forked from mnbvc188199/Warrior_EA
476 commits
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AnimateDread
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f48bc93f9b |
refactor(inputs): 96 -> 70 inputs; remove two untested/unusable filter modules
Every removal below is FINGERPRINT-NEUTRAL by construction: each retired input is pinned to the exact value it already shipped with, so running models keep their filenames and resume rather than restarting at era 0. Verified field by field against BuildConfigFingerprint. Removed as inputs, kept as pinned constants (the value was never a preference the user had a basis to change): - OutputNeuronsCount. The regression head predicts a continuous quantity the triple-barrier label does not contain; the target is an EVENT, so the right output is its probability. The regression code paths stay implemented and dormant - they cost nothing and removing them would touch every scoring path at once. - MinRecall. A safety floor, not a preference, and the only direction a user can move it is the harmful one: raising it past what the config reaches yields NO model, not a better one (observed repeatedly at 60). - SwingConfirmationBars. Stopped gating the labels with the relabel, but is STILL load-bearing for the swing-context input features - it is the ZigZag repainting embargo, and without it those 9 features read a leg the live bar could not have had yet. Pinned, not deleted. - MaxErasPerRun (runaway backstop, never reached in a healthy run), FreezePriorCalibration (unanswerable by a user; near-balanced labels make the priors stable anyway), VerboseMode (developer view, joins DebuggingMode), MACD/Ichimoku periods x6 (both indicators ship disabled, and as optimizer dimensions they are pure overfitting surface - the AI auto-tuner is the supported way to move them). - SignalClusterWindow -> 3, no longer an input. Barrier labels make consecutive setups real, which argued for 0; it is not 0 because on D1+ a 6-bar window spans over a week and two arrows a day apart on a weekly-scale move are one event. 3 splits it correctly by timeframe. - EnableOnlineLearning -> ON. Adapting to a changing market is what keeps a months-attached model from going stale, and the rolling-accuracy freeze is what makes it safe. See the caveat noted in the handoff: it had not been forward-tested on a live feed when this became default. Removed entirely: - Intraday Time Filter (5 inputs + Signals/SignalITF.mqh). Two of its five inputs were raw BITMASKS, which is an implementation detail exposed as a control. The job is covered three times over by things that are declarative or that learn: the session filter, the time-of-day/day-of-week input features (the network discovers which hours are good rather than being told), and the journal's time buckets. - Market Depth Filter (5 inputs + Signals/SignalMarketDepth.mqh, plus its OnInit probe and OnDeinit release). It needs real level-2 data that this broker - and most retail MT5 brokers - do not provide, so the module has never once executed against real data. Shipping four tuning dropdowns for an untested path is worse than shipping nothing: the only users who could enable it would be its first-ever testers, live. If DOM returns it should be a FEATURE fed to the network, not a rule-based veto with hand-tuned thresholds - imbalance is data. - IndicatorTuneTrials, replaced by ComputeTuneTrialBudget(). The useful budget depends on how many parameters are actually being searched, which depends on which features are enabled - so one number meant wildly different things run to run. The shipped 32 was ~10 candidates per dimension against one enabled indicator (wasteful: each costs GA_SEEDS full training runs) and under one per dimension against all nine (blind). Now population ~ 4 x active dimensions, clamped [8,64], with CADIndicatorTuner::ActiveDimensions() defined immediately above PerturbRandom() so the two cannot drift apart. - Six orphaned enums (TUNE_TRIALS_PRESET, DOM_*, ENTRY_HOUR_OF_DAY, TIME_FILTER_DAY_OF_WEEK), 81 lines. Other UX: - SL_ATR_x1 / TP_ATR_x3 now carry the "(classic)" default marker every other preset enum in the file already used. Nothing in the SL/TP dropdowns previously told a user which pair was the shipped default - which matters far more since the relabel, because those two define the labels and changing either forces a retrain. - Neural Network section moved directly ABOVE AI Input Features: choose the architecture, then choose what it sees. NN Optimizer / Performance stays last - the Adam/Sgd inputs are declared in AI/Network.mqh and render immediately after that divider. - News feature + window moved to the end of the AI feature list, below Wyckoff Bar Inversion. - Dropped "(0-100)" from Min vote to open - it is an enum, not a number. Both builds compile 0 errors / 0 warnings. No retrain forced. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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b4a704d309 |
feat(ai): triple-barrier labels replace exact-pivot ZigZag targets
The 31:1 class imbalance was self-inflicted by the TARGET, not a property of the market. Labelling only the exact bar where a ZigZag pivot confirms gave Buy 1164 / Sell 1164 / Neutral 35841, and every correction mechanism this codebase accumulated sits downstream of that one choice: the logit-adjusted loss and its range cap, the prior EMA, the +-3.0 output-bias seed, balanced-accuracy-then-precision selection with its coverage floor, the recall floor and its catch-22, the alternation gate, NMS, and the four oversampling designs that collapsed before them. The reference this engine is built on (references/neuronetworksbook.pdf ch. 3.1/3.3) also uses ZigZag, but targets the DIRECTION TO THE NEXT EXTREMUM on every bar - ~50/50 by construction, with no imbalance to correct at all. It never had this problem because it never asked "is this the pivot bar". Labels are now the triple barrier (Lopez de Prado ch. 3), using the EA's OWN SL_Mode/TP_Mode: does a trade opened at this bar's close reach its target before its stop, within a horizon. Buy = long resolves, Sell = short resolves, Neutral = neither. Consequences: - dir-precision in the era line stops being a proxy and becomes the win rate of the strategy under its own exit rules. - Expected balance ~25/25/50 at the shipped 1:3 (gambler's ruin), i.e. ~2:1 instead of 31:1. Measured and logged at the end of the prebuild. - Spread is charged on both legs, so it is a NET win rate. - Intrabar ambiguity resolves to the STOP. OHLC cannot order two touches inside one bar and the optimistic reading is how a backtested edge becomes a live loss. ZigZag stays as input features (EnableSwingContext) and now also supplies the vertical barrier: the horizon is the median confirmed leg length, snapped to a coarse ladder. Derived, not configured, and deliberately kept out of the filename fingerprint - a filename keyed on a measured quantity orphans a trained model the moment the measurement moves. Removed, because the premise died with the old target: - the alternation gate. Correct for pivot labels (a ZigZag cannot emit two same-type pivots in a row, so a repeat was provably a false fire), and wrong for barrier labels, which answer each bar independently. It also took its worst consequence with it: a one-sided model previously got ONE trade per backtest, a hard blocker on marketplace validation. - SignalClusterWindow now defaults off - it de-duplicated repeats that are now real trades. Kept as an opt-in display control. - LABEL_WINDOW_BARS, the pivot-widening pass, ConfirmedZigZagLabel. - the era-0 output-bias seed now needs a genuinely dominant class (0.70) rather than 0.40; at ~50% Neutral a +-3.0 seed is a distortion, not a correction. Also fixed, both found while wiring the above: 1. RefreshConvergedSignal sized its buffers from a date delta (Bars(sym, period, dtStudied, TimeCurrent())). dtStudied is a training watermark; in the tester it is loaded from a live-chart save AHEAD of the simulated date, so the interval inverted, Bars() returned ~0, and the buffer came out at exactly m_historyBars - deep enough for the OHLC window and far too shallow for the Donchian-50 / 20-bar-return / SMA extension behind it. Inference silently computed DIFFERENT features from the ones training learned on, live as well as in the tester. Now sized from what the feature builder actually needs. 2. The barrier horizon is resolved on the deployed path too. A deployed model never enters Train(), so it never reached the prebuild, and OnlineLearnStep reads the horizon as its confirmation delay - left at the fallback it would have backpropped bars whose barriers had not resolved. Silent lookahead in the one place that writes to a live model. SL_Mode/TP_Mode join the weights fingerprint: they define the labels now, so a model trained at 1:3 must never be silently reused at 1:1. This re-keys every pre-existing model by design - none were trained on this task. Inference census extended with the vote gate. LongCondition/ShortCondition open with a readiness check the refresh counters never see; in the tester it reduces to "the seeded _optcache.nnw must have LOADED", and if it did not, every vote is hard-zeroed while the model still answers Buy. The old three counters would have read that as "the model says Neutral" - false, and a completely different fix. This is the leading candidate for the zero-direction backtest and the census can now name it in one run. Both builds compile 0 errors / 0 warnings. Forces a full retrain. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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fa0455f399 |
