~2,300 lines. META had real, repeatedly measured ranking skill and ZERO
operating points that ever cleared break-even (0/350 H1 eras, 1/999 H4
pre-2-sigma, 0/8 pooled fitted points). The clinching arithmetic was edge x
width = 0.095 ATR/trade against spread 0.099 ATR/trade, and the
dose-response showed the high-conviction tail is temporally unstable -
the precision-vs-threshold slope flips sign between calib and test on 3 of
4 symbols, so no ex-ante threshold rule exists. It shipped default-off and
never gated a live entry. The self-measured tier weights are what actually
rank the vote, and all six H4 instruments converged on them alone.
RETRAIN-NEUTRAL, and that is the property that made this safe:
- The weights fingerprint emitted "|TGT:META2" or "|TGT:SWG1" from an
if/else. Every direction model already took the SWG1 arm, so
collapsing it to an unconditional append is byte-identical. No .nnw or
.cfg is orphaned or re-keyed.
- NetInputWidth() lost its "+ MetaDescWidth()" term. MetaDescWidth()
returned 0 for every direction model, so the input layer is unchanged.
- DbLegacyAiSlot()'s slot 5 was reachable only with all four Use_* NNs
off AND meta on - a config that never shipped. Every existing .db keeps
its filename.
Deleted outright: Signals/SignalMETA.mqh, Expert/Trading/MetaGate.mqh (the
directory is now empty), Expert/Training/{MetaCorpus,MetaCandidateStore,
MetaFamilies}.mqh, Tests/Test_MetaFamilies.mq5, Meta_Labeling_Design.md.
Unwound in place, the delicate part: Training.mqh carried four
IsMetaTarget() branches whose else-arm WRAPPED the direction body (pass 1
queueing, pass 2 backprop, pass 2.5 calibration, pass 3 OOS scoring). Each
wrapper is removed and the direction body promoted back to its original
nesting - the bodies were never re-indented when the wrappers were added,
so the promoted code is byte-identical to what ran before META existed.
Also gone: the ensemble verdict's meta-veto replay and its
approved/vetoed/unscored counters, the per-family/per-side OOS
decomposition arrays, the m_isTrainQueueCand parallel queue and its
lockstep shuffle, and the S2 era report.
Also removed: the CMetaGate abstraction and the live CheckOpenPosition
veto; m_gates plus AddFilter's non-voter routing and IsVotingSignal()
(META was the only non-voting child, so m_gates was always empty);
m_parentSignal/SetParentSignal (existed only to reach the root's gate);
SweepPrepare/SweepPrepareIndicator (only caller was the corpus sweep);
IsMetaTarget() from all four view interfaces and their adapters;
Use_MetaLabeling, EnableMETA, Meta_ExportDataset, m_trainTarget.
EvalShift is KEPT - HistoricalNetVote() uses it for the filtered overlay,
not just the corpus sweep; only its comment changed. The 2-output softmax
arm in NetForward.mqh is kept too: it costs nothing and is the reusable
binary-head path, now commented as unclaimed rather than as META's.
Compile-verified in _claude_stage: 0 errors, 0 warnings, matching the
pre-edit baseline.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Step 3 of the swing-pivot plan, whole-hog. The swing label is now the ONE
target and the era verdict is precision + recall per class against the
label's own base rate - no win rate, no break-even, no expectancy, no
geometry anywhere in training.
DELETED
- Expert/Excursion/ (4), Expert/BarrierHorizon/ (4), GeometrySweep,
FirstPassageLadder, Labeling/TripleBarrier.mqh (CLabelOverlap survives
in Labeling/LabelOverlap.mqh), 3 test EAs.
- TripleBarrierLabel + walk, fractal label, geometry derivation/scan/
adoption, exit-policy replay, excursion MI targets, the drift verdict
(DIRECTION_INTELLIGENT), the recall floor, balanced-accuracy telemetry,
the barrier defines, the .cfg geometry adopt (slots kept as zeros for
the positional layout), the derived-geometry live-order override.
- TRAINING_TARGET input/enum: direction models are always swing; META2
re-keys the meta head onto label agreement (descriptor loses its two
geometry slots).
REWORKED
- Labels.mqh (1795 -> ~370 lines): AdvanceSwingLabelState with
FINALITY-GATED CACHING - an unresolved bar (pivot pair uncommitted) is
never cached, so it can never freeze as a false Neutral; training,
calibration, OOS scoring and online learning all skip unresolved bars.
- SDeployVerdict: significance-only; SOosTally chance = larger
directional class share; pooled gate poolability = timeframe (record v2).
- Purge/embargo/declustering gaps: the measured mean label resolution
lag (LabelResolutionBars), not a barrier horizon.
- Pool purge key + backfill DB rows: marked at the bar the label
resolved on (m_labelResolveAge), not a fabricated barrier touch.
- Online learning frontier: finality, not a horizon delay.
- m_bestBalancedOos -> m_bestSelectionScore, m_erasSinceBestBalanced ->
m_erasSinceBest, ensemble vote outcome arrays -> label arrays.
STEP 4 folded in: Entry_Multiplier / SL_Mode / TP_Mode / tradingdirection
are inputs again - trade management is the tester GA's search space.
Fingerprints: every direction model re-keys (TGT:SWG1 now unconditional,
CUT token gone); META1 -> META2. Full retrain, as planned.
Compile-verified in _claude_stage: Warrior_EA + both surviving test EAs,
0 errors, 0 warnings each.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
It listed SignalStoch/SignalPB/SignalITF/SignalRVI and six other files as though
present, marked SignalSessionFilter '(Removed)' while its 207 lines were being
instantiated on every init, and omitted SignalMETA (578 lines), OscillatorDivergence,
SignalRiskGuard and SignalHYBRID entirely - so the one file meant to orient a reader
was wrong in both directions at once.
