forked from animatedread/Warrior_EA
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AnimateDread
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fec4d47eb2 |
refactor(labeling): CTripleBarrier - one copy of the fill/barrier arithmetic
Session B of the feature-selection/labeling refactor track. Extracts the two
pieces of triple-barrier arithmetic that were genuinely duplicated or
scattered, taking price/ATR/geometry as plain arguments - no chart, no
indicator handle - so it is testable with synthetic numbers.
CTripleBarrier::ComputeLevels() replaces the fill/barrier level arithmetic
that TripleBarrierLabel() and SimulateTradeOutcome() each spelled out by
hand; their own comments already called it "IDENTICAL... deliberately and by
copy." One caller resolves both sides at once (the both-won tie-break needs
both); the other selects the side its isLong argument names. Same for
ApplyMinStopWidening(), the broker-minimum-stop floor both walks applied.
Fuzzed 200k random (entry, spread, risk, reward, minStop, isLong) tuples
against both original hand-written forms: 0 mismatches.
CLabelOverlap replaces m_labelLifespanSum/m_labelLifespanCount - two members
reset from three separate call sites (constructor, label-cache rebuild), the
exact "N loose members cleared in more than one place" shape a candidate-
geometry incident (
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AnimateDread
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2c351a02ca |
refactor(barriers): the ladder is an object, and its snap rule is one rule
CFirstPassageLadder owns the three caches (per-rung up/down first-touch ages
plus the terminal travel) and every question asked of them. The signal keeps
one member where it kept three arrays and a lifespan scalar.
WHAT THIS ENDS. The log-space rung snap existed THREE times: once as
LadderRungFor, twice written out inline inside LadderWinShare - and
LadderRungFor's own header said "Same rule LadderWinShare snaps with, so a
rung chosen here and a rung chosen there are the same rung". A comment asking
a reader to keep three copies equal by hand is the arrangement CMetaFamilies
was built to end. It is now one static RungFor(), so the two rungs agree by
construction.
The bounds test was spelled out at four sites and the "0 means never, tie
goes to the stop" comparison at three. Now Has() and FirstTouch(), once.
The four-site bounds test was also subtly weak: it computed
`idx * COUNT` and tested only the upper end, so a negative index slipped
through into a negative array read. Row() rejects it.
Spread and horizon are ARGUMENTS, not state. The ladder is pure travel in ATR
multiples; what a spread costs and how long the walk ran are facts the caller
supplies. Every answer is now a function of its inputs alone - which is the
point, because this is the barrier arithmetic that failed its own acceptance
test in
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AnimateDread
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7452bd1ba9 |
fix(geometry): a shutdown abort cleared one tally of ten
Found by grouping, not by looking for it.
The candidate-geometry scan kept ten accumulators as ten separate
members. Era start cleared all ten in a ten-line block. The
shutdown-abort path inside the exit-policy simulation cleared
m_geoTrades and nothing else, so nine partial sums - diffSum,
diffSumSq, incSum, candSum, candSl, candTp, incOpen, candOpen,
startTick - survived the abort with the aborted era's values.
The next era then accumulated onto those sums while counting from
zero, so the paired mean is sum/trades with a numerator carrying an
extra era's worth of difference. The paired sigma is worse: diffSumSq
inherits the same contamination, so the scan reports a tighter or wider
spread than it measured depending on what the abort happened to be
holding.
That is the SAME arithmetic that failed its own acceptance test in
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AnimateDread
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042f20bdb9 |
fix(barriers): cap the horizon at what the close-all actually grants
The diagnostic shipped in
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AnimateDread
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de382bbf88 |
diag(barriers): the horizon the geometry is sized for does not exist
Every label timeout on both live charts was the scheduled close-all and
none was the horizon. Not "mostly" - all of them:
USDJPY 14417 of 14417 timeouts ended by the close-all
SP500 2434 of 2434
targetDayOfWeek is CLOSE_FRIDAY, so every position is flattened weekly.
A trading week is ~30 H4 bars and an entry lands uniformly inside it, so
the average bar is labelled under ~15 bars of runway. The horizon ladder
granted USDJPY 96 and SP500 32, and the SCALE ladder rejects rungs
against BARRIER_HORIZON_MAX (384) - a ceiling that never binds while the
one that does is invisible to it. USDJPY's chosen target is 6.00*ATR,
asked of a trade that lives ~11 bars: 78.6% of labels come back Neutral,
the base rate collapses to 14.0%, and no model can clear a 33.4%
break-even against a label that mostly cannot resolve.
The close-all itself is correct and must stay - it is what the account
actually does, and
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AnimateDread
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b5e22a1e34 |
fix(geometry): a free zero made "never resolve" the winning geometry
The CANDIDATE GEOMETRY line shipped in |
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AnimateDread
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05f1a539c4 |
feat(geometry): measure per-candidate barriers against the one global pair
Stage 2a of the candidate-conditional geometry the record has named as next and never built. MEASUREMENT ONLY - no order uses it yet. WHY THIS AND NOT META-LABELING. Meta-labeling asks take-it-or-skip-it at fixed geometry, and its verdict stands: real skill, 0 operating points clearing break-even, and the |
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AnimateDread
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b91c7b1f7a |
refactor(comments): box headers to stdlib length
The //| box blocks were excluded from |
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AnimateDread
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5efdb48de4 |
refactor(comments): stdlib comment style across the remaining in-scope files
Same pass as
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AnimateDread
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f8ac10c808 |
fix(replay): the exit replay held trades through the Friday flat that the label and the live EA both close
The EXIT-POLICY REPLAY line reported an expectancy from SimulateTradeOutcome beside a win rate read out of the label cache, and called them "the SAME calls". Same calls, two different walks - and the walks did not agree. TripleBarrierLabel stops at NextScheduledCloseAll ( |
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AnimateDread
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20b7d9967e |
chore(test-run): defaults for the horizon-break-even measurement, and give the replay line a cadence
Two diagnostics on, neither in the fingerprint, so no model is re-keyed and the run resumes: VerboseMode false -> true per-era journal instead of every 25th Run_Alglib_Baselines false -> true verifies |
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AnimateDread
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4070c5c9bd |
feat(breakeven): the break-even every layer scores against prices a trade that always resolves
CostAdjustedBreakEvenPct is risk/(risk+reward) and has no horizon term. It is the win rate a trade
needs when it is CERTAIN to end at one barrier or the other. SimulateTradeOutcome has an explicit
branch for the case where it does not - runs out of horizon, closes at the last bar seen for
whatever P&L that is - so on this label geometry the figure describes a different trade than the
one being replayed.
The gap is measurable and large. Across 21 exit replays today on SP500 H4 the geometric figure read
34.5% while the EA's own R simulation crossed zero between 27.4% (lowest positive) and 28.9%
(highest non-positive). Independent corroboration: the zero-skill reference, computed empirically
over every scored bar as max(winLong,winShort)/bars, reads 25.4% - add cost and it lands on the
same ~28%. The geometric number is the outlier, and every edge printed against it was ~6.5pp too
pessimistic: LSTM's 30.6%-win era reported -4.0pp while its replay returned +0.075 R on the same
trades.
With a timeout share t paying a mean m R apiece, expectancy is w(1+RR) + t(1+m) - 1, so
w* = (1 - t(1+m)) / (1 + RR) = CostAdjustedBreakEvenPct x (1 - t(1+m))
which needs no new geometry - the existing figure already carries 1/(1+RR).