diag: inference-path census, to explain zero-trade backtests
A backtest of the CONVERGED CONV model produced "Final directional result: 0.00000000" on every one of 1744 bars and therefore zero trades. Nothing in the log could separate the three candidate causes, and each needs a different fix: 1. RefreshLatestSignal never called (new-bar gate never fires) 2. called, but bailing at one of its two early returns 3. running fine, and the model genuinely answers Neutral every bar Counts all three plus the Buy/Sell/Neutral split, printed once at shutdown via StopTraining (which the tester reaches through OnDeinit). Three increments per bar against a full feedForward - not worth gating. Ruled out while writing this, so the next session does not re-derive it: - the alternation gate (m_lastNonNeutralSignal) is NOT the cause. It starts at Neutral, so a first Buy would still fire and show up as one non-zero direction. We saw zero. It IS still a live hazard for a one-sided model - CONV currently calls Buy:17% Sell:0%, and after the first Buy every later Buy is suppressed until a Sell that never comes - but it cannot explain an all-zero run. - shallow buffers do not hard-fail the feature builder: the swing-context Donchian loop breaks gracefully when it runs off loaded history. It does mean converged-path inference computes Donchian/return/SMA features over a TRUNCATED window versus training, which is a real train/inference skew worth its own fix, but it degrades features rather than zeroing them. Both builds 0/0. Diagnostic only. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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0261c013d0 |
fix: EMA shadow never received the LSTM weight block
Confirmed live the first run after
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AnimateDread
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ab84998d35 |
feat(ai): true multi-bar conv and true sequence LSTM
CONV and LSTM were each configured as a strictly lossier perceptron, which
is exactly what the panel showed: PAI 24% > CONV 18% > HYBRID 12% ~ LSTM
12%, monotone in how much reaches the dense stack (420 / 160 / 32 / 16).
CONV - receptive field 1 -> 3 bars, and the pool is gone.
Reading the reference kernels settled why
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AnimateDread
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b4d640d0b4 |
fix: blend-skip warning fired on layers that correctly own no weights
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AnimateDread
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32880f3728 |
diag: make BlendWeightsFrom say when it skips a layer
Every branch of the EMA shadow blend is `if(both sides return weights)
{ blend }` with no else. That silent degrade is deliberate - a partial
topology mismatch should not corrupt unrelated layers - but it also hid a
real defect for 343 eras on SP500 H1.
The HYBRID shadow's LSTM layer had never run a forward pass, so its
WeightsLSTM was still NULL and getWeightsLSTM() returned 0. The blend
skipped a 24,704-weight layer on every single era and returned true. The
only trace was on disk: the shadow .nnw is 791,120 bytes smaller than its
live net - 4 x 24,704 doubles, exactly the LSTM weight block plus its Adam
moments - while the CONV, LSTM and PAI shadows byte-match their live nets.
This matters beyond training: the shadow is the net live inference and
deployment read, so those layers are not tracking the trained model at all.
Counts skipped blocks and warns once per net, naming the layer index and
neuron type. Reporting only - the blend behaviour is unchanged, and the
underlying cause still needs a fix.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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251e9711dd |
diag: report |dW| alongside d|W| per layer, and drop two stale log claims
The per-era `dW/W` line measured the change in each layer's weight NORM. That statistic cannot separate "this layer only shrank under weight decay" from "this layer moved somewhere useful" - a rotation at constant norm and pure decay can print the same number. It matters right now: on SP500 H1 the LSTM layers print a near-constant ~1.05%/era that exactly equals their geometric norm decay over 318 eras (HYB lstm2 12.966 -> 0.755, monotone, never once up), while a sibling conv oscillates around a much slower drift. Norm-change can only hint at that. Now prints norm(d|W|% / |dW|%). Under decay alone the two are equal; any gradient component adds in quadrature to the second, so a learning layer shows the second clearly larger. Diagnostic only - no training behaviour changes, fingerprint untouched. Also removes two log lines that described machinery deleted in |
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AnimateDread
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397b0eac1f |
refactor(ai): nine class-imbalance inputs down to two
The imbalance section offered nine controls for one job. Audited against the
code, five of them did not do what their names said at the shipped defaults:
AILogitPriorStrength DEAD - Inference.mqh's post-hoc prior early-returns
whenever the adjusted loss is on, which is default.
OversampleParity DEAD in training - Training.mqh gated the replay loop
on !useLogitAdjustedLoss (correctly, citing Buda et
al. 2018). Live only in the online-learning path.
EnableMinorityReplay DEAD as replay. It survived ONLY as a focal-gamma
damper - "replay minority bars through pass-2
oversampling" was a focal-loss switch.
ConstrainReplay DEAD as a cap; it only chose damper 0.125 vs 0.25.
UseStaticPrior An exact duplicate of FreezePriorCalibration - the two
were OR'd together in the single place either is read.
So they were not five mechanisms fighting; they were one mechanism plus eight
knobs that mostly described machinery that no longer ran. That is worse than
a real conflict, because the log agreed with the names: the label-cache line
printed "reps up to 28x (90% parity) (seeding era 0's class-balance
oversampling)" on every run, describing an oversampling pass that had been
switched off. It is fixed here too - it cost this session a wrong diagnosis.
The one genuine redundancy was focal loss, running at gamma*0.125 alongside
the adjusted loss: two corrections on the same axis, the exact stacking
failure this file already cited Buda et al. for in two other places, damped
by a replay flag whose replay path was itself dead. Removed rather than
re-tuned. The plateau ladder is unaffected - its escape is the learning-rate
warm restart; the gamma anneal beside it only ever stepped toward zero.
WHAT REMAINS is logit-adjusted loss (Menon et al. 2021) plus a prior freeze:
LogitAdjustTau 0 = off; replaces the separate EnableLogitAdjusted-
Loss boolean, since a strength dial where 0 already
means off does not need an on/off switch beside it.
FreezePriorCalibration unchanged.
It is the only one of the six corrections with a consistency guarantee, and
it is consistent for exactly the balanced-error metric checkpoint selection
already ranks on - so the loss and the deploy decision optimize one thing.
The online continual-learning path keeps its own alpha-balanced focal weight,
now as constants pinned to the removed inputs' shipped defaults, so its
behaviour is unchanged. It legitimately needs its own correction:
ApplyLogitAdjustment() only runs inside a training run, so a deployed model
that was reloaded carries no logit offsets and would otherwise stream 31:1
data into itself uncorrected.
The weights-filename fingerprint is BYTE-IDENTICAL. The focal slot was a
double fed to a %d conversion and had always emitted a literal 0; the |MR:
segment is written as the constant its shipped defaults produced. Dropping
either would have re-keyed every model and forced a from-scratch retrain of
the one topology currently converged and trading.