Rewritten against the actual roster, with the removal reasons recorded here rather
than in commits nobody re-reads, and with the two conventions that are easy to get
wrong on first contact: the voting machinery is stock CExpertSignal (so 'simplify
back to stdlib' removes nothing), and Direction() is a transaction.
Co-Authored-By: Claude Opus 5 (1M context) <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>
StateRSI/StateMain, ExtStateRSI/ExtState, and CompareMaps were duplicated
verbatim (~110 lines each, even the comments) between SignalRSI.mqh and
SignalMACD.mqh - the only real difference was the oscillator value source
(RSI(pos) vs Main(pos)). Neither method touches the pattern-weight fields
LongCondition/ShortCondition read, so unlike the previously-declined
ApplyPatternWeight dedup, this extraction needed no change to the live
voting logic itself - only the ExtState(idx)/CompareMaps(...) call sites
now go through the shared collaborator.
New Signals/OscillatorDivergence.mqh: IOscillatorDivergenceSource
(abstract - oscillator value + price low/high extremum lookup) and
CDivergenceDetector (owns m_extr_osc/pr/pos/map as real members, STATEFUL,
fully exclusive - grep-confirmed no reader outside the old per-class
copies). MQL5 has no multiple inheritance and both signals already extend
CExpertSignalCustom, so each gets a thin CSignalXxxDivergenceSource
adapter (owner pointer + 3 forwards) rather than implementing the view
directly, matching the Expert/* view+adapter pattern. Added 3 small
public DivergenceOscillatorValue/DivergencePriceLow/DivergencePriceHigh
wrappers per class since the adapter is a separate object, not a
subclass, and can't reach RSI()/Main()/m_low/m_high (protected) directly.
Every moved statement verified against the pre-edit body; the only
non-mechanical change is a stale comment ("ExtStateRSI's scan") updated
to the new method name.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
CSignalMETA::AppendCandidateFeatures (training corpus rows) and
AppendLiveDescriptor (ScoreProposal's live gate descriptor) duplicated the
identical one-hot/side/tanh-squash-netVote/SL-TP-multiple/spread-ATR sequence
byte-for-byte, with a comment admitting they had to be kept in sync by hand -
the exact "copies disagreeing" failure mode this campaign exists to close,
and here it feeds both the training corpus and a live trade-gating decision.
Added private AppendDescriptorTail(slot, side, netVote, idx): slot -1 zeroes
every one-hot column (AppendLiveDescriptor's case, since the vote is not a
classic-ladder fire), any other slot sets exactly that column to 1.0 (the
training case via OneHotSlot()). Both callers now delegate to it; each
statement verified equivalent to the original inline copy before the edit
(int/char side both narrow to the same (double)side write, m_metaCands.Bar()
vs the barIdx parameter both feed the same idx argument).
Self-compiled 0 errors, 0 warnings via the _claude_stage mirror.
inTradingSession() had three near-identical if(session==...) branches
(London/NewYork/Tokyo) that each set an openUtcMin from a DST test and
called the same inTimeInterval() shape, differing only in the trade
toggle, DST function, winter-UTC open hour, and window length. Replaced
with one dispatch that fills 4 locals (winterOpenUtcHour, lengthHours,
tradeToggle, isSummer) per session name, then one shared computation +
inTimeInterval() call.
Verified branch-by-branch equivalence before compiling: London
(8,8,EuSummer), NewYork (13,9,UsSummer), Tokyo (0,9,none) reproduce the
exact original arithmetic per session, including Tokyo's already-UTC-
converted 0 (not 9, since Tokyo has no DST and JST is UTC+9). Unknown
session still returns false. This is live trade-veto logic, so no
structural reordering beyond the branch consolidation itself.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Expert/AIBase/Features.mqh (2017 lines, 38 methods) split by exclusivity grep
(whole-repo, not just Expert/): 30 methods -> Expert/Features/FeatureBuilder.mqh
(CFeatureBuilder + CFeaturesView/CAIBaseFeaturesView), 8 stay behind as a much
smaller raw partial.
CFeatureBuilder is STATEFUL, same shape as Excursion/OnlineLearning: owns the
10 feature-only indicator handles (m_Volumes/m_MA/m_RSI/m_MACDFeature/
m_Ichimoku/5 AD* CiCustom indicators - grep-verified touched nowhere else in
the repo, only their bare declarations) plus the depth-probe/handle-repair/
spread-series/detectability-latch scalars (exclusive, Lifecycle.mqh ctor-init
only elsewhere). m_Open/m_Close/m_High/m_Low/m_Time/m_ATR/m_ADZigZag stay
signal-owned - Labels.mqh/AutoTune.mqh/Training.mqh read them directly - and
are reached read-only through the view (FeatureOpenAt/FeatureHighAt/
FeatureLowAt/ChartBarClose/ChartBarTime/OnlineAtrMain, all reused where a
forward already existed).
Deliberately did NOT move InitOpen/InitClose/InitHigh/InitLow/InitTime/
InitADZigZag/ResizeBuffers/RefreshData: they manage the 7 shared indicators'
Create/BufferResize/Refresh lifecycle, which would need a pure-relay wrapper
per operation per indicator for zero coupling benefit - same judgment as
Topology's boot sequence. They stay in Expert/AIBase/Features.mqh and reach
CFeatureBuilder's 10 owned indicators through 20 new Feature*BufferResize()/
Feature*Refresh() forwards (signal calling into its own owned collaborator
directly, no view needed in that direction).
Whole-repo grep (not just Expert/) caught a real external miss the campaign's
own doctrine warns about: Signals/SignalMETA.mqh read m_spreadSeries/
m_spreadSeriesBars directly as an inherited protected field (a subclass, not
an AIBase/*.mqh partial) - fixed with two new FeatureSpreadSeriesBars()/
FeatureSpreadSeriesAt() forwards.
