Typo, and more wrong than it was: the group now holds four independent NN toggles rather
than one architecture selector.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
User request after the first ensemble run under the per-NN build: the drift verdict was
measured and printed every era while the input sat at BOTH, so it gated nothing - the
worst of both, because an authoritative-looking log line described a policy that was not
in force.
Safe as a default: the verdict fails open to BOTH (it drops a side only when the gap
clears 2 combined SEs on the overlap-deflated sample AND the weaker side sits below
cost-adjusted break-even), so an instrument with no measurable drift behaves exactly as
before. Verified it appears in no fingerprint or DB key - trade policy, not label
definition - so this changes no model identity and resets no training.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Fable 5 <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>
Two user requests, one authority: SymbolInfoSessionTrade, read fresh on
every call so DST and per-symbol schedule changes track themselves.
- WarriorMarketOpenNow(): CheckOpenPosition refuses entries outside the
symbol's trading sessions (Sunday reopen, index CFDs' daily breaks) -
a vote can no longer fire into a closed book and collect a broker
error. ENTRIES ONLY: exits, SL/TP and the scheduled close-all stay
unguarded - closing risk must never be blocked by a session boundary.
- CH_MARKET_CLOSE = 24 (appended, .set-safe): the close-all fires
"Close-all minute" minutes before that day's LAST session close.
Friday + Market close + xxH05 = flatten 5 minutes before Friday's
actual close. Resolved identically in three places: the live executor
(CExpertCustom::OnTick), the label walk's vertical barrier
(NextScheduledCloseAll - the symbol's CURRENT table stands in for
history; MT5 keeps none, and a fixed hour is wrong by more), and the
fingerprint (the |CUT: token already carries hour=24, so switching to
the dynamic mode re-keys the model exactly like any schedule change).
Training itself is deliberately NOT gated on market hours: weekend
compute is free and labels only ever exist on real bars - what the
session table gates is order placement and, via the close-all barrier,
what the labels may count as holdable.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
SQX EdgeFinder precedent (user request): adjust for the drift instead of
fighting it. The 2026-08-19 telemetry found the models leaning SHORT
(Buy recall 21% vs Sell 40%) against a long-favored market (always-long
34.3% vs always-short 29.5% at the adopted geometry).
TRADING_DIRECTION gains INTELLIGENT = 3 (appended, explicit value,
.set-safe). It resolves at runtime from the label cache's per-side win
rates - the Buy/Sell shares ARE the win rates of taking every bar
long/short at the REAL stop/target with spread charged. A side is
dropped only when BOTH hold: the drift gap clears 2 combined SEs on the
overlap-deflated effective sample (EffectiveSampleSize - labels overlap
~18x), AND the weaker side sits below cost-adjusted break-even (a side
that still clears costs is kept; drift tilt alone is not a reason to
refuse a profitable side). Fails open to BOTH: unmeasured, tiny
effective n (<30), insignificant gap, or classic-only charts (no label
cache).
One resolution point - WarriorEffectiveDirection() - feeds all three
gates so they cannot drift apart: CheckOpenLong/Short (live entries),
the filtered-view sweep (a blocked side falls into the delete branch,
mirroring live), and the vote HUD's "-> TRADE" verdict. The verdict
re-derives at every label-cache rebuild, prints only on change, and is
computed even when the input is not Intelligent (marked informational).
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Measured from the journal (2026-08-19): the era deep-dive line (~2KB) plus
the excursion verdict, tier re-rank, calibration move, barrier hold and
selection-regressed note each printed EVERY era for EVERY member - ~940
eras/member/day - long after the systems they watch were confirmed
working. Yesterday's file was 1.3GB (70% of it the news-filter calendar
spam the sweep fix already removed).
VerboseMode returns as an INPUT (demoted 2026-08-01 for the marketplace;
that track is dead since the 2026-08-16 pivot) and gains a second job:
false throttles each settled per-era print to eras 0-3 plus every
TRAIN_LOG_EVERY_ERAS-th (25 ~= one deep-dive per ~15min per member);
true restores the per-era firehose, flippable live.
Never throttled: anything that marks a CHANGE - new bests, restores +
eta decays, plateau stage transitions, deploy approvals, warnings,
errors, the label-cache/adoption one-shots, and the combined-vote gate
line (the active system's primary telemetry, still every era).
Semantic fixes over blanket gating:
- barrier hold now ARMS silently and prints only when the hold outlasts
the 2-min report interval - a brief hold every era is the design, the
long hold is the watchdog case the line exists for;
- the ensemble deploy REFUSAL prints immediately when its reason
changes (that is a finding), on cadence when unchanged;
- the filtered-view census prints when its RESULT moves (drawn count,
or strongest vote by >=2pp) and at least every 10th sweep.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Under CONSENSUS arithmetic the vote is quantized by agreement: with four
members at pooled tier weights ~29, unanimity reads ~29, 3-of-4 ~22,
2-of-4 ~14.5. The 10-point dropdown straddled every one of those rungs -
20 admitted 3-of-4, 30 admitted nothing - so the thresholds an operator
actually wants (between rungs, e.g. 25 = "unanimity or a top-tier 3-of-4")
did not exist. PERCENTAGE_PRESETS and VOTE_CLOSE_PRESETS both gain
5/15/25/35/45; members are only ever ADDED (explicit values, .set-safe),
never removed - MT5 does not validate saved enum values.
Min_Vote_Open default 40 -> 25: the 40 was priced under union arithmetic
and now sits above the unanimity ceiling (~29), i.e. a fresh attach would
silently never trade - the same fired-on-0-bars defect the 50 -> 40 move
fixed once already.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
THRESHOLD. 40 is a measured correction, not a preference. Once
RankTiersFromOos() replaced the designed tier priors with each model's real
held-out win rate, the vote converges on that win rate - logged 2026-08-18 as
pooled 23-36% across four members on three symbols - so a 50% bar could not be
reached by anything on offer and the ensemble gate fired on 0 of 4,865 OOS
bars. 40 clears the ~34% break-even those same lines report without being
unreachable. The comment says plainly not to copy the number: break-even is a
function of the barrier geometry, so read the gate's own "needs >N%" for the
config in front of you.
READOUT. One line, top-right:
VOTE SELL 37.2% peak 44.1% need 40% 3 voter(s) -> no trade
Every other number on the chart is downstream of the weighted mean the open
threshold is compared against, and that was the one quantity never displayed.
A chart with no arrows could mean the models abstained, the vote was diluted,
or the threshold is unreachable - and telling those apart meant waiting for an
era to end and reading the gate line, which is how the last two sessions went.
PEAK is the part that earns its space. A threshold above what the vote ever
attains can never fire, and that is not knowable from a single bar - it is
precisely the "unreachable gate vs merely unmet gate" confusion this project
has paid for twice. Colour carries the verdict rather than the direction:
green/red ONLY when the vote would actually place an order, grey otherwise.
Green-for-buy would make a below-threshold buy look like a trade, which is the
specific misreading the display exists to prevent.