Also removed as orphans: FOCAL_GAMMA_PRESET, MAX_OVERSAMPLE_REPLICAS,
OVERSAMPLE_PARITY_FRACTION, PLATEAU_GAMMA_STEP, and the now-unreachable
"neutralized by prior correction" diagnostic.
Both builds compile 0 errors, 0 warnings. No retrain forced.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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7eb48f5038 |
feat(trade): anchor SL and TP to the entry price, not the last swing
Stops keyed to the recent swing extreme make a trade's risk a function of how far the last swing happens to sit rather than of current volatility. On a shallow pullback the swing sits close to the fill, so the stop is tight enough to be taken out by noise on setups that then run to target - which is what the Perceptron's signals were showing. SL: lowest_low/highest_high -/+ mult*ATR -> entry -/+ mult*ATR TP: TP_PREV_SWING (opposite swing) -> removed; ATR-from-entry SL_PREV_SWING, TP_PREV_SWING -> removed from the enums The SL anchors to `price` (the resolved entry), not to base_price: with a pending entry those differ by the whole entry offset, and the risk Money sizes against is entry-to-stop. MIN_SL_ATR_MULTIPLIER 2.0 -> 0.5. That floor existed because a swing- anchored stop could land arbitrarily close to the entry and needed a bound unrelated to the chosen multiple. An entry-anchored stop is exactly mult*ATR by construction and cannot collapse, so leaving it at 2.0 would have silently overridden SL_ATR_x1 to 2*ATR - making the input a lie AND forcing TP >= 4*ATR just to clear the default 1:2 rejection filter. The broker's own stop level is enforced separately and precisely by TCAdjustStops(), so this is now a pure sanity net. Default TP_Mode TP_PREV_SWING -> TP_ATR_x3, so SL_ATR_x1 + TP_ATR_x3 gives a realised 3:1 against the 1:2 filter. TP_ATR_x2 would sit EXACTLY on the 2.0 boundary where price-normalization rounding alone can reject the setup; the default leaves a deliberate gap. This is the same interaction that once rejected 100% of setups on every symbol (see TP_INTELLIGENT_BASE_RR). Swing validity guards now reject only when the configuration actually uses a swing - i.e. ENTRY_PREV_SWING. Previously an unsynced or thin history rejected EVERY trade, including configurations whose levels no longer reference a swing at all. The guards are kept, not deleted: a bad swing must still never reach an entry price, and iLow/iHigh are no longer called with a possibly-negative index. TP_INTELLIGENT stays risk-relative. Now that risk is exactly mult*ATR the risk- and ATR-relative forms coincide, but risk-relative keeps its reward:risk guarantee exact after the floor or TCAdjustStops widens a stop. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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5a12ae08e2 |
revert(ai): restore the 4eae763 front-ends - both my rewrites stopped signaling
CONV and LSTM were signaling at |
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AnimateDread
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18f63c7a52 |
feat(ai): report per-layer weight movement each era
Adds "dW/W dense1:0.412(0.31%) conv1:0.088(0.000%) ..." to the era line: each layer's weight L2 norm and its relative change since the previous era. Why: a frozen stage and a badly-suited architecture look identical from the outside. Both give a flat metric and a retreat to the majority class, and neither the loss, the accuracy nor the per-class recall can tell them apart. This session cost two full retrain cycles guessing between them - a forget- gate bias (a real bug, measured, but not the cause of the observed failure) and a conv receptive field (which turned out to be a regression, not a fix). A layer sitting at ~0.000% era after era while its neighbours move is receiving no gradient, and no amount of retraining or hyperparameter work will change that. A net where every layer moves and the output still collapses is a genuine architecture or objective problem. The distinction is one glance at the log instead of a redeploy-and-wait cycle per hypothesis. Costs one host-side buffer read per layer per era, off the training path. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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34d6aa42a4 |
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> |
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AnimateDread
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5a7ed73df6 |
fix(ai): positive forget-gate bias, giving the LSTM its window back
The sequence rewrite made LSTM/HYBRID recurrences over 20 bars, but every weight - including the gate biases - is initialized around zero. That puts the forget gate at sigmoid(0) = 0.5, so the cell state is halved every step: the first bar survives into the output scaled by ~0.5^20, and the gradient reaches it scaled by the same factor. The layer was therefore a one-bar model wearing a 20-bar interface. A one-bar model has no signal on this task, so the head learned the base rate and emitted Neutral everywhere - the flat 0.34 IS error across twelve eras and OOS recall Neutral:100% seen on SP500 H1. Measured at the shipped H1 shapes (H=64, stepInputs=21, T=20) - influence of bar 0 on the output relative to bar 19: bias 0.0 -> 3.0e-05 forward, 3.3e-05 backward (dead) bias 1.0 -> 1.2e-02 forward, 1.4e-02 backward bias 2.0 -> 2.5e-01 forward, 2.7e-01 backward (a real 20-bar field) This is the standard fix, not a tuned knob: Gers/Schmidhuber/Cummins (2000) introduced the forget gate with a positive bias, and Jozefowicz/Zaremba/ Sutskever (ICML 2015) recommend a bias of 1 as a default (whence Keras' unit_forget_bias). Both 1 and 2 are standard; the sweep picks 2 because at a 20-step window a bias of 1 still leaves the oldest bar at ~1% influence. lstm_seq_flowcheck.cpp is added as a permanent regression check and asserts the shipped constant keeps >=5% reach in both directions. It complements lstm_seq_gradcheck.cpp: that one proves the BPTT is CORRECT, this one proves it is USABLE. The gradient check passed at 2.3e-10 throughout - correct math over a recurrence that carries nothing looks exactly like a bad architecture. Both builds compile 0 errors, 0 warnings. Initialization only, so the .nnw format is unchanged; LSTM and HYBRID must retrain to pick it up. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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b941977612 | 2 new files | ||
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AnimateDread
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7a753e54ba |
feat(ai): sequence-LSTM kernels for the OpenCL tier
Closes the gap left by
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AnimateDread
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7a081979b2 |
feat(ai): make LSTM/HYBRID actual sequence models over bars
CNeuronLSTMOCL consumed the whole flattened input in ONE gate computation and back-propagated a single timestep, which its own class comment stated. Combined with a conv stage whose window=step=neuronsCount gives it a receptive field of exactly one bar, no stage in HYBRID mixed information across time - the bars reached the dense stack as an unordered flat vector, the same thing the plain MLP sees. That predicted the measured ranking (MLP 31.5%, CONV 32.5%, LSTM 30.6%, HYBRID 14.4%): each extra bottleneck cost accuracy and bought nothing. The layer now unrolls m_historyBars timesteps, sharing one gate block across them and carrying h/c forward, with real BPTT carrying dh and dc backward. Per-step width comes from CLayerDescription::window, which CNet passes to the new SetStepWidth() - previously dead metadata. Consequences worth naming: - Weight count drops from 4H(H+420+1) to 4H(H+21+1). Weight sharing is the point of a recurrence, so ComputeLstmHiddenSize now budgets on the per-step width; H goes 16 -> 64 at H1 defaults, and the model is still smaller. - h/c start at zero per sample. The old buffers persisted across forward passes, so under shuffled training each sample inherited an unrelated sample's state. - .nnw LSTM records are versioned (LSTM_SEQ_SAVE_TAG). The old weight block is a different shape, so Load REFUSES pre-rewrite models rather than misreading one and throwing off every later layer's offset. LSTM and HYBRID must retrain. - Sequence mode has no Network.cl kernel, so it refuses the OpenCL tier loudly instead of quietly running a different architecture there than on the DLL tier - the two would train different models from one .cfg. Legacy single-timestep path kept intact for step width <= 0. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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3ea54b4f2b |
feat(dll): fused sequence-LSTM kernels with real backpropagation-through-time
The per-step entry points cannot express a sequence model. CPU_LSTMGates takes
the ENTIRE flattened input as one timestep, and CPU_LSTMGateGradient has no
parameter for dc arriving from the following step - so the recurrent gradient
path does not exist and cannot be assembled from these primitives at any call
pattern. The layer built on them is a gated dense layer that the class comment
already described honestly: "single-timestep-truncated BPTT".
