the caller retrain - see m_topologySuperseded's use in InitNeuralNetwork.
if(UsesConvStage())
{
uint loadedWindow = Net.FirstConvWindow();
uint intendedWindow = (uint)(ConvReceptiveFieldBars() * m_neuronsCount);
if(loadedWindow > 0 && loadedWindow != intendedWindow)
{
m_topologySuperseded = true;
Print(ID + ": SUPERSEDED architecture on disk - the saved model's conv receptive field is " +
IntegerToString((int)loadedWindow) + " (" + IntegerToString((int)(loadedWindow / (uint)MathMax(1, m_neuronsCount))) +
" bars), this build specifies " + IntegerToString((int)intendedWindow) + " (" +
IntegerToString(ConvReceptiveFieldBars()) + " bars). The conv weight tensor is a different" +
" shape, so this cannot be repaired in place - retraining from era 0.");
}
}
fix: stop a .nnw from pinning a superseded architecture A .nnw persists the ARCHITECTURE, not just the weights: Save writes (int)activation per neuron and Load reads it straight back. The activation chosen in BuildFreshTopology() therefore only ever reached a brand-new topology - every reload restored the file's value and the next save wrote it back out, so a wrong value could never heal while the source read as though it were already fixed. That is how five models kept training with an unbounded NONE classification head for a full day after the 07-28 revert to SIGMOID. Confirmed by parsing the binaries: 848cb42c.nnw / 2e754b43.nnw carry `act=NONE` on the 3-neuron output layer, while a genuinely reset model of the same config carries act=SIGMOID. In the log it showed as negative "OOS raw out" values - impossible under sigmoid - escalating to a 4.14e13 logit spread with all three classes numerically identical (input-independent output) and balanced accuracy pinned on the 33.3% one-class floor. - OutputLayerActivation() is now the single source of truth, called by both BuildFreshTopology() and the new load-time repair, so the two can no longer diverge the way a duplicated literal did. - CNet::EnforceOutputActivation() re-asserts it after Load and reports the stale value; CExpertSignalAIBase::EnforceTopologyContract() logs the repair loudly, since weights learned under the old head may not be worth keeping even once the head is corrected. - Hidden layers are deliberately left alone: they legitimately differ per stage (PRELU dense/conv, NONE pool, TANH LSTM). Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 12:00:40 -04:00
ENUM_ACTIVATION intended = OutputLayerActivation();
ENUM_ACTIVATION stale = intended;
if(!Net.EnforceOutputActivation(intended, stale))
return;
Print(ID + ": REPAIRED loaded model - output layer activation was " + ActivationName(stale) +
" on disk, topology specifies " + ActivationName(intended) +
". The saved file was produced by an older build; it has been corrected in memory and the next" +
" save will persist the correction. If training looks wrong from here, reset weights and retrain -" +
" these weights were learned against the stale head.");
}
//+------------------------------------------------------------------+
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
//| Persist/restore the calibration state that must survive a restart |
//| for live trading to behave like training: the true class priors |
//| and m_confidenceCalScale. Same FILE_COMMON/tester write-guard as |
//| CNet::Save so a backtest never overwrites the shared production |
//| stats. Versioned/magic-prefixed; a mismatch is treated as absent. |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::SaveModelStats(string fileName, bool common)
{
//--- mirror CNet::Save's guard: shared production stats are never written from inside a backtest
if(common && (MQLInfoInteger(MQL_OPTIMIZATION) || MQLInfoInteger(MQL_TESTER) || MQLInfoInteger(MQL_FORWARD)))
return true;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- Staged through a temp file + atomic rename (System\AtomicFile.mqh).
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 statsCommonFlag = (common ? FILE_COMMON : 0);
string statsTmpName = "";
int handle = AtomicWriteBegin(fileName + ".stats", statsCommonFlag, statsTmpName);
if(handle == INVALID_HANDLE)
{
Print(__FUNCTION__ + ": FileOpen failed for " + statsTmpName + ", error " + IntegerToString(GetLastError()) +
" - calibration/online-learning state not persisted.");
return false;
}
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- WST6 has the SAME field layout as WST5 - the bump exists to invalidate stale IS counters,
//--- whose MEANING changed: they used to count every oversampled occurrence, now they count each
//--- bar once.
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
bool ok = true;
if(FileWriteInteger(handle, 0x57535436) <= 0) // 'WST6' magic/version (WST5 = +compounded IS/OOS counts, WST4 = +live reliability, WST3 = +online state, WST2 = +CPU-marker, WST1 = base)
ok = false;
if(ok && FileWriteDouble(handle, m_priorBuy) <= 0)
ok = false;
if(ok && FileWriteDouble(handle, m_priorSell) <= 0)
ok = false;
if(ok && FileWriteDouble(handle, m_priorNeutral) <= 0)
ok = false;
if(ok && FileWriteDouble(handle, m_confidenceCalScale) <= 0)
ok = false;
//--- CPU-inference-safe marker (see ValidateCpuInference): gates whether an inference-only backtest
//--- of this model may run DLL-free. Appended after the v1 fields so a v1 reader stops cleanly before it.
if(ok && FileWriteInteger(handle, m_mqlInferenceValidated ? 1 : 0) <= 0)
ok = false;
//--- Online continual-learning state (WST3, see OnlineLearnStep) - the bar-time watermark of the
//--- newest bar already learned from, the rolling guardrail accuracy, and the cumulative update count.
//--- Appended after the WST2 fields so a WST2 reader stops cleanly before them.
if(ok && FileWriteLong(handle, (long)m_onlineLearnedUpToTime) <= 0)
ok = false;
if(ok && FileWriteDouble(handle, m_onlineRollingAcc) <= 0)
ok = false;
if(ok && FileWriteLong(handle, m_onlineSamples) <= 0)
ok = false;
//--- Deployed model's last-measured OOS reliability (WST4) - the live status panel shows this as the
//--- "signal hit-rate" so a freshly-reloaded, inference-only model still reports what to expect live
//--- (these members are computed only during training, so without persistence they read n/a on reload).
//--- Appended after the WST3 fields so a WST3 reader stops cleanly before them.
if(ok && FileWriteInteger(handle, m_lastBuyFiredPrecPct) <= 0)
ok = false;
if(ok && FileWriteInteger(handle, m_lastSellFiredPrecPct) <= 0)
ok = false;
if(ok && FileWriteInteger(handle, m_lastBuyRecallPct) <= 0)
ok = false;
if(ok && FileWriteInteger(handle, m_lastSellRecallPct) <= 0)
ok = false;
if(ok && FileWriteInteger(handle, m_lastBuyFired) <= 0)
ok = false;
if(ok && FileWriteInteger(handle, m_lastSellFired) <= 0)
ok = false;
//--- Compounded, persistent IS/OOS accuracy counts (WST5) - see m_cumIsCorrect. Carried across
//--- restarts so the panel's accuracy keeps compounding instead of restarting each session.
//--- Appended after the WST4 fields so a WST4 reader stops cleanly before them.
if(ok && FileWriteLong(handle, m_cumIsCorrect) <= 0)
ok = false;
if(ok && FileWriteLong(handle, m_cumIsTotal) <= 0)
ok = false;
if(ok && FileWriteLong(handle, m_cumOosCorrect) <= 0)
ok = false;
if(ok && FileWriteLong(handle, m_cumOosTotal) <= 0)
ok = false;
//--- A partial write (disk full mid-write) now discards the temp and leaves the previous good
//--- .stats in place, instead of publishing a truncated one that reads back as all-zero
//--- calibration state.
return AtomicWriteEnd(handle, fileName + ".stats", statsTmpName, statsCommonFlag, ok, __FUNCTION__);
}
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::LoadModelStats(string fileName, bool common)
{
if(!FileIsExist(fileName + ".stats", common ? FILE_COMMON : 0))
return false;
//--- share flags: read-only, must not fail just because another process holds the file - see CopySharedFile().
int handle = FileOpen(fileName + ".stats", (common ? FILE_COMMON : 0) | FILE_BIN | FILE_READ | FILE_SHARE_READ | FILE_SHARE_WRITE);
if(handle == INVALID_HANDLE)
return false;
int magic = FileReadInteger(handle);
if(magic != 0x57535431 && magic != 0x57535432 && magic != 0x57535433 && magic != 0x57535434 && magic != 0x57535435 && magic != 0x57535436)
{
FileClose(handle);
return false;
}
m_priorBuy = FileReadDouble(handle);
m_priorSell = FileReadDouble(handle);
m_priorNeutral = FileReadDouble(handle);
m_confidenceCalScale = FileReadDouble(handle);
//--- v2+ appended the CPU-inference-safe marker; v1 files predate it (treated as not-yet-validated so
//--- the model stays on the DLL path until re-deployed by a build that runs ValidateCpuInference).
m_mqlInferenceValidated = (magic == 0x57535432 || magic == 0x57535433 || magic == 0x57535434 || magic == 0x57535435 || magic == 0x57535436) ? (FileReadInteger(handle) != 0) : false;
//--- v3 appended the online continual-learning state. Older files predate it: leave the watermark at 0
//--- (OnlineLearnStep anchors it to the current frontier on first run - no retroactive backprop) and
//--- the rolling accuracy at -1 (re-seeded from the deploy baseline on the first update).
if(magic == 0x57535433 || magic == 0x57535434 || magic == 0x57535435 || magic == 0x57535436)
{
m_onlineLearnedUpToTime = (datetime)FileReadLong(handle);
m_onlineRollingAcc = FileReadDouble(handle);
m_onlineSamples = FileReadLong(handle);
}
//--- v4 appended the deployed model's last-measured OOS reliability (for the live status panel). Older
//--- files predate it: the members keep their -1 / 0 ctor defaults, so the panel shows no hit-rate line
//--- until the model is re-trained (or re-deployed) by a WST4+ build - exactly the pre-persistence behaviour.
if(magic == 0x57535434 || magic == 0x57535435 || magic == 0x57535436)
{
m_lastBuyFiredPrecPct = FileReadInteger(handle);
m_lastSellFiredPrecPct = FileReadInteger(handle);
m_lastBuyRecallPct = FileReadInteger(handle);
m_lastSellRecallPct = FileReadInteger(handle);
m_lastBuyFired = FileReadInteger(handle);
m_lastSellFired = FileReadInteger(handle);
}
//--- v5 appended the compounded/persistent IS/OOS accuracy counts (see m_cumIsCorrect). Older files
//--- predate it: the counts keep their 0 ctor defaults, so the panel shows "measuring" until the next
//--- era scores signals - then it resumes compounding from there.
if(magic == 0x57535435 || magic == 0x57535436)
{
m_cumIsCorrect = FileReadLong(handle);
m_cumIsTotal = FileReadLong(handle);
m_cumOosCorrect = FileReadLong(handle);
m_cumOosTotal = FileReadLong(handle);
//--- A WST5 file's IS pair counted every OVERSAMPLED OCCURRENCE, so it was measured against a
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- ~58%-directional queue instead of the real ~6% distribution - not comparable with the
//--- OOS pair beside it, and the source of the "IS 77% / OOS 12%, looks like overfitting"
//--- reading.