This commit MEASURES ONLY. The replay now separates timeout exits from barrier exits and latches
t and m for the next era to read (the accumulators are zeroed at era start and filled at era end,
so a mid-era reader sees zero trades and would fall back forever). Both break-evens print side by
side on the replay line with t and m beside them, and the threshold line's REPORTED edge - which
selects nothing - switches to the horizon-aware figure so the operator stops reading a wrong sign.
DELIBERATELY NOT CHANGED: LiveMetaGate's veto and the rung selector's BarrierMinReachPct still read
the geometric value. Both are decisions - the second re-derives geometry and therefore relabels -
and t and m have so far only been inferred from a zero-crossing, never seen on a log. One era of
this instrumentation settles that.
The file already contained the argument, one branch away, in the vote-exit comment: a vote exit
produces a CONTINUOUS payoff, not a win or a loss, and that is why an exit-aware gate cannot go on
scoring win-rate against a fixed break-even. A horizon timeout is the same thing, and unlike vote
exits it is on by default.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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667f2bcb6b |
revert(labels): drop the one-sided exit target; measure the calibration drift instead
Reverts |
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AnimateDread
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a86379621c |
feat(labels): on a one-sided book the blocked side's class is retargeted from an entry it can never take to the EXIT of the one it holds
User request: "when an asymmetry is noticed in a market (like sp500 upward drift) ... it does not need to predict shorts, but exit points. a sell signal needs to be preceded by a buy so that it can say I predict we must close that long." Until now a LONG_ONLY verdict only BLOCKED short entries. The network went on being trained to predict them - a third of its output capacity spent learning an answer the direction policy guarantees it can never act on, while the question the book actually faces (when to get out of the long) was never asked. The two are not the same event: "a short pays" needs price to travel the SHORT's target before the SHORT's stop, and at any geometry where reward != risk that is a different bar from "this long hits its stop first". The exit is the second one. So on a one-sided book TripleBarrierLabel re-cuts all three classes around the only position the book can hold: Buy = it reaches its target, Sell = it reaches its STOP first, Neutral = the horizon expired with it still open. Both come off the allowed side's own barriers, which the walk already computed - this reads longLost where it used to read shortWon, so it costs nothing. Label lifespan and the timeout flag follow the allowed side too, so the overlap correction is sized on the window this label actually spans. DECIDED ONCE, AT ERA 0, AND PINNED. m_exitTargetSide goes in the .cfg beside the derived geometry under the same doctrine and for the same reason: it decides what Buy and Sell MEAN, and a target that moved mid-run would retrain a fitted model against something it never saw. A .cfg from before this ends early and reads 0/0 - "not decided, symmetric" - which is exactly what every existing model was trained as, so nothing needs migrating. The weights fingerprint keys on the INPUT only (explicit Long only / Short only); under Intelligent the measured verdict must never reach a filename, or the model is orphaned the moment more history downloads. THE DRIFT VERDICT HAD TO MOVE OFF THE LABELS FIRST, and it turns out it was measuring the wrong thing anyway. It counted m_labelCacheBuy/Sell and called them "always-long vs always-short win rate", but the label pair is the COLLAPSED first-touch verdict: a bar where both sides reached their target carries only the side touched first, so long wins were undercounted by the both-won-goes-to-short share. m_winLongCache/m_winShortCache are the actual per-side win rates, published before the collapse, and that is what it reads now. Necessary as well as more correct - deriving the verdict from labels the verdict shapes is a feedback loop, since Sell-as-exit is near complementary to Buy and would close the very gap that produced it. The gap's SE now leans conservative rather than anti-conservative for the same reason. LIVE. The retargeted class is wired to close the position, or training it would be pointless: CheckClosePosition's "never vote-exit a certified position" rule keeps governing symmetric books and gains a one-sided exception, and the replay reads the identical rule through one LiveVoteExitThreshold() so certified and traded cannot describe different policies. Armed only when the operator picks a close threshold (Signal_ThresholdClose ships Disabled) AND the model's own pin says its blocked-side class means "close" - a model trained symmetric never fires it, whatever the verdict has since become. This does trade a different game from the one the win-rate certificate grades; the era's EXIT-POLICY REPLAY line already reports expectancy in R for exactly this case and says so in words. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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4346dd3c24 |
refactor(stdlib): the vote thresholds are ints on the library's scale, not "confidence %"
The MECHANISM was already stdlib and is untouched: ThresholdOpen() ->
m_threshold_open, tested as `m_direction >= m_threshold_open` exactly as
CExpertSignal does it. What was wrong was the presentation. Both inputs
were preset ENUMS labelled "Min confidence to open/close (%)", which
names the wrong quantity - m_direction is a WEIGHTED MEAN OF PATTERN
WEIGHTS, not a probability, and nothing in this path is a confidence.
They are now plain ints named the way the MQL5 wizard names them:
input int Signal_ThresholdOpen = 25; // [0...100]
input int Signal_ThresholdClose = 101; // [0...100, 101 = never]
Values are exactly what shipped, so behaviour is unchanged. 101 rather
than the library's default of 100 for close: a weighted mean of pattern
weights cannot REACH 101, which is how the shipped config disables the
vote exit, and quietly lowering it to 100 would re-arm a live exit route
as a side effect of a naming change.
VOTE_CLOSE_PRESETS is deleted (its only user is gone). PERCENTAGE_PRESETS
stays - MinRecall genuinely is a percentage.
** ACTION NEEDED ON DEPLOYED CHARTS: the inputs are RENAMED, so saved
.set files no longer match and charts fall back to the defaults above.
Those defaults are the current shipped values, so a chart on 25/Disabled
needs nothing; a tuned one does.
Comment cleanup in the same pass, and this part was not cosmetic - three
blocks documented mechanisms that no longer exist:
- the AI early-exit route (deleted in
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AnimateDread
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77594ef5fb |
refactor(stdlib): one quantile definition, from Math\Stat
The codebase had THREE conventions for the same statistic. AltData took a
true median; the barrier horizon and the derived input window took the
upper of the two middle values; the MI terciles and the barrier stop
ladder used nearest-rank indexing. All four now go through MathMedian /
MathQuantile, which is R's type 7 and the library's one answer.
System\AltData.mqh column median -> MathMedian (exact, no change)
AIBase\Labels.mqh swing median -> MathMedian
leg-range med -> MathMedian
stop ladder -> MathQuantile, read in one call
AIBase\Topology.mqh window median -> MathMedian
AIBase\AutoTune.mqh MI terciles -> MathQuantile + MathMin/MathMax
Signals\SignalSessionFilter DST last Sunday-> CDateTime::DaysInMonth()
gaps[]/legs[] change from int to double so MathMedian can read them; the
values are bar counts either way.
VALUES MOVE. Even-sample medians shift by half a bin and the quantile
reads interpolate, so the barrier geometry and the derived input window
can land on different rungs - re-keying fingerprints and forcing a
retrain. Accepted deliberately: stdlib consistency was the ask, and three
private conventions for one statistic is what it buys out.
Two YAGNI finds fell out of the ladder rewrite. MathQuantile sorts its own
copy, so DeriveBarrierGeometry no longer sorts up[]/dn[] in place - which
means upUnsorted[], a full array copy kept only to undo that sort, is
gone. ArraySort(up) had no consumer needing order at all; it was pure
work. The library call also gets a failure guard the hand-rolled indexing
never needed but the ladder read does.
