Warrior_EA/Enumerations/InputEnums.mqh

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fix(ai): drop the conv pooling stage - it reduced across filters, not time FeedForwardConv emits POSITION-MAJOR output, matrix_o[out + window_out * i], so one bar's window_out filter responses are contiguous and consecutive bars sit window_out apart. Both pooling implementations (FeedForwardProof and CPU_FeedForwardProof) slide FLAT over that buffer - pos = i * step, reducing `window` CONSECUTIVE elements. On a position-major layout those neighbours are different FILTERS of the same bar, never one filter across time. At the shipped 3/2 the pool computed max(bar0_f0, bar0_f1, bar0_f2), then max(bar0_f2, bar0_f3, bar0_f4), with every 8th window straddling a bar boundary. So it collapsed unrelated feature detectors into whichever fired hardest, passed gradient to that winner only, and halved the feature map while doing it - all below every learnable layer, where nothing above can recover it. The removed inputs' own labels ("3 Bars") show time-axis pooling was the intent throughout. Measured cost: CONV sat pinned at ~40% balanced accuracy for 510 eras with Sell recall 0%, while plain MLPs on the same data reached 57-61%. HYBRID, which also carried this stage, came second-worst of the batch-norm group. Not fixable in the topology: pooling one filter across time needs a stride of window_out BETWEEN samples within a window, which a consecutive-window kernel cannot express at any window/step. That needs a stride-aware kernel in Network.cl + WarriorCPU.cpp + WarriorDML.cpp and a DLL rebuild, and is only worth doing if a conv front-end earns its place without downsampling first - with 20 sliding positions there is little to gain by halving them. ConvPoolWindow/ConvPoolStep and their enums are removed with it, along with the |CP: fingerprint term added earlier today. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 19:28:44 -04:00
//+------------------------------------------------------------------+
//| CustomEnums.mqh |
//| AnimateDread |
//| https://www.mql5.com |
//+------------------------------------------------------------------+
#property copyright "AnimateDread"
#property link "https://www.mql5.com"
//--- Weight-update optimizer. This is really an AI\Network.mqh library type; a guarded duplicate is
//--- kept here so Variables\Inputs.mqh (which uses it for the TrainingOptimizer input) can be included
//--- before the AI headers - putting the EA's own inputs at the top of the Inputs tab. Keep in sync
//--- with AI\Network.mqh's copy; the shared WARRIOR_ENUM_OPTIMIZATION_DEFINED guard prevents a
//--- duplicate definition whichever header is parsed first.
#ifndef WARRIOR_ENUM_OPTIMIZATION_DEFINED
#define WARRIOR_ENUM_OPTIMIZATION_DEFINED
//--- A third DFA entry was removed 2026-07-28 - see AI\Network.mqh's copy for the full rationale (it was
//--- a deterministic index-parity sign flip on the gradient, i.e. ascent on half of every weight tensor,
//--- not Direct Feedback Alignment). SGD/ADAM keep ordinals 0/1: they feed the weights-filename
//--- fingerprint and must never be renumbered.
enum ENUM_OPTIMIZATION
{
SGD, // SGD + Momentum (heavy-ball, simpler, needs more eras)
ADAM // Adam (adaptive step, faster convergence, can overfit)
};
#endif
//--- Logical, commonly-used Moving Average / RSI periods only - keeps the Classic Signals inputs (and
//--- the AutoTuneIndicators search space over them, see ADIndicatorTuner.mqh) from being set/perturbed
//--- to an arbitrary, non-standard period.