Guarded on `total > 0` for the same reason the normalization is: Direction()
is inherited as-is by every leaf filter, so without it each filter would write
its own opinion into the one shared label and the last to run would win - the
reader would be looking at an arbitrary member's number believing it was the
vote. Drawn after the +-100 range check, so it shows what the threshold is
actually tested against.
CORNER_RIGHT_UPPER: the status lines, control panel and ensemble panel all
live on the left. Registered in WarriorChartPrefixes() explicitly even though
the "Warrior" catch-all already reaches it - that catch-all exists because the
list has drifted twice, not to make entries optional.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
User request: "the entry/exit thresholds are manual numbers, I would like
them to be confidence percentages, so the current 20 would be only 20%
confidence in a profitable trade."
WHY 20 WAS EVER SENSIBLE. Under UseDatabaseRanking both factors of a filter's
contribution are win rates: the pattern weight is that pattern's measured win
rate (UpdateSignalsWeights -> ApplyPatternWeight) and m_weight is the filter's
average win rate over its patterns, /100. Dividing the sum by the VOTER COUNT
therefore produced a mean of PRODUCTS of two win rates - a genuinely
60%-accurate filter firing a 60% pattern scored 0.60 x 60 = 36. The number was
never on a probability scale, so its magnitude meant nothing on its own.
Dividing by Sum(m_weight) instead makes it a weighted MEAN of win rates, which
is a win rate: result = Sum(w_i*p_i)/Sum(w_i). Every voter at 60% now reads 60;
MACD's double-divergence pattern (weight 100) voting alone reads 100. m_weight
stops being a discount on the probability and becomes how much a filter's
opinion COUNTS - which is what a module weight should always have been.
Default Min_Vote_Open 20 -> 50: not a tightening, the same bar re-expressed.
ONE SCALE, EVERYWHERE - the part that made this bigger than a rescale. Three
other places compared against a 0..1 softmax confidence and would each have
become a fresh currency mismatch the moment the input changed meaning:
* the AI early-exit route (LiveSignedConfidence vs m_ai_exit_threshold) now
reads m_lastAiVote - the AI filters' own weighted mean, undiluted by the
classic side, which is the only reason that route exists - against the
same m_threshold_close the averaged vote uses. m_ai_exit_threshold is
retired rather than left dangling.
* m_oosDecisionSeries now carries the vote, not the confidence, so the exit
SIMULATION stops modelling a close rule the EA does not run.
* ExitPolicy() clamped anything > 1.0 to zero. Passing the unscaled input
through that would have silently switched vote exits off in the
simulation while live went on running them - found before it shipped;
the bound now tracks the scale.
LiveSignedConfidence() is deliberately untouched and still 0..1: MM sizing,
SL/TP scaling and the intelligent trailing want a model confidence, not a win
rate.
CALIBRATION CAVEAT, stated in the code where the claim is made: this is only a
real probability to the extent the pattern weights are. A pattern with fewer
than MIN_TRADES_FOR_WIN_RATE journaled trades keeps its DEFAULT weight - a
designed prior (25/50/75/100 for the AI tiers), not a measurement. Until the
signal DB fills, "60" means "the designed conviction of the patterns that
fired". Closing that gap is the next commit.
Also corrects VOTE_CLOSE_PRESETS' comment, which documented the two scales
this removes.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Adds DrawUnfilteredSignals (default OFF) and, with it off, replaces the
per-model arrow layer with the decision the EA would really have made.
THE FILTERED ARROW IS DRAWN AT THE ORDER, NOT AT THE THRESHOLD. Clearing
Min_Vote_Open is not the same as trading: a setup can pass the vote and still
never reach the broker (invalid SL/TP, stops-level, ATR warm-up, unsynced
swing history), and every one of those lands in OpenParams' failure branch.
So DrawVoteArrow() fires only after the order parameters validate, and the
failure branch withdraws any arrow already standing on that bar. One arrow is
one entry the EA would have placed - carrying the vote, the threshold it
cleared, and the SL/TP the order would have had.
Classic signals now draw too, under their own name and weight, so a chart
running MA/RSI/MACD/Ichimoku alongside the nets reads the same way an
ensemble chart does. They can only be drawn from the aggregate's once-per-bar
pass, because unlike the AI members they have no cached per-bar scan.
Two subtleties that would each have produced a quietly wrong chart:
- The raw classic draw sits AFTER filter.Direction(), not beside the
journaling block. GetActivePattern*() are CONSUMING reads holding the
PREVIOUS evaluation - "one tick later", which at Expert_EveryTick=false is
one BAR later. Keyed off those and placed at StartIndex(), every classic
arrow would have been drawn one bar early, which on a chart is
indistinguishable from a model that genuinely leads. Peek*() accessors
(non-consuming) let pattern, weight and bar come from one evaluation.
- CExpertSignalAIBase::DrawObject() early-returns instead of gating its five
call sites, so the switch cannot be honoured in three passes and missed in
the fourth. Its delete counterparts stay ungated so flipping the input off
and rescanning clears the raw layer rather than stranding it.
SIG_ARROW_PREFIX and g_signalsVisible move from ExpertSignalAIBase.mqh down
to ExpertSignalCustom.mqh - the nearest common ancestor - because the classic
signals cannot see the AI header (it is included later in Warrior_EA.mq5).
The vote layer gets SIG_VOTE_PREFIX under the same bare prefix, so
WarriorChartPrefixes()' purge still reaches every arrow without knowing they
exist.
NOT YET BUILT: the reconstructed history behind attach. Filtered arrows
currently start where the EA starts. See the next commit.
NOT COMPILED - user compiles in MetaEditor.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Option (a) from the exit-policy question: the certified number must be the traded
number. Plus the modularity correction the user called for on 778b6c0.
1. VOTE COMBINING BELONGS TO THE ORCHESTRATOR, NOT TO A MEMBER.
778b6c0 fixed the last-writer-wins bug on g_LiveAISignedConfidence by having a
member average its siblings through g_warriorEnsemble. That trades a scheduling bug
for a coupling bug, and it is the wrong shape for this EA: every signal runs in its
own instance, minds its own state, and VOTES to the orchestrator, which is the only
thing allowed to combine opinions.
Replaced with a publish/aggregate pair in Variables\ConfidenceBridge.mqh whose split
is enforced by shape rather than by convention:
- PublishAIVote(slot, conf) - a member writes ONLY its own slot, reads nobody's;
- AggregateAIVotes() - called by CExpertSignalCustom::LiveSignedConfidence.
CExpertSignalAIBase::EnsembleLiveSignedConfidence is gone. The orchestrator also
republishes the aggregate into g_LiveAISignedConfidence, because the intelligent
trailing reads that global directly and must act on the same number the exit route
does rather than on a leftover from whichever member ticked last. A solo AI signal
owns slot 0, so the non-ensemble path is unchanged.
