forked from animatedread/Warrior_EA
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AnimateDread
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168422ff7a |
fix(labels): the 128-bar horizon ceiling was truncating the shipped label
The corrected geometry scan exposed something bigger than the geometry
question it was asked. Every pairing from 2:6 upward came back CLAMPED -
including 2:6, the SHIPPED configuration.
First-passage time for a driftless walk leaving [-m,+k] goes as m*k, and
the measured swing median here is ~12 bars at m*k=1, so 2:6 wants ~144
bars and 3:10 wants ~360. The ladder stopped at 128. A clamped label
stops meaning "does the target come before the stop" and quietly becomes
"...within 128 bars", while the deployed EA holds until SL or TP with no
bar limit. So the target the models have been trained on all along was
not the strategy the EA executes, and the trades it silently reclassified
as Neutral were the SLOW WINNERS - precisely the ones a 1:3 barrier
exists to capture. Timeout share stayed ~0% throughout, which is why this
never showed up: the truncation lands in Neutral, not in the timeout
counter that was watching for it.
Ladder extended to 384 (12..128, 192, 256, 384) so every selectable
geometry gets an honest horizon. Cost is one embargo of at most 384 bars
out of ~38k.
Second fix, same class of error as the H(Y) one: the scan's "best
eligible" was 2:2, a 1:1 barrier, against a shipped Min_Risk_Reward_Ratio
of 1:2. Training four topologies on that target would have produced a
model whose every setup is rejected at the door - the exact failure
behind four consecutive Market rejections for "no trading operations".
Sub-minRR geometries are now ineligible and marked [<minRR], printed
rather than hidden.
Also drops the dense-depth tag from the display name ("Perceptron 3L" ->
"Perceptron"). Depth is derived, so it names nothing a user chose; the
config tag [PAI-0be2] already disambiguates concurrent charts and does it
for every input rather than one. Full topology still logged by "config -".
Compiles 0 errors / 0 warnings, standard and Market. Build tag
horizon-384-v1. Changes the LABEL for every geometry, so the next scan
supersedes the previous numbers - and a retrain is required before any
model trained under the truncated target means anything.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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40af4a4b5b |
fix(labels): the geometry scan rewarded the labels it should reject
First run named 3:10 on all four charts, at 2.3x the configured 2:6. That answer was wrong and the fault was the ranking statistic. 3:10 wants a horizon of ~swingMedian*30 (~320 bars) and gets BARRIER_HORIZON_MAX. Clamped, most trades never resolve, the unresolved remainder all lands in Neutral, and H(Y) collapses. The old statistic divided the excess BY H(Y) - so a collapsing denominator made the most degenerate label look like the most predictable one. Every geometry from 2:6 upward was already showing the clamped h128, and the two widest scored highest, which is the fingerprint of the artefact rather than of signal. Two fixes: Rank on the raw excess in nats. Subtracting each geometry's OWN measured null already removes the class-balance bias, which is the only thing the normalisation was ever needed for. Disqualify clamped geometries outright rather than ranking them down. The deployed EA holds until SL or TP with no bar limit, so a truncated label trains the model on a question the strategy never asks. They are still printed, marked '!', so the disqualification is visible instead of a silent omission - and the scan now says so explicitly when nothing eligible is left, because "the limit is the feature set, not the target" is itself the finding in that case. The scan also reports each geometry's directional share and timeout share now. A label nobody can trade is not a candidate however well it scores, and that has to be visible in the same line as the score. Compiles 0 errors / 0 warnings. Build tag geometry-scan-v2. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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f97ab9f1d6 |
feat(labels): measure which barrier is predictable at entry, don't guess
The alignment scan settled the shape of the problem: 4.7x more is knowable 5 bars into a 128-bar window than at the entry the model actually trades. A 6xATR target reached over 128 bars is decided overwhelmingly by what happens DURING the window, so whatever the entry state knows is buried under 128 bars of later noise. That is a property of the TARGET, and it is why four different architectures all landed on precision exactly equal to the base rate - no topology can undo it. So measure the target. For each SL/TP pairing a user can actually select, relabel the same sampled bars and score how much the SAME features say about THAT outcome at entry. Seconds, no training, no topology, and it runs on the diagnostic path that already exists. Ranked on excess over its OWN null as a share of its OWN H(Y), never on raw nats: each geometry has a different class balance, hence a different finite-sample bias and a different amount of information there to find, so raw MI would rank the most BALANCED label rather than the most PREDICTABLE one. The break-even win rate m/(m+k) is printed beside each so the ranking is read next to the bar the model must clear. Stated in the output because it is the easy thing to get wrong: chance precision EQUALS break-even at every geometry, so a tighter target does not hand you expectancy. It buys predictability - less noise piled on top of what the entry state knows - which is the one thing changing topology cannot do. Read-only by construction: it relabels a sampled copy via TripleBarrierLabel(), never writes the label cache (which belongs to the configured geometry), and restores the horizon and overrides it borrowed. The overrides apply only when BOTH are positive, so a half-set pair can never silently relabel a live run. Compiles 0 errors / 0 warnings, standard and Market. Build tag geometry-scan-v1. Redeploy only - no retrain to READ the ranking. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com> |
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AnimateDread
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d7eea325fb |
refactor(ai): extract Layer.mqh and deduplicate AI config
- Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean - Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function - Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets |
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AnimateDread
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25813523d3 |
fix: refuse invalid SL/TP, fix the unreachable deploy floor, scale the horizon
Three defects found by reading the 2026-08-01 training logs, all of which
only became visible because the relabel made the numbers mean something.
1. A STALE ENUM TRAINED FOUR MODELS ON THE WRONG TARGET.
`OnInit: trade settings snapshot - SL_Mode=1 TP_Mode=-101`
-101 was TP_PREV_SWING, deleted from TAKE_PROFIT_MODE on 2026-07-31 in
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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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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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2de93539d4 |
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> |