- Replaced standard library signal modules with custom implementations to allow for named patterns and improved voting.
- Added new input parameters for module weights, allowing for optimization of individual signal contributions.
- Enhanced the management of trades with new options for breakeven and management cut.
- Introduced a mechanism for dynamic ranking of signal weights based on historical performance.
- Improved initialization logic to ensure proper registration of filters and handling of trading conditions.
- Added detailed logging for trading permissions and account status during initialization.
WHAT AND WHY. ed91919 removed the four classic votes because all 26 patterns measured AT CHANCE as
standalone entries - pre-registered, nothing fitted, and the +4 sigma that had once appeared was two
bars of lookahead. That result stands. It is also not the claim being made here.
That test measured UNCONDITIONAL edge: fired blind, does this pattern beat a coin on average. No -
after the lookahead fix MACD_p4 read -0.02pp at -0.02 sigma, flat rather than weak. A signal that is
zero on average can still be strongly positive on a SUBSET, and finding that subset is exactly what
meta-labelling is for. The primary supplies direction and an entry BAR and is judged on RECALL; the
net decides which firings to take.
THE MEASURED PROBLEM THIS ATTACKS. Under the leg-ride label the primary was "the ZigZag leg in
progress", so entry landed on an ARBITRARY bar inside a move already underway. Measured 2026-09-05
on the converged fleet: the ORACLE ride is +4.2 to +4.6 ATR per leg on every chart, and the models
captured +0.08 to +0.38 over a 14-17 bar hold - under 10%, because most of the leg was gone before
entry. Always-ride scores -0.14 to +0.10, i.e. an arbitrary entry inside a leg is worth nothing. A
pattern fires at a CHOSEN bar with the move ahead of it.
RESTORED: SignalMA (4 patterns), SignalRSI (4), SignalMACD (6), SignalIchimoku (12),
OscillatorDivergence, plus Bill Williams SignalAO (4) and SignalAC (3) recovered from 1073262.
33 patterns across six voting modules. The only edit needed was dropping SweepPrepareIndicator(),
whose base-class method no longer exists.
THE WEIGHTS ARE FIXED PRIORS (Variables\ClassicSignals.mqh), set from structural strength and rarity
in the spirit of the standard library's own ladder, NOT fitted to returns:
10 confirming state ("price is on the right side") - every module's pattern 0
15-30 simple state or weak-grade event (overbought reversal, cross in a poor location)
30-50 a crossing or completed pull-back
50-70 structural: divergence, cloud breakout, strong-grade cross
80-100 rare confluence: double divergence (90), Sanyaku Kouten (95)
33 numbers tuned against measured returns would be 33 free parameters and would hand back the
family-wise problem this project keeps rediscovering. They are priors and must stay priors.
AND THE FEEDBACK LOOP IS CUT. DB_RankingFeedsWeights=false splits the signal database in two:
ProcessBufferedSignals() still RECORDS every firing, direction, entry, exit and outcome - that
corpus is the input to the meta-labelling work - while UpdateSignalsWeights() no longer writes win
rates back into the pattern weights. That loop is why the classic votes were never evaluable: the
same setup contributed a different amount at different times, so the vote drifted era to era under
the model and nothing could be measured against it. UseDatabaseRanking gates BOTH halves, so turning
the master switch off would have stopped the collection too.
RARITY CUTS BOTH WAYS, recorded rather than glossed: double divergence earns 90 BECAUSE it is rare,
which also means the net will see very few examples and can say little about it. The prior carries
those patterns; the model will learn mostly about the common, low-weight ones.
Compiles clean. Not yet deployed - the Bill Williams suite (Alligator, Fractals, Gator, BWMFI) and
the stdlib ports land next, and one restart should carry all of it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
research/classic.py transcribed all 26 shipped vote patterns (MA 4, RSI 4, MACD 6,
Ichimoku 12) with their constructor weights and tested them as entries on 178k-bar
histories, four instruments x three barrier geometries. Nothing separated from
chance - not one pattern, not the averaged vote at any threshold 10-70, not a
2/3/4-module quorum, not event-plus-confirmation. Residual E[R] everywhere was
-0.01 to -0.08 R, which is approximately the spread. The +4 sigma reading that had
once justified the set was two bars of lookahead: closing it took MACD_p4 on EURUSD
from +5.05pp to -0.02pp.
All four inputs have shipped false ever since, so this deletes dormant code rather
than changing behaviour.
RETRAIN-NEUTRAL, deliberately. EnableMA and EnableRSI were hashed UNCONDITIONALLY
into the DB config fingerprint, so they become literal 0 legacy slots - the same
treatment the ind_Periods slot two lines above already uses, and every existing
database keeps its key. EnableMACD/EnableIchimoku were appended only when enabled,
so with both gone the segment simply never appears, which is byte-identical to
today. No .nnw or .db is orphaned.
WHAT THIS COSTS, STATED PLAINLY: these four were CSignalMETA's only wired candidate
sources, so the on-chart ladder sweep (BuildCorpusBySweep) now has nothing to sweep
and a META chart is no longer self-contained. That is survivable rather than fatal
because MetaPrepareEra already falls back to CMetaCorpus::LoadLargestOnDisk, and its
own comment names this exact case - "charts whose classic filters are disabled".