2. THE GATE NOW REPLAYS THE REAL EXIT RULE.
SimulateTradeOutcome() walks the same price series with the same fill/barrier/spread
convention as ComputeLabelForBar - deliberately by copy, so a disagreement between
the two can only be a policy effect and never a discrepancy between two pieces of our
own arithmetic - and terminates at the FIRST of stop / target / vote reversal /
horizon. Barriers are tested before the vote on the same bar: intrabar we cannot know
which came first, and the barrier is what the broker executes automatically, so
checking the vote first would credit the exit policy with escapes a real stop would
have taken out of its hands.
It runs AFTER pass 3, not inside it. A vote-flip exit for a trade entered at bar r is
decided by the model's output at bars r-1, r-2, ... - NEWER bars - and pass 3 walks
oldest-to-newest, so at the moment r is graded its own exit does not exist yet. Only
once m_oosDecisionSeries is complete over the whole OOS window can the replay run.
In ensemble mode that series carries the member's adjusted decision and the live exit
reads the ensemble aggregate, which is the coupling the user identified: an LSTM entry
really can be closed by the ensemble turning against it.
3. THE STATISTIC HAS TO CHANGE WITH THE POLICY, AND THAT IS THE REAL FINDING.
A barrier exit pays a fixed R. A vote exit pays whatever the close happens to be. So
the moment vote exits are enabled the payoff is CONTINUOUS, and "win rate vs
break-even" stops being a meaningful test - there is no fixed break-even for a
variable payoff. SimulateTradeOutcome therefore returns R rather than a bool, and the
replay reports expectancy in R with its SE taken from the R distribution (overlap-
deflated on the same EffectiveSampleSize doctrine as every other SE here), not from a
binomial.
This is the same class of error as win-based scoring in 2026-08-09: measuring a
variable-payoff process with a fixed-payoff statistic. Naming it now, while vote exits
are still off, is much cheaper than discovering it after they go on.
4. WHY THIS IS SAFE TO SHIP TODAY.
Min_Vote_Close ships Disabled (101, unreachable on both scales it drives) and reaches
the AI signal through the new ExitPolicy() setter as 1.01, which the setter turns into
0.0 = no vote exit. Under that policy every replayed trade resolves at a barrier and
the simulation is arithmetically the same trade the deploy gate already certifies -
they cannot drift. The report says so explicitly, and prints ONCE per run in that
state; when vote exits are on it prints every era, because then the divergence is the
thing to watch. Nothing about today's numbers moves.
The gate switchover is wired but dormant by construction: it becomes exit-aware the
moment the input is enabled, which is exactly what "the certified number is the traded
number" has to mean.
KNOWN LIMIT, stated rather than hidden: only the AI early-exit route is replayed. The
rule-based averaged-vote close (m_threshold_close) depends on every other filter's
live vote, which pass 3 does not reproduce, so a position the classic filters would
have closed is held to its barrier here. The replay therefore holds LONGER than live
and overstates barrier-reached outcomes. Faithful only while the AI is the dominant
vote - which is the configuration this is being built for.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
NOT COMPILED - user compiles.
The private build still DEFAULTED to TARGET_FRACTAL, so every fresh attach was
training the target adjudicated dead that morning (5,700 model-eras flat at -2pp,
best-of-243 p=0.17). The campaign closed; the default was never flipped back.
Rather than re-default it, the input is withdrawn entirely (user: "remove the
option if there is only one choice for now"). An input offering a single live
choice is worse than no input - it presents a dead option as supported, and an
operator picking it silently trains a model already known to carry nothing.
Direction models are now unconditionally triple-barrier.
Removed: the input, the TrainTargetFractal() call in the signal setup, and the
HoldToBarrier() exit-policy block (which existed only because the fractal vote
flips at swing-marker cadence, ~3-5 bars, far inside the barrier's travel time -
barrier-target models keep vote exits and always did, their label IS the vote's
horizon). Verified no code reference to TrainingTarget survives; the four
remaining mentions are comments.
Kept deliberately, so a rerun is a re-enable and not a rebuild: the TRAINING_TARGET
enum, the fractal label itself, its |TGT:FRA1 fingerprint token, its conditional
barrier-geometry derivation, HoldToBarrier()/m_holdToBarrier, and the campaign's
trained models on disk. Three lines bring it back; Inputs.mqh names them.
ALSO CORRECTS THE RECORD from 1b5a412. I claimed the live run was on the barrier
target, "confirmed" by break-even 34.3% matching the 0.62/1.18 geometry. That
proved nothing - break-even comes from BarrierMultiples, which grades wins
identically under either target. Neutral's share is the real tell: ~31% would say
barrier, the measured 10.6% says fractal. So the imbalance finding stands and its
mechanism is unchanged, but the cause of Neutral being rare was the FRACTAL
target, not the triple-barrier relabel. Neutral fell twice - 94% under the old
exact-pivot ZigZag label, ~31% under triple-barrier, 10.6% under fractal - and
the correction was re-checked at neither step. The fractal campaign was chosen
FOR its balanced classes and did balance Buy vs Sell (48.3/41.1) while quietly
making Neutral the thin residual the correction then subsidised.
Under the barrier target the same geometry gives roughly 34/34/31, where Neutral
is neither rare nor dominant, so 1b5a412's fix should be close to a no-op there -
which is the right answer when there is nothing to correct. It stays: never
subsidising the abstain class is correct under both targets.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Systematic audit of the alt-data stack against "any symbol, any timeframe",
prompted by the H4 surprise. Findings, each fixed:
CAPACITY: ALTDATA_MAX_FEATURES was 16 with FX symbols already at 15 - the next
added column would have been silently truncated by a MathMin. Raised to 32,
pin-chars 512 -> 1024.
SYMBOL NAMES: the panel reload path re-derived symbol/timeframe by splitting
the file path on its FIRST underscore - mis-parsing every symbol containing
one (OANDA-style EUR_USD and US_500 are in our own alias lists) and knowing
only three timeframes. It now stores the (symbol, period) Load() was called
with and reuses them verbatim. Path-hostile characters in broker symbols
("EUR/USD") are sanitized by a shared AltDataFileSymbol() used by the panel,
the fetcher and TunedPeriods, so a slash cannot route a write into an
unintended subfolder.
DOWNLOAD VALIDATION: every FRED-family fetch now enforces a per-series
plausibility band (VIX 1-200, yields -5..30, CPI index 20-1000, ...) -
StringToDouble on transport garbage returns 0.0, and one absurd value poisons
every change/percentile feature computed across it (the BatchNorm NaN-latch
incident came from exactly one huge-but-finite input). Rejected rows are
counted and reported, never dropped silently.
LOUD EMPTINESS: a successful response with zero observations on an empty
cache now says so - naming the series (wrong id / format drift) or the COT
predicate (the unverified like-clauses) instead of leaving 0-filled features
unexplained. GEX gains a truncation guard: a day-over-day contract-count
collapse >50% is the fingerprint of a partial 13 MB download, not of markets,
and is skipped rather than recorded as a plausible-but-wrong number.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
EIA (user directive: "the NN might find patterns in it for both oil and regular
symbols"). Weekly Petroleum Status Report via the v2 API - crude stocks ex-SPR,
field production, refinery utilization - three features (1y percentile, 4w
change, utilization) on EVERY catalog symbol, not just oil. EIA screened NULL on
WTI's short 7y sample, so these ship as EXPLORATORY inputs: the deploy gate, not
the screen, decides whether a model trained on them trades. Publication stamp
observed+6d mirrors research/altdata/eia.py.
