BuildFeatureWindow() replaces eight hand-rolled copies of the same loop
and feeds the window OLDEST BAR FIRST. Every copy fed it newest-first,
because MQL5 timeseries indices run backwards and `r + b` with b ascending
walks into the past.
Harmless for PAI and CONV - a dense layer learns a weight per position
either way, a conv learns time-mirrored kernels. Not harmless for the
recurrent stacks:
- LSTM_SeqStepForward reads `inputs + t*Iw`, so step t is block t.
- It writes output[] only when t == steps-1: the visible output IS the
last hidden state.
- c_t = f*c_{t-1} + i*g decays toward the start of the sequence.
lstm_seq_flowcheck.cpp measured block 0's influence on the output at
1.2e-2 of block T-1's, at the shipped forget bias of 1.0.
So the bar being PREDICTED sat at the far end of the decay and the output
was handed to the OLDEST bar in the window - the exact inverse of what the
window is for. ~80x backwards on LSTM and HYBRID, on all three tiers
(OpenCL kernel, CPU DLL, pure-MQL5 inference), which is why it never
surfaced as a backend discrepancy.
This does not create edge - the MI diagnostics read at the noise floor
(p=0.4975) with a working positive control. It makes the one hypothesis
those diagnostics explicitly do NOT cover testable: they are marginal and
per-bar, and state they "cannot rule out one that only exists in
combination or across time". The sequence model is the instrument for
across-time structure and it has been crippled, so that hypothesis has
never been honestly tested.
Fingerprint gets an unconditional |WIN:2 - the vector keeps its shape and
its features, so a stale .nnw would load cleanly and run a model fitted to
one ordering against the other, silently. Re-keying every config is the
point, not collateral damage. FORCES A FULL RETRAIN.
Also: the now-relative bar caches are re-keyed on the two live paths.
EnsureBarCachesCapacity() was only ever called from training paths, but
once m_trainingComplete is set ScheduleTrainingIfNeeded() routes every bar
to RefreshConvergedSignal() and Train() is never re-entered - so nothing
cleared the feature cache again for the life of the process. A chart that
trained to convergence kept replaying the rows computed for the last
training era's bar grid: the live signal froze at its convergence-time
value, and OnlineLearnStep() backpropped those stale features against
freshly resolved labels. Backtests were never affected (an inference-only
process never allocates the arrays, so every read recomputes).
Compiles clean: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Adds spread/ATR and the spread change ratio as network inputs (EnableSpreadFeature,
default on). Spread is the one microstructure channel that is both FX-available and
genuinely historical in the Strategy Tester - "during testing, the spread is not modeled
but is taken from historical data" - so unlike swap, signed tick flow or depth of market it
is something a backtest can honestly validate.
What it encodes, stated precisely because the raw measurement overstates it.
research/test_spread.py found spr/atr the strongest single feature in this codebase, on 5
of 8 instrument/geometry cells at 2-4x any volume feature. But the barrier LABEL charges
the spread inside its own barriers, so a wide-spread bar is mechanically likelier to
resolve as a loss and the feature would partly be predicting its own cost model. Relabelling
at zero cost and re-measuring the identical feature showed 20-40% of it WAS that tautology
and the majority was not (XAUUSD retained 97%). What survives is a volatility-regime
reading: spread is near-fixed while ATR is not, so the ratio runs high exactly when
realised volatility is below its own ATR estimate, which genuinely predicts whether
ATR-scaled barriers get reached. It is UNSIGNED - Neutral-vs-directional only, never a side.
Also fixes a stale-index bug I introduced with the cross-asset panel and had just repeated
in the spread series. Both cached on length alone:
if(m_crossAsset.Bars() >= bars) return true;
MQL5 series indices are relative to NOW, so one new closed candle shifts every index by
one. Keyed only on length, the panel keeps serving its index 0 as a bar that is no longer
the newest, and every cross-asset value is read one bar out of step with the price features
sitting beside it in the same vector - silently, with no error and no shape change. This is
the same class of defect as the dtStudied watermark behind the zero-direction backtests.
Both now carry a datetime anchor on m_Time.GetData(0), the same invalidation key the
label/feature bar caches already use.
And a performance fix that fell out of it: with correct invalidation the panel rebuilds on
every new bar, and RefreshConvergedSignal runs per bar - which in the tester would mean one
full multi-symbol resample per simulated bar at training depth. Inference only reads bars
0..m_historyBars-1 plus the panel's own slow window, so it now requests exactly that. The
cache check is >=, so a deeper panel left from training still satisfies it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Two defects surfaced by research/test_classic.py, both verified fixed by re-running the
transcription against 178k bars of EURUSD H1.