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(magic == 0x57535435)
{
m_cumIsCorrect = 0;
m_cumIsTotal = 0;
}
}
FileClose(handle);
return true;
}
//+------------------------------------------------------------------+
//| Deploy-time self-check (chart only, where a compute backend |
//| exists): run the just-saved deployed model through both the |
//| backend Net and a throwaway pure-MQL5 clone (CNet::SetCpuInference|
//| loaded from the same .nnw) on one real input window, and return |
//| true only if their outputs match within CPU_INFERENCE_MAX_DIFF. |
//| This is what lets an inference-only backtest run DLL-free; any |
//| error, size mismatch, or a not-yet-ported architecture (conv/LSTM |
//| CPU load fails) returns false -> the model stays on the DLL path. |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::ValidateCpuInference(void)
{
//--- Chart-only: needs a real backend to compare against, and only the shared production model (not a
//--- per-agent optimization cache) is ever seeded into a buyer's inference-only backtest.
if(MQLInfoInteger(MQL_TESTER) || MQLInfoInteger(MQL_OPTIMIZATION) || MQLInfoInteger(MQL_FORWARD))
return false;
if(CheckPointer(Net) == POINTER_INVALID || CheckPointer(TempData) == POINTER_INVALID)
return false;
fix: the sequence models were reading the window backwards BuildFeatureWindow() replaces eight hand-rolled copies of the same loop and feeds the window OLDEST BAR FIRST. Every copy fed it newest-first, because MQL5 timeseries indices run backwards and `r + b` with b ascending walks into the past. Harmless for PAI and CONV - a dense layer learns a weight per position either way, a conv learns time-mirrored kernels. Not harmless for the recurrent stacks: - LSTM_SeqStepForward reads `inputs + t*Iw`, so step t is block t. - It writes output[] only when t == steps-1: the visible output IS the last hidden state. - c_t = f*c_{t-1} + i*g decays toward the start of the sequence. lstm_seq_flowcheck.cpp measured block 0's influence on the output at 1.2e-2 of block T-1's, at the shipped forget bias of 1.0. So the bar being PREDICTED sat at the far end of the decay and the output was handed to the OLDEST bar in the window - the exact inverse of what the window is for. ~80x backwards on LSTM and HYBRID, on all three tiers (OpenCL kernel, CPU DLL, pure-MQL5 inference), which is why it never surfaced as a backend discrepancy. This does not create edge - the MI diagnostics read at the noise floor (p=0.4975) with a working positive control. It makes the one hypothesis those diagnostics explicitly do NOT cover testable: they are marginal and per-bar, and state they "cannot rule out one that only exists in combination or across time". The sequence model is the instrument for across-time structure and it has been crippled, so that hypothesis has never been honestly tested. Fingerprint gets an unconditional |WIN:2 - the vector keeps its shape and its features, so a stale .nnw would load cleanly and run a model fitted to one ordering against the other, silently. Re-keying every config is the point, not collateral damage. FORCES A FULL RETRAIN. Also: the now-relative bar caches are re-keyed on the two live paths. EnsureBarCachesCapacity() was only ever called from training paths, but once m_trainingComplete is set ScheduleTrainingIfNeeded() routes every bar to RefreshConvergedSignal() and Train() is never re-entered - so nothing cleared the feature cache again for the life of the process. A chart that trained to convergence kept replaying the rows computed for the last training era's bar grid: the live signal froze at its convergence-time value, and OnlineLearnStep() backpropped those stale features against freshly resolved labels. Backtests were never affected (an inference-only process never allocates the arrays, so every read recomputes). Compiles clean: 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 18:28:44 -04:00
//--- Build the same latest-bar input window RefreshLatestSignal() feeds the deployed model - same
//--- builder, so "the same" is structural rather than a comment that has to stay true by hand.
if(!BuildFeatureWindow(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
return false;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- Reference: the compute backend Net actually trained on. See CNet::SetBatchNormFrozen. A no-
//--- op on topologies without normalization.
fix(ai): freeze batch-norm statistics when comparing two forward passes With normalization enabled a forward pass is not a pure function of its input - it also advances the running mean/variance. ValidateCpuInference compares the live backend net against a throwaway pure-MQL5 clone loaded from the just-saved .nnw, so its own reference pass left the live model one EMA step ahead of the file the clone reads. The check would then have been measuring its own side effect, and a marginal result decides whether buyers' backtests are allowed to run DLL-free. Adds CNet::SetBatchNormFrozen / CNeuronBatchNormOCL::SetStatsFrozen - classic batch-norm inference semantics, statistics used but not updated - and freezes both sides for the duration of the comparison. Not persisted: it is a transient evaluation mode, not model state. Default stays adaptive, which is what the rest of the system (online continual learning) is built around. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 12:37:50 -04:00
Net.SetBatchNormFrozen(true);
bool refOk = Net.feedForward(TempData);
Net.SetBatchNormFrozen(false);
if(!refOk)
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
return false;
CArrayDouble *refOut = new CArrayDouble();
if(CheckPointer(refOut) == POINTER_INVALID)
return false;
Net.getResults(refOut);
//--- Candidate: a throwaway pure-MQL5 clone loaded from the just-saved deployed weights.
CNet *cpu = new CNet(NULL);
if(CheckPointer(cpu) == POINTER_INVALID)
{
delete refOut;
return false;
}
cpu.SetCpuInference(true);
double e, u, f;
datetime tm;
long era;
bool complete;
double ip[];
bool loaded = cpu.Load(m_activeFileName + ".nnw", e, u, f, tm, m_activeFileCommon, era, complete, ip);
fix(ai): freeze batch-norm statistics when comparing two forward passes With normalization enabled a forward pass is not a pure function of its input - it also advances the running mean/variance. ValidateCpuInference compares the live backend net against a throwaway pure-MQL5 clone loaded from the just-saved .nnw, so its own reference pass left the live model one EMA step ahead of the file the clone reads. The check would then have been measuring its own side effect, and a marginal result decides whether buyers' backtests are allowed to run DLL-free. Adds CNet::SetBatchNormFrozen / CNeuronBatchNormOCL::SetStatsFrozen - classic batch-norm inference semantics, statistics used but not updated - and freezes both sides for the duration of the comparison. Not persisted: it is a transient evaluation mode, not model state. Default stays adaptive, which is what the rest of the system (online continual learning) is built around. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 12:37:50 -04:00
//--- Frozen for the same reason as the reference pass above - both sides must evaluate the SAME
//--- statistics, which are the ones sitting in the .nnw.
if(loaded)
cpu.SetBatchNormFrozen(true);
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
bool pass = false;
double maxDiff = DBL_MAX;
if(loaded && cpu.feedForward(TempData))
{
CArrayDouble *cpuOut = new CArrayDouble();
if(CheckPointer(cpuOut) != POINTER_INVALID)
{
cpu.getResults(cpuOut);
if(cpuOut.Total() == refOut.Total() && refOut.Total() > 0)
{
maxDiff = 0.0;
for(int i = 0; i < refOut.Total(); i++)
maxDiff = MathMax(maxDiff, MathAbs(refOut.At(i) - cpuOut.At(i)));
pass = (maxDiff <= CPU_INFERENCE_MAX_DIFF);
}
delete cpuOut;
}
}
delete cpu;
delete refOut;
PrintVerbose(ID + ": CPU-inference validation " + (pass ? "PASSED - backtests may run DLL-free" :
"FAILED - backtests keep using the DLL") + " (max |delta| = " +
(maxDiff == DBL_MAX ? "n/a" : DoubleToString(maxDiff, 8)) + ", tol " +
DoubleToString(CPU_INFERENCE_MAX_DIFF, 8) + ")");
return pass;
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
feat(nn): derive dense depth, train on all history, pin the shape in .cfg Completes the derived-topology work. Three inputs removed. AIType loses its depth suffix - AI_MLP/AI_CONV/AI_LSTM/AI_HYBRID, five entries instead of eight. Depth is now derived from the two endpoints the taper already has to connect (derived first-layer width, output-tied final width) at a 2x per-layer compression target, clamped [2..5]. Asking a user to pick a layer count while the code derives the widths those layers taper between was asking for half a decision: at 64 units tapering to 12, four layers compress by 1.4x per step and five by 1.3x, so the extra depth bought no abstraction. On the shipping H1/10y default the derivation lands on 3 layers - the depth that actually won Run 2. StudyPeriods removed. There is no case for training on less data than the broker provides at a ~6% directional base rate; the honest generalization read comes from the OOS holdout, not from withholding history. Training now starts at the earliest available bar, floored by MinTrainYear, which answers a different question (excluding dubious pre-history) and stays. That required closing the hazard the old code documented: the capacity budget now MEASURES the symbol's real bar count, and a topology derived from a measurement would widen as history downloads. Both ends are now pinned. Every derived value left the weights-filename fingerprint - keying a filename on a measured quantity means the EA looks for a file that does not exist, starts from era 0 and orphans a trained model, silently, because a missing cache is the normal first-run state. The shape lives in the .cfg instead, where LoadAndCompare now ADOPTS the four derived fields rather than diffing them; a mismatch there would discard a fully-trained model over nothing the user did. Two fields appended to the .cfg for the conv/LSTM stages, length-guarded on read because FileReadInteger past EOF returns 0 with no error. ForceHiddenLayers, a compile-time constant like DebuggingMode, pins depth for diagnostic comparisons. It joins the fingerprint only when non-zero, so forced depths get their own files - sequential comparisons only, not simultaneous from one .ex5. Derived shape, H1/10y defaults (21 features x 20 bars): first layer 64, 3 dense, 8 conv filters, 16 LSTM units. The LSTM block halves from ~58k to ~28k weights. Both builds compile 0 errors, 0 warnings. Re-keys existing models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-30 10:05:40 -04:00
bool CExpertSignalAIBase::SaveTopologyConfiguration(string fileName, int initialNeuronsCount, int hiddenLayersCount, double neuronsReduction, int minNeuronsCount, int optimizationAlgo, int historyBars, int outputNeuronsCount, int neuronsCount, int studyPeriod, int minTrainYear, bool isInitialized, int stopTrainWR, int fractalPeriods, int convFilterCount, int lstmHiddenSize, bool common)
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
{
string configFileName = fileName + ".cfg";
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- Staged through a temp file + atomic rename (System\AtomicFile.mqh). It also stops an
//--- exclusive writer from blocking that reader's FILE_SHARE_READ|FILE_SHARE_WRITE open on
//--- another instance.
