A resumed META model hot-looped pass 1 (0->100% scan oscillation, silent for
3 minutes until the stall reporter fired) because EVERY window failed at the
first AD/Wyckoff feature: the init-time param adoption called
ReInitADIndicators unconditionally, destroying five freshly-calculating
indicator instances to recreate them with BYTE-IDENTICAL params (verified by
parsing the .nnw header - the MI tuner had kept the configured settings), at
process start, on a box with 1 GB free of 31. The replacements sat cold for
6+ minutes while full-history resweeps starved the indicator threads harder.
- AdoptIndicatorParams: installs a loaded param set into the tuner and
rebuilds handles ONLY when the set actually differs from what the live
indicators run. Both call sites (resume init + panel reload) use it.
- Resumed models get the same 3 warm-up passes as fresh ones. The skip was
the shared root cause of the cold-ATR (ba13eef), cold-AD (2026-08-11) and
this incident - custom indicators recompute from scratch every process
start regardless of what the .nnw proves.
- Cold-sweep backoff: a pass-1 sweep in which every window failed on a
TRANSIENT cause arms a 5s era-start pause instead of an immediate
full-history resweep, so the retry loop stops consuming the CPU/memory the
warming indicators need. The stall reporter names the backoff branch.
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>
The reference-pair set was re-discovered from Market Watch on every
build, so adding or removing a terminal symbol silently changed what a
trained model's six cross-asset features meant - the last open
train/serve parity gap from the 2026-08-11 audit. The set a model's
FIRST successful build actually used is now stamped into its .cfg
(append-and-length-guard, adopt-don't-compare - the derived-barrier
pattern) and every later build constructs the panel from exactly that
list; a pinned pair that is temporarily unavailable is skipped, never
substituted.
Also warms SymbolSelect/SeriesInfo for every reference symbol at
InitNeuralNetwork, so the terminal's ~minute of async cross-symbol
download starts at init instead of when the first Build() trips over
an unselected symbol - the source of the startup 'only 0 usable
reference pairs' console failures.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Three findings from the 2026-08-11 audit:
1. The excursion head's trailing-quantile ring was deliberately never cleared
between eras ("a rolling estimate of the market, not of the era") - but
pass 3 re-walks the SAME OOS window every era, so at each walk's restart
the ring still held the outcome masks of the newest OOS bars from the
previous walk: the chronological FUTURE of the bars about to be scored.
For the first ~window+horizon pushes of every era the "trailing" incumbent
was partly a leading one - conservative for the gate (an informed incumbent
is a harder hurdle) but exactly the self-made-artifact class 06d4785 hunts.
The ring now clears at era-score reset; the warm-up bars simply don't score
the trail race, which the m_excTrailN gating already accounts for.
2. skillTrail compared the head's FULL-block Brier (pro-rated by coverage)
against the incumbent's subset sum - valid only if head skill is uniform
across the OOS walk, while the trail-scored subset systematically excludes
each era's warm-up bars. The audit also found m_excBrierHeadD/BaseD/
m_excOosHitsD declared, zeroed and never accumulated (dead since e2c9593
made every scored bar disjoint). The dead trio is replaced by
m_excBrierHeadT: the head's Brier accumulated only on the bars the warm
incumbent also scored, so the race now compares both predictors on an
identical bar set.
3. The AD/Wyckoff feature blocks read GetData with no EMPTY_VALUE guard; a
cold (still-calculating) indicator returns EMPTY_VALUE everywhere, the
sanitize loop rewrote that to 0.0, and the bar SUCCEEDED - so
BufferTempData cached an all-zero Wyckoff block as a success for the whole
bar frame: the one path the f6150ee only-cache-successes rule cannot see,
because it never fails (the ba13eef class, arriving through values that
never fail; a resumed model's era-0 prebuild starts milliseconds after
OnInit). ADIndicatorCold() probes the NEWEST bar - EMPTY_VALUE there means
async warm-up (transient reject, retried), while deep bars beyond the
buffered depth keep the sanitize loop's neutral-fill so degraded history
still trains. Also fixed m_featureCacheValid's declaration comment, which
still described the pre-f6150ee cached-miss semantics.
Compile: 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
ba13eef cached a miss unless it was flagged transient, and flagged
exactly two guards: the EMPTY_VALUE open and the cold ATR. Every other
rejection in BufferTempDataCompute - an indicator buffer not yet
calculated, a panel not yet built, a series not yet loaded, a failed Add -
still cached as PERMANENT.