Verified: if(/for(/while( counts identical between the original file and the
new split (269/20/1); return-count delta (+12) fully accounted for by the 12
new trivial one-line forwards added (10 indicator BufferResize + 2 spread-
series getters); quoted-string-literal diff empty except two doc-comment
paraphrases. Self-compiled 0 errors, 0 warnings.
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.
The gate-ownership commit put CMetaGate *m_metaGate on CExpertSignalCustom
while SMetaGateTelemetry m_metaGate already sat on CExpertSignalAIBase,
which derives from it - so the derived name hid the base one.
The telemetry is m_metaTelemetry now, and the declaration says why:
m_metaGate is the gate the root signal OWNS; this is the RECORD of what a
gate did. Different things, and they read differently at a glance.
Checked the rest of the chain for the same shape - no other member name is
declared in more than one of CExpertSignalCustom / CExpertSignalAIBase /
CSignalMETA.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Same value in this EA (CExpert::Init is given Period()), but the filter
exists so the rows resolve onto the grid MetaPrepareEra resolves them onto,
and that grid is m_period.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
CMetaCorpus already existed (S1, report-only) with a proper SMetaCandidate
row. CSignalMETA kept a SECOND corpus of the same journaled candidates
right next to it: six parallel arrays, a second walk of the same 52 pattern
tables, a second row-filling loop, and THREE six-line ArrayResize blocks
keeping the six arrays the same length by hand. Same rows, same tables,
same meaning - and neither copy was reviewable without the other.
Now one class with one row schema and three sources:
LoadFromConfigDb() was Load(). This chart's fingerprinted DB via dbm;
what the S1 report reads.
LoadLargestOnDisk() moved in from CSignalMETA, header and all - the
symbol+period filter and the read-only open are the
point of it, and so is NOT going through the config
fingerprint (the trap that burned four corpus-build
runs). CountDbPatternRows moved with it as the one
"how big is this corpus" table walk.
Add() the on-chart ladder sweep, which needs the EA's live
filters and so stays in CSignalMETA - but stores here.
Storage is encapsulated: Count() is the truth, Reserve()/ShrinkToFit() are
hints, and every read is bounds-checked with an out-of-range answer that
cannot pass for a real candidate. That retires the sweep's hand-rolled
capacity block, which had already failed both ways - silently truncating
the corpus at a bar boundary on SP500, and running off the end mid-bar on
USDJPY/XAUUSD/XTIUSD because it only reserved room for two appends. The
lesson stays in the comment; the arithmetic does not.
SignalMETA.mqh 713 -> 573 lines. Also moved MetaPrepareEra's header
comment back above MetaPrepareEra - it had drifted two functions away.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Five parallel arrays, a count and a two-array intrusive chain sat on
CExpertSignalAIBase - inherited by every direction model, filled and read
by exactly one subclass. CMetaCandidateStore takes all eight.
What that fixes beyond the clutter:
- THE CHAIN WAS LINKED BY HAND. MetaPrepareEra wrote next[id] = head[bar]
then head[bar] = id itself, after six ArrayResize calls it also wrote out
itself. Add() does the linking, Reset() does the sizing, and a bar off
the grid now cannot be stored at all rather than stored unreachable.
- THE BOUNDS TEST HAD FOUR SITES AND THREE IMPLEMENTATIONS.
MetaCandidateWon indexed side[] with no test at all and answered
"short" for any id out of range - the same shape as the ladder's
negative-index read (2c351a0). Side() is three-state here, IsLong() and
SideIndex() are the safe ways to ask, and the per-side era tally in
RunOosPass is now guarded exactly like the per-family one beside it,
which always was.
Like the ladder and the OOS tally, none of it needs a chart, a net or a
broker: hand it bars and rows and every answer is a function of those.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Since S3 (f64e0f8) the meta head casts no vote - it scores an entry the
consensus already cleared and vetoes the ones under the cost-adjusted
break-even. The code still said otherwise. LiveMetaGate() was a virtual on
CExpertSignalCustom, so MA, RSI, MACD, Ichimoku, the four direction nets,
the session and news filters and the risk guard each carried a meta-gate
method they had no business having; one class implemented it and a dozen
inherited it. The trading pipeline held the gate as a CExpertSignalCustom*
- a signal pointer, with a signal's two hundred other methods reachable
from the entry path.
Expert\Trading\MetaGate.mqh now owns the abstraction:
CMetaGate one pure virtual, Evaluate(), and the two static
readings of a verdict (Blocks / Scored)
META_GATE_* names for the four codes the three call sites used
to spell as bare 0/1/2 and test three different ways
(`< 0` here, `== 2` there, `else` for the rest).
Codes unchanged; only ONE of them blocks, and that
asymmetry is now stated where it lives.
SMetaGateTelemetry the five m_metaGate* members that were on the AI
signal base - inherited by every direction model,
meaningful for none of them. One lifetime, one
writer, one object; the arm latch and the two
counters are a set that clears together.
g_warriorMetaGate is a CMetaGate*. MQL5's single inheritance means the head
cannot also BE one (it already extends the AI base for the net, the era
loop, the feature windows, the label caches and persistence), so it owns a
bound CMetaGateAdapter and hands that out - the same shape CTrainingDataView
uses for the same reason. LiveMetaGate() is gone from the signal base.
Behaviour unchanged: same codes, same thresholds, same fail-open doctrine,
same live-only telemetry rule. The adapter fails open when unbound, on that
same doctrine.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The flat walk replaced `for(f) for(p) for(s)` with one TableAt() index, but
LoadMetaCorpus's row body still read `s == 0` to stamp m_corpusSide. The
compiler caught it - `undeclared identifier 's'` - which is the good case.
Worth naming the near-miss anyway: had an outer `s` been in scope, this
would have compiled and stamped every candidate with one side. The side is
now taken from TableAt's own isBuy, so the name that opens the table is the
name that labels its rows - one source, not two.