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 cfgCommonFlag = (common ? FILE_COMMON : 0);
string cfgTmpName = "";
int handle = AtomicWriteBegin(configFileName, cfgCommonFlag, cfgTmpName);
if(handle == INVALID_HANDLE)
{
Print("Error: Unable to open file ", cfgTmpName, " : Error code: ", GetLastError());
ResetLastError();
return false;
}
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- ON-DISK LAYOUT - DO NOT REORDER OR RETYPE. Appending a NEW field at the end is the only
//--- backward-safe change. LoadAndCompareTopologyConfiguration() reads these back positionally,
//--- and every existing .cfg on every deployed install has this exact sequence; changing it
//--- silently invalidates them all (-> "configuration mismatch" -> retrain from era 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
bool ok = (FileWriteInteger(handle, initialNeuronsCount) >= sizeof(int));
if(ok && FileWriteInteger(handle, hiddenLayersCount) < sizeof(int)) ok = false;
if(ok && FileWriteDouble(handle, neuronsReduction) < sizeof(double)) ok = false;
if(ok && FileWriteInteger(handle, minNeuronsCount) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, optimizationAlgo) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, historyBars) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, outputNeuronsCount) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, neuronsCount) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, studyPeriod) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, minTrainYear) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, isInitialized) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, stopTrainWR) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, fractalPeriods) < sizeof(int)) ok = false;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- APPENDED 2026-07-30, which the note above names as the only backward-safe change. A .cfg
//--- written before this shipped simply ends here; the loader checks the file length before
//--- reading them.
feat(nn): derive dense depth, train on all history, pin the shape in .cfg Completes the derived-topology work. Three inputs removed. AIType loses its depth suffix - AI_MLP/AI_CONV/AI_LSTM/AI_HYBRID, five entries instead of eight. Depth is now derived from the two endpoints the taper already has to connect (derived first-layer width, output-tied final width) at a 2x per-layer compression target, clamped [2..5]. Asking a user to pick a layer count while the code derives the widths those layers taper between was asking for half a decision: at 64 units tapering to 12, four layers compress by 1.4x per step and five by 1.3x, so the extra depth bought no abstraction. On the shipping H1/10y default the derivation lands on 3 layers - the depth that actually won Run 2. StudyPeriods removed. There is no case for training on less data than the broker provides at a ~6% directional base rate; the honest generalization read comes from the OOS holdout, not from withholding history. Training now starts at the earliest available bar, floored by MinTrainYear, which answers a different question (excluding dubious pre-history) and stays. That required closing the hazard the old code documented: the capacity budget now MEASURES the symbol's real bar count, and a topology derived from a measurement would widen as history downloads. Both ends are now pinned. Every derived value left the weights-filename fingerprint - keying a filename on a measured quantity means the EA looks for a file that does not exist, starts from era 0 and orphans a trained model, silently, because a missing cache is the normal first-run state. The shape lives in the .cfg instead, where LoadAndCompare now ADOPTS the four derived fields rather than diffing them; a mismatch there would discard a fully-trained model over nothing the user did. Two fields appended to the .cfg for the conv/LSTM stages, length-guarded on read because FileReadInteger past EOF returns 0 with no error. ForceHiddenLayers, a compile-time constant like DebuggingMode, pins depth for diagnostic comparisons. It joins the fingerprint only when non-zero, so forced depths get their own files - sequential comparisons only, not simultaneous from one .ex5. Derived shape, H1/10y defaults (21 features x 20 bars): first layer 64, 3 dense, 8 conv filters, 16 LSTM units. The LSTM block halves from ~58k to ~28k weights. Both builds compile 0 errors, 0 warnings. Re-keys existing models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-30 10:05:40 -04:00
if(ok && FileWriteInteger(handle, convFilterCount) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, lstmHiddenSize) < sizeof(int)) ok = false;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- APPENDED 2026-08-07, same backward-safe rule. Written unconditionally; the loader length-
//--- guards them exactly as it does the two above, so a .cfg from an older build simply ends
//--- before them.
feat: entry/SL/TP stop being inputs - the barrier geometry is measured Three enums left the Inputs tab. They were three things a user had to pick and, in the tester, three more axes for a genetic optimization to overfit. Entry_Multiplier is pinned to MARKET. Its pending modes place the entry at a LEVEL while the rest of the pipeline measures from the bar open - the exact mismatch that manufactured the +0.097 R "retail fade" result later retracted as a fill artifact. This codebase's fill model cannot honestly simulate a pending entry, so it is no longer offered. SL_Mode/TP_Mode become a STARTING pair. ReportBarrierGeometryScan now ADOPTS its winner instead of printing "set SL_Mode/TP_Mode to X and retrain": - only when it clears the family-wise gate from 04ee2e1 (beat the null of the MAXIMUM, not merely the incumbent). This is why that gate had to land first: without it, removing the inputs would hand a noise-picked geometry direct control over the training target with no human in the loop - strictly worse than the input it replaced. On SP500 H1 today it does NOT clear (p=0.1463), so 2:6 is what you get - now chosen by measurement rather than assumed. - only at m_eraCount == 0. Relabelling a partly-trained net moves the target out from under weights already fitted to the old one. THE GEOMETRY LEFT THE WEIGHTS-FILENAME HASH, because it is now measured. Same rule that moved the horizon and the derived topology values out: a filename keyed on a measured quantity changes the moment the measurement does - a few more bars shift which pairing wins - and the EA then looks for a file that does not exist, starts from era 0 and orphans a trained model silently. It is PINNED IN THE .cfg instead: appended at the end (the only backward-safe change), length-guarded like the 2026-07-30 derived pair, and ADOPTED on load rather than compared, so a trained model keeps the barriers it actually learned and never re-measures. Two traps closed while wiring it, neither of which announces itself: - m_barrierHorizonResolved latches the horizon ONCE PER PROCESS. Adopting 2:8 (wants ~192 bars) after it settled for 2:6 (128) would label the new target against the old ceiling - the truncation fixed in 168422f, where every model learned "target within 128 bars" while the EA holds to SL/TP. It lands in Neutral, not in the timeout counter watching for it. Unlatched on adoption, along with the label cache the old barriers filled. - the .cfg adopt runs at init, before the horizon latches and before any label is computed, so a resumed model has its pinned pair in place first. Verified, not assumed. FORCES A FULL RETRAIN: the fingerprint change orphans every existing .nnw. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 09:39:30 -04:00
if(ok && FileWriteInteger(handle, m_sl_mode) < sizeof(int)) ok = false;
if(ok && FileWriteInteger(handle, m_tp_mode) < sizeof(int)) ok = false;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- APPENDED 2026-08-07 after the two ints above, which is why those stay: a .cfg written
//--- earlier today ends after them and its length guard below simply finds no doubles.
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
if(ok && FileWriteDouble(handle, m_derivedSlMult) < sizeof(double)) ok = false;
if(ok && FileWriteDouble(handle, m_derivedTpMult) < sizeof(double)) ok = false;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- APPENDED 2026-08-09, same append-and-length-guard convention as everything above it.
feat: fitted directional confidence threshold - selectivity gets a mechanism The training loss and the selection metric wanted different things and only the second one knew it. Logit-adjusted cross-entropy has no term for "how often should I trade", so the head calls a direction on 87-91% of bars. The selection metric is precision x coverage credit, saturating at the coverage floor - above the floor extra calls earn NOTHING and only precision counts. So selection wanted few good calls, the loss produced many mediocre ones, and all selection could do was pick the least-bad era out of what it was handed. Nothing pushed the model toward selectivity. This gives the decision RULE the policy instead of distorting the loss (which is estimating class probabilities correctly, and a probability estimate should not be bent to encode a trading policy - Elkan 2001: estimate, then choose the operating point separately). AdjustedSignalFromSoftmax now abstains unless the winning direction's softmax margin over its best rival clears a fitted threshold. Margin, not the winning probability: the latter moves with overall calibration rather than with how close the decision actually was. Fitted on IS, applied to OOS and live. Pass 2 already forward-passes every IS sample, so the margin histogram is harvested there for free (primary occurrences only, so the oversampled replay queue cannot skew the operating point); the fit runs at the end of pass 2, BEFORE pass 3, so the deploy gate grades the thresholded model on bars the threshold never saw. Fitting on pass 3's own predictions would be choosing the operating point on the data being graded - the best-of-N error corrected in five other places here. Objective: maximise IS directional precision subject to still clearing the SAME coverage floor the deploy gate uses (base rate x 0.25, re-derived locally so the two cannot drift apart). Swept top-down in one pass; ties go to the LOWER threshold, since equal precision for less coverage is strictly worse. Under DIR_CONF_MIN_FIT_CALLS (200) it runs unthresholded rather than on a guess. The threshold is part of the MODEL, not the run: captured with Net.CaptureWeights(), restored with the weights at both restore sites, and appended to the .cfg under the same length-guard convention so a deployed model reloads at the operating point its gate actually cleared. A pre-2026-08-09 .cfg reads 0.0, which is exactly the behaviour it was trained under. Per-era line now prints "@margin>=X.XX" next to coverage, so a coverage drop can be attributed to the operating point rather than guessed at. Both build variants compile 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-09 15:04:37 -04:00
if(ok && FileWriteDouble(handle, m_bestDirConfThreshold) < sizeof(double)) ok = false;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- APPENDED 2026-08-11, same append-and-length-guard convention. Written as char-count then
feat: pin the cross-asset pair set train->serve + warm the sync at init The reference-pair set was re-discovered from Market Watch on every build, so adding or removing a terminal symbol silently changed what a trained model's six cross-asset features meant - the last open train/serve parity gap from the 2026-08-11 audit. The set a model's FIRST successful build actually used is now stamped into its .cfg (append-and-length-guard, adopt-don't-compare - the derived-barrier pattern) and every later build constructs the panel from exactly that list; a pinned pair that is temporarily unavailable is skipped, never substituted. Also warms SymbolSelect/SeriesInfo for every reference symbol at InitNeuralNetwork, so the terminal's ~minute of async cross-symbol download starts at init instead of when the first Build() trips over an unselected symbol - the source of the startup 'only 0 usable reference pairs' console failures. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-11 21:29:14 -04:00
//--- characters; empty (no set pinned yet) writes 0 and no string, which the reader adopts as
//--- "nothing pinned" - the state a fresh model is in.
int xaPinLen = StringLen(m_crossAssetPairsPinned);
if(ok && FileWriteInteger(handle, xaPinLen) < sizeof(int)) ok = false;
if(ok && xaPinLen > 0 && FileWriteString(handle, m_crossAssetPairsPinned) <= 0) ok = false;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- APPENDED 2026-08-16, same append-and-length-guard convention. Width changes already re-key
//--- the weight fingerprint through neuronsCount; this records which NAMES that width was made
//--- of.
feat(altdata): external feature block wired into the NN feature window - System\AltData.mqh: CAltDataPanel - publication-stamped CSV panel (Common\Files\Warrior_EA\AltData\{SYM}_{TF}.csv), as-of lookup by bar open, 0-fill degradation (mirrors cross-asset), hourly live refresh - Topology: width block AFTER the .cfg name-list pin is pre-read (ReadAltDataPinFromCfg) so a grown export can never mismatch a resumed model's width or shift its slots - Persistence: alt pin appended to the .cfg (append-and-length-guard convention), adopt-don't-compare on load - Features: emit block after Wyckoff SBI; EnsureFresh probe in BuildFeatureWindow (never fires in tester) - export.py: fixed a-priori scale constants (never data-fitted) Widths change SP500 +4 / USDJPY +3 / XAUUSD +1 (fingerprint re-keys -> fresh models on redeploy); EURUSD exports nothing and resumes unchanged. Compiles 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-16 13:39:00 -04:00
int altPinLen = StringLen(m_altDataNamesPinned);
if(ok && FileWriteInteger(handle, altPinLen) < sizeof(int)) ok = false;
if(ok && altPinLen > 0 && FileWriteString(handle, m_altDataNamesPinned) <= 0) ok = false;
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(!ok)
{
Print("Error writing ", configFileName, " : Error code: ", GetLastError());
ResetLastError();
}
return AtomicWriteEnd(handle, configFileName, cfgTmpName, cfgCommonFlag, ok, __FUNCTION__);
}
//+------------------------------------------------------------------+
//| |
//+------------------------------------------------------------------+
refactor: derive history bars for input sequences and update related configurations
2026-08-11 21:53:37 -04:00
bool CExpertSignalAIBase::LoadAndCompareTopologyConfiguration(string fileName, int &initialNeuronsCount, int &hiddenLayersCount, double neuronsReduction, int minNeuronsCount, int optimizationAlgo, int &historyBars, int outputNeuronsCount, int neuronsCount, int minTrainYear, bool isInitialized, int stopTrainWR, int fractalPeriods, int &convFilterCount, int &lstmHiddenSize, bool common)
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
{
string configFileName = fileName + ".cfg";
if(!FileIsExist(configFileName, common ? FILE_COMMON : 0))
{
//--- Not an error: no cached config for this parameter set yet - normal on the first run of a config
//--- (and every first tester/optimizer pass on a fresh agent). The caller treats a false return as
//--- "start fresh", so log it as informational rather than "Error" (which read as a real failure).
PrintVerbose(__FUNCTION__ + ": no cached topology config at " + configFileName + " yet - treating as a fresh start for this configuration");
return false;
}
//--- share flags: read-only, see CopySharedFile().
int handle = FileOpen(configFileName, FILE_READ | FILE_BIN | FILE_SHARE_READ | FILE_SHARE_WRITE | (common ? FILE_COMMON : 0));
if(handle == INVALID_HANDLE)
{
Print("Error: Unable to open file ", configFileName);
return false;
}
int savedInitialNeurons = FileReadInteger(handle);
int savedHiddenLayers = FileReadInteger(handle);
double savedReductionFactor = FileReadDouble(handle);
int savedMinNeurons = FileReadInteger(handle);
int savedOptimizationAlgo = FileReadInteger(handle);
int savedHistoryBars = FileReadInteger(handle);
int savedOutputNeuronsCount = FileReadInteger(handle);
int savedNeuronsCount = FileReadInteger(handle);
int savedStudyPeriod = FileReadInteger(handle);
int savedMinTrainYear = FileReadInteger(handle);
bool savedIsInitialized = FileReadInteger(handle); // read for layout only - NOT compared (see below)
int savedStopTrainWR = FileReadInteger(handle); // retired MinWR slot - layout only, NOT compared (see below)
int savedFractalPeriods = FileReadInteger(handle);
feat(nn): derive dense depth, train on all history, pin the shape in .cfg Completes the derived-topology work. Three inputs removed. AIType loses its depth suffix - AI_MLP/AI_CONV/AI_LSTM/AI_HYBRID, five entries instead of eight. Depth is now derived from the two endpoints the taper already has to connect (derived first-layer width, output-tied final width) at a 2x per-layer compression target, clamped [2..5]. Asking a user to pick a layer count while the code derives the widths those layers taper between was asking for half a decision: at 64 units tapering to 12, four layers compress by 1.4x per step and five by 1.3x, so the extra depth bought no abstraction. On the shipping H1/10y default the derivation lands on 3 layers - the depth that actually won Run 2. StudyPeriods removed. There is no case for training on less data than the broker provides at a ~6% directional base rate; the honest generalization read comes from the OOS holdout, not from withholding history. Training now starts at the earliest available bar, floored by MinTrainYear, which answers a different question (excluding dubious pre-history) and stays. That required closing the hazard the old code documented: the capacity budget now MEASURES the symbol's real bar count, and a topology derived from a measurement would widen as history downloads. Both ends are now pinned. Every derived value left the weights-filename fingerprint - keying a filename on a measured quantity means the EA looks for a file that does not exist, starts from era 0 and orphans a trained model, silently, because a missing cache is the normal first-run state. The shape lives in the .cfg instead, where LoadAndCompare now ADOPTS the four derived fields rather than diffing them; a mismatch there would discard a fully-trained model over nothing the user did. Two fields appended to the .cfg for the conv/LSTM stages, length-guarded on read because FileReadInteger past EOF returns 0 with no error. ForceHiddenLayers, a compile-time constant like DebuggingMode, pins depth for diagnostic comparisons. It joins the fingerprint only when non-zero, so forced depths get their own files - sequential comparisons only, not simultaneous from one .ex5. Derived shape, H1/10y defaults (21 features x 20 bars): first layer 64, 3 dense, 8 conv filters, 16 LSTM units. The LSTM block halves from ~58k to ~28k weights. Both builds compile 0 errors, 0 warnings. Re-keys existing models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-30 10:05:40 -04:00
//--- Appended 2026-07-30 - guard on the actual file length rather than reading optimistically, because
//--- FileReadInteger past the end returns 0 with no error, and adopting a conv filter count of 0 would
//--- build a degenerate topology out of a file that was merely written by an older build.
bool haveDerivedStages = (FileSize(handle) >= (ulong)FileTell(handle) + 2 * sizeof(int));
int savedConvFilters = haveDerivedStages ? FileReadInteger(handle) : 0;
int savedLstmHidden = haveDerivedStages ? FileReadInteger(handle) : 0;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- Appended 2026-08-07, length-guarded for the same reason: FileReadInteger past the end
//--- returns 0 with no error, and adopting SL/TP mode 0 would relabel the whole run against a
//--- barrier nobody chose.
feat: entry/SL/TP stop being inputs - the barrier geometry is measured Three enums left the Inputs tab. They were three things a user had to pick and, in the tester, three more axes for a genetic optimization to overfit. Entry_Multiplier is pinned to MARKET. Its pending modes place the entry at a LEVEL while the rest of the pipeline measures from the bar open - the exact mismatch that manufactured the +0.097 R "retail fade" result later retracted as a fill artifact. This codebase's fill model cannot honestly simulate a pending entry, so it is no longer offered. SL_Mode/TP_Mode become a STARTING pair. ReportBarrierGeometryScan now ADOPTS its winner instead of printing "set SL_Mode/TP_Mode to X and retrain": - only when it clears the family-wise gate from 04ee2e1 (beat the null of the MAXIMUM, not merely the incumbent). This is why that gate had to land first: without it, removing the inputs would hand a noise-picked geometry direct control over the training target with no human in the loop - strictly worse than the input it replaced. On SP500 H1 today it does NOT clear (p=0.1463), so 2:6 is what you get - now chosen by measurement rather than assumed. - only at m_eraCount == 0. Relabelling a partly-trained net moves the target out from under weights already fitted to the old one. THE GEOMETRY LEFT THE WEIGHTS-FILENAME HASH, because it is now measured. Same rule that moved the horizon and the derived topology values out: a filename keyed on a measured quantity changes the moment the measurement does - a few more bars shift which pairing wins - and the EA then looks for a file that does not exist, starts from era 0 and orphans a trained model silently. It is PINNED IN THE .cfg instead: appended at the end (the only backward-safe change), length-guarded like the 2026-07-30 derived pair, and ADOPTED on load rather than compared, so a trained model keeps the barriers it actually learned and never re-measures. Two traps closed while wiring it, neither of which announces itself: - m_barrierHorizonResolved latches the horizon ONCE PER PROCESS. Adopting 2:8 (wants ~192 bars) after it settled for 2:6 (128) would label the new target against the old ceiling - the truncation fixed in 168422f, where every model learned "target within 128 bars" while the EA holds to SL/TP. It lands in Neutral, not in the timeout counter watching for it. Unlatched on adoption, along with the label cache the old barriers filled. - the .cfg adopt runs at init, before the horizon latches and before any label is computed, so a resumed model has its pinned pair in place first. Verified, not assumed. FORCES A FULL RETRAIN: the fingerprint change orphans every existing .nnw. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 09:39:30 -04:00
bool haveBarrierGeometry = (FileSize(handle) >= (ulong)FileTell(handle) + 2 * sizeof(int));
int savedSlMode = haveBarrierGeometry ? FileReadInteger(handle) : 0;
int savedTpMode = haveBarrierGeometry ? FileReadInteger(handle) : 0;
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
bool haveDerivedGeometry = (FileSize(handle) >= (ulong)FileTell(handle) + 2 * sizeof(double));
double savedDerivedSl = haveDerivedGeometry ? FileReadDouble(handle) : 0.0;
double savedDerivedTp = haveDerivedGeometry ? FileReadDouble(handle) : 0.0;
feat: fitted directional confidence threshold - selectivity gets a mechanism The training loss and the selection metric wanted different things and only the second one knew it. Logit-adjusted cross-entropy has no term for "how often should I trade", so the head calls a direction on 87-91% of bars. The selection metric is precision x coverage credit, saturating at the coverage floor - above the floor extra calls earn NOTHING and only precision counts. So selection wanted few good calls, the loss produced many mediocre ones, and all selection could do was pick the least-bad era out of what it was handed. Nothing pushed the model toward selectivity. This gives the decision RULE the policy instead of distorting the loss (which is estimating class probabilities correctly, and a probability estimate should not be bent to encode a trading policy - Elkan 2001: estimate, then choose the operating point separately). AdjustedSignalFromSoftmax now abstains unless the winning direction's softmax margin over its best rival clears a fitted threshold. Margin, not the winning probability: the latter moves with overall calibration rather than with how close the decision actually was. Fitted on IS, applied to OOS and live. Pass 2 already forward-passes every IS sample, so the margin histogram is harvested there for free (primary occurrences only, so the oversampled replay queue cannot skew the operating point); the fit runs at the end of pass 2, BEFORE pass 3, so the deploy gate grades the thresholded model on bars the threshold never saw. Fitting on pass 3's own predictions would be choosing the operating point on the data being graded - the best-of-N error corrected in five other places here. Objective: maximise IS directional precision subject to still clearing the SAME coverage floor the deploy gate uses (base rate x 0.25, re-derived locally so the two cannot drift apart). Swept top-down in one pass; ties go to the LOWER threshold, since equal precision for less coverage is strictly worse. Under DIR_CONF_MIN_FIT_CALLS (200) it runs unthresholded rather than on a guess. The threshold is part of the MODEL, not the run: captured with Net.CaptureWeights(), restored with the weights at both restore sites, and appended to the .cfg under the same length-guard convention so a deployed model reloads at the operating point its gate actually cleared. A pre-2026-08-09 .cfg reads 0.0, which is exactly the behaviour it was trained under. Per-era line now prints "@margin>=X.XX" next to coverage, so a coverage drop can be attributed to the operating point rather than guessed at. Both build variants compile 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-09 15:04:37 -04:00
//--- Appended 2026-08-09. A .cfg from before then simply ends here and the guard yields 0.0, which is
//--- exactly the right default: unthresholded, i.e. the behaviour that model was trained under.
bool haveDirConf = (FileSize(handle) >= (ulong)FileTell(handle) + sizeof(double));
double savedDirConf = haveDirConf ? FileReadDouble(handle) : 0.0;
feat: pin the cross-asset pair set train->serve + warm the sync at init The reference-pair set was re-discovered from Market Watch on every build, so adding or removing a terminal symbol silently changed what a trained model's six cross-asset features meant - the last open train/serve parity gap from the 2026-08-11 audit. The set a model's FIRST successful build actually used is now stamped into its .cfg (append-and-length-guard, adopt-don't-compare - the derived-barrier pattern) and every later build constructs the panel from exactly that list; a pinned pair that is temporarily unavailable is skipped, never substituted. Also warms SymbolSelect/SeriesInfo for every reference symbol at InitNeuralNetwork, so the terminal's ~minute of async cross-symbol download starts at init instead of when the first Build() trips over an unselected symbol - the source of the startup 'only 0 usable reference pairs' console failures. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-11 21:29:14 -04:00
//--- Appended 2026-08-11: the cross-asset pair set this model was trained against. Length-guarded
//--- like everything above; a sanity cap on the count rejects a garbage length from a truncated or
//--- misaligned file rather than asking FileReadString for megabytes.
bool haveXaPin = (FileSize(handle) >= (ulong)FileTell(handle) + sizeof(int));
int xaPinLen = haveXaPin ? FileReadInteger(handle) : 0;
string savedXaPairs = "";
if(haveXaPin && xaPinLen > 0 && xaPinLen <= 16 * CROSSASSET_MAX_PAIRS)
savedXaPairs = FileReadString(handle, xaPinLen);
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- Appended 2026-08-16: the alt-data feature name list.
feat(altdata): external feature block wired into the NN feature window - System\AltData.mqh: CAltDataPanel - publication-stamped CSV panel (Common\Files\Warrior_EA\AltData\{SYM}_{TF}.csv), as-of lookup by bar open, 0-fill degradation (mirrors cross-asset), hourly live refresh - Topology: width block AFTER the .cfg name-list pin is pre-read (ReadAltDataPinFromCfg) so a grown export can never mismatch a resumed model's width or shift its slots - Persistence: alt pin appended to the .cfg (append-and-length-guard convention), adopt-don't-compare on load - Features: emit block after Wyckoff SBI; EnsureFresh probe in BuildFeatureWindow (never fires in tester) - export.py: fixed a-priori scale constants (never data-fitted) Widths change SP500 +4 / USDJPY +3 / XAUUSD +1 (fingerprint re-keys -> fresh models on redeploy); EURUSD exports nothing and resumes unchanged. Compiles 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-16 13:39:00 -04:00
bool haveAltPin = (FileSize(handle) >= (ulong)FileTell(handle) + sizeof(int));
int altPinLen = haveAltPin ? FileReadInteger(handle) : 0;
string savedAltNames = "";
if(haveAltPin && altPinLen > 0 && altPinLen <= ALTDATA_MAX_PIN_CHARS)
savedAltNames = FileReadString(handle, altPinLen);
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
FileClose(handle);
feat: entry/SL/TP stop being inputs - the barrier geometry is measured Three enums left the Inputs tab. They were three things a user had to pick and, in the tester, three more axes for a genetic optimization to overfit. Entry_Multiplier is pinned to MARKET. Its pending modes place the entry at a LEVEL while the rest of the pipeline measures from the bar open - the exact mismatch that manufactured the +0.097 R "retail fade" result later retracted as a fill artifact. This codebase's fill model cannot honestly simulate a pending entry, so it is no longer offered. SL_Mode/TP_Mode become a STARTING pair. ReportBarrierGeometryScan now ADOPTS its winner instead of printing "set SL_Mode/TP_Mode to X and retrain": - only when it clears the family-wise gate from 04ee2e1 (beat the null of the MAXIMUM, not merely the incumbent). This is why that gate had to land first: without it, removing the inputs would hand a noise-picked geometry direct control over the training target with no human in the loop - strictly worse than the input it replaced. On SP500 H1 today it does NOT clear (p=0.1463), so 2:6 is what you get - now chosen by measurement rather than assumed. - only at m_eraCount == 0. Relabelling a partly-trained net moves the target out from under weights already fitted to the old one. THE GEOMETRY LEFT THE WEIGHTS-FILENAME HASH, because it is now measured. Same rule that moved the horizon and the derived topology values out: a filename keyed on a measured quantity changes the moment the measurement does - a few more bars shift which pairing wins - and the EA then looks for a file that does not exist, starts from era 0 and orphans a trained model silently. It is PINNED IN THE .cfg instead: appended at the end (the only backward-safe change), length-guarded like the 2026-07-30 derived pair, and ADOPTED on load rather than compared, so a trained model keeps the barriers it actually learned and never re-measures. Two traps closed while wiring it, neither of which announces itself: - m_barrierHorizonResolved latches the horizon ONCE PER PROCESS. Adopting 2:8 (wants ~192 bars) after it settled for 2:6 (128) would label the new target against the old ceiling - the truncation fixed in 168422f, where every model learned "target within 128 bars" while the EA holds to SL/TP. It lands in Neutral, not in the timeout counter watching for it. Unlatched on adoption, along with the label cache the old barriers filled. - the .cfg adopt runs at init, before the horizon latches and before any label is computed, so a resumed model has its pinned pair in place first. Verified, not assumed. FORCES A FULL RETRAIN: the fingerprint change orphans every existing .nnw. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-08-07 09:39:30 -04:00
if(haveBarrierGeometry && savedSlMode != 0 && savedTpMode != 0
&& (savedSlMode != m_sl_mode || savedTpMode != m_tp_mode))
{
PrintFormat("%s: adopting the barrier geometry this model was trained on - SL %d TP %d (was about "
"to use SL %d TP %d). Measured once at era 0 and pinned; it is not re-measured for an "
"existing model.", __FUNCTION__, savedSlMode, savedTpMode, m_sl_mode, m_tp_mode);
m_sl_mode = savedSlMode;
m_tp_mode = savedTpMode;
}
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 multiples, same adopt-don't-compare rule and the same length guard. A model that carries
//--- them was TRAINED on them, so re-deriving would relabel a finished run against a target it never
//--- saw - the drift the fingerprint change was made to prevent, arriving through the .cfg instead.
if(haveDerivedGeometry && savedDerivedSl > 0.0 && savedDerivedTp > 0.0)
{
m_derivedSlMult = savedDerivedSl;
m_derivedTpMult = savedDerivedTp;
m_geometryDerived = true;
//--- Block the fixed-point iteration: it only ever runs for a model that has none pinned.
m_geometryDerivePasses = BARRIER_DERIVE_MAX_PASSES;
fix: a restart no longer loses the measured geometry or the training window Terminal restart, 22:25: all four resumed models sat on empty windows with enum 2:6 barriers. Three interlocking causes, all visible in one log excerpt: 1) THE PRE-SCAN WINDOW WAS SIZED BY THE SAVED WATERMARK. A resumed model's dtStudied sits at its last studied bar, so Bars(dtStudied, now) ~ 0 and the resumed-model MI pre-scan built a zero-bar "complete" label cache - logged as "Buy: 0 | Sell: 0 | Neutral: 0". Train()'s own era start RESETS dtStudied to the training-window rule before computing its window; the pre-scan did not. The rule is now factored into TrainWindowStart() and both use it. The scan also refuses to arm before SERIES_SYNCHRONIZED (it ran in the same second as OnInit), and deployed models keep their watermark - for them it gates inference recency, not a training window. 2) THE HORIZON LATCHED ON AN INDICATOR WARM-UP. ComputeBarrierHorizonBars ran against a ZigZag with 0 calculated legs, fell back, and EnsureBarrierHorizon latched fallback(32) x slMult x tpMult = 384 for the process lifetime. A leg-starved horizon is now PROVISIONAL: re-resolved on the next rebuild, the label cache wiped if it moved (labels from two horizons answer different questions), and the geometry deriver refuses to run from it - a pair derived over a warm-up window would get PINNED. 3) THE DERIVED GEOMETRY WAS NEVER PERSISTED. The .cfg is written at model creation and at weights-reset - both BEFORE era 0 derives - so the measured pair lived only in memory: every restart read back zeros, adopted nothing, fell back to the enum barriers, and the era-0-only gate meant a resumed model could NEVER re-derive. A full day of training on 3.33/1.62 resumed as 2:6. Now: the settled pair is pinned to the .cfg the moment derivation completes (one-shot, atomic write), and the derive gate accepts any model with no pinned pair, not just era 0 - mid-run stability is carried by m_geometryDerived itself, which never allows a second derivation. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-09 22:40:43 -04:00
//--- Already on disk - that is where it was just read from - so the post-derivation pin is moot.
m_geometryCfgSaved = true;
fix: live trades now use the geometry the gate certifies; perf: BN kernels Three changes, one theme: the trade placed, the trade graded, and the trade computed are now the same trade. 1) GEOMETRY WIRE (correctness, the ranked #1 open issue). The measured barrier pair reached the LABELS only - OpenParams still placed orders at the enum geometry (2*ATR/6*ATR), so the deploy gate certified "reaches 1.62*ATR before 3.33*ATR above break-even" about trades the EA never placed. Published via g_DerivedSlAtrMult/g_DerivedTpAtrMult (ConfidenceBridge, same same-tick contract as the confidence globals, because OpenParams runs on the root signal which has no pointer to the AI filter). Two writers: DeriveBarrierGeometry at era 0, and the .cfg adoption a deployed model takes. Overrides both legs and both Intelligent modes - the certificate is exact or it is nothing. TP is ATR-anchored like the label, NOT risk-relative, so a floor-widened stop cannot reshape the certified target. 2) BATCH NORM RUNS DEVICE-SIDE ON OPENCL. Four kernels in Network.cl - forward, hidden gradient, gamma/beta accumulate, gamma/beta apply - each a line-for-line transcription of the host implementation (NormalizeHost / HiddenGradHost / StepGammaBeta) including every NaN guard, clamp, and the exact moment-write ordering. The host copies remain the runtime for the DLL and pure-MQL5 tiers and the reference the kernels must match. Because this box has no OpenCL platform, the safety story is layered: - shim validation: kernels compiled as C and driven against a fp64 host transcription over NaN-poisoned stats, NaN gamma, over-clamp inputs, the frozen path, both optimizers, 3 batches - ALL PASS, worst normalized diff 0.132 vs tolerance 1.0 - in-situ self-check: each kernel is compared against its host twin ON FIRST USE on the real device (SelfCheckBn*), covering what the shim cannot - arg indices and buffer bindings. Any disagreement resyncs from the good copy, latches all BN kernels off process-wide, and training continues host-side. A transcription bug costs a warning and some speed, never a poisoned .nnw. - sync discipline: BatchOptions is now a CBufferDouble with explicit authority tracking (m_bnDeviceAuthoritative). Checkpoints/saves pull read-only; restores/loads/resets push; a mid-batch handover drains the device gamma/beta accumulator into the host arrays so no sample is lost. 3) SMALL FIXES. Apply-kernel build failure now latches the dispatch path at init (one warning instead of warning + failed Execute). Build tag bumped to win-scoring-gpu-v1 - first tag change since expectancy-stop-v1 despite five binary-changing commits. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-09 17:51:40 -04:00
//--- Publish to the LIVE order path too - this is the second of the two writers of these globals
//--- (DeriveBarrierGeometry is the other), and the one a DEPLOYED model reaches: a tester or live
//--- run that loads a trained model never derives, it adopts, and without this line its orders
//--- would quietly revert to the enum geometry the gate never certified.
g_DerivedSlAtrMult = m_derivedSlMult;
g_DerivedTpAtrMult = m_derivedTpMult;
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
PrintFormat("%s: adopting the DERIVED barrier this model was trained on - stop %.2f*ATR, target "
fix: live trades now use the geometry the gate certifies; perf: BN kernels Three changes, one theme: the trade placed, the trade graded, and the trade computed are now the same trade. 1) GEOMETRY WIRE (correctness, the ranked #1 open issue). The measured barrier pair reached the LABELS only - OpenParams still placed orders at the enum geometry (2*ATR/6*ATR), so the deploy gate certified "reaches 1.62*ATR before 3.33*ATR above break-even" about trades the EA never placed. Published via g_DerivedSlAtrMult/g_DerivedTpAtrMult (ConfidenceBridge, same same-tick contract as the confidence globals, because OpenParams runs on the root signal which has no pointer to the AI filter). Two writers: DeriveBarrierGeometry at era 0, and the .cfg adoption a deployed model takes. Overrides both legs and both Intelligent modes - the certificate is exact or it is nothing. TP is ATR-anchored like the label, NOT risk-relative, so a floor-widened stop cannot reshape the certified target. 2) BATCH NORM RUNS DEVICE-SIDE ON OPENCL. Four kernels in Network.cl - forward, hidden gradient, gamma/beta accumulate, gamma/beta apply - each a line-for-line transcription of the host implementation (NormalizeHost / HiddenGradHost / StepGammaBeta) including every NaN guard, clamp, and the exact moment-write ordering. The host copies remain the runtime for the DLL and pure-MQL5 tiers and the reference the kernels must match. Because this box has no OpenCL platform, the safety story is layered: - shim validation: kernels compiled as C and driven against a fp64 host transcription over NaN-poisoned stats, NaN gamma, over-clamp inputs, the frozen path, both optimizers, 3 batches - ALL PASS, worst normalized diff 0.132 vs tolerance 1.0 - in-situ self-check: each kernel is compared against its host twin ON FIRST USE on the real device (SelfCheckBn*), covering what the shim cannot - arg indices and buffer bindings. Any disagreement resyncs from the good copy, latches all BN kernels off process-wide, and training continues host-side. A transcription bug costs a warning and some speed, never a poisoned .nnw. - sync discipline: BatchOptions is now a CBufferDouble with explicit authority tracking (m_bnDeviceAuthoritative). Checkpoints/saves pull read-only; restores/loads/resets push; a mid-batch handover drains the device gamma/beta accumulator into the host arrays so no sample is lost. 3) SMALL FIXES. Apply-kernel build failure now latches the dispatch path at init (one warning instead of warning + failed Execute). Build tag bumped to win-scoring-gpu-v1 - first tag change since expectancy-stop-v1 despite five binary-changing commits. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-09 17:51:40 -04:00
"%.2f*ATR. Measured once from the excursion distribution and pinned; not re-derived. "
"Live orders use this pair, overriding the SL_Mode/TP_Mode enums.",
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
__FUNCTION__, m_derivedSlMult, m_derivedTpMult);
}
feat: fitted directional confidence threshold - selectivity gets a mechanism The training loss and the selection metric wanted different things and only the second one knew it. Logit-adjusted cross-entropy has no term for "how often should I trade", so the head calls a direction on 87-91% of bars. The selection metric is precision x coverage credit, saturating at the coverage floor - above the floor extra calls earn NOTHING and only precision counts. So selection wanted few good calls, the loss produced many mediocre ones, and all selection could do was pick the least-bad era out of what it was handed. Nothing pushed the model toward selectivity. This gives the decision RULE the policy instead of distorting the loss (which is estimating class probabilities correctly, and a probability estimate should not be bent to encode a trading policy - Elkan 2001: estimate, then choose the operating point separately). AdjustedSignalFromSoftmax now abstains unless the winning direction's softmax margin over its best rival clears a fitted threshold. Margin, not the winning probability: the latter moves with overall calibration rather than with how close the decision actually was. Fitted on IS, applied to OOS and live. Pass 2 already forward-passes every IS sample, so the margin histogram is harvested there for free (primary occurrences only, so the oversampled replay queue cannot skew the operating point); the fit runs at the end of pass 2, BEFORE pass 3, so the deploy gate grades the thresholded model on bars the threshold never saw. Fitting on pass 3's own predictions would be choosing the operating point on the data being graded - the best-of-N error corrected in five other places here. Objective: maximise IS directional precision subject to still clearing the SAME coverage floor the deploy gate uses (base rate x 0.25, re-derived locally so the two cannot drift apart). Swept top-down in one pass; ties go to the LOWER threshold, since equal precision for less coverage is strictly worse. Under DIR_CONF_MIN_FIT_CALLS (200) it runs unthresholded rather than on a guess. The threshold is part of the MODEL, not the run: captured with Net.CaptureWeights(), restored with the weights at both restore sites, and appended to the .cfg under the same length-guard convention so a deployed model reloads at the operating point its gate actually cleared. A pre-2026-08-09 .cfg reads 0.0, which is exactly the behaviour it was trained under. Per-era line now prints "@margin>=X.XX" next to coverage, so a coverage drop can be attributed to the operating point rather than guessed at. Both build variants compile 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-09 15:04:37 -04:00
//--- Operating point, adopted on the same grounds. Unlike the barrier it IS re-fitted every era while
//--- training continues (the margin distribution moves with the weights), so this restores the value a
//--- DEPLOYED model should trade at and the value a resuming run starts from until its next pass 2.
if(haveDirConf && savedDirConf > 0.0)
{
m_dirConfThreshold = savedDirConf;
m_bestDirConfThreshold = savedDirConf;
PrintFormat("%s: adopting the directional confidence threshold this model was trained with - %.2f. "
"Below that winner-vs-rival softmax margin it abstains rather than trading.",
__FUNCTION__, m_dirConfThreshold);
}
feat: pin the cross-asset pair set train->serve + warm the sync at init The reference-pair set was re-discovered from Market Watch on every build, so adding or removing a terminal symbol silently changed what a trained model's six cross-asset features meant - the last open train/serve parity gap from the 2026-08-11 audit. The set a model's FIRST successful build actually used is now stamped into its .cfg (append-and-length-guard, adopt-don't-compare - the derived-barrier pattern) and every later build constructs the panel from exactly that list; a pinned pair that is temporarily unavailable is skipped, never substituted. Also warms SymbolSelect/SeriesInfo for every reference symbol at InitNeuralNetwork, so the terminal's ~minute of async cross-symbol download starts at init instead of when the first Build() trips over an unselected symbol - the source of the startup 'only 0 usable reference pairs' console failures. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-11 21:29:14 -04:00
//--- Cross-asset pair set: adopt, don't compare, same grounds as the derived barrier - it records
//--- the panel this model's features were trained against. Already on disk, so the one-shot
//--- re-save in BuildCrossAssetPanel is moot for this model.
if(savedXaPairs != "")
{
m_crossAssetPairsPinned = savedXaPairs;
m_crossAssetCfgSaved = true;
PrintFormat("%s: adopting the cross-asset pair set this model was trained on - [%s]. The panel "
"builds from exactly this set; Market Watch changes do not alter it.",
__FUNCTION__, savedXaPairs);
}
feat(altdata): external feature block wired into the NN feature window - System\AltData.mqh: CAltDataPanel - publication-stamped CSV panel (Common\Files\Warrior_EA\AltData\{SYM}_{TF}.csv), as-of lookup by bar open, 0-fill degradation (mirrors cross-asset), hourly live refresh - Topology: width block AFTER the .cfg name-list pin is pre-read (ReadAltDataPinFromCfg) so a grown export can never mismatch a resumed model's width or shift its slots - Persistence: alt pin appended to the .cfg (append-and-length-guard convention), adopt-don't-compare on load - Features: emit block after Wyckoff SBI; EnsureFresh probe in BuildFeatureWindow (never fires in tester) - export.py: fixed a-priori scale constants (never data-fitted) Widths change SP500 +4 / USDJPY +3 / XAUUSD +1 (fingerprint re-keys -> fresh models on redeploy); EURUSD exports nothing and resumes unchanged. Compiles 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-16 13:39:00 -04:00
//--- Alt-data name list: same adopt-don't-compare grounds. ReadAltDataPinFromCfg() normally
//--- adopted this before the width sum; a mismatch here means the .cfg changed between the two
//--- reads (or the pre-reader could not open it) - adopt and say so, because training on inputs
//--- whose names have silently shifted is exactly the misalignment the pin exists to prevent.
if(savedAltNames != "" && savedAltNames != m_altDataNamesPinned)
{
PrintFormat("%s: alt-data pin from the .cfg [%s] differs from the one applied at width "
"derivation [%s] - adopting the .cfg's. If the width no longer matches, this run "
"will (correctly) refuse the saved weights.",
__FUNCTION__, savedAltNames, m_altDataNamesPinned);
m_altDataNamesPinned = savedAltNames;
m_altData.SetPinnedNames(savedAltNames);
}
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- isInitialized is DELIBERATELY excluded from the comparison: it is a runtime lifecycle flag,
//--- not a topology/input parameter, and it is always false at the point the compare and the
//--- fresh-start save run (set true only at the end of InitNeuralNetwork).
feat(nn): derive dense depth, train on all history, pin the shape in .cfg Completes the derived-topology work. Three inputs removed. AIType loses its depth suffix - AI_MLP/AI_CONV/AI_LSTM/AI_HYBRID, five entries instead of eight. Depth is now derived from the two endpoints the taper already has to connect (derived first-layer width, output-tied final width) at a 2x per-layer compression target, clamped [2..5]. Asking a user to pick a layer count while the code derives the widths those layers taper between was asking for half a decision: at 64 units tapering to 12, four layers compress by 1.4x per step and five by 1.3x, so the extra depth bought no abstraction. On the shipping H1/10y default the derivation lands on 3 layers - the depth that actually won Run 2. StudyPeriods removed. There is no case for training on less data than the broker provides at a ~6% directional base rate; the honest generalization read comes from the OOS holdout, not from withholding history. Training now starts at the earliest available bar, floored by MinTrainYear, which answers a different question (excluding dubious pre-history) and stays. That required closing the hazard the old code documented: the capacity budget now MEASURES the symbol's real bar count, and a topology derived from a measurement would widen as history downloads. Both ends are now pinned. Every derived value left the weights-filename fingerprint - keying a filename on a measured quantity means the EA looks for a file that does not exist, starts from era 0 and orphans a trained model, silently, because a missing cache is the normal first-run state. The shape lives in the .cfg instead, where LoadAndCompare now ADOPTS the four derived fields rather than diffing them; a mismatch there would discard a fully-trained model over nothing the user did. Two fields appended to the .cfg for the conv/LSTM stages, length-guarded on read because FileReadInteger past EOF returns 0 with no error. ForceHiddenLayers, a compile-time constant like DebuggingMode, pins depth for diagnostic comparisons. It joins the fingerprint only when non-zero, so forced depths get their own files - sequential comparisons only, not simultaneous from one .ex5. Derived shape, H1/10y defaults (21 features x 20 bars): first layer 64, 3 dense, 8 conv filters, 16 LSTM units. The LSTM block halves from ~58k to ~28k weights. Both builds compile 0 errors, 0 warnings. Re-keys existing models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-30 10:05:40 -04:00
if(savedInitialNeurons > 0)
initialNeuronsCount = savedInitialNeurons;
if(savedHiddenLayers > 0)
hiddenLayersCount = savedHiddenLayers;
if(savedConvFilters > 0)
convFilterCount = savedConvFilters;
if(savedLstmHidden > 0)
lstmHiddenSize = savedLstmHidden;
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- historyBars joined the adopt list 2026-08-11 when the window became DERIVED
//--- (DeriveHistoryBars): the measurement moves as history downloads, so comparing it would
//--- discard a trained model for nothing the user did - the exact failure mode this block exists
//--- to prevent.
refactor: derive history bars for input sequences and update related configurations
2026-08-11 21:53:37 -04:00
if(savedHistoryBars > 0 && savedHistoryBars != historyBars)
{
PrintFormat("%s: adopting the input window this model was trained with - %d bars (a fresh "
"derivation would have said %d).", __FUNCTION__, savedHistoryBars, historyBars);
historyBars = savedHistoryBars;
}
feat(nn): derive dense depth, train on all history, pin the shape in .cfg Completes the derived-topology work. Three inputs removed. AIType loses its depth suffix - AI_MLP/AI_CONV/AI_LSTM/AI_HYBRID, five entries instead of eight. Depth is now derived from the two endpoints the taper already has to connect (derived first-layer width, output-tied final width) at a 2x per-layer compression target, clamped [2..5]. Asking a user to pick a layer count while the code derives the widths those layers taper between was asking for half a decision: at 64 units tapering to 12, four layers compress by 1.4x per step and five by 1.3x, so the extra depth bought no abstraction. On the shipping H1/10y default the derivation lands on 3 layers - the depth that actually won Run 2. StudyPeriods removed. There is no case for training on less data than the broker provides at a ~6% directional base rate; the honest generalization read comes from the OOS holdout, not from withholding history. Training now starts at the earliest available bar, floored by MinTrainYear, which answers a different question (excluding dubious pre-history) and stays. That required closing the hazard the old code documented: the capacity budget now MEASURES the symbol's real bar count, and a topology derived from a measurement would widen as history downloads. Both ends are now pinned. Every derived value left the weights-filename fingerprint - keying a filename on a measured quantity means the EA looks for a file that does not exist, starts from era 0 and orphans a trained model, silently, because a missing cache is the normal first-run state. The shape lives in the .cfg instead, where LoadAndCompare now ADOPTS the four derived fields rather than diffing them; a mismatch there would discard a fully-trained model over nothing the user did. Two fields appended to the .cfg for the conv/LSTM stages, length-guarded on read because FileReadInteger past EOF returns 0 with no error. ForceHiddenLayers, a compile-time constant like DebuggingMode, pins depth for diagnostic comparisons. It joins the fingerprint only when non-zero, so forced depths get their own files - sequential comparisons only, not simultaneous from one .ex5. Derived shape, H1/10y defaults (21 features x 20 bars): first layer 64, 3 dense, 8 conv filters, 16 LSTM units. The LSTM block halves from ~58k to ~28k weights. Both builds compile 0 errors, 0 warnings. Re-keys existing models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-30 10:05:40 -04:00
//--- savedStudyPeriod is read for layout only and NOT compared - the StudyPeriods input it mirrored was
//--- removed 2026-07-30 (training covers all available history), so like the retired MinWR slot its
//--- value says nothing about whether the saved WEIGHTS are compatible.
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
string diff = "";
if(savedReductionFactor != neuronsReduction) diff += " reduction " + DoubleToString(savedReductionFactor, 4) + "->" + DoubleToString(neuronsReduction, 4) + ";";
if(savedMinNeurons != minNeuronsCount) diff += " minNeurons " + IntegerToString(savedMinNeurons) + "->" + IntegerToString(minNeuronsCount) + ";";
if(savedOptimizationAlgo != optimizationAlgo) diff += " optimizer " + IntegerToString(savedOptimizationAlgo) + "->" + IntegerToString(optimizationAlgo) + ";";
if(savedOutputNeuronsCount != outputNeuronsCount) diff += " outputs " + IntegerToString(savedOutputNeuronsCount) + "->" + IntegerToString(outputNeuronsCount) + ";";
if(savedNeuronsCount != neuronsCount) diff += " inputWidth " + IntegerToString(savedNeuronsCount) + "->" + IntegerToString(neuronsCount) + " (a feature toggle changed);";
if(savedMinTrainYear != minTrainYear) diff += " minTrainYear " + IntegerToString(savedMinTrainYear) + "->" + IntegerToString(minTrainYear) + ";";
//--- (savedStopTrainWR deliberately NOT compared - retired input, see the note above)
if(savedFractalPeriods != fractalPeriods) diff += " fractalPeriods " + IntegerToString(savedFractalPeriods) + "->" + IntegerToString(fractalPeriods) + ";";
if(diff != "")
{
Print("Configuration mismatch for ", configFileName, " -> retraining from era 0. Changed:", diff,
" (a saved model only reloads when these parameters match exactly; revert the changed input to resume the existing model.)");
FileDelete(configFileName, common ? FILE_COMMON : 0);
return false;
}
return true;
}
//+------------------------------------------------------------------+
refactor(comments): box headers to stdlib length The //| box blocks were excluded from 0b06f8e and 5efdb48 and were what remained: 160 of them ran to 10+ lines, the longest to 88. Compressed to their leading topic sentences - 5 lines for a function header, 8 for a file header - keeping the box format and the standard MQL5 name/author lines verbatim. Verified at the BYTE level this time, across every in-scope file: the list of non-comment lines is byte-identical to HEAD and braces balance. The first check compared a locale-decoded 'git show' against a UTF-8 read and flagged 25 files that had not changed at all - every BOM and every non-ASCII line mismatched. 47,696 -> 40,665 lines in scope; comment share 38% -> 26%. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:30:14 -04:00
//| Early read of ONE appended .cfg field: the alt-data feature name |
//| list. |
feat(altdata): external feature block wired into the NN feature window - System\AltData.mqh: CAltDataPanel - publication-stamped CSV panel (Common\Files\Warrior_EA\AltData\{SYM}_{TF}.csv), as-of lookup by bar open, 0-fill degradation (mirrors cross-asset), hourly live refresh - Topology: width block AFTER the .cfg name-list pin is pre-read (ReadAltDataPinFromCfg) so a grown export can never mismatch a resumed model's width or shift its slots - Persistence: alt pin appended to the .cfg (append-and-length-guard convention), adopt-don't-compare on load - Features: emit block after Wyckoff SBI; EnsureFresh probe in BuildFeatureWindow (never fires in tester) - export.py: fixed a-priori scale constants (never data-fitted) Widths change SP500 +4 / USDJPY +3 / XAUUSD +1 (fingerprint re-keys -> fresh models on redeploy); EURUSD exports nothing and resumes unchanged. Compiles 0 errors / 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-16 13:39:00 -04:00
//+------------------------------------------------------------------+
string CExpertSignalAIBase::ReadAltDataPinFromCfg(void)
{
bool inTesterOrOpt = (bool)MQLInfoInteger(MQL_TESTER) || (bool)MQLInfoInteger(MQL_OPTIMIZATION);
string fileName = inTesterOrOpt ? (m_fileName + "_optcache") : m_fileName;
int commonFlag = inTesterOrOpt ? 0 : FILE_COMMON;
if(!FileIsExist(fileName + ".cfg", commonFlag))
{
fileName = m_fileName;
commonFlag = FILE_COMMON;
if(!FileIsExist(fileName + ".cfg", commonFlag))
return "";
}
int handle = FileOpen(fileName + ".cfg", FILE_READ | FILE_BIN | FILE_SHARE_READ | FILE_SHARE_WRITE | commonFlag);
if(handle == INVALID_HANDLE)
return "";
string pin = "";
//--- fixed layout: 12 ints + 1 double (see SaveTopologyConfiguration's write order)
bool ok = FileSeek(handle, 12 * sizeof(int) + sizeof(double), SEEK_SET);
//--- appended segments, in order: conv/lstm ints, sl/tp ints, derived sl/tp doubles,
//--- dirConf double, xa pin (int + chars), alt pin (int + chars)
if(ok && FileSize(handle) >= (ulong)FileTell(handle) + 2 * sizeof(int))
ok = FileSeek(handle, 2 * sizeof(int), SEEK_CUR);
else ok = false;
if(ok && FileSize(handle) >= (ulong)FileTell(handle) + 2 * sizeof(int))
ok = FileSeek(handle, 2 * sizeof(int), SEEK_CUR);
else ok = false;
if(ok && FileSize(handle) >= (ulong)FileTell(handle) + 2 * sizeof(double))
ok = FileSeek(handle, 2 * sizeof(double), SEEK_CUR);
else ok = false;
if(ok && FileSize(handle) >= (ulong)FileTell(handle) + sizeof(double))
ok = FileSeek(handle, sizeof(double), SEEK_CUR);
else ok = false;
if(ok && FileSize(handle) >= (ulong)FileTell(handle) + sizeof(int))
{
int xaLen = FileReadInteger(handle);
if(xaLen > 0 && xaLen <= 16 * CROSSASSET_MAX_PAIRS)
FileReadString(handle, xaLen); // skip the cross-asset pin (length in CHARS, as written)
else if(xaLen != 0)
ok = false; // garbage length: stop walking, yield no pin
}
else ok = false;
if(ok && FileSize(handle) >= (ulong)FileTell(handle) + sizeof(int))
{
int altLen = FileReadInteger(handle);
if(altLen > 0 && altLen <= ALTDATA_MAX_PIN_CHARS)
pin = FileReadString(handle, altLen);
}
FileClose(handle);
return pin;
}
//+------------------------------------------------------------------+
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
//| See the declaration comment - retries a FileCopy FROM FILE_COMMON |
//| that raced a concurrent writer (typically a live chart's own |
//| atomic Save(), mid write-then-rename on the SAME source file). |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::CopyFileWithRetry(string srcFileName, string dstFileName)
{
refactor(comments): stdlib comment style across the remaining in-scope files Same pass as 0b06f8e, applied file by file: comment runs of 4+ lines compressed to their leading topic sentences, capped at 4 lines, whole sentences only. Warning sentences (NEVER / MUST / trap / would-have) survive the budget. Every file was checked the same way before committing: the list of non-comment lines is byte-identical to HEAD, and braces balance. No code was touched. Panel/, Enumerations/ and the already-terse System headers needed little or nothing - PooledGate, TradeChecks, BinomialStats and Random came through with no blocks over the threshold at all. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:25:52 -04:00
//--- Retries are only a backstop for a genuinely transient hiccup.
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 int RETRY_ATTEMPTS = 5;
const int RETRY_DELAY_CAP_MS = 1000;
int delayMs = 150;
bool ok = false;
for(int attempt = 0; attempt < RETRY_ATTEMPTS && !ok; attempt++)
{
if(attempt > 0)
{
Sleep(delayMs);
delayMs = (int)MathMin(delayMs * 2, RETRY_DELAY_CAP_MS);
}
//--- quiet on every attempt but the last, so an ordinary race that resolves on retry 2 doesn't
//--- spam the journal with scary per-attempt failures.
ok = CopySharedFile(srcFileName, dstFileName, attempt < RETRY_ATTEMPTS - 1);
}
if(!ok)
Print(__FUNCTION__ + ": WARNING - failed to copy " + srcFileName + " (shared folder) -> " + dstFileName +
" after " + IntegerToString(RETRY_ATTEMPTS) + " attempts (see the per-stage reason above)." +
" This run will train from scratch instead of reusing the deployed model.");
return ok;
}
//+------------------------------------------------------------------+
refactor(comments): box headers to stdlib length The //| box blocks were excluded from 0b06f8e and 5efdb48 and were what remained: 160 of them ran to 10+ lines, the longest to 88. Compressed to their leading topic sentences - 5 lines for a function header, 8 for a file header - keeping the box format and the standard MQL5 name/author lines verbatim. Verified at the BYTE level this time, across every in-scope file: the list of non-comment lines is byte-identical to HEAD and braces balance. The first check compared a locale-decoded 'git show' against a UTF-8 read and flagged 25 files that had not changed at all - every BOM and every non-ASCII line mismatched. 47,696 -> 40,665 lines in scope; comment share 38% -> 26%. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-22 00:30:14 -04:00
//| Share-aware streamed file copy FROM the shared (FILE_COMMON) |
//| folder INTO this program's own sandbox (the tester agent's local |
//| MQL5\Files when running under the Strategy Tester). |
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
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::CopySharedFile(string srcFileName, string dstFileName, bool quiet)
{
ResetLastError();
int src = FileOpen(srcFileName, FILE_COMMON | FILE_BIN | FILE_READ | FILE_SHARE_READ | FILE_SHARE_WRITE);
if(src == INVALID_HANDLE)
{
//--- Distinguish the two failure stages explicitly - a source failure here (with the share flags
//--- already set) would mean the shared folder itself is unreachable from this sandbox, which is a
//--- completely different problem from a destination/sandbox write failure below.
if(!quiet)
Print(__FUNCTION__ + ": cannot open SOURCE " + srcFileName + " in the shared folder, error " +
IntegerToString(GetLastError()) + " (share flags were set, so this is not a lock).");
return false;
}
ulong size = FileSize(src);
uchar buf[];
//--- 0-byte source would produce a 0-byte model file that CNet::Load rejects later as a stub - refuse
//--- it here instead, so the caller falls back to a fresh topology with an accurate reason logged.
if(size == 0 || ArrayResize(buf, (int)size) != (int)size)
{
FileClose(src);
if(!quiet)
Print(__FUNCTION__ + ": refusing to copy " + srcFileName + " - source is " + IntegerToString((int)size) +
" bytes (empty, or too large to buffer).");
return false;
}
uint read = FileReadArray(src, buf, 0, (int)size);
FileClose(src);
if(read != (uint)size)
{
if(!quiet)
Print(__FUNCTION__ + ": short read on " + srcFileName + " (" + IntegerToString((int)read) + " of " +
IntegerToString((int)size) + " bytes) - not copying a partial model.");
return false;
}
//--- destination is this program's OWN sandbox (no FILE_COMMON) - never the shared production folder.
string tmpName = dstFileName + ".copytmp";
ResetLastError();
refactor(stdlib): adopt Math\Stat for the deploy gate's normal tail; retire the b1/b2/lr/momentum macros The gate's NormalUpperTail was a hand-rolled Abramowitz & Stegun 26.2.17 approximation. Its own comment gave the reason - "drags a chain of headers behind it" - and that turned out to be one file: Math\Stat\Normal.mqh includes only Math.mqh, which includes nothing. Swapped for Cody's rational approximation in the library (~18 significant digits vs |error| < 7.5e-8). No past verdict changes: at the z the gate operates on, the difference is orders of magnitude below DEPLOY_FAMILY_WISE_ALPHA. Adopting it needed the four bare macros in AI\Network.mqh gone first. "#define b1 AdamBeta1" collides with an identifier in Math.mqh, so the include would have macro-expanded the library's own local and failed to compile - the same landmine that made the original author rename the approximation's coefficients to ntB1..ntB5 rather than use the reference's b1..b5. lr, b2 and momentum are the same class of hazard: single-token global macros in a 52k-line codebase. All four now resolve to the input names they always aliased, which is a pure textual identity - verified zero bare occurrences remain. Also: - SelectionSort over the buffered signals was O(n^2) with an O(n^2) count of StructToTime calls, because the comparison rebuilt both datetimes from the six int date fields every time. Now materialises the keys once and does an insertion sort; ArraySort cannot permute a struct array. IsEarlier goes with it, MakeDateTime becomes SignalTime. - Seven FileOpen sites lacked FILE_SHARE_READ|FILE_SHARE_WRITE, including AtomicWriteBegin, which stages every model save. All 43 sites now carry them - an exclusive open fails outright when another process holds the path, which here has meant a silently skipped save. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-19 19:31:36 -04:00
int dst = FileOpen(tmpName, FILE_BIN | FILE_WRITE | FILE_SHARE_READ | FILE_SHARE_WRITE);
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(dst == INVALID_HANDLE)
{
if(!quiet)
Print(__FUNCTION__ + ": cannot open DESTINATION " + tmpName + " in this agent's sandbox, error " +
IntegerToString(GetLastError()) + ".");
return false;
}
uint written = FileWriteArray(dst, buf, 0, ArraySize(buf));
FileFlush(dst);
FileClose(dst);
if(written != (uint)size)
{
FileDelete(tmpName);
if(!quiet)
Print(__FUNCTION__ + ": short write to " + tmpName + " (" + IntegerToString((int)written) + " of " +
IntegerToString((int)size) + " bytes) - discarded the partial copy.");
return false;
}
//--- atomic swap into place, so a reader never sees a half-written file
if(!FileMove(tmpName, 0, dstFileName, FILE_REWRITE))
{
if(!quiet)
Print(__FUNCTION__ + ": atomic rename " + tmpName + " -> " + dstFileName + " failed, error " +
IntegerToString(GetLastError()) + ".");
ResetLastError();
return false;
}
return true;
}
//+------------------------------------------------------------------+
//| See the declaration comment - retries Net.Load() against the |
//| active file. Covers the same transient-lock class as |
//| CopyFileWithRetry (e.g. antivirus briefly holding the file just |
//| written into the tester's local sandbox) rather than assuming any |
//| single failed read means "no model"/"corrupt file". |
//+------------------------------------------------------------------+
bool CExpertSignalAIBase::LoadNetWithRetry(double &indicatorParams[])
{
//--- same exponential-backoff reasoning as CopyFileWithRetry - see its declaration comment.
const int RETRY_ATTEMPTS = 5;
const int RETRY_DELAY_CAP_MS = 2000;
int delayMs = 200;
bool loaded = false;
for(int attempt = 0; attempt < RETRY_ATTEMPTS && !loaded; attempt++)
{
if(attempt > 0)
{
Sleep(delayMs);
delayMs = (int)MathMin(delayMs * 2, RETRY_DELAY_CAP_MS);
}
loaded = Net.Load(m_activeFileName + ".nnw", dError, dUndefine, dForecast, dtStudied, m_activeFileCommon, m_eraCount, m_trainingComplete, indicatorParams);
}
return loaded;
}
#endif // WARRIOR_AIBASE_PERSISTENCE_MQH
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