Symbol handling was hardcoded to three if-blocks; it is now a catalog of 24
instruments x alias lists covering The5ers/FTMO/AvaTrade/Dukascopy/OANDA/IC
Markets naming, with prefix matching for the broker suffix zoo (US500.cash,
XAUUSDm, EURUSD.r). Adding an instrument is one AddSpec row. COT caches are
named by CANONICAL so two brokers' names for one contract share a download.
Unrecognised symbol -> a chart dialog (Panel\AltDataMapDialog.mqh, CAppDialog +
dropdown) asks which instrument it is; the answer persists in symbol_map.cfg and
"No alternative data" is a recorded choice, not a nag. Non-blocking by design:
an unmapped symbol contributes 0 features and must never hold up a chart.
Also: UrlEncodePart now escapes '%' - SoQL like-predicates use it as the
wildcard and an unescaped one corrupts the query; docs/ gains the whitelist
URLs, an API-key backup, and the catalog reference.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The AltData folder in Common\Files gets wiped before every fresh test, and
keys.txt died with it (2026-08-16 silent-FRED incident). The credential now
travels with the EA: FredApiKey input, owner key as default; keys.txt demoted
to a fallback consulted only when the input is blanked. EiaApiKey stored the
same way - reserved, nothing consumes it since the WTI screen came back null.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- PeriodMA/MA_Type/PeriodRSI: input -> const seeds (closing the set: every
indicator parameter is now tuner-owned)
- Variables\TunedPeriods.mqh: chart-level tuned-period state. A gated
install writes TunedPeriods_{SYM}_{TF}.cfg; next attach reads it BEFORE
the DB fingerprint and classic-signal config, so classic votes, DB key,
and tuner seeds always describe the same indicators regardless of
classic/AI/hybrid use. Restart-grained adoption by design (no mid-run
handle churn); new periods re-key the signal DB (semantics rule).
- EnableAltData input in AI Input Features (consumption gate only;
collection keeps running); |ALT DB-fingerprint token; opt-out on an
alt-trained model correctly starts fresh via the width compare.
- Defaults: all four classic votes OFF (AI-first; WARRIOR_MARKET_BUILD
branches collapsed with the marketplace pivot), order-flow/Wyckoff NN
features OFF (alt data is the default information diet; toggles stay).
Compiles 0 errors / 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The 18 inputs added 2026-08-08 (when the tuner defaulted off and the
values needed an operator path) become compile-time aliases of their own
defaults - same names, zero consumer churn, byte-identical values. The
tuner is now the only path by which these values move: it defaults ON
(the 08-08 off-flip was measured against the direction target's flat
landscape; the objective is now RANGE, which has signal), searches from
the seeds under the Sidak family-wise gate, and persists winners in the
.nnw beside the weights. ADP fingerprint token retired (deviation now
impossible by construction; tuned values were never its job).
Menu shrinks 102 -> 84 inputs. Compiles 0 errors / 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The user is right that no special combination logic is needed: the AI
signals are ordinary voting filters, and the aggregate already has
union semantics - abstaining filters do not dilute the average, so an
ensemble chart trades whenever ANY deployed member clears the vote
threshold and disagreeing members net out. What the ensemble preset
actually adds:
- AI_CHOICE value 4 renamed AI_CONVLSTM (the name says the front-end);
enum VALUES stable, CSignalHYBRID class and State\HYBRID\ folder kept,
so saved configs and trained models keep their identity.
- New AI_HYBRID = 6: enables PAI+CONV+LSTM+CONVLSTM together on one
chart - replaces four separate charts of the same symbol. Each member
trains and self-gates independently; only certified members ever vote.
- |ENS1 fingerprint token on every member, so an ensemble member's
weight files can never collide with a solo model of identical
settings on another chart of the same symbol (the duplicate-chart
guard would otherwise correctly fight over one .nnw).
- Private default AIType = AI_HYBRID: one D1 drop now yields every
topology's gate verdict for that symbol.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
User direction (2026-08-15): back to predicting swing turns, D1 charts,
fractals over ZigZag pivots (their call - balances classes, matches the
reference library target, and a 5-bar fractal confirms 2 bars after its
extreme so labels resolve nearly to the present with no repaint embargo).
- TRAINING_TARGET enum + TrainingTarget input: TARGET_BARRIER (Market
default - existing models keep their meaning and fingerprints) or
TARGET_FRACTAL (private default).
- FractalDirectionLabel (Labels.mqh): per-bar 3-class label = direction
from the bar close to the next confirmed strict 5-bar fractal extreme,
costs charged in the same bid-series convention as the barrier label,
Neutral when the move cannot clear max(2 spreads, 0.10 ATR) or on an
outside bar (both-extreme bars are unorderable within OHLC).
- The barrier walk still runs in full: measured SL/TP geometry, the
expectancy scan, excursion caches and the era gate all keep scoring
what a trade at the EA's own stop/target actually collected - only the
TRAINING label changes. NOT the pre-b4a704d "is this bar the pivot"
form; that target's 31:1 imbalance stays retired.
- Fingerprint token |TGT:FRA1 so switching targets trains a separate
model; AI_META unaffected (guarded setter).
- Private defaults: AIType back to AI_HYBRID (direction topology needed)
+ TrainingTarget=TARGET_FRACTAL = drop-on-D1-chart workflow.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
User request: attaching a chart must need zero Inputs-tab edits. Private
(non-Market) build now defaults to AIType=META, all four classic families
ON (they are the sweep's candidate sources), Meta_ExportDataset=true.
Market-build defaults unchanged (AI_NONE, MA/RSI only, no export);
UseDatabaseRanking=true applies to both per the earlier request.
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 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>
Implements stage S1 of Meta_Labeling_Design.md, superseding the
original "training-time ladder sweep": the per-side journaling from
652bf81/195be20 already produces the exact candidate stream a sweep
would compute - every pattern instance the live ladders fire, both
sides, uncensored, with netVote and touchable entry price - so the
corpus is READ from the DB instead of re-implementing 26 ladder
conditions in training code. That eliminates the silent-divergence
trap outright: the corpus is by construction identical to live
behaviour. Accepted costs are documented in the module and the doc:
coverage equals the populating backtest, and sampling is one
candidate per fire-stretch (the right dedup for training anyway).
- Expert\AIBase\MetaCorpus.mqh: CMetaCorpus reader (52 tables ->
SMetaCandidate rows) + VerboseMode OnInit report: volume/closed/
S&R-win-rate per family, span, and the GMT->server bar-offset
match table (offsets +0..+3h) that S2''s label plumbing pins to -
measured, not assumed.