Verified while here: Math\Stat\Math.mqh's MathAbs/MathMax/MathSqrt/MathPow
and friends are ARRAY overloads, not scalar redefinitions, so pulling it
into the translation unit shadows no builtin.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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29c82ad50b |
refactor(dry): one binomial arithmetic for every "is this edge real" test
The formula p(1-p)/n was transcribed nine times across six files - the two deploy gates, the two edge floors, the collapse recall floor, the barrier rung ladder, the inference bin SE, the pooled inverse-variance weights and both detectability reports. System\BinomialStats.mqh now holds it once, as free functions with no class dependency, so the god-class declaration does not grow to host pure math. BinomialVar(p, n) p(1-p)/n BinomialSEPct(p, n) 100*sqrt(p(1-p)/n) BinomialCallsForEdge(p, edge, sigmas) the same, solved for n NormalUpperTailQ(z) Q(z), via Math\Stat\Normal.mqh SidakFamilyP(z, N) 1-(1-Q(z))^N Value-preserving by construction: rates go in as probabilities so no call site gained a *100/100 round-trip, and BinomialSEPct is written through BinomialVar so the multiply order is the one it replaced. Every degenerate guard each site carried (p<=0, p>=1, n<=0) now lives in one place and returns the 0 those sites already treated as "no bar to clear". CExpertSignalAIBase::NormalUpperTail is gone; NormalUpperTailQ replaces it. What consolidating SURFACED, and is deliberately NOT changed here: the two Sidak selection gates compute their SE on the RAW call count, while every other SE in the project deflates by EffectiveSampleSize() for triple- barrier label overlap. That makes them the most permissive test in the codebase, by ~sqrt(mean label lifespan). Correcting it tightens a live deploy bar, which is a policy decision, not a refactor - flagged in the code at both sites. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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2be2970434 |
fix(topology): the capacity budget counted overlapping bars as independent examples
EstimatedInSampleBars() returned raw bars (11372 on SP500 H4) and every derived capacity decision spent that: first-layer width, conv filters, LSTM hidden size. But triple-barrier labels overlap - mean lifespan 9.4 bars - so the label cache line on the same run already reports those bars are worth ~1210 independent observations. Sizing a network against RAW bars while grading it against EFFECTIVE ones is two subsystems disagreeing about one sample, and it disagreed in the dangerous direction because the capacity side was the optimistic one: the warning's "roughly 1.1 weights per training bar" is nearer 11 per independent observation. EffectiveSampleSize() has existed since 2026-08-17 and is applied at eight sites, all of them statistics. This adds the ninth, in the one place that decides how many parameters get fitted. Applied inside EstimatedInSampleBars() rather than at the call sites, because that function exists precisely so the three stages spend one budget. SELF-ENABLING AND THEREFORE INERT WHERE IT MATTERS MOST, which is why this is two changes and not one. MeanLabelLifespan() is 1.0 until a label cache has measured something, so on a model's first build - before any label exists - the deflation is correctly the identity: an unmeasured overlap must not invent a shrink. A fresh attach constructs a fresh object, so its counters are zero too; only a mid-session weights reset carries real evidence into a rebuild. That is deliberately safe (no attach can now re-derive a narrower topology and discard trained weights) but it would have left the first build - the case you most want the truth for - quoting the flattering figure. So ReportDetectability now restates capacity against the effective sample at the first moment L is real, for the topology already pinned. It re-sizes nothing; it reports what was bought. Placed ABOVE that function's break-even guard on purpose - a degenerate geometry is exactly when you want to know the net is over-parameterised, and "it only fires for sane configs" is how the 2026-08-18 IS-error stop managed never to fire at all. The warning also names its basis now (independent observations and L, or an explicit "overlap NOT YET MEASURED, this is an UPPER BOUND"), so a flattering number can never again read as a measured one. Also factors FirstLayerFanIn() out of ComputeFirstLayerWidth so the capacity REPORT charges for exactly what the capacity DECISION charged for - same reason RequiredHorizonBars was factored out after the 2026-08-17 divergence - and makes MeanLabelLifespan()/EffectiveSampleSize() const so the const budget path can call them. Verified: no recursion (EstimatedInSampleBars -> EffectiveSampleSize -> EstimatedInSampleBarsRaw, which computes from Bars() alone); both new StringFormat sites hand-counted (basis 3/3 and 1/1, CAPACITY 10 specifiers / 10 arguments). NOT COMPILED - user compiles in MetaEditor. |
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AnimateDread
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caad156464 |
fix(geometry): the ratio raise was reported against reachability, not bounded by it
First clean derivation after |
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AnimateDread
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c3daded397 |
feat(geometry): 1:2 becomes a FLOOR the swing legs may raise, and the scan can no longer undercut it
Two coupled changes, both from measurements in today's SP500 H4 log. 1. THE SCAN WAS OVERRIDING THE DERIVER ON THE WRONG OBJECTIVE. At 13:37:47 DeriveBarrierGeometry produced stop 1.21*ATR / target 2.41*ATR - break-even 33.3%. Thirty-seven seconds later the barrier-geometry scan adopted 2:2 - break-even 50.9% - because it carried 0.0143 nats of entry-time information against the configured pair's 0.0075, and cleared its family-wise gate. Information is not expectancy, and the scan says so itself; nothing checked what the adoption did to the operating point. It did this: the fitted thresholds immediately after read 38.8% win vs 50.9% break-even (-12.2pp) and 48.3% vs 50.9% (-2.6pp), where the earlier model on this instrument at a 1:2 geometry had fitted +1.8pp. The deriver applies the ratio as user RISK POLICY; a scan that can crown 1:1 makes two subsystems disagree about one geometry - the same split this file already fixed once for the clamped-horizon rule. The scan now enrols and crowns only pairings at or above the floor; sub-floor pairs are still scored and printed (marked 'r') so the choice stays auditable. This is NOT the min-RR rule removed on 2026-08-09 - that one guarded a rejection filter that no longer exists. 2. THE RATIO IS A FLOOR, NOT A CAP (user: "the ratio of 1:2 is a minimum that I want, but it should not cap to that if the average zigzag moves gives more room"). BARRIER_TARGET_RR -> BARRIER_TARGET_RR_MIN. ComputeBarrierHorizonBars already scanned ZigZag pivots for leg DURATION; it now harvests leg RANGE in the same pass - two properties of one object, so the horizon and the target describe the same legs instead of two windows. The per-rung ratio is the floor raised toward median-leg/stop, snapped DOWN to a coarse ladder (2.0/2.5/3.0/4.0/5.0). The ladder is coarse on purpose: PooledGate pools only instruments whose structural break-even matches, and continuous per-instrument ratios would never match and would silently empty the pool. A leg is the right yardstick precisely because it owes NOTHING to the barrier - sizing a target off travel measured over the barrier's own horizon is the circular loop that ran EURUSD/USDCAD away to 14-31*ATR in 2026-08-07. The raise stays bounded by the three tests already in the ladder: reachability, the horizon ceiling (first-passage time grows with stop x target), and the cost fraction. Consequential fixes: the reachability floor was a macro keyed to the fixed ratio and is now BarrierMinReachPct(rr) evaluated per rung (a raised ratio has a lower break-even, so a fixed floor would be the wrong strictness); the detectability break-even likewise; PooledGate now writes and matches the ACTUAL ratio (TargetRR()) rather than the floor. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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6717509c8b |