enum MA_PERIOD_PRESETS
{
MA_PERIOD_5 = 5, // 5
MA_PERIOD_8 = 8, // 8
MA_PERIOD_9 = 9, // 9
MA_PERIOD_10 = 10, // 10
MA_PERIOD_13 = 13, // 13
MA_PERIOD_20 = 20, // 20
MA_PERIOD_21 = 21, // 21
MA_PERIOD_50 = 50, // 50
MA_PERIOD_100 = 100, // 100
MA_PERIOD_200 = 200, // 200
};
feat(indicators): run the built-in iMA and MetaTrader's ZigZag; add a classic-vote shift MA: CustomIndicators\ADMovingAverage is replaced by the built-in iMA (CiMA) on both consumers - the classic vote and the NN MA input feature. This drops the five advanced types ALMA/DEMA/ZLEMA/T3/Kalman, which have no iMA equivalent; MA_TYPE_PRESETS is now ENUM_MA_METHOD's own codes and the tuner searches all four. It also removes a documented failure mode: a custom indicator's depth is bounded by TERMINAL_MAXBARS, and m_MA was the one whose feature block REJECTS the bar on a short read - the "feature 25 fails on every bar" incident of 2026-08-17. A built-in is served at any depth. MIGRATION. SMA moves from code 5 to 0, so persisted type codes change meaning. SanitizeMaType() is the single validity rule; TunedPeriods records now carry a version field and a v1 record remaps 5..8 -> 0..3, falling back to SMA for a stored advanced type (unrecoverable - old 0..4 are indistinguishable from valid new codes). Existing .nnw files re-key on their own, because MA_Type is hashed into the topology fingerprint, so models retrain rather than silently running on different MA values. EXPECT A FULL RETRAIN. ZigZag: ADZigZag was a byte-identical rename of MetaQuotes' Examples\ZigZag - verified by normalising identifiers and stripping comments, 233 significant lines each with only renamed symbols differing. It now loads the stock one, so nothing is bundled and MetaQuotes' fixes arrive without a rebuild here. Both #resource entries are gone. Classic_Shift: a new input, the BAR the four classic votes evaluate on (0 = forming, 1 = last closed, default 1). One implementation on CExpertSignalCustom, inherited by all four rather than repeated per module. Defaults to a sentinel meaning "unset", so the AI signals and the aggregate keep the stock every_tick rule and their feature/label alignment is untouched. The META corpus sweep still takes precedence. CExpertBase::StartIndex turns out to be virtual, so this is a real override, not the name-hiding the old comment claimed. Not compiled - MetaEditor compile pending. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-19 19:09:58 -04:00
//--- Moving-average TYPE. VALUES ARE ENUM_MA_METHOD's own codes and MUST stay in sync with it - both the
//--- classic MA vote (Signals\SignalMA.mqh) and the NN MA input feature now run the BUILT-IN iMA via
//--- CiMA, so a value here is passed straight through as the ma_method argument. Auto-tuner-searchable.
//---
//--- 2026-08-19: replaced CustomIndicators\ADMovingAverage. That indicator offered five extra types
//--- (ALMA/DEMA/ZLEMA/T3/Kalman) on codes 0..4 with SMA/EMA/SMMA/LWMA on 5..8; those five have no iMA
//--- equivalent and are GONE, and the four survivors renumbered to match ENUM_MA_METHOD. Anything that
//--- persists a type code across that boundary must migrate - see LoadTunedPeriods().