Also restores stdlib indentation at three sites where the removed nesting
left braces at the old depth.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
MetaCorpus was already a class, so the raw-include problem was not the
one here. The problem was that the rule for naming a signal-DB pattern
table
MetaFamilyName(f) + "_Pattern_" + p + ("_Buy" | "_Sell")
was written out FOUR times, each wrapped in its own identical
family/pattern/side triple loop: the corpus loader, the stale-DB guard,
META's row counter and META's exporter. Four chances for a rename to
leave three of them querying an absent table and reporting it as
"family disabled" - which is what that code says when a table is
missing, so the failure would have looked like normal operation.
CMetaFamilies now owns the taxonomy and that rule. Callers walk ONE
flat index over all 52 tables and never spell a name:
for(int ti = 0; CMetaFamilies::TableAt(ti, table, f, p, isBuy); ti++)
Enumeration order is unchanged - Buy then Sell within a pattern,
families in order - so the corpus is assembled in exactly the same
sequence as before.
META's OneHotSlot had a second hardcoded 0/4/8/14 ladder with a comment
reading "matches MetaFamilyPatterns' 4/4/6/12" - a note asking a reader
to keep two constants in step by hand. The ladder is now summed from
the pattern counts, so they agree by construction. The bound against
META_ONE_HOT_SLOTS stays in META: the head's input width is that
class's business, and a taxonomy grown past it must be caught rather
than silently truncated.
Caught while re-reading the rewritten loop: my first counter was `t`,
and the body declares `MqlDateTime t`. Renamed to `ti` at all four
sites before it reached a compile.
MetaCorpus.mqh moves to Expert\Training\ with the other real classes.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Every AI signal repeated the same five-line InitIndicators override that
did nothing but call InitNeuralNetwork. The cause was an access mismatch,
not a design: CExpertSignalCustom declares InitIndicators public, the AI
base redeclared it PROTECTED, and each subclass had to redeclare it
public to be reachable by CExpert. Worse, the base's own override does a
different job entirely - it creates the OHLC/ZigZag feature indicators -
and InitNeuralNetwork called it back scope-qualified to stop the virtual
dispatch landing in the subclass. Two jobs, one virtual name, and a
recursion trap held off by a scope qualifier.
The feature-indicator step is now InitFeatureIndicators() (protected,
non-virtual, named for what it does) and the AI base carries the single
public InitIndicators override. CONV/HYBRID/LSTM/PAI/META drop their
copies and are now purely identity plus topology, which is the classic
signal file's shape.
Comment pass on ExpertSignalAIBase.mqh, -100 lines with every constant
and every measured number kept. Three claims in the tier block were
stale and inverted - it named CalibratedConfidenceMagnitude() as the
tiering input where the code deliberately uses the RAW magnitude, and it
described the signal DB as re-ranking each tier when ApplyPatternWeight
declines the DB from the end of era 1. Also dropped a paragraph whose
subject was a previous version of the comment, and moved two notes down
onto the constants they document (CONV_COMPRESSION_DIVISOR was 16 lines
and three unrelated defines away from its own text).
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The codebase had THREE conventions for the same statistic. AltData took a
true median; the barrier horizon and the derived input window took the
upper of the two middle values; the MI terciles and the barrier stop
ladder used nearest-rank indexing. All four now go through MathMedian /
MathQuantile, which is R's type 7 and the library's one answer.
System\AltData.mqh column median -> MathMedian (exact, no change)
AIBase\Labels.mqh swing median -> MathMedian
leg-range med -> MathMedian
stop ladder -> MathQuantile, read in one call
AIBase\Topology.mqh window median -> MathMedian
AIBase\AutoTune.mqh MI terciles -> MathQuantile + MathMin/MathMax
Signals\SignalSessionFilter DST last Sunday-> CDateTime::DaysInMonth()
gaps[]/legs[] change from int to double so MathMedian can read them; the
values are bar counts either way.
VALUES MOVE. Even-sample medians shift by half a bin and the quantile
reads interpolate, so the barrier geometry and the derived input window
can land on different rungs - re-keying fingerprints and forcing a
retrain. Accepted deliberately: stdlib consistency was the ask, and three
private conventions for one statistic is what it buys out.
Two YAGNI finds fell out of the ladder rewrite. MathQuantile sorts its own
copy, so DeriveBarrierGeometry no longer sorts up[]/dn[] in place - which
means upUnsorted[], a full array copy kept only to undo that sort, is
gone. ArraySort(up) had no consumer needing order at all; it was pure
work. The library call also gets a failure guard the hand-rolled indexing
never needed but the ladder read does.
Verified while here: Math\Stat\Math.mqh's MathAbs/MathMax/MathSqrt/MathPow
and friends are ARRAY overloads, not scalar redefinitions, so pulling it
into the translation unit shadows no builtin.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The gate's NormalUpperTail was a hand-rolled Abramowitz & Stegun 26.2.17
approximation. Its own comment gave the reason - "drags a chain of headers
behind it" - and that turned out to be one file: Math\Stat\Normal.mqh
includes only Math.mqh, which includes nothing. Swapped for Cody's rational
approximation in the library (~18 significant digits vs |error| < 7.5e-8).
No past verdict changes: at the z the gate operates on, the difference is
orders of magnitude below DEPLOY_FAMILY_WISE_ALPHA.
Adopting it needed the four bare macros in AI\Network.mqh gone first.
"#define b1 AdamBeta1" collides with an identifier in Math.mqh, so the
include would have macro-expanded the library's own local and failed to
compile - the same landmine that made the original author rename the
approximation's coefficients to ntB1..ntB5 rather than use the reference's
b1..b5. lr, b2 and momentum are the same class of hazard: single-token
global macros in a 52k-line codebase. All four now resolve to the input
names they always aliased, which is a pure textual identity - verified zero
bare occurrences remain.