Adds CPU_LSTMSeqForward / CPU_LSTMSeqBackward: the whole unrolled sequence in
one call each, weights shared across timesteps, dW accumulated over all of them
(the per-step CPU_LSTMWeightsGradient assigns rather than accumulates, so it
could not have been reused even with the dc term). Fused rather than dispatched
per step because the recurrence is sequential - T round trips would serialise T
lock/dispatch pairs for a few thousand FLOPs each.
h_{-1} and c_{-1} are zero per sample. The old layer carried its cell state
across forward passes, so under shuffled training every sample inherited the
state of an unrelated one.
DirectML gets the same math host-side (readback, compute in double, upload)
rather than HLSL: the recurrence needs a barrier per timestep, the GPU buffers
are float and BPTT accumulation is where that hurts most, and no D3D12 device
exists on this machine to test a shader against. Documented at the definition.
Verified with lstm_seq_gradcheck.cpp - central-difference check of dW and dX
against an asymmetric loss over the final hidden state. Max relative error
2.3e-10 on both, with a non-trivial gradient magnitude asserted so the check
cannot pass on an all-zero result.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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4eae763849 |
fix(ai): report the metric actually compared; surface the derived front-end
The plateau/regression line printed balancedOosEra as the current value while comparing against m_bestBalancedOos, which has held the SELECTION score since |
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AnimateDread
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ff06583680 |
feat(ui): drop the config tag from the plain-language panels
"Hybrid 3L [HYB-9369] - learning (era 4, 12%)" leads with a fingerprint hash that means nothing to an owner. The tag earns its place in the journal and the State\ folders, where telling one chart's model files from another's is the whole point - but the default panel is the commercial surface and should not open with a debug token. New DisplayName() strips the bracketed suffix; the two plain-language panels (training and live/idle) use it. Logs, the VerboseMode panels and the auto-tune line keep the full ID, so nothing needed for diagnosis is lost. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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efdd36d183 |
fix(ai): stop the shutdown save from resurrecting reset weights; size HYBRID's LSTM to its real fan-in
ResetWeights already deletes the whole model set - .nnw, .cfg, _ckpt.tmp,
.stats, _shadow.nnw - and clears both the .arrows sidecar and the drawn
chart objects. What undid it was PersistWeightsOnShutdown: detaching the EA
after a reset but before an era completed re-created a .nnw from the
freshly-built, never-run net, so the next attach loaded an era-0 stub
instead of starting clean. For LSTM/HYBRID that stub is worse than nothing -
a layer that has never run a forward pass has m_iInputs<=0, so Save omits
every LSTM buffer (see
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AnimateDread
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413ff7e9a2 |
fix(ai): allocate LSTM cell state on the load path, unpinning HYBRID from Neutral
CNeuronLSTMOCL::Save early-returns when m_iInputs<=0, writing no LSTM buffers at all - correct, since a layer that has never run a forward pass has no weights to persist. But Load mirrored that early return BEFORE allocating Memory (the c_prev cell state), and SetInputs - the lazy sizing path that runs on the next feedForward - allocates the weight buffers but never Memory. Both Init overloads allocate it unconditionally, so only the save-then-load round trip could produce the gap. Net effect: any net serialized before its first feedForward came back with Memory==NULL. LSTMGates then failed on every call, short-circuiting the || before LSTMState could dereference the null buffer, so instead of crashing the layer computed nothing forever. Observed on HYBRID after a weights-reset-then-detach: all three class outputs pinned at exactly 1.000 (spread 0.0000), IS error stuck at 0.78, Buy/Sell recall 0%, and 636k "Error of execution DirectML LSTM feedForward" lines in one 55MB journal. The model looked like a converged Neutral collapse; it was a dead layer. Allocate Memory in Load ahead of the early return, and harden SetInputs to guarantee every buffer LSTMGates/LSTMState touch exists before it returns - loudly, since the failure it replaces was indistinguishable from a healthy net that simply never fires. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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1039ad936f |
feat(ai): measure precision per confidence tier; fix stale metric labels
Two things the 2026-07-30 run exposed.
1. Every user-facing message still called the selection metric "balanced
accuracy". It has ranked on directional precision since
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AnimateDread
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ce90fc74b6 |
fix(ai): discount selection precision by coverage shortfall
The 2026-07-30 run caught a bug in the precision-led selection metric within 8 eras. HYBRID made exactly ONE directional call in era 7, got it right, scored 100% precision, and locked that in as best-ever. Nothing can beat 100%, so the checkpoint froze on a single sample and the run could only burn to the era cap deploying it. The coverage floor already existed and already blocked that era from being DEPLOYABLE - but the ranking ignored coverage entirely whenever no era had qualified yet, which is precisely the phase where the ranking is the only thing steering the run. Precision is now discounted by coverage/floor, capped at 1.0. Continuous rather than a threshold: an era at half the floor scores half its precision, so coverage and precision both improve rank and neither can be traded away. Above the floor the credit saturates, so ranking among genuinely deployable eras is unchanged pure precision. Also: the startup config line printed "tau 1.00" while every chart was actually running the capped 0.35 - the effective value depends on the measured class priors and is not knowable at init. Now reads "1.00 requested"; ApplyLogitAdjustment still logs the real figure. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
45b35b3d1d |
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> |
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AnimateDread
|
3bc551b6e1 |
feat(nn): derive conv filter count and LSTM hidden size from the data
Same defect the first-layer width had before 2026-07-29: both were inputs whose defaults were fixed constants picked with no reference to the input they sit on, which is the only thing that decides whether either number is sane. The conv layer is a per-bar projection - AddConvStage sets window = step = one bar's features - so its filter count should be read against the per-bar feature count. Sixteen filters COMPRESSED a 50-feature configuration 3x but EXPANDED a minimal 4-feature one 4x, and the expanding case adds parameters below every learnable layer without adding information. Now derived as half the per-bar feature count, snapped down a power-of-two ladder. The LSTM stage was the bigger miss. Its weight count is exactly 4*H*(H+inputs+1) (CNeuronLSTMOCL::SetInputs) and AddLstmStage feeds it the whole flattened vector, so the shipped 32 units against a 540-wide input is ~73k weights - more than DOUBLE the entire derived dense taper it feeds. It was the one stage the capacity budget never covered, which is why deriving the dense stack alone did not stop LSTM and HYBRID from being over-parameterized. Now solved from the same one-weight-per-in-sample-bar budget the first layer spends. Factored EstimatedInSampleBars() out of ComputeFirstLayerWidth so all three decisions spend one budget rather than each guessing at the training-set size separately. Both new values are assigned alongside the first-layer width, before the fingerprint that hashes them, and are functions of inputs already in that hash - so they need no entry of their own, and the same reasoning removes them from the DB config key. Both builds compile 0 errors, 0 warnings. Re-keys existing models. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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ebf2e73667 |
fix(ui): unique chart tag, product-grade panel, responsive under load