fix(build): four compile faults - one was a SILENT enum collision that inverted the direction policy
Reported by the user's MetaEditor compile of |
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AnimateDread
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1445f175ce |
feat(consistency): the five review flaws fixed - training wears the live constraints, the gate wears the policy
1. Labels and the exit simulator go through the broker's stop-distance check: risk/reward widen to SYMBOL_TRADE_STOPS_LEVEL exactly as TCAdjustStops does at order time - the M5/tight-ATR case where live trades ran wider geometry than training measured. Current stops level stands in for history (like the spread); measured quantity, so it does not key the fingerprint. 2. The Intelligent drift verdict moved into RefreshDriftVerdict(), which RESCANS the label cache and now runs at every era end beside RankTiersFromOos - era-cadence instead of waiting for rare full rebuilds. Prints only on change. 3. Session filter is any-broker: sessions defined on their financial centres' civil clocks (London 08-16 Europe/London, NY 08-17 America/New_York, Tokyo 09-18 Asia/Tokyo), converted to UTC by each centre's own computed DST rule (EU last-Sun-Mar/Oct, US 2nd-Sun-Mar/1st-Sun-Nov), then to broker time by the MEASURED server-vs-GMT offset (half-hour brokers included). Windows may wrap midnight in broker time - the interval test handles it. Replaces the EET-hardcoded anchors, which were correct on exactly one broker and got Tokyo wrong by an hour each European summer. 4. The current-session-table-for-history caveat resolved by analysis: the bars bound the error - a too-late assumed close meets no bars (zero error), a too-early one truncates conservatively (<=1h, never optimistic, cannot manufacture edge). Documented at the site. 5. The ensemble deploy gate mirrors the direction policy: blocked-side fires are not fired bars (certified == traded), the zero-skill reference uses only ACHIEVABLE baselines (always-short is not a strategy a long-only book can run), and one-sidedness BY POLICY is not degeneracy - the two-sided requirement applies only when both sides are allowed. Sell predictions keep their other jobs (exit triggers, consensus dilution) untouched. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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1a46dfdad9 |
feat(sessions): market-hours entry gate + "Market close" close-all option, both live from the symbol's session table
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> |
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AnimateDread
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3e467f92e9 |
feat(labels): the scheduled close-all is now a vertical barrier in the label walk
User report: "I exit everything on Friday close to avoid weekend swap... if the NN training thinks I hold over the weekend it could produce inaccurate results" - it thought exactly that. TripleBarrierLabel walked its full horizon (64 bars, mean lifespan ~18 H4 bars ~ 3 days) straight through the scheduled flat, scoring trades the deployed EA is guaranteed to have closed on Friday 23:45. SQX applies this rule when building strategies; the EA's own labels did not. NextScheduledCloseAll() mirrors CExpertCustom::OnTick's live check exactly (same three inputs, same -1 disabled sentinels, same CLOSE_EVERYDAY semantics, same server clock). The walk stops at the first bar that does not END by the cutoff - OHLC cannot order the tradable fraction of a partial bar, and ties go to the refusal, as everywhere in this file. An unresolved trade at the cutoff times out to Neutral, exactly as live would flatten it. Excursions, the first-passage ladder and the label lifespan truncate with the walk, so the DERIVED geometry is automatically sized to the tradable window - a target the flat rule never lets price reach stops counting as reachable. The prebuild census now splits timeouts: "horizon too short?" vs "ended by the scheduled close-all" - different questions, different fixes. Schedule disabled = no cutoff, exactly like live. Models trained under weekend-blind labels are fitted to a different target; charts with the close-all enabled (the default) should be reset to retrain under the honest labels. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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348492fb3b |
feat(direction): INTELLIGENT trade direction - the measured drift picks the side(s)
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> |
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AnimateDread
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4858507146 |
feat(vote): thresholds become confidence percentages, on ONE scale everywhere
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>
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AnimateDread
|
65a3e4e877 |
fix(chart): a purge that reports "zero leftovers" was only ever checking its own list
2026-08-17 21:58: all three charts hit "Abnormal termination" ~5.3 s into
OnDeinit with NO cleanup-timings line - the teardown was starved again. The
22:00 init purge then removed 993 / 1373 / 1557 stranded objects and reported
ZERO by-name leftovers on every chart, and the charts still came up with
duplicated panels. "Nothing matching our prefixes remains" and "the chart is
clean" are different statements and only the first was being made.
Three changes, in the order they matter:
1. WHY the teardown starved, and it is a gap in
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AnimateDread
|
ad80e0bb57 |
fix(shutdown): make ExitPolicy public, and stop every long loop the moment MT5 asks
Two things, one of which was a compile error.
1. ExitPolicy() was declared in the protected block but is pushed in from
Warrior_EA.mq5:770. Moved to public beside the other EA-facing setters.
2. Chart objects surviving OnDeinit. The 4,500 ms teardown budget is measured
from the STOP REQUEST, not from OnDeinit's first line, and OnDeinit cannot
begin until whatever is in flight returns - so a scan still running after
_StopFlag is raised does not delay the cleanup, it SPENDS it, and the purge
never gets its turn.
New CExpertSignalAIBase::ShutdownRequested() = IsStopped() || m_shutdownInProgress.
Deliberately NOT m_trainingStopRequested: that latches, and a latched flag
would permanently disable scans that must run again on the next Start.
Guarded, longest first:
- TuneIndicatorsByFilter - per candidate, restoring the OPERATOR's settings
on the way out (best[] is mutated in place; the tuner otherwise keeps the
last trial's parameters, which nothing chose).
- ReportBarrierGeometryScan - per pairing, breaking to ONE restore point so
m_barrierScanLiveLabels can never be left true (that makes ComputeLabelForBar
read the last candidate's multiples as the configured geometry).
- ReportFeatureLabelInformation / ReportExcursionInformation / lag profile -
nulls ABANDON rather than truncate: fewer draws is not a smaller null, it
is a wrong one, and p shifts toward significance. m_dirEvidence staying
false is the safe direction.
- SimulateExitPolicyOutcomes - zeroes its accumulators so the divergence line
is dropped instead of latching a partial expectancy as the run's only report.
- ReportGeometryExpectancyScan - per ladder rung.
- HttpGet - one choke point for up to a dozen blocking WebRequests per
first-pass Update(). An in-flight request cannot be cancelled; refusing to
start another is the whole remedy.
- PollTraining, OnChartEventHandler's study event, TuneIndicatorsAndTrain -
entry points, so a queued event cannot open an era during teardown.
TuneIndicatorsAndTrain's guard is the first statement, ahead of the
m_tuneFilterDone / g_ensembleChartTuneDone latches.
- OnTick / OnTimer / OnChartEvent.
Training's own bar loops already honoured this (pass 1 per bar, passes 2/2.5/3
yield on a 120 ms budget); the warm-up scans did not, and they are the longest
uninterruptible stretches the EA has.