enum MA_TYPE_PRESETS
{
feat(indicators): run the built-in iMA and MetaTrader's ZigZag; add a classic-vote shift MA: CustomIndicators\ADMovingAverage is replaced by the built-in iMA (CiMA) on both consumers - the classic vote and the NN MA input feature. This drops the five advanced types ALMA/DEMA/ZLEMA/T3/Kalman, which have no iMA equivalent; MA_TYPE_PRESETS is now ENUM_MA_METHOD's own codes and the tuner searches all four. It also removes a documented failure mode: a custom indicator's depth is bounded by TERMINAL_MAXBARS, and m_MA was the one whose feature block REJECTS the bar on a short read - the "feature 25 fails on every bar" incident of 2026-08-17. A built-in is served at any depth. MIGRATION. SMA moves from code 5 to 0, so persisted type codes change meaning. SanitizeMaType() is the single validity rule; TunedPeriods records now carry a version field and a v1 record remaps 5..8 -> 0..3, falling back to SMA for a stored advanced type (unrecoverable - old 0..4 are indistinguishable from valid new codes). Existing .nnw files re-key on their own, because MA_Type is hashed into the topology fingerprint, so models retrain rather than silently running on different MA values. EXPECT A FULL RETRAIN. ZigZag: ADZigZag was a byte-identical rename of MetaQuotes' Examples\ZigZag - verified by normalising identifiers and stripping comments, 233 significant lines each with only renamed symbols differing. It now loads the stock one, so nothing is bundled and MetaQuotes' fixes arrive without a rebuild here. Both #resource entries are gone. Classic_Shift: a new input, the BAR the four classic votes evaluate on (0 = forming, 1 = last closed, default 1). One implementation on CExpertSignalCustom, inherited by all four rather than repeated per module. Defaults to a sentinel meaning "unset", so the AI signals and the aggregate keep the stock every_tick rule and their feature/label alignment is untouched. The META corpus sweep still takes precedence. CExpertBase::StartIndex turns out to be virtual, so this is a real override, not the name-hiding the old comment claimed. Not compiled - MetaEditor compile pending. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-19 19:09:58 -04:00
MA_TYPE_SMA = MODE_SMA, // SMA (simple)
MA_TYPE_EMA = MODE_EMA, // EMA (exponential)
MA_TYPE_SMMA = MODE_SMMA, // SMMA (smoothed)
MA_TYPE_LWMA = MODE_LWMA, // LWMA (linear weighted)
};
feat(indicators): run the built-in iMA and MetaTrader's ZigZag; add a classic-vote shift MA: CustomIndicators\ADMovingAverage is replaced by the built-in iMA (CiMA) on both consumers - the classic vote and the NN MA input feature. This drops the five advanced types ALMA/DEMA/ZLEMA/T3/Kalman, which have no iMA equivalent; MA_TYPE_PRESETS is now ENUM_MA_METHOD's own codes and the tuner searches all four. It also removes a documented failure mode: a custom indicator's depth is bounded by TERMINAL_MAXBARS, and m_MA was the one whose feature block REJECTS the bar on a short read - the "feature 25 fails on every bar" incident of 2026-08-17. A built-in is served at any depth. MIGRATION. SMA moves from code 5 to 0, so persisted type codes change meaning. SanitizeMaType() is the single validity rule; TunedPeriods records now carry a version field and a v1 record remaps 5..8 -> 0..3, falling back to SMA for a stored advanced type (unrecoverable - old 0..4 are indistinguishable from valid new codes). Existing .nnw files re-key on their own, because MA_Type is hashed into the topology fingerprint, so models retrain rather than silently running on different MA values. EXPECT A FULL RETRAIN. ZigZag: ADZigZag was a byte-identical rename of MetaQuotes' Examples\ZigZag - verified by normalising identifiers and stripping comments, 233 significant lines each with only renamed symbols differing. It now loads the stock one, so nothing is bundled and MetaQuotes' fixes arrive without a rebuild here. Both #resource entries are gone. Classic_Shift: a new input, the BAR the four classic votes evaluate on (0 = forming, 1 = last closed, default 1). One implementation on CExpertSignalCustom, inherited by all four rather than repeated per module. Defaults to a sentinel meaning "unset", so the AI signals and the aggregate keep the stock every_tick rule and their feature/label alignment is untouched. The META corpus sweep still takes precedence. CExpertBase::StartIndex turns out to be virtual, so this is a real override, not the name-hiding the old comment claimed. Not compiled - MetaEditor compile pending. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-19 19:09:58 -04:00
//--- The ONE validity rule for a persisted MA type code. iMA rejects anything outside ENUM_MA_METHOD,
//--- and a stored code can predate the ADMovingAverage removal, so every load path runs it through
//--- here. Old codes 5..8 were SMA/EMA/SMMA/LWMA and map cleanly; old 0..4 were the five advanced
//--- types that no longer exist and are indistinguishable from valid new codes, so they cannot be
//--- rescued - callers that know they are reading a pre-migration file pass legacy=true to convert.