- DB_MaxRowsPerTable input (default 1000 = old MAX_TABLE_ROWS): a
corpus build raises it (e.g. 20000) so a 15-20 year backtest
isn''t pruned; wired through CExpertSignalCustom::MaxTableRows().
- Report-only stage: nothing downstream consumes the corpus yet.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The DB system logs objectively; the decision layer reads it to compute
win rates and adjust weights. The journaling path still had one
decision-layer tendril: rows were only written when the root''s
OpenLongParams()/OpenShortParams() succeeded. Those calls validate
ORDER PLACEMENT (broker stops-level, ATR warm-up, entry-mode
rejection) and their failures cluster in volatility/spread conditions,
so the gate non-randomly censored exactly those bars out of every
pattern''s win-rate sample - the same censoring class 652bf81/c8ef478
removed, one layer down. The ledger never needed placement to be
possible: entries are marked at the touchable side of the spread and
exits are same-pattern reversals, not broker fills.
Also documents netVote for what it is: a record of the decision
layer''s state at log time (per-pattern weights inside it drift as
ranking updates land), not an objective measure - the objective part
of a row is pattern/direction/price/result.
SIGNAL_DB_SEMANTICS_VERSION 4 -> 5: row populations gain the
previously censored bars, so the database re-keys.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Verification of 652bf81 on a fresh 7-month backtest DB surfaced the
last one-sided mechanism: ProcessSignal absorbed a reversing signal as
the exit of the opposite trade and skipped registering it. For pure
EVENT patterns that strictly alternate (MACD model 3, the zero-line
cross), every reversal was consumed and the whole ledger landed on
whichever side fired first - 60 Buy rows, 0 Sell rows - so the silent
side never earned a win rate and UpdateSignalsWeights() weighted the
pattern from one side only. State patterns escaped by re-firing one
bar later.
The reversal now closes the opposite trade AND registers its own row;
the existing duplicate/outdated/open-trade checks still bound the
table at one open trade per pattern+side. Row populations change
meaning, so SIGNAL_DB_SEMANTICS_VERSION 3 -> 4 re-keys the database
(the v3 file is orphaned, not wiped - schema is unchanged).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The labelMatchesVote gate compared a single last-writer-wins label
(LongCondition then ShortCondition) against the net vote sign, which
structurally censored the pattern tables: a long event co-occurring
with any short-side state model lost its label to the later writer and
was dropped, while the mirrored short event journaled fine. Ichimoku
models 0/3 and MA model 1 could not produce a row at all by
construction (MA model 1 was "revived" in 8710240 yet still could
never journal - its weight-10 vote is exactly cancelled by the
opposing Pattern_0 state), and every pattern's win rate was measured
on a with-trend-only subset - the exact statistic
UpdateSignalsWeights() feeds back into the weights, self-sealing:
no rows -> no win rate -> default weight -> still censored.
- Direction() now evaluates the two ladders separately and snapshots
each ladder's matched pattern into its own side slot; each side that
matched journals its own row. The flat-vote poisoning the old gate
fixed stays fixed: a label can no longer contradict its side.
- The filter's net vote (raw pattern-weight units) is stored as a new
netVote column - data, never a drop filter. Snapshot is keyed on the
ladder setting a label, not on its weight, so a 0%-win-rate pattern
keeps journaling and can recover.
- SIGNAL_DB_SEMANTICS_VERSION is folded unconditionally into the DB
filename fingerprint: pattern-definition changes (b2069bc, 8710240)
re-key the database instead of blending incompatible Pattern_N
populations under one key, which the input-hash fingerprint cannot
see. 7 months of mixed-semantics rows shared one file because of it.
- dbVersion 2.0 -> 3.0: schema changed, and inserts carry the new
column, so the version-mismatch folder wipe is the migration.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Direction is closed - normalised asymmetry fails on three instruments
with a working positive control, and the classifier's own best-of-999
era-cap test agrees (+0.9pp = 1.48 sigma, family-wise p=1.0000). SIZE is
a different question and RANGE clears at ~4x its null.
Checked the denomination before building on that, since the source memo
warns to: m_excUpCache holds (maxHigh - fill)/ATR, so "RANGE is
predictable" is a claim about travel RELATIVE to current ATR, not a
restatement of "ATR is autocorrelated". It is exactly the part a fixed
multiple (stop 3.31*ATR, target 1.64*ATR) discards.
A second small CNet, 760 -> 24 -> 32 sigmoid outputs = P(price reaches
ladder rung k) upward and downward. Survival parameterisation rather than
regressing the multiple, because it needs nothing new from CNet: sigmoid
outputs and the per-neuron delta the `total != 3` branch already applies
(a quantile head would need a linear activation and a pinball gradient in
Network.mqh, Network.cl and the DirectML path, on a class four topologies
share). Targets are free - m_ladderUpAt already records first-touch age
per rung with 0 meaning never reached.
Separate net, not extra outputs on the classifier: more outputs would
change m_outputNeuronsCount, the .nnw shape and the fingerprint, and push
the count off 3 - the exact condition backProp uses to select the joint
softmax gradient the 3-class head depends on. The classifier is
bit-for-bit unaffected and this is removable without trace.
STAGE 1 PLACES NO ORDERS. It reports a Brier skill score against the
constant per-rung base rate - the baseline a fixed ATR multiple already
assumes - with both predictors fitted IS and evaluated OOS, so neither
gets a look at the test set. Positive skill justifies Stage 2 (drive
SL/TP and sizing off ExcursionQuantile, which is defined and deliberately
uncalled). Zero or negative means ATR already carries everything and
Stage 2 must not be built.
Trains only on primary occurrences: the replay queue oversamples for
CLASS balance, and a direction-balanced sample is a biased SIZE sample.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
SignalClusterWindow 3 -> 10 for all topologies. On H1 a 3-bar window
collapsed only the tightest runs and left visible clusters at every
turn; 10 bars is closer to the spacing of genuinely distinct setups.
ALTERNATION. Rule 1 only collapses a same-direction run INSIDE the
window; past it a second Buy is emitted with no Sell between, giving
Buy/Buy/Buy/Sell. With both directions tradeable that sequence is the
model re-entering a move it is already in rather than finding a new
one. The kept sequence must now alternate: the first signal passes,
and after that a direction passes only if the last KEPT signal was the
opposite one.
Added to ALL THREE consumers, with identical logic, because they must
agree:
- NmsLiveAccept -> the live trade
- pass 3's OOS replay -> the tally the deploy gate grades
- PruneDirectionalClusters -> the drawn history
A rule applied to only some of these certifies one strategy and trades
another - the same defect class as the geometry the gate certified
while OpenParams placed something else (9a7c37f) - and would draw the
user arrows the EA would never have taken.
Deliberately NOT applied to the LABEL. The barrier target has no "must
flip" invariant: consecutive Buy labels are routinely correct, and an
earlier alternation gate was removed with the triple-barrier relabel
for exactly that reason. This filters what is ACTED ON, which is what
"applies to training" can honestly mean here - pass 3's declustered
tally is the training-side number that decides deployment.