Observed 2026-08-11: the MI pre-scan runs ~3 s after OnInit and touches
all 54k bars while the indicators are still warming. The log announced it
immediately and unmistakably:
feature/label information - ... (0 samples 19 bars apart
= 0 independent blocks over a 64-bar horizon, 0.0s)
Zero usable rows, four seconds in. Training then stalled at era 0 for an
hour with "NOT ONE of 54681 scanned bars produced a usable feature
window" on all four charts. Both charts reporting cross-asset PRESENT and
both reporting ABSENT got 0 samples, so the optional block was not the
discriminator - the cache was.
Enumerating which rejections are "really" permanent is the wrong shape of
fix: it is a list that must be re-audited every time a feature block is
added, and being wrong once costs the whole run silently - which is
exactly how the two-guard version failed. Caching only successes needs no
list and cannot be wrong.
Cost is bounded and small: in steady state the only bars that still fail
are the handful at the deep end of history inside the indicators' own
warm-up, so an era recomputes ~ind_Periods bars rather than 54k.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Era 0 stalls with "NOT ONE of 54681 scanned bars produced a usable
feature window, windows ok=0 failed=54681" and nothing else. That line
reads identically for a cold ATR, a conditionally-missing optional
feature block and an out-of-range index, so it cannot be diagnosed
without one restart per hypothesis.
Two changes:
1. WIDTH CONTRACT in BufferTempData. Every enabled block must emit
exactly m_neuronsCount values on EVERY bar. A block that emits its
values on some bars and skips them on others (indicator, panel or
series unavailable for that bar) does not merely shorten the window -
it SHIFTS every feature after it into the wrong slot, and the net
then trains on silently misaligned inputs that still look like a
valid window to everything downstream. Now rejected, rolled back and
reported once, naming the optional blocks (XA / SPR / swing context)
as the ones carrying an availability test. Worth having independently
of the current stall.
2. BuildFeatureWindow records WHICH lookback slot rejected and how much
of the window was assembled, and the pass-1 stall report renders it:
"slot 0 of 20 REJECTED (window had 0 of 760)" is an indicator warm-up
or history-edge read; "every lookback bar ACCEPTED and the window was
still short: 640 of 760" is a missing 6-value block.
No behaviour change on a healthy run: the width check is an equality
that already holds, and the diagnostics render only inside the
total-failure branch.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
BufferTempData cached EVERY failure - m_featureCacheHasValue[idx]=true
with m_featureCacheValid[idx]=false - and the cache never re-tries a
miss. So a single feature read taken before the terminal had finished
calculating the indicator buffers marked those bars unusable for the
rest of the process, even though the data arrived milliseconds later.
MT5 fills an indicator's buffers asynchronously after the handle is
created, and a cold ATR returns 0 for EVERY index, not just its warm-up
tail. BufferTempDataCompute rejects a bar with no ATR (correctly - the
price features would be meaningless), so the whole window failed, and
the whole cache was poisoned.
Only resumed models were hit, because only they read features that
early. Topology.mqh sets m_warmupPassesRemaining = netLoaded ? 0 : 3:
a fresh start sits through three separately-scheduled Train() calls
before anything touches a feature, which is exactly what those passes
are for. A resumed one skips them and TuneIndicatorsAndTrain drives
StartLabelCachePrebuild and the MI report from the first chart event.
Its rationale - "a restart already has a proven-synced history" - holds
for HISTORY and not for INDICATORS, which are recreated every process
start.
Downstream: BuildFeatureWindow failed on every bar of every era, so
add_loop never went true, so pass 2, pass 3, the era counter and the
checkpoint were all skipped and pass 1 swept 0->100% forever. The
"0 samples" MI report line at startup was the same failure, four
seconds earlier, already visible in the log.
- a miss is now cached only when it is PERMANENT; the two "not ready
yet" guards mark m_featureFailTransient and are recomputed on the
next visit. Steady-state cost is ~ind_Periods bars per era, not 54k.
- an era that discards itself now drops the feature cache before
restarting, so any remaining cause of this state self-heals instead
of looping.
Deleting the .nnw "fixed" this only by turning the model back into a
fresh one.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Completes the 2026-08-09 training audit. FORCES A RETRAIN of every
Wyckoff-enabled config (N1 re-keys the fingerprint), and BOTH DLLs must be
redeployed alongside the .ex5 - they carry new exports.