CSignalMA model 1 could never fire. For any recursive average - and MA_TYPE_EMA is the
shipped default - MA(i) = a*Close(i) + (1-a)*MA(i+1), so
DiffMA(i) = a * (Close(i) - MA(i+1))
DiffCloseMA(i) = (1-a) * (Close(i) - MA(i+1))
are positive multiples of one quantity and always share a sign. Model 1 asks for a close
BELOW a RISING average, which is precisely the combination that identity forbids: 0.000%
of bars, either direction, any symbol. The MQL5 standard library this was ported from
defaults to MODE_SMA, where the two are merely correlated - the bug arrived with the EMA
default, not with the port. Reading the slope one bar back (DiffMAPrev) breaks the tie for
every MA type while keeping the model's stated meaning. Now fires on 7.92% of bars.
CSignalIchimoku model 11 fired on 27% of bars at weight 100. Sanyaku is three standing
STATES conjoined with no transition term, so it held across long stretches - and being
last in the if-chain at the top weight, the module's highest-conviction reading was also
its most common one, overwriting all eight event models below it on a quarter of all bars.
The old comment rejected an event form because "demanding all three flip on the same bar
would fire almost never" - true, but that is not the alternative. Kouten is the TURN: the
ALIGNMENT transitions, and only one role need change for it to. Testing !Sanyaku(idx+1)
fires once per aligned stretch. Now 2.17%, in line with Kumo breakout (2.4%) and the
strong TK cross (1.1%). DataReady() extended one bar deeper to cover the lookback.
Neither pattern showed edge before or after; this is about the models meaning what they
say and the vote not being dominated by a constant.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- 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
Neutral collapse on all four topologies by era 5 with a 2:6 barrier
(recall Buy 0% / Sell 0% / Neutral 100%), and the panel stuck on
"measuring...". One root cause, and it was not the barrier.
The labels were fine: Buy 25.4% / Sell 22.0% / Neutral 52.5%, which is
exactly gambler's ruin for m=2,k=6 (2/8 = 25% per side), with only 0.1%
of Neutral coming from the vertical barrier - so the new m*k horizon
scaling is right, arguably generous.
What was broken: Train()'s era-start block wrapped UpdateClassPriors() in
`if(!m_evalMode)`. The auto-tune GA scores every candidate in eval mode,
and AutoTuneIndicators ships ON, so on a default configuration EVERY era
of the search ran with unmeasured priors. ApplyLogitAdjustment() requires
measured priors; without them it calls ClearLogitAdjustment() and returns.
So the entire search trained under PLAIN cross-entropy. With a 52.5%
majority class the optimum of plain CE is "always predict Neutral", and
that is precisely what all four models found. The panel followed: its
counters only advance on bars the model CALLED Buy or Sell, so a
collapsed model leaves them at zero and the line reads "measuring..."
forever.
This was latent, not new. It has been true for every auto-tuned run, but
it was invisible while the labels were near-balanced - last night's
accidental 1:1 barrier gave 43/40/17, where plain CE has no majority to
collapse into. Widening the stop to 2*ATR (correctly - 1*ATR is too tight
to survive noise) moved Neutral to the majority and exposed it.
The guard's stated fear cannot happen. These priors are measured from the
LABEL distribution, and the tuner only perturbs indicator periods
(MA/RSI/MACD/Ichimoku/AD). The barrier label depends on ATR, SL_Mode and
TP_Mode - none of which the search touches - so every candidate sees
byte-identical labels and identical priors. There is nothing to
contaminate. What the guard actually protected was the .stats write, and
that is gated separately: eval candidates never checkpoint and never
persist.
Also, because this is the THIRD quiet no-op to cost a run in this
codebase (after the fictional oversampling log line and the shadow-blend
skip):
- ApplyLogitAdjustment() now WARNS when it declines to install, instead
of silently clearing. A mechanism that cannot announce it is not
running is indistinguishable from one that is.
- The panel distinguishes "measuring..." (before era 1, nothing scored
yet - an honest warm-up) from "no directional calls yet" (eras trained,
zero calls - a finding, not a wait).
Both builds compile 0 errors / 0 warnings. No retrain forced by this
commit itself, but the collapsed models must be discarded.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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>
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>
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>
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>
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>
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>
AnimateDread