BothDirectionsTradeable() is the stated precondition (with one side
disabled there is no opposite to wait for, so alternation would
suppress everything after the first call). This build has no
long-only/short-only input, so it is constant true - kept as a named
predicate so a future direction restriction has one place to change
rather than three call sites silently assuming both sides.
Build tag -> nms-alternate-v4.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Three changes, one theme: the trade placed, the trade graded, and the trade
computed are now the same trade.
1) GEOMETRY WIRE (correctness, the ranked #1 open issue). The measured barrier
pair reached the LABELS only - OpenParams still placed orders at the enum
geometry (2*ATR/6*ATR), so the deploy gate certified "reaches 1.62*ATR before
3.33*ATR above break-even" about trades the EA never placed. Published via
g_DerivedSlAtrMult/g_DerivedTpAtrMult (ConfidenceBridge, same same-tick
contract as the confidence globals, because OpenParams runs on the root signal
which has no pointer to the AI filter). Two writers: DeriveBarrierGeometry at
era 0, and the .cfg adoption a deployed model takes. Overrides both legs and
both Intelligent modes - the certificate is exact or it is nothing. TP is
ATR-anchored like the label, NOT risk-relative, so a floor-widened stop cannot
reshape the certified target.
2) BATCH NORM RUNS DEVICE-SIDE ON OPENCL. Four kernels in Network.cl -
forward, hidden gradient, gamma/beta accumulate, gamma/beta apply - each a
line-for-line transcription of the host implementation (NormalizeHost /
HiddenGradHost / StepGammaBeta) including every NaN guard, clamp, and the
exact moment-write ordering. The host copies remain the runtime for the DLL
and pure-MQL5 tiers and the reference the kernels must match.
Because this box has no OpenCL platform, the safety story is layered:
- shim validation: kernels compiled as C and driven against a fp64 host
transcription over NaN-poisoned stats, NaN gamma, over-clamp inputs, the
frozen path, both optimizers, 3 batches - ALL PASS, worst normalized diff
0.132 vs tolerance 1.0
- in-situ self-check: each kernel is compared against its host twin ON FIRST
USE on the real device (SelfCheckBn*), covering what the shim cannot - arg
indices and buffer bindings. Any disagreement resyncs from the good copy,
latches all BN kernels off process-wide, and training continues host-side.
A transcription bug costs a warning and some speed, never a poisoned .nnw.
- sync discipline: BatchOptions is now a CBufferDouble with explicit
authority tracking (m_bnDeviceAuthoritative). Checkpoints/saves pull
read-only; restores/loads/resets push; a mid-batch handover drains the
device gamma/beta accumulator into the host arrays so no sample is lost.
3) SMALL FIXES. Apply-kernel build failure now latches the dispatch path at
init (one warning instead of warning + failed Execute). Build tag bumped to
win-scoring-gpu-v1 - first tag change since expectancy-stop-v1 despite five
binary-changing commits.
Both build variants compile 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The barrier geometry is derived from the instrument's own excursion
distribution (stop at q75 of adverse travel, target at q50 of favourable),
and then a 1:2 floor was applied on top, raising the target to twice whatever
the stop happened to be. On SP500 H1 that pushed the target to 6.66*ATR,
reached on 3.3% of bars inside the horizon - so the label became "almost
never a win" and every topology was trained to predict an event that
essentially does not occur. A measured target has to stay measured.
The ratio never bought what it was believed to buy. A reward:risk floor does
not create expectancy; it trades hit rate against payoff at a break-even the
geometry already fixes - which this project has separately MEASURED (payoff
0.92 -> 5.72 with expectancy flat). What it did buy was two outages: four
consecutive Market validation rejections for "no trading operations" when it
rejected 100% of setups, and the label corruption above.
Removed:
- the input and the RISK_REWARD_RATIO enum (deleted, not left dangling - a
live enum with no input behind it is the shape of the stale-.set incident
that trained ~250 eras on the wrong target)
- the forced target raise in the label geometry
- the rrOK eligibility gate in the barrier-geometry scan, so every unclamped
pairing now competes on the measurement alone. Clamping stays disqualifying
for its own unrelated reason.
- the reward < minRR*risk veto in OpenParams
Kept: g_TradeRewardRiskRatio still computed and still bridged to Kelly sizing
in MoneyIntelligent - the ratio as a SIZING input was always the sound use.
Risk stays bounded where it actually is - account risk % and CRiskBudget.
The low-reachability warning survives but is re-aimed: with nothing inflating
the target, a target the market rarely reaches can only mean the horizon is
truncating the excursions the geometry is derived from.
Both build variants compile 0 errors / 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
AutoTuneIndicators now defaults to FALSE, and the 33 AD/Wyckoff parameters
it used to search are now inputs.
WHY THE DEFAULT FLIPPED - not because the search is broken. It is correct,
and its own Sidak gate is what proves it: 324 candidates per model on
SP500 H1, "no improvement" on all four topologies (0.00236 -> 0.00236 on
the AD configs, 0.00370 -> 0.00370 on PAI), winner rejected at p=1.0000.
It cannot do better here by construction - it ranks candidates by MARGINAL
MI, and the headline MI is 0.00370 nats against a shuffled null of
0.00379 +/- 0.00061 (p=0.4975), so every candidate is a noise draw and the
maximum over N of them is noise too. The cost is 45-56 min per model in
one synchronous call with no yield, and it was the amplifier for the
handle leak fixed in 33f106d. The EA's own report says it plainest: "no
per-feature indicator retuning will help."
THE INPUT STAYS. TuneIndicatorsByFilter is one function of twelve in
AIBase/AutoTune.mqh; the other eleven are the MI/lag/excursion/geometry
diagnostics that produced every verdict this project relies on, and they
run regardless of this flag. Removing the input invites removing the file.
WHY THE INPUTS WERE NEEDED. All 33 were literals in CADIndicatorTuner's
constructor with no input of any kind, while MA/RSI/MACD/Ichimoku have had
their periods exposed from the start. On the AD configs those indicators
contribute 28 of 64 features per bar. Survivable while the tuner searched
them; indefensible with it off, where they would freeze at values nobody
chose.
CONSOLIDATED 33 -> 18. volClimax/volHigh/rangeClimax/rangeSignificant/
stVolRatio/atr were duplicated verbatim across CumulativeDelta, Wyckoff
Events, Failed Structure and Bar Inversion - the same constants restated
3-4 times. One concept, one input. They are SEEDS: each fans out to the
indicator's own struct field, so with the tuner on it retains full
per-indicator freedom to move them apart. Same contract as PeriodMA.
NO RETRAIN. Every default is byte-identical to the literal it replaces,
and the fingerprint's new ADP token is appended ONLY on deviation
(MACD/Ichimoku/BN/XA convention), gated on the AD features being enabled.
At defaults the token is absent, so every model on disk keeps its filename
and stays loadable. Without that guard, merely EXPOSING these parameters
would have re-keyed every config and forced a from-scratch retrain of all
four topologies for a change that alters no number anywhere.