StopTraining() is unchanged: the operator's Stop still finalises synchronously.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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62a719f04c |
fix(consistency): one geometry authority, one exit authority, and the MI screen finally gets a veto
Consistency pass before a fresh deployment. Three places where two systems were choosing the same thing and one of them silently lost. 1. THE GEOMETRY SCAN'S DECISION WAS INERT - measured, not suspected. USDJPY, 2026-08-17: 14:24:12.844 adopting barrier geometry 2:8 ... Relabelling and training on it. 14:24:12.979 triple-barrier labels - stop 1.61*ATR, target 3.21*ATR ... this is what trains It adopted 2:8 and trained on 1.61:3.21. ReportBarrierGeometryScan wrote only m_sl_mode/m_tp_mode, and BarrierMultiples ranks the DERIVED pair ABOVE those ints - so on any model carrying a derived pair (every model with a .cfg, including a fresh one whose weights are gone but whose sidecar survived) the adoption changed nothing. Worse, had it changed something it would have been undone immediately: the adoption sets m_labelCachePrebuilt = false, and that prebuild re-runs DeriveBarrierGeometry at era 0, which overwrites m_derivedSl/TpMult from the excursion quantiles. ONE AUTHORITY: the derived pair, because it is what the labels read, what the deploy gate certifies, what g_Derived*AtrMult places on the live order, and what the .cfg pins across restarts. The scan now writes THAT (floored by MIN_SL_ATR_MULTIPLIER, the same floor DeriveBarrierGeometry applies so the live stop can never be wider than the labelled one), republishes to the bridge immediately rather than at the next era end, and forces the sidecar to be rewritten. m_geometryAdopted latches it so the derive pass the adoption itself triggers cannot overwrite it. The scan outranks the derive for an evidential reason, not an architectural one: its winner cleared a permutation test against the null of the MAXIMUM over every eligible pairing, and it scores the incumbent derived pair as a peer in that same field. The derive is a descriptive quantile read with no significance test attached. BEHAVIOURAL CHANGE, and the reason to flag it before a fresh test: barrier geometry will now actually move when the scan says so. Until today it never did. 2. ONE EXIT AUTHORITY, tied to whose certificate the trade was placed under. CheckClosePosition had two routes. The AI early-exit reads the AI vote undiluted and is exactly what the new exit replay reproduces. The blended route thresholds m_direction, the average over EVERY filter including classic ones whose live votes pass 3 never computes - so it can close a position the certificate never modelled, and no replay can ever check it. When g_DerivedSlAtrMult > 0 the AI's measured geometry is on the order, which means the deploy gate's certificate is the reason the trade exists. In that state the AI now governs the exit and the blended route is suppressed. Classic-only configurations are untouched: there the blended route is the only exit opinion and stays exactly as it was. Nothing moves at the shipped defaults either way (Min_Vote_Close = Disabled). 3. EDGEFINDER, SECOND HALF: THE MEASUREMENT NOW STEERS. The MI suite has always printed its verdicts and then trained the direction target regardless of what they said. That gap IS the difference between this and the EdgeFinder discipline: measure what the market offers, THEN aim. m_dirEvidence is set when EITHER the feature/label mutual information OR the normalised excursion asymmetry clears its block-permuted null - an OR, because the two look for the same thing by different routes and requiring both would reject on the weaker of two independent measurements. Normalised asymmetry specifically, never the raw one, which is the volatility confound. Deploy - solo AND ensemble - now requires it. A run without it still trains, and keeps its checkpoint: the research value is real and the measurement can be wrong. It simply may not go live. Reported separately from the statistical gate because the remedy is different: a failed selection test says train differently, this says look somewhere else. Excursion SIZE keeps clearing where direction does not, and that is a risk-control head rather than an entry signal. For the ensemble the check is per-chart by construction - the MI suite runs once and shares its outcome across members - which is the honest treatment: four models finding nothing between them is not four chances at an edge, it is four fits to the same absent information. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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94019f363e |
feat(gate): grade OOS calls on the exit policy actually in force, and move vote combining out of the members and into the orchestrator
Option (a) from the exit-policy question: the certified number must be the traded number. Plus the modularity correction the user called for on |
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AnimateDread
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d9f834d01d |
fix(buffers): revert the MA +1 - it asked for a bar that does not exist and stopped every chart
REGRESSION I INTRODUCED IN
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AnimateDread
|
45c9e211b3 |
feat(depth): prime -> settle -> sweep, and name which handle is short
"Max bars in chart" is set to Unlimited, so the static-terminal-limit reading in |
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AnimateDread
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7e63a8be01 |
fix(depth): route EVERY ResizeBuffers call site through one indicator-depth gate
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AnimateDread
|
2b5d0f8355 |
feat(measurement): fix zero-skill denominator, publish the deploy bar, measure lifespan per rung, add a MEASURE scale objective
The last run could not have demonstrated an edge either way, and nothing in the
log said so. Four changes so it does.
1. THE ZERO-SKILL LINE DIVIDED BY THE WRONG DENOMINATOR. m_oosWinLongTotal resets
every era; m_oosSamples only resets on a full model reset. So 'always-long %'
decayed as ~1/era: a run whose true rate is 37% printed 1.2% at era 33 and
0.0% at era 2219. This is the SAME bug already found and fixed for
logBuyPredPct thirty lines above ('era-15 Buy:2% that was really ~30%'), left
in the one line whose whole job is to be the reference every other number is
read against. Correct at era 1, wrong everywhere after - including the '62%
zero-skill' figure in the 2026-08-16 notes. Now per-era, and always-short is
finally readable.
2. THE DEPLOY GATE STATES ITS OWN BAR. 'edge -1pp' era after era cannot separate
'short by a hair' from 'short by an amount no strategy could cover'. The era
line now prints the required win rate, the SE, the effective n and the
lifespan it was deflated by; above 100% it says UNREACHABLE. At 4,738 OOS bars
and L=75.6 there are ~63 independent observations, putting the bar near 66% at
typical coverage.
3. LIFESPAN MEASURED PER RUNG. The first-passage cache already stores touch ages
at every ladder level, so each candidate geometry's resolution time is
readable without training on it - L-vs-width becomes a measurement across the
whole ladder in ONE run rather than a second chart. Each rung reports L,
n_eff, min provable edge and min provable EV.
4. SCALE OBJECTIVE IS PHASE-AWARE, defaulting to MEASURE. Width and detectability
are opposed: labels overlap by L, L grows like m*k = width^2 at fixed ratio,
so min provable EV ~ width^2 while the cost saving from width is only linear.
Doubling width quadruples the smallest EV you can prove. DEPLOY (widest that
clears reachability) is right once an edge is known; MEASURE (narrowest that
keeps round-trip spread under BARRIER_MAX_COST_FRACTION_PCT) is right while it
still has to be shown. The direction does not depend on the exponent, and
item 3 makes the exponent checkable.
Fixed in review: m_lastRungLifespan is cleared on every LadderWinShare entry or a
rejected rung reports the previous rung's lifespan as its own; per-rung
detectability is labelled IS-sample based (the deriver may not see the holdout),
so absolute figures are optimistic while the ranking is unaffected.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
|
d1ac18ebdb |
fix(labels): correct EffectiveSampleSize clamp order, share the horizon ladder, retract a false justification
Self-review of |
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AnimateDread
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1540ba8e64 |
fix(labels): overlapping-label sample correction + horizon cap on the scale ladder
Three defects, all surfaced by the 2026-08-17 SP500 H4 run that shipped
stop 4.86 / target 9.71 (width 14.57*ATR, horizon 384).
1. EVERY STANDARD ERROR ASSUMED INDEPENDENT SAMPLES. Triple-barrier labels
started one per bar overlap by the label's lifespan, so n calls are worth
~n/L independent observations (Lopez de Prado, AFML ch. 4 - sample
uniqueness). All three sqrt(p(1-p)/n) sites divided by the RAW count.
The tell: the operating point's null-of-the-maximum gate is family-wise and
should fire on ~5% of eras under the null. Measured fire rates - PAI 47/73
(64%), ConvLSTM 9/24, LSTM 8/21 (38%), CONV 4/62 (6%). CONV, the only model
whose margin distribution admits few bins, sat on the null; the rest cleared
a bar that was too low by ~sqrt(L). PAI's deployed threshold consequently
alternated between the ENDS of its own range era to era (0.10 -> 0.88 ->
0.86 -> 0.66; coverage 16% <-> 73%).
TripleBarrierLabel now records when each label became KNOWABLE - the first
winning touch, or both stops, or the timeout - and the prebuild accumulates
the mean. EffectiveSampleSize() feeds the operating point, the member deploy
gate and the ensemble vote gate. Conservative by construction (n/L is an
upper bound on the damage); gates get harder, never easier.