Also:
- SelectionSort over the buffered signals was O(n^2) with an O(n^2) count of
StructToTime calls, because the comparison rebuilt both datetimes from the
six int date fields every time. Now materialises the keys once and does an
insertion sort; ArraySort cannot permute a struct array. IsEarlier goes
with it, MakeDateTime becomes SignalTime.
- Seven FileOpen sites lacked FILE_SHARE_READ|FILE_SHARE_WRITE, including
AtomicWriteBegin, which stages every model save. All 43 sites now carry
them - an exclusive open fails outright when another process holds the
path, which here has meant a silently skipped save.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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>
User design (2026-08-19): 'remove the enum menu that selects neural networks... individual
inputs for every NN just like classic signals... the META NN should be integrated into the
voting decision pipeline when enabled... as a bonus meta labelling is applied to enabled NNs.'
- AI_CHOICE is GONE (tombstoned per the stale-.set doctrine). Use_MLP/Use_CONV/Use_LSTM/
Use_CONVLSTM are ordinary bools like the classic votes; the ensemble arithmetic adapts to
any subset because the consensus divisor is the enabled capable weight. Two or more
enabled = ensemble (|ENS1 token + joint gate, exactly the old AI_HYBRID fingerprints, so
existing weight files keep loading); one = the old solo preset; none = classic-only.
- Use_MetaLabeling un-couples META from the direction NNs (the old selector made them
mutually exclusive). S3 ships: CSignalMETA::LiveMetaGate scores each vote-cleared entry
(shared window at bar 1 + proposal descriptor: side, net vote, live geometry, spread/ATR;
pattern one-hot ZEROED - ranking, not calibrated probability, documented in the body) and
vetoes below the cost-adjusted break-even. Entries only; fail-open everywhere, loudly.
- COEXISTENCE HAZARDS closed: VoteCapableWeight()=0 and ProspectiveVote()=false for the
meta target - solo-only until today, a trained META would otherwise sit in the consensus
divisor as a permanent abstainer and shrink every vote by its module weight.
- CERTIFIED == TRADED: the ensemble era verdict replays the identical veto through the same
g_warriorMetaGate pointer over its OOS fired bars (bar re-resolved from the row's own
time; fail-open counted as fires and reported: 'metaGate: N approved, M vetoed, K
unscored'). The overlay deliberately does NOT replay it (veto-filter-in-replay class,
calendar-cliff precedent) - documented at the sweep site. Solo charts' own gate does not
model the veto - the standing solo-gate caveat, documented at the input.
- DB continuity: the pattern/journal DB fingerprint's first slot was (int)AIType;
DbLegacyAiSlot() maps every legacy-expressible config to its OLD value (new 2-3 member
subsets get 100+bitmask, outside the legacy range) so no existing database re-keys.
filterID becomes the enabled roster via one EnabledNNSummary().
- HUD: the meta line shows the gate (armed/(trn), last P vs BE, ok/veto tally); the
armed/disarmed announcement fires on state change via one latch (MetaGateArmedNow), not
only when an entry happens to be proposed.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
1. Labels and the exit simulator go through the broker's stop-distance
check: risk/reward widen to SYMBOL_TRADE_STOPS_LEVEL exactly as
TCAdjustStops does at order time - the M5/tight-ATR case where live
trades ran wider geometry than training measured. Current stops level
stands in for history (like the spread); measured quantity, so it
does not key the fingerprint.
2. The Intelligent drift verdict moved into RefreshDriftVerdict(), which
RESCANS the label cache and now runs at every era end beside
RankTiersFromOos - era-cadence instead of waiting for rare full
rebuilds. Prints only on change.
3. Session filter is any-broker: sessions defined on their financial
centres' civil clocks (London 08-16 Europe/London, NY 08-17
America/New_York, Tokyo 09-18 Asia/Tokyo), converted to UTC by each
centre's own computed DST rule (EU last-Sun-Mar/Oct, US
2nd-Sun-Mar/1st-Sun-Nov), then to broker time by the MEASURED
server-vs-GMT offset (half-hour brokers included). Windows may wrap
midnight in broker time - the interval test handles it. Replaces the
EET-hardcoded anchors, which were correct on exactly one broker and
got Tokyo wrong by an hour each European summer.
4. The current-session-table-for-history caveat resolved by analysis:
the bars bound the error - a too-late assumed close meets no bars
(zero error), a too-early one truncates conservatively (<=1h, never
optimistic, cannot manufacture edge). Documented at the site.
5. The ensemble deploy gate mirrors the direction policy: blocked-side
fires are not fired bars (certified == traded), the zero-skill
reference uses only ACHIEVABLE baselines (always-short is not a
strategy a long-only book can run), and one-sidedness BY POLICY is
not degeneracy - the two-sided requirement applies only when both
sides are allowed. Sell predictions keep their other jobs (exit
triggers, consensus dilution) untouched.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
User decision: "stick to the broker's time throughout the codebase and
analysis, session filter, programmed close time etc". Investigation
found the GMT choice was not just inconsistent but broken: live
journaling stamped DB rows with TimeGMT() while the online-learning
backfill stamped them with BAR time (server) - two clocks ~3h apart in
the same column. The newest-row duplicate guard compares them on one
axis, so a live row landing within the offset after a backfill row was
silently rejected as "outdated". dbVersion 3.0 -> 4.0 wipes the Signals
store: the only honest reset for a mixed-basis corpus.
- Direction()'s clock (stamps every journaled row, keys the per-second
vote window): TimeGMT -> TimeCurrent, variables renamed so the name
cannot lie about the basis.
- UpdateSignalsWeights' future-row bound: same clock as the rows.