All-or-nothing rather than per-input, so the token can never encode a
partial picture of what the features were built from.
Compiles clean: 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The daily (4%) and total (8%) rules bound how FAST an account can lose. Nothing
noticed WHETHER it was losing. A negative-expectancy signal traded at 1% inside
that envelope breaches no rule and still arrives at zero - it just takes longer,
with every limit green the whole way down. That is the realistic way this EA
destroys an account, and no existing guard could see it.
THE ARITHMETIC THIS ENFORCES. Expected value per trade is p*TP - (1-p)*SL - cost.
With no directional edge p equals SL/(SL+TP), which is also the break-even rate,
so the payoff terms cancel exactly and EV = -cost. Expected P&L is -(trades) x
cost: strictly negative, proportional to activity. Measured here: directional
precision 23-24% against a 25% break-even, flat across every confidence tier,
with 58 points of spread on SP500. Sizing, stop placement and trailing move
variance around that mean; none of them changes its sign.
So every closed position now reports its result in R (net profit over money
actually at risk) and the running mean is tested against zero. Above the
configured minimum sample, if mean + sigma*SE < 0, new entries stop.
- SIGNIFICANTLY below, not merely below. A run of losers is ordinary variance
even for a profitable system; halting on the raw mean would be the same
act-on-noise error the MI gates exist to prevent. Using the standard error
means a wide spread simply demands more trades before the rule can fire.
- NET of swap and commission (ResolveClose already sums all three). Deliberate
and load-bearing: when the edge is zero, cost IS the expectancy, so a gross
version would measure a strategy nobody can trade.
- Reported in R so symbols, lot sizes and balances share one scale and one
mean. Trades without a stop are not scored rather than assigned a guessed R.
- LATCHED across restarts, like the daily halt and for the same reason: a
latch a reattach clears is not a latch. Clearing it means deleting the risk
state file, deliberately, after looking at why.
State is appended to the risk file length-guarded, so files written before this
still load and start their sample at zero rather than misreading.
Defaults 40 trades / 2 sigma; ExpectancyMinTrades = 0 disables it.
This does not make the strategy profitable and is not meant to. It stops paying
tuition on one the results say is losing, and does it on measurement rather than
on a drawdown limit finally being reached.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Three enums left the Inputs tab. They were three things a user had to pick and,
in the tester, three more axes for a genetic optimization to overfit.
Entry_Multiplier is pinned to MARKET. Its pending modes place the entry at a
LEVEL while the rest of the pipeline measures from the bar open - the exact
mismatch that manufactured the +0.097 R "retail fade" result later retracted as
a fill artifact. This codebase's fill model cannot honestly simulate a pending
entry, so it is no longer offered.
SL_Mode/TP_Mode become a STARTING pair. ReportBarrierGeometryScan now ADOPTS its
winner instead of printing "set SL_Mode/TP_Mode to X and retrain":
- only when it clears the family-wise gate from 04ee2e1 (beat the null of the
MAXIMUM, not merely the incumbent). This is why that gate had to land first:
without it, removing the inputs would hand a noise-picked geometry direct
control over the training target with no human in the loop - strictly worse
than the input it replaced. On SP500 H1 today it does NOT clear (p=0.1463),
so 2:6 is what you get - now chosen by measurement rather than assumed.
- only at m_eraCount == 0. Relabelling a partly-trained net moves the target
out from under weights already fitted to the old one.
THE GEOMETRY LEFT THE WEIGHTS-FILENAME HASH, because it is now measured. Same
rule that moved the horizon and the derived topology values out: a filename
keyed on a measured quantity changes the moment the measurement does - a few
more bars shift which pairing wins - and the EA then looks for a file that does
not exist, starts from era 0 and orphans a trained model silently. It is PINNED
IN THE .cfg instead: appended at the end (the only backward-safe change),
length-guarded like the 2026-07-30 derived pair, and ADOPTED on load rather than
compared, so a trained model keeps the barriers it actually learned and never
re-measures.
Two traps closed while wiring it, neither of which announces itself:
- m_barrierHorizonResolved latches the horizon ONCE PER PROCESS. Adopting 2:8
(wants ~192 bars) after it settled for 2:6 (128) would label the new target
against the old ceiling - the truncation fixed in 168422f, where every model
learned "target within 128 bars" while the EA holds to SL/TP. It lands in
Neutral, not in the timeout counter watching for it. Unlatched on adoption,
along with the label cache the old barriers filled.
- the .cfg adopt runs at init, before the horizon latches and before any label
is computed, so a resumed model has its pinned pair in place first. Verified,
not assumed.
FORCES A FULL RETRAIN: the fingerprint change orphans every existing .nnw.
Co-Authored-By: Claude Opus 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.
- 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>
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>
MEASURED COST OF THE GA, which is what retired it. Per generation:
rung 0: 8 cand x 3 seeds x 3 eras = 72 eras
rung 1: 4 cand x 3 seeds x 8 eras = 96
rung 2: 2 cand x 3 seeds x 20 eras = 120
= 288 eras/generation x 4 generations = 1152 eras BEFORE the winner's
real training began. Against the observed era times on SP500 H1:
PAI 29.1 s/era -> 9.3 h (matches the observed 00:37 -> 09:22)
CONV 41.3 s/era -> 13.2 h
LSTM 150.4 s/era -> 48.1 h
HYBRID 154.6 s/era -> 49.5 h
Two days to tune is not a first-run experience, and it is the phase in
which the panel goes quiet, which is what made it look like a hang.
It also bought nothing. The space is 90 points (10 MA periods x 9 MA
types), so 1152 evaluations revisited each point ~13 times; and rungs of
3 and 8 eras cannot separate two MA periods at all. The 2026-08-01 run
proves it: every finalist scored 25.0-25.9% balanced accuracy - below the
33.3% one-class floor, i.e. indistinguishable noise - and the search then
"deployed the winner" of that.
THE ERROR WAS THE SCORING FUNCTION, not its constants. Using a full
training run to choose a feature's period is a wrapper method paying
wrapper prices for a decision that does not need one. The reference book
does not do this: ch. 3.3 selects inputs by measuring each candidate
indicator's CORRELATION with the target and dropping the ones with none,
with no network involved.
So: rank candidates by the MUTUAL INFORMATION between the resulting
feature vector and the triple-barrier label. MI rather than correlation
because the label is 3-class categorical and the features are not
monotonically related to it. Equal-FREQUENCY binning (rank-based),
because these features are ATR-normalised and heavy-tailed - fixed-width
bins put nearly everything in one bucket and report ~0 information for a
genuinely useful feature.
Scoring is arithmetic over the feature cache, so it costs seconds and its
cost is independent of topology: LSTM now tunes as fast as the MLP.
Coordinate sweep, not product sweep - cost is the SUM of per-parameter
candidate counts, so enabling every indicator stays affordable - with a
second pass that breaks early once nothing moves.
Sampling is IS-ONLY. Letting the OOS window influence which indicator
settings ship would mean the holdout had been used for selection and had
stopped being a holdout.