2. THE SCALE LADDER RAN AWAY, again. Horizon scales as swingMedian*sl*tp, and
since
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AnimateDread
|
4d8cb08501 |
fix(geometry): the reachability floor measured the WRONG WINDOW - my bug from bc57aca, and it cost real width
RECONCILED: the derivation reported "target reached on 17.7% of bars" while the
label cache reported Buy on 35.9%. Nothing was broken. They measure different
windows, and both are correct:
EXCURSION window ~12 bars (the SWING MEDIAN) - what m_excUpCache accumulates
over. Deliberately short: sizing a barrier off travel
measured over a horizon that itself scales with the barrier
is circular, and it ran away to 14-31*ATR on EURUSD/USDCAD
in 2026-08-07. That guard is correct and stays.
BARRIER horizon 64 bars - what the LABEL walk and the first-passage ladder
run over, and how long the EA actually holds the trade.
So `up >= target` is a 12-bar question and `label == Buy` is a 64-bar one, and
the second can freely exceed the first. TripleBarrierLabel gates the excursion
accumulation on `idx - t <= excWindow` while the barrier walk and the ladder run
the full horizon - the split is explicit and intentional.
THE BUG IS MINE. bc57aca's scale ladder tested reachability with `up[i] >= tp`,
i.e. it asked the 12-bar question about a 64-bar trade. That understates
reachability by ~2x, which is why EVERY wide rung was rejected and the geometry
fell back to the tightest rung at 1.61/3.21. The data supported considerably
wider; the test was just asking the wrong question.
FIX: LadderWinShare() reads the answer off the first-passage ladder - target
touched strictly before the stop, over the full horizon, tie to the stop. That
is the identical question the label walk asks, so the ladder share and the Buy
rate should now agree to within rung discretisation. Both legs snap to the
SMALLEST rung at or above the requested multiple (harder target, harder stop) so
the floor stays conservative.
Expect the scale ladder to select a WIDER rung on the next relabel. On this
data the excursion test read 17.7% at q50 where the true full-horizon share is
35.9%, so rungs that scored 8.1% and 2.8% were likely well above the floor.
ALSO:
- Window reconciliation now PRINTED every derivation: excursion travel share,
ladder win share, and the label cache's Buy share side by side, with the
ladder-vs-label gap flagged if it exceeds rung discretisation. Those two must
agree; if they ever stop agreeing, one of them is wrong and the line says so.
- Renamed tpReach/slReach -> tpTravel/slTravel and relabelled the log line. They
describe the EXCURSION window and are near-tautological there (a q50 stop is
exceeded by ~50% of bars); calling them "reached within the horizon" is what
made the two quantities look like one.
- BARRIER_MIN_TP_REACH_PCT is now BARRIER_MIN_REACH_FRACTION_OF_BE (0.60) x
break-even instead of a hardcoded 20.0. Break-even for 1:RR is 100/(1+RR), so
the absolute floor silently tightened as RR rose - 0.60x at RR=2 but 0.80x at
RR=3, penalising the user for asking for a bigger target. Evaluates to exactly
20.0% at the shipped RR=2, so this is a no-op today and correct if the knob
moves.
NOT COMPILED - user compiles.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
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bc57aca15d |
fix(geometry): the target was small BY CONSTRUCTION - ratio is now policy, scale is measured, ladder ceiling removed
The derivation read the stop from q75 of ADVERSE travel and the target from q50
of FAVOURABLE travel. Over one horizon those distributions are broadly the same
shape, so q75 > q50 MECHANICALLY - the target came out smaller than the stop no
matter what the market did. SP500 H4 shipped stop 3.07 / target 1.70: a 0.55:1
payoff needing 64.3%. That was never a measurement, it was two mismatched
constants.
The reachability line printed beside it - "target on 50.0% of bars, stop on
25.0%" - is exactly 1-q50 and 1-q75. Tautological. It cannot disconfirm
anything, and it read as validation.
WIDTH AND RATIO ARE INDEPENDENT AND ONLY ONE PAYS. EV = edge x width;
ratio is EV-neutral (a driftless walk reaches +m before -k with probability
k/(k+m), which IS break-even). Width is what buys cost efficiency: the spread
is a fixed 0.047*ATR here, so the shipped 4.77*ATR width paid it 21 times per
unit of travel. So:
RATIO = policy. BARRIER_TARGET_RR = 2.0 (user's 1:2). Break-even 33.3%.
SCALE = measured. The stop quantile is chosen from a ladder, WIDEST FIRST,
taking the first rung whose implied 2x target is still reached often
enough to be a trainable class.
That last clause is the difference from the min-reward:risk raise removed in
2026-08-09, which forced target = 2 x stop with NO reachability test, landed on
6.66*ATR reachable on 3.3% of bars, and trained the model to predict something
that essentially never happened. Same ratio; the scale now retreats until the
data says the target is attainable. Every rung is logged.
LADDER CEILING REMOVED. BARRIER_LADDER stopped at 5.00 and the expectancy scan's
"best resolvable pair on width alone" came back as stop 5.05 / target 4.95 - it
pinned to the top rung. A recommendation landing exactly on the edge of its own
search space is a boundary, not a finding: it cannot tell "5 ATR is optimal"
from "5 ATR is all we allowed". Extended to 20*ATR (8 -> 14 rungs). Nothing else
needs editing - every consumer is parameterised by BARRIER_LADDER_COUNT - and
the horizon constraints (decided >= 60%, reachability floor) now bind instead of
a constant.
THE SCAN COULD NOT SEE THE SHIPPED GEOMETRY. ReportBarrierGeometryScan looked
the configured pair up in its integer grid, and DeriveBarrierGeometry produces
CONTINUOUS multiples (3.07/1.70) that can never equal a grid point - so
cfgExcess stayed at its -1.0 sentinel and the report printed "configured 3:2
scores -1.00000", which reads as a catastrophic score and actually means "never
evaluated". Worse, the grid skipped target<stop entirely because it "inverts the
trade's whole premise" - while the derivation was shipping exactly that. The
incumbent is now always scored as a peer (never crowned; it is already in force
and is not an enum pairing the scan could adopt).
BREAK-EVEN NOW INCLUDES THE SPREAD. Every report quoted the frictionless
SL/(SL+TP). On SP500 H4 that read 64.3% while the MEASURED zero-skill rate was
62.1% - a 2.2pp gap that IS the cost, and that made every model look 2.2pp
better than it was. CostAdjustedBreakEvenPct() prices a win at (TP - spread) and
a loss at (SL + spread), matching the expectancy scan's convention exactly so
the two reports cannot disagree.
It also feeds FitDirConfThreshold, which is the correctness half: the operating
point subtracts break-even from precision, so the frictionless figure made every
candidate threshold look better by the width of the spread - 2.2pp against a
measured edge of 2.3pp, i.e. very nearly all of it.
Era line now carries both: "break-even 64.3% frictionless, 66.6% AFTER SPREAD".
Forces a full relabel and retrain. Requested.