Three separate reports from one deploy. 1. CONV, LSTM and HYBRID all came back tagged [4109]. The weights fingerprint omits the topology type on purpose - the file path already separates it (State\CONV\ vs State\LSTM\ vs State\HYB\) and hashing a value that is constant within a folder buys nothing while re-keying every trained model into a forced retrain. So the files were never at risk, but the tag could not do its one job. Prefixing the short id makes it unique on the display side only; the hex half still greps straight to the .nnw inside the folder the prefix names. 2. The default panel read like a training console. Six lines down to three, each answering a question an owner actually has. The deploy internals (best score, eras-since-best, ladder stage) were developer diagnostics describing a recall floor that no longer decides anything, and were already in the era-end journal line. In-sample accuracy left the panel too: it grades the model on bars it trained on, so it always flatters, and showing it beside the honest number invites reading the wrong one. New compile-time DebuggingMode constant - deliberately not an input - carries the IS/OOS pair and the resolved model path into the journal instead. No extra Inputs row, no extra Market description line, no user-reachable firehose. 3. Panel drag and buttons stuttered under training load, exactly as the 2026-07-26 note raising the chunk budget to 200ms warned they might. Backed off to the documented 120ms - worst-case click latency is that budget - and the derived topology (~292k weights to ~29k) makes the throughput this costs far cheaper than when that note was written. Also halved the panel redraw rate to 2.5 Hz: ChartRedraw repaints the whole chart, so its cost scales with accumulated arrows, and 5 Hz was the larger half of the stutter. Era-end still force-refreshes. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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16632c1c3c |
feat: make batch normalization mandatory, and record the run-3 results
EnableBatchNorm and BatchNormWindow demoted from inputs to constants. Batch norm is required, not optional: measured on identical MLP_3L topologies it was worth +11.3 points of balanced accuracy (57.0% with, 45.7% without), stable across 150+ and 200+ eras, and the no-BN control converged to ~5% IS and OOS accuracy with no chart signals at all. A user cannot make a good decision here and can easily make a ruinous one, so the choice is not offered. BatchNormWindow goes with it - a running-statistics window in samples has no meaningful setting a trader could reason about, and its only other reachable state (<=1) silently disables the layer. Kept as named constants rather than deleted: the topology builder, the weights fingerprint and the .cfg guard all read them, and a constant keeps those paths - and the ability to flip one for a diagnostic rebuild - intact. Fewer knobs also means a shorter Market description and less room for a buyer to misconfigure. EXPERIMENTS.md records runs 2 and 3, since the MT5 logs are wiped between runs and these measurements are what the design decisions rest on. Run 3 (12h, uncapped tau=1.0) is a write-off: zero eras out of 1,993 across the five batch-norm charts ever called a direction on fewer than half of all bars, at a median precision equal to the ~6.1% base rate. The damage was present at era 1 and never recovered over 292-766 eras. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
a142749a87 |
feat(ai): rank checkpoints on directional precision, not balanced accuracy
Balanced accuracy is maximized by exactly the model this system must never
deploy. Measured frontier at fixed signal strength, base rate 6.1%:
tau 0.00 -> calls 0.2% of bars at 27.3% precision, balanced 34.0%
tau 0.35 -> calls 2.0% of bars at 15.5% precision, balanced 36.3%
tau 1.00 -> calls 49.6% of bars at 6.4% precision, balanced 53.5%
It rises monotonically as the model calls MORE and is right LESS, because
two of its three terms are directional recalls that a call-everything model
drives to ~95%, while the Neutral term it sacrifices counts for only a
third. The 2026-07-29 run landed exactly there: balanced 58-64% while
calling a direction on ~100% of bars at a 5-7% win rate against a ~6% base
rate. Only the per-class recall floor stopped those deploying - a guard
doing the job the objective should have been doing - and that same guard
also rejected the genuinely useful sparse-but-precise checkpoints.
Ranking is now DIRECTIONAL PRECISION: of the bars called Buy or Sell, how
many were right. That is what a trading edge is. Two anti-degenerate floors
bracket it, since precision alone is trivially maximized by calling almost
nothing: coverage must reach a fraction of the true directional base rate
(derived, not configured - it adapts to any symbol/timeframe/label rule),
and precision must at least beat that base rate.
Against the same frontier the deploy order inverts from
tau 1.00 > 0.50 > 0.35 > 0.15 (old, worst model first)
to
tau 0.35 > 0.50 > 1.00 (new; 0.00/0.15 rejected on coverage)
Balanced accuracy is kept in the log as a diagnostic and marked as such, so
a run where the two disagree - the signature of an over-caller - is visible
at a glance. MinRecall no longer decides what ships; it now only drives the
diagnostic recall line and is a candidate for removal.
Both builds compile 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
|
0e5f1bb2f6 |
fix(ai): cap logit-adjustment strength to the head's usable logit range
tau=1.0 inverted the collapse instead of curing it. The head is SIGMOID, so each output is bounded to [0,1] and the widest logit gap the net can express between two classes is CLASS_LOGIT_SCALE * (1-0) = 6. The offsets are tau*log(prior_c), whose spread on this 30:1 imbalance is 3.42 - so tau=1.0 spent 57% of the ENTIRE expressible range on the prior correction. The network did the only thing available to it: saturate Buy/Sell outputs to 1.0 to overcome a -3.42 training handicap. The offsets are absent at inference, so that surplus made every bar directional. Measured across all five still-training charts: Neutral recall 0%, directional calls on ~100% of bars, win rate 5-7% against a ~6% base rate - no information whatsoever - while balanced accuracy read a flattering 58-64% because two of its three terms sat near 95%. OOS accuracy 6%. Menon et al. assume an unbounded logit head where a 3.42 shift is negligible against the reachable range. It is not negligible here, so the strength is now expressed RELATIVE to the range actually available: tau_eff = min(tau_cfg, LOGIT_ADJUST_MAX_RANGE_FRACTION * SCALE / spread) At 20% that gives tau 0.35 on this data. Deliberately a fraction rather than a tau ceiling: it stays correct if CLASS_LOGIT_SCALE changes, if the head becomes unbounded, or on any symbol whose imbalance differs. The input remains effective below the cap, so dialling it down needs no rebuild. Simulated at a signal strength where the task is genuinely learnable, the precision/recall frontier is monotone: tau 1.0 -> 49.6% call rate at 6.4% precision (base rate 6.1%, i.e. worthless); tau 0.35 -> 2.0% at 15.5%; tau 0.15 -> 0.2% at 33.3%. The capped value lands in the same regime the pre-logit-adjustment run occupied (1-6% of bars at 20-35% win rate). Also logs the measured priors, the spread, and whether the cap bound. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
000c45fdbb |
feat: print a self-verifying config line per chart at startup
A multi-chart comparison is only valid if every chart is identical except the axis under test, and a drifted setting was previously invisible: the model filename carries a HASH, so two charts that should match and do not look merely "different" with no indication of which field moved. Each signal now logs its effective config plus the raw fingerprint string, unconditionally (not gated on VerboseMode). The six lines diff directly, so an accidental divergence in study period, feature set, focal gamma or anything else feeding training shows up at startup rather than as an unexplained result hours later. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
70fed28015 |
docs: correct the conv-pool rationale against the reference contract
The previous note claimed the pooling stage was unfixable in the topology. That is only true of TIME-axis pooling. The NeuroNet_DNG reference - whose conv and pool kernels are byte-identical to ours - ties the pool to the filter count (window = step = window_out), producing a clean non-overlapping max-over-channels emitting one value per bar. So a correct channel-pooling configuration does exist and needs no kernel change. The real defect was that our window/step were never tied to window_out: 3/2 against 16 filters overlapped across the filter axis and straddled bar boundaries. Removal still stands, for a different and narrower reason: max-over-channels at 16 filters reduces 320 conv outputs to 20 - one scalar per bar for a 420-wide input - and the first dense layer would fan OUT 20 -> 64 instead of funnelling. The reference could afford that at window_out=4. Comment-only. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
70cdec2717 |
fix(ai): drop the conv pooling stage - it reduced across filters, not time
FeedForwardConv emits POSITION-MAJOR output, matrix_o[out + window_out * i],
so one bar's window_out filter responses are contiguous and consecutive bars
sit window_out apart. Both pooling implementations (FeedForwardProof and
CPU_FeedForwardProof) slide FLAT over that buffer - pos = i * step, reducing
`window` CONSECUTIVE elements. On a position-major layout those neighbours
are different FILTERS of the same bar, never one filter across time.