Use_MetaLabeling ships false regardless. SignalMETA.mqh is otherwise UNTOUCHED, and
its 26-slot one-hot stays at 26: a tester-built corpus on disk still encodes those
pattern ids, and narrowing the descriptor would invalidate every stored corpus.
Signals/SignalMA.mqh SignalRSI.mqh SignalMACD.mqh SignalIchimoku.mqh deleted
Signals/OscillatorDivergence.mqh deleted - RSI and MACD were its only users
Classic_Shift deleted - the four votes were its only readers
Compile-verified in the stage copy: 0 errors, 0 warnings, against a 0/0 baseline
taken before any edit.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
CSignalMA/RSI/MACD/Ichimoku each re-overrode SweepPrepare() with an identical body -
call the base, resize/refresh one indicator buffer, return - differing only by the
buffer's field name. Base class now does the shared price-series prep once and calls
a new SweepPrepareIndicator() hook; each signal overrides only the hook. Compiled clean.
MA: CustomIndicators\ADMovingAverage is replaced by the built-in iMA (CiMA) on
both consumers - the classic vote and the NN MA input feature. This drops the
five advanced types ALMA/DEMA/ZLEMA/T3/Kalman, which have no iMA equivalent;
MA_TYPE_PRESETS is now ENUM_MA_METHOD's own codes and the tuner searches all
four. It also removes a documented failure mode: a custom indicator's depth is
bounded by TERMINAL_MAXBARS, and m_MA was the one whose feature block REJECTS
the bar on a short read - the "feature 25 fails on every bar" incident of
2026-08-17. A built-in is served at any depth.
MIGRATION. SMA moves from code 5 to 0, so persisted type codes change meaning.
SanitizeMaType() is the single validity rule; TunedPeriods records now carry a
version field and a v1 record remaps 5..8 -> 0..3, falling back to SMA for a
stored advanced type (unrecoverable - old 0..4 are indistinguishable from valid
new codes). Existing .nnw files re-key on their own, because MA_Type is hashed
into the topology fingerprint, so models retrain rather than silently running
on different MA values. EXPECT A FULL RETRAIN.
ZigZag: ADZigZag was a byte-identical rename of MetaQuotes' Examples\ZigZag -
verified by normalising identifiers and stripping comments, 233 significant
lines each with only renamed symbols differing. It now loads the stock one, so
nothing is bundled and MetaQuotes' fixes arrive without a rebuild here. Both
#resource entries are gone.
Classic_Shift: a new input, the BAR the four classic votes evaluate on (0 =
forming, 1 = last closed, default 1). One implementation on CExpertSignalCustom,
inherited by all four rather than repeated per module. Defaults to a sentinel
meaning "unset", so the AI signals and the aggregate keep the stock every_tick
rule and their feature/label alignment is untouched. The META corpus sweep still
takes precedence. CExpertBase::StartIndex turns out to be virtual, so this is a
real override, not the name-hiding the old comment claimed.
Not compiled - MetaEditor compile pending.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The user should not need a tester corpus run per symbol. Every pattern
condition in Signals\Signal{MA,RSI,MACD,Ichimoku}.mqh anchors its reads on
`int idx = StartIndex()` with zero hardcoded indices (verified), so a
name-hiding StartIndex override + EvalShift(i) on CExpertSignalCustom makes
the EXACT live ladder code answer "what would you have fired at bar i" -
the silent-divergence trap that justified the DB corpus does not exist on
this path, and neither do the GMT-offset ambiguity, the DB row caps, or
the wipe procedure.
- CExpertSignalCustom: m_evalShift + StartIndex()/EvalShift() +
SweepPrepare(bars) (deep-resizes the shared price series); the four
classic signal classes override SweepPrepare to deep-resize their own
indicator buffers.
- CSignalMETA::BuildCorpusBySweep: per bar x per source filter, run
Direction() shifted, harvest the per-side pattern slots + netVote into
the same corpus arrays the DB loader fills; entry=bar open so
MetaPrepareEra's resolution matches at offset +0 with zero price error.
DB corpus remains the fallback when classic filters are disabled.
- Warrior_EA.mq5: META gets the enabled classic filters as candidate
sources (family ids match the descriptor one-hot).
- UseDatabaseRanking default false -> true (user request): a META chart
journals + ranks out of the box.
Workflow per symbol is now: attach ONE chart with AIType=META (optionally
Meta_ExportDataset=true for the offline pool) - candidates, labels,
training and export all happen in place, ~10 seconds of sweep instead of a
tester run.
Co-Authored-By: Claude Fable 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
Add `MA_TYPE_PRESETS` enum covering advanced (ALMA, DEMA, ZLEMA, T3, Kalman) and standard (SMA, EMA, SMMA, LWMA) moving averages. Integrate `maType` and `bestMaType` into `CADIndicatorTuner` struct, update flatten/unflatten routines, and bump `AD_TUNE_PARAM_COUNT` to 33. This allows the auto-tuner to search over MA type alongside period, improving feature discovery.
Remove verbose book references from input parameter comments in
Network.mqh for clarity. Add #ifndef guard around ENUM_OPTIMIZATION
to allow inclusion from multiple headers without redefinition.
Document the MQL5 Market DLL restriction in NeuronDirectML.mqh and
introduce WARRIOR_MARKET_BUILD macro to conditionally compile out
DirectML DLL imports for Market-compliant builds.