NOT COMPILED - user compiles.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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AnimateDread
|
430cdbe650 |
feat(ai): conditional barrier geometry for the fractal target - MFE/MAE measured leg-by-leg at labeled bars
The derived stop/target were quantiles of EVERY bar''s excursions over a fixed horizon - q75 adverse gave a 2.6-3.5*ATR stop against a ~1.7*ATR target (user: "looks limiting"). That pooled measurement was correct when direction was dead (any subset of bars had the same distribution) and is provably mis-sized now that the gate certifies the label carries information: the bars the model trades are the labeled bars, and their excursions differ from the pool. FractalDirectionLabel now records, for every Buy/Sell-labeled IS bar during the prebuild, the favourable and adverse travel in ATR units over exactly the LEG the label points at - entry close through the next fractal extreme (user request: "from a fractal to the next for maximum accuracy"). DeriveBarrierGeometry reads the same q75-adverse/q50- favourable quantiles off that conditional sample instead of the pool, with a logged fallback to pooled when fewer than the minimum legs exist. Quantiles kept over averages deliberately: a mean MFE is dominated by runaway legs and would set an unreachable target. No circularity: the fractal label does not depend on SL/TP (the barrier label does - this path must never feed it). Recording stops the moment geometry is derived and pinned, so pass 2 relabels and later bars cannot silently re-shape a certified pair. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
|
61a8c42a9c |
feat(ai): TrainingTarget input - fractal-direction label for the direction models
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> |
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AnimateDread
|
320f13253f |
feat: first-passage ladder + expectancy scan - price every geometry, not just the chosen one
Corrects the premise of the previous plan. Break-even is NOT a ceiling. If the model shifts the win probability on the bars it selects from p0 = m/(m+k) to p0 + d, then EV = (p0+d)*k - (1-p0-d)*m = d*(k+m) because p0*k - (1-p0)*m is zero by construction. The stop:target RATIO is expectancy-neutral - a punishing break-even is exactly repaid by the payoff - and only the real edge d and the TOTAL WIDTH (k+m) move EV. Width matters because the spread is charged once per trade however wide the barriers are, so a narrow barrier spends much of its own range on costs. DeriveBarrierGeometry's own comment already said the ratio buys nothing; the objective just never followed from it. Blocker this had to solve first: m_excUpCache/m_excDownCache hold only MAXIMUM travel each way, and a maximum cannot say which side was reached FIRST - so any geometry other than the walked one was undecidable on precisely the bars where both barriers were touched, ~28% of the sample. - BARRIER_LADDER: per bar, the first-touch AGE for 8 travel distances in each direction, filled during the walk the labels already run. Cursors keep it O(1) amortised per walked bar rather than 16 comparisons. Levels are travel FROM ENTRY, not barrier prices, so one ladder serves both directions and the spread is applied analytically when a level converts back to an SL/TP multiple - storing prices would need four ladders and bake today's spread into the cache. Sized, invalidated and validity-gated with the label caches. - ReportGeometryExpectancyScan: every ladder pair priced exactly off that cache - width in ATR and in SPREADS (cost efficiency, knowable without knowing d), break-even, both base rates, the share of bars resolved inside the horizon, and EV per unit of edge. Compares the widest resolvable pair against the quantile rule's pick. MEASUREMENT ONLY - the quantile rule still chooses. Nothing here can measure d, and width buys nothing if the wider target is less predictable. Base rates are printed beside each break-even because a persistent gap is DRIFT and must not be credited to the model. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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AnimateDread
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0c85c54a5b |
fix: a restart no longer loses the measured geometry or the training window
Terminal restart, 22:25: all four resumed models sat on empty windows with enum 2:6 barriers. Three interlocking causes, all visible in one log excerpt: 1) THE PRE-SCAN WINDOW WAS SIZED BY THE SAVED WATERMARK. A resumed model's dtStudied sits at its last studied bar, so Bars(dtStudied, now) ~ 0 and the resumed-model MI pre-scan built a zero-bar "complete" label cache - logged as "Buy: 0 | Sell: 0 | Neutral: 0". Train()'s own era start RESETS dtStudied to the training-window rule before computing its window; the pre-scan did not. The rule is now factored into TrainWindowStart() and both use it. The scan also refuses to arm before SERIES_SYNCHRONIZED (it ran in the same second as OnInit), and deployed models keep their watermark - for them it gates inference recency, not a training window. 2) THE HORIZON LATCHED ON AN INDICATOR WARM-UP. ComputeBarrierHorizonBars ran against a ZigZag with 0 calculated legs, fell back, and EnsureBarrierHorizon latched fallback(32) x slMult x tpMult = 384 for the process lifetime. A leg-starved horizon is now PROVISIONAL: re-resolved on the next rebuild, the label cache wiped if it moved (labels from two horizons answer different questions), and the geometry deriver refuses to run from it - a pair derived over a warm-up window would get PINNED. 3) THE DERIVED GEOMETRY WAS NEVER PERSISTED. The .cfg is written at model creation and at weights-reset - both BEFORE era 0 derives - so the measured pair lived only in memory: every restart read back zeros, adopted nothing, fell back to the enum barriers, and the era-0-only gate meant a resumed model could NEVER re-derive. A full day of training on 3.33/1.62 resumed as 2:6. Now: the settled pair is pinned to the .cfg the moment derivation completes (one-shot, atomic write), and the derive gate accepts any model with no pinned pair, not just era 0 - mid-run stability is carried by m_geometryDerived itself, which never allows a second derivation. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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9a7c37f334 |
fix: live trades now use the geometry the gate certifies; perf: BN kernels
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> |
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AnimateDread
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5cef0947f4 |
fix: the deploy gate was benchmarking a win rate against a label frequency
The gate rests on an invariant stated at ExpertSignalAIBase.mqh:199 - under a
driftless walk P(touch +k before -m) is m/(m+k), and break-even for a k:m trade
is ALSO m/(m+k), so "beats chance" and "is profitable" are the same test.
That invariant needs reward >= risk, and the measured geometry no longer
satisfies it. With target 1.62*ATR and stop 3.33*ATR, break-even is 67.3%, but
both-won bars were stripped out of Buy and Sell so the label base rate read
37.5%. chancePrecPct is max(BuyTotal,SellTotal)/bars, so the gate was clearing
models nearly 30pp short of break-even: 42% "directional precision" is +4 sigma
against 37.5% and loses money on every single trade. Live since
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AnimateDread
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ce5265488e |
fix: both-won bars were labelled "do not trade" - resolve by first touch
Removing the min-reward:risk raise let the MEASURED geometry come back with the target NEARER than the stop (SP500 H1: target 1.62*ATR at q50 of favourable, stop 3.33*ATR at q75 of adverse). That reopened a branch the code called unreachable: price can reach +target and -target inside one horizon, winning in BOTH directions, and those bars fell through to Neutral. Neutral has only three producers, both-lost is unreachable (you cannot touch -3.33 without crossing -1.62 first, which wins the short), and timeouts logged at 1.0% of Neutral - so ~27% of ALL bars were being handed to the model as the abstain class when a trade either way would have collected its target. The cleanest positives in the sample, labelled "do not trade", while the fitted confidence threshold was being asked to find selectivity in what was left. Resolved by FIRST TOUCH: the target reached earlier is the trade that would have closed first. Same forward window, no extra lookahead. Same-bar ties stay Neutral - OHLC cannot order two touches, and unlike an intrabar stop tie there is no pessimistic side to fall to, so a guess would inject a coin-flip direction into the target. Also: - count both-won and its same-bar tie subset in the prebuild line, so the share is measured rather than inferred from arithmetic on a log line - scope the timeout counter to IS, matching the tally it is reported as a percentage OF; it was incremented over the whole scan and divided by an in-sample denominator - clear m_lastBarrierTimedOut at the top of the walk with the excursions, not at the bottom - the two early returns published the previous bar's verdict - mark the pass-1 label line PROVISIONAL. It prints the enum fallback because geometry can only be derived from excursions that do not exist yet, and it reads exactly like a config change that failed to take effect FORCES RETRAIN. Both build variants compile 0 errors, 0 warnings. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> |
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AnimateDread
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217b9bc9bf |
feat: remove Min_Risk_Reward_Ratio - a guess was overriding a measurement
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> |
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AnimateDread
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b3b7e7bceb |
fix: excursion window must not depend on the barrier it sizes
DIRECTION IS NOT THERE, and this run is what establishes it. Three symbols:
raw ASYMMETRY clears on all three (p=0.0199 / 0.0050 / 0.0050)
norm ASYMMETRY collapses on all three (p=0.3433 / 0.5075 / 0.2736),
USDCAD landing BELOW its own null
RANGE control strengthens to 3-5x its null everywhere
Divide sigma out and the apparent directional signal vanishes entirely. What
cleared was volatility leaking through an unnormalised difference. Note this
would have passed any replication test: three instruments at p=0.005 is exactly
the evidence one would accept before committing to a rebuild, and the confound
reproduces perfectly. Replication was never going to catch it - only the
normalisation could.