F4 - mini-batch accumulation, TRAIN_BATCH_SIZE=32. Training was pure online
SGD (one weight update per bar), which is the mechanical source of the
era-to-era whipsaw every downstream guard was built to cope with. The O(n^2)
outer product is native - AccumulateWeightGrad / AccumulateWeightGradConv /
AccumulateBufferInto in Network.cl, WarriorCPU and WarriorDML - while the
optimizer step is host-side MQL5 shared by all tiers (ApplyAccumToBlock), so
there is one Adam/SGD implementation instead of four that can drift.
- the LSTM needs no outer-product kernel (WeightsGradient already holds the
sample's full dW) but could NOT simply be left un-zeroed between samples:
CPU_LSTMSeqBackward/DML_LSTMSeqBackward memset it on entry. Hence a
separate accumulator plus an elementwise add.
- batch-norm gamma/beta accumulate in host arrays, not new BatchOptions
slots - BN_OPT_STRIDE is baked into every persisted .nnw.
- scoped to pass 2; online learning keeps immediate updates. Every save /
checkpoint / scoring boundary flushes, scaling by the real sample count.
- degrades to per-sample updates (one log line) on a tier that cannot
accumulate, so old devices and DLL-free builds are unaffected.
- verified offline: DirectML/batch_accum_check.cpp drives the real exports
against an independent reference; at B=1 the accumulator matches the
shipped unbatched kernel's own gradient to 1.1e-16. Math only - the
in-situ check remains the per-layer dW/W report on a real era.
F6 - ComputeFirstLayerWidth budgeted against the RAW input width even where a
conv/LSTM front end had already reduced it, so an LSTM's dense stack was
charged for 1,280 inputs when it receives 64. Confirmed from the deployed
.cfg files: CONV, LSTM and HYBRID were all pinned at the 16-unit floor. Now
budgeted against the front-end output and capped at it (never fan out), with
the derivation reordered so both stages settle first.
N1 - EventCode/EventPhase/StructuralPhase are signed categoricals packing
direction and Wyckoff stage into one scalar across a sign discontinuity. Split
into direction + [0,1] magnitude, the same convention the base OHLC block uses.
Information-preserving; 13 readings now occupy 16 inputs.
Compiled clean (0 errors, 0 warnings); both DLLs rebuilt.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
This is what killed CONV and LSTM on 2026-08-07. Terminal journal:
19:19:40 6664 x "VirtualAlloc failed in large allocator"
19:19:40.829 expert Warrior_EA (SP500,H1) removed <- CONV
19:29:55 2048 x "VirtualAlloc failed in large allocator"
19:29:55.359 expert Warrior_EA (SP500,H1) removed <- LSTM
50ms and 71ms after each printed its "logit adjustment" line, i.e. the
instant era 0 tried to allocate its training queues. They did not hang -
MT5 shot them for running out of memory.
ReInitADIndicators() re-Create()s every enabled indicator and released
nothing. The comment above it asserted "CiCustom.Create() already
releases its old handle"; MQL5's CIndicator::Create is
m_handle = IndicatorCreate(symbol, period, type, num_params, params);
a plain overwrite, whose only success-path IndicatorRelease is in
~CIndicator. IndicatorRelease appeared nowhere in this codebase.
That function is the indicator tuner's inner loop. AutoTuneIndicators
scored 324 candidates per model on SP500 H1, so ~324 x 6 orphaned
terminal-side instances, each holding a full-history buffer set -
ADWyckoffEventStream is 14 buffers x ~38k bars x 8 bytes = ~4.3 MB each.
Gigabytes. Confirmed in the shutdown teardown, where a single surviving
expert still held 70 x ADWES, 53 x ADWyckoffEventStream, 23 x
WFS(48,3,1.80,1.10), 19 x ADMovingAverage, 18 x WYSB(162). Those
parameter sets are the tuner's candidate grids.
Release is UNCONDITIONAL, not gated on the handle having changed: MT5
refcounts instances by (symbol, period, params), so re-creating with
IDENTICAL params returns the SAME handle with the count incremented -
the "23 x WFS(48,3,1.80,1.10)" pattern. Either way Create() added one
reference and we hold one handle, so one release is owed.
Released AFTER the re-creates, never before: dropping the terminal's
last reference first would tear the instance down, so an identical-params
Create() would rebuild it from scratch instead of reusing the live one -
turning a refcount bump into a full recalculation over all history, 324
times over.
PAI was unaffected because it has no AD/Wyckoff indicators enabled (35
candidates, built-ins only). HYBRID survived on luck: CONV and LSTM died
2 and 12 minutes before its own sweep finished, freeing the memory.
Compiles clean: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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>
ROOT CAUSE of the zero spread measured on SP500 H1 2026-08-07 (all 17 candidates
returned exactly 0.00359 nats): the tune loop re-inits the indicators and then
scores, with no RefreshData() between.