int SanitizeMaType(const int stored, const bool legacy)
{
if(legacy)
return (stored >= 5 && stored <= 8) ? stored - 5 : (int)MA_TYPE_SMA;
return (stored >= MODE_SMA && stored <= MODE_LWMA) ? stored : (int)MA_TYPE_SMA;
}
enum RSI_PERIOD_PRESETS
{
RSI_PERIOD_2 = 2, // 2
RSI_PERIOD_5 = 5, // 5
RSI_PERIOD_7 = 7, // 7
RSI_PERIOD_9 = 9, // 9
RSI_PERIOD_14 = 14, // 14 (classic)
RSI_PERIOD_21 = 21, // 21
RSI_PERIOD_25 = 25, // 25
};
//--- MACD periods (Signals\SignalMACD.mqh classic vote + the MACD input feature). The preset SETS are
//--- deliberately chosen so that EVERY fast/slow combination satisfies CSignalMACD::ValidationSettings()'s
//--- "slow must exceed fast" rule - the fast list tops out at 15, the slow list starts at 17. A trader
//--- picking two legal-looking values from the dropdowns can therefore never produce a combination that
//--- fails init, and the auto-tuner (ADIndicatorTuner::PerturbRandom) can perturb either one in isolation
//--- without having to know the other's current value.
enum MACD_FAST_PRESETS
{
MACD_FAST_5 = 5, // 5
MACD_FAST_8 = 8, // 8
MACD_FAST_12 = 12, // 12 (classic)
MACD_FAST_15 = 15, // 15
};
enum MACD_SLOW_PRESETS
{
MACD_SLOW_17 = 17, // 17
MACD_SLOW_21 = 21, // 21
MACD_SLOW_26 = 26, // 26 (classic)
MACD_SLOW_34 = 34, // 34
MACD_SLOW_50 = 50, // 50
};
enum MACD_SIGNAL_PRESETS
{
MACD_SIGNAL_5 = 5, // 5
MACD_SIGNAL_7 = 7, // 7
MACD_SIGNAL_9 = 9, // 9 (classic)
MACD_SIGNAL_12 = 12, // 12
};
//--- Ichimoku periods (Signals\SignalIchimoku.mqh classic vote + the Ichimoku input feature). Same
//--- all-combinations-are-legal design as the MACD presets above, against
//--- CSignalIchimoku::ValidationSettings()'s "Tenkan < Kijun < Senkou B" rule: Tenkan tops out at 20,
//--- Kijun spans 22-40, Senkou B starts at 44. The classic 9/26/52 triple is in the middle of each.
enum ICHIMOKU_TENKAN_PRESETS
{
ICHI_TENKAN_7 = 7, // 7
ICHI_TENKAN_9 = 9, // 9 (classic)
ICHI_TENKAN_12 = 12, // 12
ICHI_TENKAN_20 = 20, // 20
};
enum ICHIMOKU_KIJUN_PRESETS
{
ICHI_KIJUN_22 = 22, // 22
ICHI_KIJUN_26 = 26, // 26 (classic)
ICHI_KIJUN_30 = 30, // 30
ICHI_KIJUN_40 = 40, // 40
};
enum ICHIMOKU_SENKOU_PRESETS
{
ICHI_SENKOU_44 = 44, // 44
ICHI_SENKOU_52 = 52, // 52 (classic)
ICHI_SENKOU_60 = 60, // 60
ICHI_SENKOU_120 = 120, // 120
};
//--- custom enumerations for certain settings, minimizes overfitting
enum IND_PERIODS_PRESETS
{
PERIOD_5 = 5, // 5 Periods
PERIOD_10 = 10, // 10 Periods
PERIOD_14 = 14, // 14 Periods (classic)
PERIOD_20 = 20, // 20 Periods
PERIOD_30 = 30, // 30 Periods
PERIOD_50 = 50, // 50 Periods
PERIOD_100 = 100, // 100 Periods
PERIOD_200 = 200, // 200 Periods
};
enum TRAINING_YEARS_PRESET
{
YEARS_1 = 1, // 1 year
YEARS_2 = 2, // 2 years
YEARS_5 = 5, // 5 years
YEARS_10 = 10, // 10 years
YEARS_20 = 20, // 20 years
};
feat(trade): anchor SL and TP to the entry price, not the last swing 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>
2026-07-31 10:11:13 -04:00
//--- Stop-loss sizing mode. The ATR_* presets place the SL a fixed multiple of ATR FROM THE ENTRY
//--- PRICE. SL_INTELLIGENT uses the same entry anchor but tightens the distance as live AI/DB
//--- confidence rises (see CExpertSignalCustom::OpenParams()'s AI_SL_TIGHTEN_FACTOR) - a
//--- high-conviction setup gets a tighter stop, a marginal one keeps the full ATR cushion. Negative
//--- sentinel so it can never be mistaken for a literal ATR multiple.