Two defects of mine, both surfaced by the same run.
1. THE GEOMETRY DERIVATION WAS DIVERGING, NOT CONVERGING. It produced a
14.57*ATR stop and a 29.14*ATR target that only 5.7% of bars ever reach.
Excursions were measured over the barrier horizon; the horizon scales with
the target; the target is a quantile of the excursions - so target ->
horizon -> excursions -> target ran away, and "settled" only because the
horizon ladder caps at 384 bars. A saturated runaway, which the iteration
guard could not catch because it watches for OSCILLATION.
Fixed at the root: excursions now accumulate only over m_swingMedianBars -
the UNSCALED median ZigZag leg, a property of the instrument that owes
nothing to the barrier. The barrier walk still runs the full horizon,
because that is how long the trade is held; only the MEASUREMENT used to
size the barrier is confined to a geometry-independent window.
(The Min_Risk_Reward_Ratio warning fired correctly and is what flagged it -
the diagnostic worked while the derivation behind it did not.)
2. THE CONFOUND VERDICT WAS UNREACHABLE. `sizeCleared && !asymCleared` was
tested first and is true whenever size clears - i.e. always - so the branch
that NAMES the volatility confound never printed; all three symbols showed
the generic size-not-direction message instead. Verdict chain rewritten with
the specific case first, and the dangling elses my first patch introduced
removed.
FORCES A FULL RETRAIN (the excursion window changes every derived barrier).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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32ffeb99f3 |
fix: normalise the asymmetry target - the raw one is confounded by volatility
Three symbols ran the excursion test. RANGE/UP/DOWN cleared on all three;
raw ASYMMETRY cleared on EURUSD and USDCAD at p=0.0050 and not on SP500
(p=0.1045). That looked like the first directional signal this project has
found. It probably is not, and the test as built could not tell.
(up-dn) IS NOT SCALE-FREE. If sigma is predictable - and RANGE clears at ~4x its
null on every instrument - and the directional part is symmetric noise eps, then
up-dn ~ sigma*eps, so a large sigma pushes the value into BOTH outer terciles. A
pure volatility predictor scores positive MI against a 3-bin (up-dn) while
carrying no directional information at all. Crucially that confound REPLICATES,
so reproducing on two instruments is not evidence against it - and the effect
sizes fit it: asymmetry runs 1.3-1.6x its null where RANGE runs ~4x, and carries
~0.1% of the target's entropy against RANGE's ~0.9%. That is the shape of a
leaked fraction of the volatility signal, not an independent one.
So add (up-dn)/(up+dn): bounded in [-1,+1], volatility divided out, and the only
target a directional claim may rest on. The verdict now separates the cases and
NAMES the confound when raw clears while normalised does not, instead of
reporting the raw line as a finding.
Two bugs of mine in the same block, both caught by output rather than review:
- The derived-geometry line had a MISORDERED argument list: it printed
"stop 25.00*ATR (q3 of adverse travel)" - the quantile percentage as the
multiple and the multiple as the quantile. Real values were 2.61 stop /
8.03 target. A 25*ATR stop is absurd on its face, which is why it was seen.
- THE STOP QUANTILE WAS BACKWARDS, and this one changes labels. It was 0.25
"so ordinary noise does not reach it", but q25 means 75% of bars EXCEED the
stop - hit three times in four. The printed reachability said exactly that
("stop on 75.0% of bars"). Now 0.75. A quantile is a threshold, not a rate.
This is the entire reason reachability is measured and printed rather than
assumed.
Also raises BARRIER_DERIVE_MAX_PASSES 3 -> 5: SP500 did not settle in 3 (stop
still moving ~14% per pass) while EURUSD and USDCAD converged on pass 2. And
bounds both quantile indices with MathMin(..., n-1) so q=1.0 cannot run off the
end of the sorted array.
The geometry from the previous run is NOT usable and the asymmetry result is
unresolved, not established. Both are decided by the next run.
FORCES A FULL RETRAIN (the stop quantile changes every label).
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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AnimateDread
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a7701f032b |
feat: derive the ATR multiples from measured excursions - no hardcoded geometry
The barrier was still two constants. SL_Mode/TP_Mode left the Inputs tab in |
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AnimateDread
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2c78f3b90d |
diag: is "optimal SL/TP" learnable? Score the features against excursions
Proposed direction: train the net to predict entry/SL/TP that maximise return
and minimise drawdown, rather than to classify direction. Before rebuilding a
head, measure whether the target is learnable at all.
That question splits into two that behave nothing alike:
HOW FAR price travels (MFE/MAE) - essentially volatility, and volatility
clustering is about the most robust regularity in markets.
WHICH WAY it goes first (the asymmetry) - direction, which is what every
noise-floor verdict in this project has been about.
Expectancy comes ONLY from the second. The first buys position sizing and
drawdown control - worth having under prop-firm limits, but not an edge: exit
management on RANDOM entries already moved the payoff ratio 0.92 -> 5.72 with
expectancy FLAT.
Crucially this is NOT already answered. Every MI figure here scored the
triple-barrier label, i.e. one specific question at one fixed geometry. A
noise-floor result there says nothing about whether excursion MAGNITUDE is
learnable - different target, different answer.
Four targets, and the verdict is the CONTRAST, printed explicitly because the
dangerous misreading of "UP clears" is "we can predict profitable trades":
RANGE (up+dn) - realised volatility, included as a POSITIVE CONTROL that
SHOULD clear. Every prior verdict here lacked a control
expected to pass; a range target at the floor indicts the
measurement, not the market.
UP / DOWN - MFE / MAE.
ASYMMETRY - up-dn, the only one that can pay.
Collected inside the walk the label already does (one max, one min per bar).
The early-out when both barriers resolved is GONE: it would have truncated the
excursions at whichever bar tripped the last barrier, making the measurement a
function of the CURRENT SL/TP - the circularity this is trying to escape. The
loop was already bounded by the horizon, so only the average cost moves.
Discretised into 3 EQUAL-FREQUENCY bins, so every downstream piece (block
permutation, null, p-value) is reused unchanged. Equal-frequency because MFE is
fat-tailed and fixed-width bins would put nearly every row in bin 0; it also
pins H(Y) at ln(3)=1.099 for all four, making them comparable to each other and
to the barrier label's ~1.02 instead of confounded by class balance.
Two bugs fixed in this code before it ever ran, both of which would have
produced a plausible quiet wrong answer rather than an error:
- TripleBarrierLabel early-returns on invalid ATR/close BEFORE the point the
accumulators were reset, so one bar's excursions would be cached under
another bar's index. Cleared at the top now, ahead of every return.
- An unresolvable bar is still flagged as labelled but carries excursions of
exactly 0. Under equal-frequency binning a block of identical zeros drags
the lowest cut onto zero and a third of the sample lands in one
uninformative bin - a depressed score that reads as "not predictable", a
false negative in the direction that would wrongly kill the idea. Rows
where both excursions are zero are dropped; price cannot travel zero both
ways over a whole horizon.
Read-only diagnostic. No topology or label change: no retrain of its own.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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