At the shipped 3/2 the pool computed max(bar0_f0, bar0_f1, bar0_f2), then
max(bar0_f2, bar0_f3, bar0_f4), with every 8th window straddling a bar
boundary. So it collapsed unrelated feature detectors into whichever fired
hardest, passed gradient to that winner only, and halved the feature map
while doing it - all below every learnable layer, where nothing above can
recover it. The removed inputs' own labels ("3 Bars") show time-axis pooling
was the intent throughout.
Measured cost: CONV sat pinned at ~40% balanced accuracy for 510 eras with
Sell recall 0%, while plain MLPs on the same data reached 57-61%. HYBRID,
which also carried this stage, came second-worst of the batch-norm group.
Not fixable in the topology: pooling one filter across time needs a stride
of window_out BETWEEN samples within a window, which a consecutive-window
kernel cannot express at any window/step. That needs a stride-aware kernel
in Network.cl + WarriorCPU.cpp + WarriorDML.cpp and a DLL rebuild, and is
only worth doing if a conv front-end earns its place without downsampling
first - with 20 sliding positions there is little to gain by halving them.
ConvPoolWindow/ConvPoolStep and their enums are removed with it, along with
the |CP: fingerprint term added earlier today.
Both builds compile 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
|
f2ec1edf84 |
feat(ai): logit-adjusted loss, replacing oversampling and the post-hoc prior
Menon et al. 2021 (ICLR), "Long-tail learning via logit adjustment": add tau*log(prior_c) to each class logit inside the training gradient. Softmax CE on adjusted logits is consistent for BALANCED error - the metric checkpoint selection already ranks on - so the loss and the deploy decision finally optimize the same thing. The engine already computed a true softmax + categorical-CE gradient and wrote it over the per-neuron sigmoid delta, so this is an offset added to three logits in the two places that gradient is built (backProp scalar path and backPropOCL). No backend, kernel or DLL change; the forward pass and every inference path are untouched, which is the point - the network learns to absorb the offset, so its raw argmax becomes the balanced-optimal decision with nothing applied at inference. Replaces rather than stacks. Minority replay is disabled while this is on, and the post-hoc inference prior is forced off. Stacking is not a theoretical worry: simulated on the measured 1118/1119/34298 distribution in the weak-signal regime, plain CE collapses to Neutral (33.4% balanced, Buy 0%), replay reaches 48.1%, logit adjustment 50.9% with better balance - and BOTH together score 45.4% with Neutral recall at 0%, worse than either alone. Buda et al. 2018 predicts exactly that. Motivation from the six-chart run: every topology took one direction to ~50% recall and abandoned the other, the direction chosen arbitrarily (the batch-norm control went Buy 1% / Sell 42%, the inverse of the other five). One era in 1,301 cleared the per-class recall floor. Fingerprinted conditionally, so the converged 60.7% models on disk keep their filenames and stay loadable as the fallback. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
2695a961c4 |
refactor(perf): pin CPU threads per network, drop the TargetCPULoad input
Dividing a machine budget by the live chart count was wrong twice over. The count is a snapshot taken when each net's pool is built, and charts attach one at a time: five charts measured 10/6/5/4/4% of the same budget, because the first only ever saw itself and the last saw all five. So the earliest chart got several times the threads of the latest - skewing any cross-topology comparison run on those charts, which is the exact thing the setting existed to make fair. Nothing rebalanced afterwards either, and rebalancing would mean tearing down a DLL context under a live trainer. Both problems disappear once the answer stops depending on how many charts are running. Each net now asks for a fixed 2 worker threads, converted to the percentage the DLL wants from the detected core count. Two is not a compromise: since the topology became data-derived the widest dense layer is 64 units, so each ParallelFor has almost nothing to split and per-dispatch overhead dominates. An MLP era cost ~66s at a wildly oversubscribed 12 threads and ~80s at 1 thread - a 20% spread across a 12x difference in thread count. Two per net also lands six concurrent charts exactly on a 12-core box. Removing the input costs nothing on the product side: a Market build has no DLL tier at all, so it was already compiled out to a constant there and no buyer could reach it. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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a2d4a7b218 |
fix: tag AI signal names with the config fingerprint
The dense-depth tag separates MLP_3L from MLP_4L but not two charts that differ by anything else - the batch-norm control is 3L on both sides, so it put two identical "Perceptron 3L" streams in the log. Any config difference at all changes the fingerprint by construction, so it is the only discriminator that cannot go stale as inputs are added. The 4 hex digits match the model filename's first 4, so a log line greps straight to its .nnw. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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d4dea8d6f2 |
fix: close five weight-affecting inputs missing from the model fingerprint
Audited every input in Variables\Inputs.mqh against the filename hash.
Five changed the trained weights without changing the filename, so
switching any of them silently re-adopted a model trained under the old
value - the .cfg guard only catches it when the topology also differs, and
says nothing at all when it does not.
ConvPoolWindow / ConvPoolStep the Pool layer's window/step set how many
neurons it emits, resizing every dense
matrix above it
EnableMinorityReplay gates the replay loop and scales focal
gamma
OversampleParity sets the minority replica count
ConstrainReplay caps replicas and gamma
VolumeData tick vs real feeds different numbers into
the same input slot
PeriodMA / PeriodRSI seed the indicator tuner exactly as
MA_Type does - MA_Type was already hashed,
these two were not
Pool geometry is unconditional, matching how m_convFilterCount and
m_lstmHiddenSize are already treated. The replay knobs nest under
EnableMinorityReplay so turning replay off cannot re-key a model over a
parity value nothing reads. The three feature-value inputs are conditional
on the AI feature that consumes them, following the MACD/Ichimoku rule -
they also drive the classic MA/RSI votes, which are inference-only.