HONEST LIMIT, recorded because it is the price: MI is marginal, so a
parameter that only pays off in combination with another can be missed
(Guyon & Elisseeff 2003, filter vs wrapper). Given the wrapper it
replaces was ranking pure noise at 48 h a run, this is strictly better.
Deleted with it: GaRungEras/GaExtract/GaStore/GaMutate/GaRandomCandidate/
GaBlockCrossover/GaSortAliveByScoreDesc/GaBreedNextGeneration, 14 m_ga*
members, the GA_*/TUNE_POP_* constants, and ComputeTuneTrialBudget.
AND m_evalMode/m_evalEraBudget, because nothing set them any more - 28
read sites all permanently inert. That is not a tidy-up: the `if
(!m_evalMode)` guard on UpdateClassPriors is exactly what silently
disabled the imbalance correction for entire runs two commits ago. Dead
machinery that still reads like live machinery is this codebase's most
expensive recurring bug, and leaving 28 more instances of it would have
been indefensible.
The panel's tuning-progress state goes too - tuning no longer takes long
enough to need one.
Both builds compile 0 errors / 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
The AutoTuneIndicators input is now automatically derived via `ComputeTuneTrialBudget()`, so the default is set to false to prevent manual interference.
Adjust default stop-loss mode from SL_ATR_x1 to SL_ATR_x2 and default take-profit mode from TP_ATR_x3 to TP_ATR_x6. This improves the risk-reward alignment in line with the recommended minimum ratio and ensures setups are not rejected under the EA's target reward parameters.
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 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>
The imbalance section offered nine controls for one job. Audited against the
code, five of them did not do what their names said at the shipped defaults:
AILogitPriorStrength DEAD - Inference.mqh's post-hoc prior early-returns
whenever the adjusted loss is on, which is default.
OversampleParity DEAD in training - Training.mqh gated the replay loop
on !useLogitAdjustedLoss (correctly, citing Buda et
al. 2018). Live only in the online-learning path.
EnableMinorityReplay DEAD as replay. It survived ONLY as a focal-gamma
damper - "replay minority bars through pass-2
oversampling" was a focal-loss switch.
ConstrainReplay DEAD as a cap; it only chose damper 0.125 vs 0.25.
UseStaticPrior An exact duplicate of FreezePriorCalibration - the two
were OR'd together in the single place either is read.
So they were not five mechanisms fighting; they were one mechanism plus eight
knobs that mostly described machinery that no longer ran. That is worse than
a real conflict, because the log agreed with the names: the label-cache line
printed "reps up to 28x (90% parity) (seeding era 0's class-balance
oversampling)" on every run, describing an oversampling pass that had been
switched off. It is fixed here too - it cost this session a wrong diagnosis.
The one genuine redundancy was focal loss, running at gamma*0.125 alongside
the adjusted loss: two corrections on the same axis, the exact stacking
failure this file already cited Buda et al. for in two other places, damped
by a replay flag whose replay path was itself dead. Removed rather than
re-tuned. The plateau ladder is unaffected - its escape is the learning-rate
warm restart; the gamma anneal beside it only ever stepped toward zero.
WHAT REMAINS is logit-adjusted loss (Menon et al. 2021) plus a prior freeze:
LogitAdjustTau 0 = off; replaces the separate EnableLogitAdjusted-
Loss boolean, since a strength dial where 0 already
means off does not need an on/off switch beside it.
FreezePriorCalibration unchanged.
It is the only one of the six corrections with a consistency guarantee, and
it is consistent for exactly the balanced-error metric checkpoint selection
already ranks on - so the loss and the deploy decision optimize one thing.
The online continual-learning path keeps its own alpha-balanced focal weight,
now as constants pinned to the removed inputs' shipped defaults, so its
behaviour is unchanged. It legitimately needs its own correction:
ApplyLogitAdjustment() only runs inside a training run, so a deployed model
that was reloaded carries no logit offsets and would otherwise stream 31:1
data into itself uncorrected.
The weights-filename fingerprint is BYTE-IDENTICAL. The focal slot was a
double fed to a %d conversion and had always emitted a literal 0; the |MR:
segment is written as the constant its shipped defaults produced. Dropping
either would have re-keyed every model and forced a from-scratch retrain of
the one topology currently converged and trading.
Also removed as orphans: FOCAL_GAMMA_PRESET, MAX_OVERSAMPLE_REPLICAS,
OVERSAMPLE_PARITY_FRACTION, PLATEAU_GAMMA_STEP, and the now-unreachable
"neutralized by prior correction" diagnostic.
Both builds compile 0 errors, 0 warnings. No retrain forced.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Stops keyed to the recent swing extreme make a trade's risk a function of
how far the last swing happens to sit rather than of current volatility. On
a shallow pullback the swing sits close to the fill, so the stop is tight
enough to be taken out by noise on setups that then run to target - which is
what the Perceptron's signals were showing.
SL: lowest_low/highest_high -/+ mult*ATR -> entry -/+ mult*ATR
TP: TP_PREV_SWING (opposite swing) -> removed; ATR-from-entry
SL_PREV_SWING, TP_PREV_SWING -> removed from the enums
The SL anchors to `price` (the resolved entry), not to base_price: with a
pending entry those differ by the whole entry offset, and the risk Money
sizes against is entry-to-stop.
MIN_SL_ATR_MULTIPLIER 2.0 -> 0.5. That floor existed because a swing-
anchored stop could land arbitrarily close to the entry and needed a bound
unrelated to the chosen multiple. An entry-anchored stop is exactly
mult*ATR by construction and cannot collapse, so leaving it at 2.0 would
have silently overridden SL_ATR_x1 to 2*ATR - making the input a lie AND
forcing TP >= 4*ATR just to clear the default 1:2 rejection filter. The
broker's own stop level is enforced separately and precisely by
TCAdjustStops(), so this is now a pure sanity net.
Default TP_Mode TP_PREV_SWING -> TP_ATR_x3, so SL_ATR_x1 + TP_ATR_x3 gives a
realised 3:1 against the 1:2 filter. TP_ATR_x2 would sit EXACTLY on the 2.0
boundary where price-normalization rounding alone can reject the setup; the
default leaves a deliberate gap. This is the same interaction that once
rejected 100% of setups on every symbol (see TP_INTELLIGENT_BASE_RR).
Swing validity guards now reject only when the configuration actually uses a
swing - i.e. ENTRY_PREV_SWING. Previously an unsynced or thin history
rejected EVERY trade, including configurations whose levels no longer
reference a swing at all. The guards are kept, not deleted: a bad swing must
still never reach an entry price, and iLow/iHigh are no longer called with a
possibly-negative index.
TP_INTELLIGENT stays risk-relative. Now that risk is exactly mult*ATR the
risk- and ATR-relative forms coincide, but risk-relative keeps its
reward:risk guarantee exact after the floor or TCAdjustStops widens a stop.
Both builds compile 0 errors, 0 warnings.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