- Session filter: broker-time anchors (London 10-18, NY 15-23:59, Tokyo
2-11). The GMT anchors were backwards for an EET-family broker - such
a broker follows European DST, so London is DST-STABLE in broker time
and moved twice a year in GMT. Tokyo drifts 1h each European summer
(no DST to track) - accepted, smallest error on offer. Also fixed:
inTimeInterval ignored its datetime parameter and called TimeGMT
fresh - a dead parameter hiding a hardwired clock.
- MetaCorpus/SignalMETA: rows pre-4.0 are GMT, broker since; the
GMT->server offset scan is KEPT because it measures rather than
assumes - it pins 0 on new corpora and still resolves old ones.
- AltDataFetch deliberately stays on GMT: FRED/COT/EIA release schedules
are external UTC-anchored events; the as-of join maps them onto server
bars downstream.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The ensemble chart UI had a shared-namespace defect that answered the user
question "what do the arrows represent?" with "a bug": all four members drew
arrows under the same WarSig_<bartime> object names, so the chart showed
whichever member rendered LAST, one member Neutral deleted another member Buy
at the same bar, each member init sweep wiped the arrows the previous member
had just restored, and SaveChartSignals - which rebuilds the sidecar by
SCANNING the chart - persisted every other member arrows into its own history
(the exact cross-model laundering its own header warns about, now happening
BETWEEN ensemble members).
Arrows are now namespaced per member (WarSig_PAI_, WarSig_CONV_, WarSig_LSTM_,
WarSig_HYB_): draw, delete, restore, prune, member init sweep, destructor
purge and the sidecar scan are all member-scoped, and the tooltip names the
model. Global purges keep matching the bare WarSig_ prefix, which covers all
member namespaces plus old-format leftovers from earlier builds.
Labels: the ensemble panel header no longer says "HYBRID ensemble" (HYBRID is
one member; the header is the ensemble) and the CONVLSTM member displays as
ConvLSTM instead of Hybrid. Its SHORT id stays HYB deliberately - it names the
model folder and changing it would orphan every model trained under that path.
Deinit: the alt-data mapping dialog namespace (WarriorAltMap_) joins
WarriorChartPrefixes, so both the OnInit purge and the deinit final sweep now
cover it - it was in neither list, so a dialog starved of its own Destroy()
left its controls on the chart permanently.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The candidate sweep reserved bars*2 slots and guarded the bar loop with
room for only 2 appends, but every bar can append m_srcCount*2 candidates
(4 families x 2 sides) and STATE-model patterns stay active on most bars.
Two failure modes, both observed on the first multi-chart attach:
- USDJPY/XAUUSD/XTIUSD H1: mid-bar overflow -> "array out of range in
SignalMETA.mqh (369/379,25)" -> EA dead on the chart, panel frozen at
"getting ready".
- SP500 H1: the guard tripped exactly at cap (109,508 = 54,754*2), a
SILENT truncation that dropped the newest bars from the corpus - the
sweep walks oldest-first, so what fell off was the most recent history.
The arrays now grow 1.5x whenever headroom for one full bar is missing,
the loop runs to completion on every symbol, and a shrink-to-fit after
the loop returns the slack.
Co-Authored-By: Claude Fable 5 <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>
Meta_ExportDataset input: with AIType=META the chart writes its complete
training set once per attach - every resolved+labeled candidate as
[barTime|family|pattern|side|won|NetInputWidth floats] using the SAME
window builder, descriptor and label caches pass 2 trains on, so offline
examples are byte-equivalent to the EA's own. Sidecar .meta.csv carries
layout + the geometry/BE the labels were computed at. Files land in
Common\Files\Warrior_EA\MetaExport\<sym>_<period>.f32.
This is the pooling architecture decision: multi-symbol training INSIDE the
per-chart God-class would be the riskiest surgery this codebase has seen;
instead each chart exports, the pooled head trains offline (small dense+BN
net, minutes on this box), is validated per-symbol under the same
chronological splits and coverage x (p - BE) gate, and only a WINNER gets
written back into a .nnw for the EA to load natively (format fully mapped).
Also turns every future meta experiment from a 20-minute tester cycle into
minutes of offline iteration.
Cost-model note for the record (user challenge, verified): spread is 0.099
ATR = ~2% of the 4.74 ATR trade width - tiny per bar, but expressed in
win-rate points it is 0.099/4.74 = 2.1pp, which is the measured base-vs-BE
gap and the size of the entire observed skill lift. Zero-spread relabeling
would put base == BE by construction. Multi-day holds additionally pay swap,
which the label does NOT charge - the true bar is higher, not lower.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The S2 verdict localized precisely: the meta head's edge x width (0.02 x
4.74 ATR = 0.095 ATR/trade) equals the measured spread (0.099 ATR/trade) -
real signal, consumed exactly by cost. The breakdown line adds: the lift is
LONG-ONLY (shorts anti-selected) and MA-family-strongest (67-70% traded win,
<1 sigma over BE on ~350 trades, best-of-32 cells - not family-wise
evidence).
Next experiment, pre-registered in Meta_Labeling_Design.md before any H4
data exists: SP500 H4 doubles ATR against a fixed spread, halving the cost
drag (~1.3pp) that the ~+2pp lift must clear. Same pipeline end to end;
deployability still decided by the unchanged 2-sigma gate. H3 (the honest
risk) is that the lift decays with timeframe as fast as cost does - the
tick-flow failure shape - which would close the single-instrument well and
leave cross-sectional pooling as the only lever.
Enabler fixed here: LoadMetaCorpus picked the LARGEST .db on disk, so an H4
chart would have adopted the (bigger) H1 corpus and resolved candidates onto
wrong bars - and a chart could even adopt another SYMBOL's corpus. The
loader now requires a <symbol>_<period>_ filename match and says so when
nothing matches.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The NN now has a target that is not per-bar direction (closed, best-of-999
p=1.0000): P(win | this journaled candidate, at the EA's own SL/TP, net of
cost). One net for all 52 pattern-sides, AIType=AI_META.