Re-keys existing models. Deliberate and free this cycle: the derived
first-layer width and the |BN: term already re-keyed everything, so this
is the cheapest moment it will ever cost.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
|
8ae27bec08 |
fix: name AI signals by dense depth so concurrent charts are separable
MLP_3L and MLP_4L both identify as "Perceptron", so running them side by side writes two interleaved streams of identically-prefixed lines and the log cannot be split back apart afterwards - half a comparison run lost to a naming collision rather than anything technical. The dense layer count is exactly what AIType varies between them, so the name now carries it: "Perceptron 3L", "Perceptron 4L", "Convolutional 2L". Display only. m_id (the State\<id>\ folder) and the config fingerprint are untouched, so no model file is re-keyed. Idempotent, because a failed init leaves m_isInitialized false and this point can be reached twice on one object. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
2ad9fdd531 |
fix: refuse to start when another chart owns the same model files
Two charts with the same AIType and the same retrain-affecting inputs resolve to one .nnw/.cfg/.stats/checkpoint set. Both train and both save, so whichever writes last wins and the other's eras vanish - silently, because every individual file operation succeeds. A five-chart comparison run hit this today: one chart was left at the AIType default (HYBRID_2L), so two Hybrids shared State\HYB\...nnw and the intended MLP_3L never ran. The only evidence anywhere in the log was that model path appearing twice as often as the others. The claim is a terminal-wide temporary global variable keyed on an FNV-1a hash of the resolved filename - which is the correct lock identity, since every retrain-affecting input is already folded into that name. GlobalVariableTemp() gives an atomic create-if-absent, and a temporary variable dies with the terminal, so a crash cannot leave a stale lock blocking the next start. Within a session, a claim whose owning chart no longer runs an expert is taken over; a chart reclaims its own entry across a parameter change or recompile. Live charts only - tester and optimizer agents are separate processes writing sandboxed _optcache copies, and running one config across many agents is the point of an optimization. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
54519e9917 |
fix(perf): treat TargetCPULoad as a machine budget split across charts
The CPU-DLL thread pool was sized from TargetCPULoad undivided, on the reasoning that only one pool is ever actively computing at a time. That is true WITHIN a chart - MQL5 gives one chart's EA a single execution thread, and every WarriorCPU.dll entry point blocks it until its ParallelFor() completes, so the live net, the EMA shadow and HYBRID's fused pair take turns. It does not hold ACROSS charts, which each get their own execution thread and really do run their pools simultaneously. At the 100% default on a 12-core box, five training charts asked for 12 threads each: 60 threads contending for 12 cores. Measured today, dropping to ~2 threads apiece made every chart train "super fast". This had previously been read as one architecture being mysteriously 10x slower than another on the CPU-DLL tier while identical on OpenCL - oversubscription of that degree degrades superlinearly and punishes whichever model issues the most small sequential dispatches, which fits an MLP being the victim. TargetCPULoad now means the budget for the whole machine, divided by the number of charts running this EA. Counting charts is the correct axis: concurrency here is one execution thread per chart, not one per CNet, and the within-chart division that was previously removed stays removed. Snapshot at pool-creation time on purpose - attaching another chart later does not resize pools that already exist, because that would mean tearing down a DLL context underneath a live training run. Skipped entirely in the tester/optimizer, where the terminal already pins one strategy per agent. This matters most for buyers: a Market build compiles the input out and pins it to 100%, so they cannot reach the setting at all and would have hit the pathological case with no way to diagnose or fix it. The tier log line now reports the split rather than just the result. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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|
AnimateDread
|
692cb0eeaa |
refactor(ai): derive the dense taper's shape, not just its first layer
Deriving the first layer's width left NeuronsReduction and MinNeuronsCount
behind as inputs calibrated for something that no longer exists. Against a
hand-picked 500-wide first layer "keep 30%, floor at 20" produced a genuine
funnel - 500 -> 150 -> 45. Against the derived 64 it degenerates to
64 -> 20 -> 20: the reduction factor stops mattering after one step, and
"minimum neurons per layer" silently becomes the width of every layer but
the first. Two knobs whose labels no longer describe what they do.
The taper now runs geometrically from the derived first-layer width down to
a final hidden layer sized off the output count, spread evenly over however
many layers the chosen AIType implies:
MLP_3L 64 -> 28 -> 12 -> 3 29,151 dense weights
MLP_4L 64 -> 37 -> 21 -> 12 -> 3 30,450
CONV/LSTM/HYBRID_2L 64 -> 12 -> 3 27,763
and it stays a funnel at the floor, where the old rule could not:
D1 (first layer floored to 16) 16 -> 14 -> 12 -> 3
Both inputs are removed. With the width derived there is no freedom left in
the taper, so keeping either would only let the user contradict the
derivation. The layer COUNT stays selectable, because it is bundled into
AIType alongside the conv/LSTM front-end - depth is an architecture choice,
not a data-derived quantity, and pairing them means the two cannot
contradict each other.
m_minNeuronsCount / m_neuronsReduction survive as frozen members: nothing
reads them to build a topology any more, but they hold positional slots in
the .cfg sidecar and the weights fingerprint, and changing either value
would re-key every model on disk for no behavioural reason.
The DB config fingerprint drops both terms.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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af209997fc |
refactor(ai): derive the first dense layer's width instead of asking for it
InitialNeurons was an input whose only defensible value depends on two
things the user cannot see when picking from a dropdown: how wide the input
vector ended up after feature selection, and how much in-sample data the
study period actually yields. Left to a hand-picked constant it was badly
wrong - 500 units against a 420-wide input is 210,500 weights, 72% of a
292,583-weight model, against ~36,500 training bars of which only ~2,236
are directional. That is 6.6 weights per training bar, and it EXPANDS a set
of highly correlated inputs rather than compressing them.
The symptom was already in the logs and had been read as a depth problem:
the shallowest topology consistently beat the deepest (perceptron 52.7%
balanced, hybrid 41.3%). Over-parameterization predicts that ordering just
as well as covariate shift does, and only one of the two had been addressed.
ComputeFirstLayerWidth() budgets roughly one first-layer weight per
in-sample bar. Measured across the configurations in use:
M15 10y -> 256 units, 129,071 weights, 0.73 per bar
H1 10y -> 64 units, 28,727 weights, 0.65 per bar
H4 10y -> 16 units, 7,559 weights, 0.68 per bar
Two design points that matter:
- It estimates in-sample bars from the STUDY PERIOD and timeframe, not
from Bars(). What is downloaded grows over a terminal's lifetime, and a
topology that widened as history filled in would re-key its own weights
file and discard a trained model.
- The result is snapped down to a coarse power-of-two ladder, so the
estimate would have to be wrong by ~2x to change the answer.
Every field it reads is already part of the weights-filename fingerprint,
so the derived value needs no fingerprint entry of its own. The public
setter is removed - it could only have been called after construction, and
would either be ignored or silently re-key the model mid-run.
Where the data cannot support even the floor (D1 over 10 years is under
2,000 bars) it now says so and names the fixes, rather than quietly
training a model with more weights than examples.
The DB config fingerprint drops the term too, which re-keys existing
pattern databases once - correct, since a model an order of magnitude
smaller should not inherit the old one's win-rate history.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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77184cdb11 |
docs: record the .nnw architecture-persistence root cause and the batch-norm gap
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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65dc1bddb4 |
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> |
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AnimateDread
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30206cbabc |
feat(ai): batch normalization between dense layers
The only bounded stage in the entire forward path was the sigmoid
classification head - every hidden stage is PRELU. That is a network with
no internal scale control, and the failure ordered exactly by depth: on
SP500 H1 the shallow perceptron held ~52% balanced accuracy while the
deepest topology sat on the 33.3% one-class floor, with the per-bar logit
spread decaying monotonically (0.45 -> 0.38 over ~200 eras) until the
evidence tilt fell under the class-prior tilt. That is the signature of
internal covariate shift, which chapter 6.1 of the reference book is
entirely about and which the NeuroNet_DNG engine addresses with a layer
this project never had.
Two mechanisms make this the right fix rather than more hyperparameter
nudging:
- it decouples WEIGHT_DECAY from the learned function (van Laarhoven
2017) - with a normalized layer downstream, decay can no longer grind
the discriminative signal away, it only rescales the effective
learning rate;
- it is the precondition for ever running an unbounded logit head here.
The 2026-07-27 attempt blew up (IS error 5.6e15) precisely because
nothing upstream constrained scale.