ReInitADIndicators() does its part - Create() builds a NEW handle carrying the
new parameters, and the feature cache is flagged stale so features really are
recomputed. But BufferTempDataCompute() reads the CIndicatorBuffer objects, and
only Refresh() copies data out of a handle into those. So every candidate was
scored on values still held from the PREVIOUS handle. My earlier guess in the
diagnostic ("suspect the feature cache") was wrong: the cache invalidation works.
Two things land together, because neither is safe alone:
1. RefreshData() after the re-init, so a candidate is scored on its own features.
2. A SELECTION GATE on the install. bestScore is a MAXIMUM over candidates, and
the maximum of N draws from a null beats its incumbent almost every time - so
"it beat the incumbent" installs noise. This selector is the highest-stakes of
the three found in this audit because it ACTS: it overwrites the user's
configured indicator settings and forces BuildFreshTopology(), so the network
then trains on whatever the noise picked. Fixing (1) without (2) would have
made a dormant bug actively harmful.
The gate draws the winner's own permutation null once, then corrects the p-value
for having chosen it out of N with Sidak: p_family = 1 - (1-p)^N. Sidak rather
than the max-of-N resample used by the geometry scan because each candidate here
has a DIFFERENT feature set, so their draws cannot be pooled; Sidak needs only
the one null. Exact under independence, mildly anti-conservative under positive
dependence - stated in the comment rather than hidden. A rejected winner restores
the configured settings, which best[] cannot do since the descent mutates it.
Also reports the least-ready tunable handle's BarsCalculated(). IndicatorCreate()
calculates asynchronously, so if the spread is STILL zero the handles simply are
not done and the tuner needs to yield between candidates rather than score them
back to back - a state machine like the label prebuild. That distinction is now
readable from the log instead of requiring another guess.
No input, topology or label change: no retrain.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- BufferDouble: replace hardcoded "DirectML/CPU-DLL" with dynamic backend name
and add buffer index/element count to all error prints for easier debugging.
- NetPersistence: distinguish missing file from transient lock by probing
FileIsExist before logging, eliminating false "sharing violation" warnings
when no saved model exists on first run.
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>
The block fed exactly one number: (v[i] - v[i-1]) / v[i-1]. That is the first difference,
and it cannot express three things that matter - the LEVEL relative to a baseline (two
dead bars and two frantic bars both read ~0 change), and the two volume-vs-range
interactions, where heavy participation that went NOWHERE (absorption) and heavy
participation that travelled (continuation) mean opposite things and currently collapse
onto the same value.
research/test_volume.py measures each candidate's mutual information with the triple-
barrier label across 3 instruments x 2 geometries, against a BLOCK-permutation null -
blocks sized to the barrier horizon, because adjacent labels share almost their entire
outcome window and a free shuffle yields a null so tight that everything looks
significant. Finite-sample MI bias (~7/n here) is reported alongside rather than
subtracted, since the permutation null already absorbs it.
Result: volLevel beats the shipped change ratio outright on 4 of 6 cells (EURUSD 2:3
+0.000118 excess at p=0.006, USDJPY 1:2 +0.000284 at p=0.002); absorption is the single
strongest reading anywhere in the sweep at EURUSD 1:2 (+0.000404, p=0.002) though it is
null on XAUUSD; vol x range clears on 4 of 6. The shipped change ratio is itself
significant on 5 of 6, so it stays.
Kept OUT: a session-relative z-score against the same hour-of-day's own recent history.
It was the weakest candidate - null on both EURUSD cells - and it is the only one needing
per-hour rolling bookkeeping in MQL5. Not worth the state for a reading that did not
survive its own null on the primary instrument.
Magnitudes, stated plainly because they are the point: the excess MI is ~2e-4 nats against
a label entropy near 1.05. That is under a tenth of one percent of the label's
uncertainty. It is real, it repeats across instruments, and it is nowhere near an edge -
this is worth having because it costs one 50-bar loop, not because it changes the answer.
Prior work stands: the whole single-series feature family measured at the noise floor.
m_neuronsCount is already in the fingerprint, so the width change re-keys existing caches
by itself, which is correct - the input vector genuinely changed shape.
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>
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>
REFACTOR_NOTES.md records what was found, what was changed, what was
deliberately left alone, and the one investigation that is still open (the
MLP CPU-DLL slowdown, with the parameter counts that rule out my earlier
"largest weight matrix" explanation).
Also restores the missing opening rule on ReInitADIndicators' comment banner.
Compiles 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>