- NetForward.mqh: the host-side softmax+CE gradient generalized total==3 ->
2||3 on both backprop paths; a 2-class softmax IS a logistic head, and no
compute backend changes.
- SignalMETA.mqh (new): corpus loaded read-only from the LARGEST signal DB on
disk (decoupled from the config fingerprint that burned four S1 runs); the
GMT->server offset is measured PER ROW against entryPrice vs bar open
(DST-immune, histogram logged); a window-span regime filter drops the
pre-2017 daily-backfill rows; 31-feature setup descriptor appended at the
input (26 one-hot + side + tanh netVote + SL/TP ATR + spread/ATR).
- Training.mqh: candidate-queued pass 1, binary-target pass 2, per-candidate
calibration (2.5) and OOS (3) walks. Counter mapping win->Buy / loss->Sell
lets checkpoint selection, the edge floor, the plateau ladder and the
family-wise deploy gate run UNCHANGED: precision reads as win rate among
traded candidates, chance as the base win rate, recalls as sensitivity/
specificity. Era-end META line: coverage x (p - break-even) vs the null.
- Labels are the side-conditional triple-barrier win caches - never the DB's
stop-and-reverse outcome. Logit adjustment deliberately skipped (~40% base
rate). Live inference + online learning guarded off until S3.
- Fingerprint: conditional |TGT:META1; State\META\ folder + 2-output filename
slot keep meta models fully separate from direction models.
Compiles clean (0 errors, 0 warnings). S2 run = attach a chart with
AIType=AI_META; S3 wires the votes via the per-side hooks.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Implemented sqx_audit.py to audit StrategyQuant X trade lists, focusing on performance metrics and cost analysis.
- Created sqx_portfolio.py to evaluate portfolio performance based on uncorrelated components and their impact on risk and return.
- Developed swing.py to analyze cost ratios across different holding periods and assess swing trading structures.
- Introduced test_management.py to investigate the effectiveness of exit rules on random entries and their impact on expectancy.
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
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>
The plateau/regression line printed balancedOosEra as the current value while
comparing against m_bestBalancedOos, which has held the SELECTION score since
a142749. Two different metrics in one sentence, so HYBRID logged "regressed
from best 14.4% to 34.0%" a hundred times - a regression to a higher number,
which is not a thing. The comparison itself was right (selectionScore, coverage
weighted, genuinely below best); only the print was wrong. 1039ad9 relabelled
these strings but missed that this site passes the wrong variable.
The startup config line had the same shape of gap: it printed the dense taper
and called itself self-verifying while the DERIVED conv and recurrent stages -
the ones that dominate CONV/LSTM/HYBRID - were invisible. It now shows the
width into and out of each front-end stage, and flags the case where the dense
stack is wider than the vector reaching it (a linear fan-out cannot recover
what the bottleneck discarded; it only adds parameters). Flagged, not silently
reshaped - that would re-key trained topologies mid-comparison.
UsesConvStage()/UsesLstmStage() replace HasConvBeforeLstm() as the primitive,
so each subclass declares its composition once and both the capacity budget and
the config line derive from it rather than restating it.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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 413ff7e). Skip the save when no era completed and no
model was loaded; that is exactly the post-reset and first-attach state.
Also sweep _shadowclone.tmp, which the reset did not cover.
Separately, ComputeLstmHiddenSize budgeted every topology against the
flattened input (historyBars x neuronsCount). True for LSTM, wrong for
HYBRID, where AddConvStage runs first and the LSTM is fed the conv feature
map - historyBars x convFilterCount, 160 rather than 420 at H1 defaults.
The quadratic is dominated by the inputs term, so overstating the fan-in
2.6x cost a full ladder step (16 units where the budget affords 32). New
virtual HasConvBeforeLstm() feeds LstmFanIn(), so composition decides this
rather than an AIType check. desc.window is advisory only - CNet never
passes it to the layer - but is now truthful for the same reason.
Derived values stay out of the weights-filename fingerprint and are adopted
from the .cfg, so existing models keep their saved width; only fresh ones
pick up the corrected budget.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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>
DRY - topology construction
---------------------------
CSignalCONV and CSignalHYBRID each built the Conv+Pool front-end from scratch;
CSignalLSTM and CSignalHYBRID each built the LSTM stage from scratch. The
duplicates had already drifted: HYBRID guarded the LSTM step with
MathMax(1, historyBars/2), CSignalLSTM divided unguarded, so a historyBars of 1
gave two different steps for what is documented as the same layer.
Extracted AddConvPoolStage() and AddLstmStage() onto CExpertSignalAIBase. The
three overrides are now compositions:
CONV = AddConvPoolStage
LSTM = AddLstmStage
HYBRID = AddConvPoolStage && AddLstmStage
HYBRID's "matches the standalone CONV front-end exactly, then adds LSTM" is
enforced by construction instead of by comment. Took the guarded step for both.
Also fixed a descriptor leak the duplicates shared: on a failed topology.Add()
the CLayerDescription was neither owned by the array nor deleted.
Dead code
---------
- CNet::SaveCheckpoint / CNet::LoadCheckpoint (123 lines). Superseded by the
in-memory CaptureWeights/RestoreWeights pair; Network.mqh:1312 already said so
("This replaces the file-based SaveCheckpoint/LoadCheckpoint"). Zero call
sites - every remaining mention was a comment. The five comments that
referenced them have been reworded rather than left dangling.
- CExpertSignalCustom::CheckForDuplicateTrade / FindLastTradeIndex /
UpdateTradeStatusAndExit: declared, never defined anywhere, never called.
They only made it look as though duplicate-trade detection existed.