Implementation notes:
- CNeuronBatchNormOCL computes host-side rather than as a fourth copy of
a kernel across Network.cl + WarriorCPU.cpp + WarriorDML.cpp. The math
is elementwise O(n); this way it behaves identically on all four
compute tiers, needs no DLL rebuild, and cannot drift between
backends. Same precedent as the softmax+CCE gradient and the
per-sample loss weighting, both computed in MQL5 for that reason.
- Statistics are exponential moving, not a stored mini-batch: training
is pure online SGD, one update per sample, so there is no batch to
average over. BatchNormWindow is an EMA window length.
- gamma/beta are excluded from weight decay, deliberately - decaying
gamma toward zero is the exact pathology being fixed.
- The layer self-sizes from whatever sits below it, because a conv/pool
stage's output width is derived inside the CNet constructor and is not
knowable to the topology builder.
- Checkpoint capture/restore/blend carry gamma/beta and the running
statistics alongside the dense matrix, so the plateau ladder cannot
restore a mismatched pair.
- SeedOutputLayerBias accepted only an exact defNeuronBaseOCL as the
weight-carrying penultimate layer; with normalization enabled that is
the batch-norm layer, so the cold-start bias seed would have silently
stopped being applied.
- Refuses to build, loudly, if a topology asks for normalization with no
compute backend at all - rather than quietly training a different
architecture than the one requested.
EnableBatchNorm (default on) and BatchNormWindow (1000 samples) are
inputs so the effect can be A/B'd without a recompile. Both feed the
weights-filename fingerprint, appended conditionally so existing non-BN
configs keep their fingerprints and are not forced to retrain.
Verified: analytic gradients match finite differences to 1.5e-7 relative
over 200 random cases; a faithful port of the full forward/backward chain
collapses to the 33.3% floor by era 4 without this layer and holds
36-43% with it. Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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1aa7df9096 |
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> |
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AnimateDread
|
4cb888e1b1 |
fix: clear stale signal arrows when a fresh model starts at era 0
Arrow cleanup existed on two paths - the panel's reset-weights, and the
topology-mismatch discard - but both are gated on there being a saved .nnw to
delete. The third case had no cleanup at all: a fresh topology at era 0 with no
weights behind it, which is what a changed config produces. A new fingerprint
makes a new m_fileName, so the previous model's files are not "discarded", they
are simply not this model's files, and nothing ever cleared the chart.
That is not cosmetic. Arrows outlive the model that drew them twice over:
1. The chart objects live in the CHART, not the sidecar, so they survive a
remove/re-add, a recompile, a restart and a fresh deploy no matter what
happens to any file on disk.
2. SaveChartSignals() rebuilds the sidecar by SCANNING the chart for
SIG_ARROW_PREFIX objects. So the first save of the fresh run adopts the
dead model's calls and writes them out under the NEW model's filename -
laundering them into the new model's history where nothing can separate
them afterwards.
Extracted the duplicated cleanup into ClearPersistedChartSignals(reason) - it
cancels the deferred restore queue, deletes m_fileName + ".arrows", clears the
namespaced chart objects and logs why - and called it from all three paths.
The call sits at the BuildFreshTopology() call site, not inside it: the genetic
tuner rebuilds a throwaway topology per candidate (AutoTune.mqh) and must never
touch the chart. All three sites run after m_fileName has its config fingerprint
appended, so they target the right sidecar.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
|
cc625c827e |
fix(training): escape the recall-gate catch-22 that let runs decay unchecked
Evidence (MQL5\Logs, SP500 H1, 2026-07-29): Perceptron era 61 Buy 32% Sell 27% Neut 94% bal 51% LSTM era 160 Buy 16% Sell 11% Neut 98% bal 42% (peaked 49% @ era 44) Hybrid era 179 Buy 5% Sell 2% Neut 99% bal 35% (peaked 41%) CONV era 228 Buy 2% Sell 4% Neut 99% bal 35% (peaked 40% @ era 122) Every model peaks early then decays monotonically toward Neutral, and nothing stops it: the restore-best-weights + decay-eta handler is gated on m_bestPassedRecall, which stays false forever when no checkpoint ever clears the per-class floor. CONV ran 228 eras with eta pinned at its 0.000300 start. The plateau ladder cannot end such a run either (stage 3 refuses to deploy without a recall pass, so it resets ~27 times), making it a 1000-era one-way trip. The gate's own justification had expired. It was written when the pre-pass tiebreak was blended-accuracy-only, where "best" really did mean "called Neutral most confidently". The balanced-selection change replaced that with `balancedOosEra > m_bestBalancedOos` plus an isFullyCollapsedEra exclusion, so a Neutral-only era now scores ~33% - the FLOOR of the balanced metric - and cannot anchor the checkpoint at all. Pre-pass "best" now means "most class-balanced so far", which is worth defending; and isWorseEra is itself a balanced-accuracy regression, so it cannot fire merely for trading Neutral calls for Buy/Sell. The original concern still holds while the best-so-far IS near-collapse, so the escape is margin-guarded: defend the checkpoint only once balanced accuracy sits more than BALANCED_WORTH_DEFENDING_MARGIN_PCT (5pp) above the one-class floor of 100/3. Against the run above that engages for all three stuck topologies (42.3/41.3/50.0 vs a 38.3 threshold) while a genuinely collapsed run still explores freely. Two inputs restored to the regime that actually produced a deploy: - MinRecall 60 -> 40. The one successful auto-deploy in the logs (Hybrid, 28th 00:50, best balanced 66.0%) ran against a 40% floor. 60 has never been shown reachable here - a floor above what the config can reach is the same "target set too high" failure the surrounding comment already warns about. - OversampleParity 60 -> 90. 60 overcorrected. Runs now START Neutral-dominant (Buy 0-11% recall at era 1) and call Buy/Sell on 0-4% of bars against a ~6% true base rate - under-calling, with no headroom to converge down from. The deploying run began at Buy 90% / Sell 36%, 24% of bars called, and settled into the floor from above. Raw over-calling is the intended starting condition; live calls are base-rate-calibrated by AILogitPriorStrength, which is why the input's own note says to judge over-calling by live-fired precision, not raw counts. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
41341f44c1 |
fix(panel): show the metric that actually gates deployment
The simple panel showed "Buy/Sell accuracy: IS x% OOS y%" from m_cumIs*/ m_cumOos*, which are monotonic lifetime counters: never reset per era (only on reset-weights) and restored from .stats across restarts. So the number is the average over EVERY era ever trained. At era 217 one more era moves it by well under a percent - it reads flat whether training is healthy or dead, and a model that started badly and has since recovered still shows low. That is the only number the non-verbose panel offered, so there was no way to tell "still improving" from "stuck" while watching four charts. Added the actual gate. The plateau ladder only auto-deploys a checkpoint that cleared m_minDirectionalRecallPct on EVERY class (Buy AND Sell AND Neutral, default MinRecall=60%). If nothing ever clears it, stage 3 deliberately refuses to deploy, resets the ladder and keeps training to the era cap - correct anti-collapse behaviour, but externally indistinguishable from being stuck. Panel now shows: - era against the cap, not just the era number - the accuracy line explicitly labelled "(lifetime avg)" - best balanced accuracy vs the per-class floor it must clear - eras since best + ladder stage, so plateau escapes are visible Display only - no training, selection or convergence logic touched. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
|
26ac479bae |
docs: refactor + audit notes; fix malformed comment banner
REFACTOR_NOTES.md records what was found, what was changed, what was deliberately left alone, and the one investigation that is still open (the MLP CPU-DLL slowdown, with the parameter counts that rule out my earlier "largest weight matrix" explanation). Also restores the missing opening rule on ReInitADIndicators' comment banner. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |