- 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>