Compiles 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Add MACD_FAST, MACD_SLOW, MACD_SIGNAL presets and Ichimoku Tenkan, Kijun, Senkou presets to InputEnums.mqh. All combinations are designed to satisfy the respective indicator's validation rules (fast < slow for MACD, Tenkan < Kijun < Senkou B for Ichimoku), eliminating init errors and allowing the auto-tuner to perturb settings independently.
Introduce VOTE_CLOSE_PRESETS enum with a Disabled option (value 101) that bypasses vote-driven position closing via arithmetic thresholding, removing the need for a separate boolean flag. This ensures positions exit only via stop-loss, take-profit, or trailing when disabled.
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.
- Added optional `weighScale` parameter (default -1.0) to `CNeuronBase::Init` and `CLayer::CreateElement`.
- Updated `CNeuronPool::Init` to use LeCun-uniform scaling (1/sqrt(window+1)) for its base initialization.
- Updated `CNet::CNet` to use He-scaled initialization (sqrt(2/neurons)) for dense layers.
- These changes enable more flexible and statistically sound weight initialization, matching the rationale used in OCL-based implementations, leading to better training stability and convergence.
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.
Replace old ATR_MULTIPLIER, THRESHOLDS_PRESET enums with new
STOP_LOSS_MODE, TAKE_PROFIT_MODE, AI_EXIT_MODE enums that support
ATR-based, intelligent confidence-scaled, and swing-anchored modes.
Also fix LSTM signal identity string.
Audit turned up a real gap for a prop-firm-portfolio-manager use
case: nothing in this codebase watched for account-level daily-loss
or max-drawdown breaches - the single most common way a prop-firm
evaluation actually gets failed.
New Signals/SignalRiskGuard.mqh (CSignalRiskGuard), wired into the
exact same filter-composition chain as SignalNewsFilter/
SignalSessionFilter (CreateSignalWithRetry/AddFilterToSignal, no new
architecture). Vetoes new entries only (never closes existing
positions - a materially bigger behavior change, left to the
trader/EA's own SL/TP handling) once either MaxDailyLossPct or
MaxDrawdownPct (new RISK_LIMIT_PCT_PRESET inputs, both default
disabled) is breached. Peak equity and the current broker day's
starting balance persist to a small local per-symbol-per-magic state
file - peak equity in particular must survive a restart to mean
anything, otherwise a restart would silently reset drawdown tracking.
New RISK_LIMIT_PCT_PRESET enum (2/3/4/5/8/10/15/20%) rather than
reusing PERCENTAGE_PRESETS, which steps by 10 starting at 10 - too
coarse for prop-firm-style limits (commonly single-digit daily loss,
~8-10% max drawdown). Compiled clean (MetaEditor, 0 errors/0
warnings).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
CalendarValueHistory() was called with no country filter at all, so
ANY country's economic calendar event vetoed a trade regardless of
relevance - a JPY release blocked a EURUSD trade just as readily as a
USD one, making NF_MinImpact's fine-tuning far noisier than intended.
Adds System/NewsRelevance.mqh (GetRelevantCountryCodes/
ImpactWeightedProximity), a shared utility that cross-references
CalendarCountries() against the symbol's base/quote currency to get
the actually-relevant ISO country codes, then uses
CalendarValueHistory()'s country_code-filtering overload. Shared so
the upcoming NN news-input feature reuses the same relevance logic
rather than duplicating it.
Compiled clean (MetaEditor, 0 errors/0 warnings).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- Variables/Inputs.mqh: drop the dead commented-out HybridSignals input
line; add a one-line note above the section-header input strings
clarifying they're intentional MetaTrader GUI dividers (consumed by
the terminal, not any MQL5 statement) so a future audit doesn't
re-flag them as unwired.
- Signals/SignalSessionFilter.mqh: replace the never-filled-in MQL5
Wizard template header (ProjectName/CompanyName placeholders) with
this codebase's real header, matching every other Signals/*.mqh file.
- Signals/SignalNewsFilter.mqh: remove the stale NEWS_IMPACT template
macro - it was only ever used as the constructor default, immediately
overridden by the real NF_MinImpact input at wiring time.
- Expert/ExpertSignalAIBase.mqh: guard the SERIES_LASTBAR_DATE read in
ScheduleTrainingIfNeeded() so a failed lookup (0) can't silently be
read as "no new bar pending" and stall training/signal refresh.
Compiled clean (MetaEditor, 0 errors/0 warnings) after each change.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
Replace hardcoded lr and momentum with new input variables for Adam and
SGD+momentum. Add OpenCL kernel LSTM_UpdateWeightsMomentum alongside the
existing Adam kernel. Update comments and revert beta1 to book default 0.9.
- Ensure `tick_volume` array is set as series in ADShorteningOfThrust.mq5 to prevent future-data leak in volume calculations.
- Ensure `open` array is set as series in ADWyckoffFailedStructure.mq5 to prevent future-data leak in structure detection.
- Add missing PReLU gradient scaling (multiply by 0.01 for negative outputs) in CPU_CalcHiddenGradient and DirectML shader to match expected derivative behavior across all backends.
Warrior_EA.mq5: clear Comment() and destroy the control panel before
Expert.Deinit()'s object-purge cascade runs out from under it; stop
re-registering the same signal filters on every DB retry (was causing a
double-delete of the same pointer on shutdown).
DirectML/WarriorCPU.cpp: bound the worker-thread join in ThreadPool::Stop()
instead of blocking forever - CPU_Shutdown() held g_mutex across an unbounded
join, so a watchdog-killed calling thread could leave it locked forever,
poisoning every future call into the DLL (matches reports of the EA getting
stuck on "initializing" after being removed and re-added to a chart).
Also includes prior era-0 label-cache prebuild and pullback/reversal
label-quality work in AI/Network.mqh, Expert/ExpertSignalAIBase.mqh, and
Variables/Inputs.mqh.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