//--- SWING-ANCHORED STOPS WERE REMOVED 2026-07-31. Both the ATR presets ("N ATR beyond the swing") and
//--- SL_PREV_SWING ("exactly at the swing") keyed the stop to the recent swing high/low, which makes
//--- the risk on a trade a function of how far away the last swing happens to sit rather than of
//--- current volatility: a shallow pullback produced a stop tight enough to be taken out by noise on a
//--- setup that then ran to target. Anchoring to the entry makes risk exactly N*ATR by construction,
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>
2026-08-09 14:51:59 -04:00
//--- which is also what kept the old minimum-reward:risk rejection satisfiable without depending on
//--- swing geometry. That filter is gone (2026-08-09); the coupling is still the right shape.
refactor(inputs): 96 -> 70 inputs; remove two untested/unusable filter modules 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>
2026-07-31 21:22:02 -04:00
//--- The "(classic)" marker on the shipped default follows the same convention as every period preset
//--- below. It matters more here than anywhere else in this file: since the 2026-08-01 triple-barrier
//--- relabel, SL_Mode and TP_Mode DEFINE THE TRAINING LABELS, so they are in the weights-filename
//--- fingerprint and changing either one re-keys the model and starts a fresh retrain. A user needs to
//--- be able to see which pair the shipped model was actually trained on.
enum STOP_LOSS_MODE
{
SL_INTELLIGENT = -1, // Intelligent (AI-confidence scaled)
perf(autotune): replace the genetic search with a filter score - hours to seconds 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>
2026-08-01 10:29:31 -04:00
SL_ATR_x1 = 1, // ATR * 1 from entry
feat(trade): anchor SL and TP to the entry price, not the last swing 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>
2026-07-31 10:11:13 -04:00
SL_ATR_x2 = 2, // ATR * 2 from entry
SL_ATR_x3 = 3, // ATR * 3 from entry
};
//--- Take-profit sizing mode. The ATR_* presets set the TP a fixed multiple of ATR FROM THE ENTRY
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>
2026-08-09 14:51:59 -04:00
//--- PRICE (no longer derived from the reward:risk ratio - that ratio was a pure
feat(trade): anchor SL and TP to the entry price, not the last swing 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>
2026-07-31 10:11:13 -04:00
//--- rejection filter). TP_INTELLIGENT scales the target UP with confidence (lets high-conviction
//--- winners run further). Negative sentinel as above.
//--- TP_PREV_SWING REMOVED 2026-07-31 alongside the swing-anchored stops: targeting the opposite swing
//--- caps the reward at whatever structure happens to be overhead, which on a trending signal exits
//--- well before the move is done and, paired with a swing-anchored stop, made the realised
//--- reward:risk a property of the chart's geometry rather than of the setup.
enum TAKE_PROFIT_MODE
{
TP_INTELLIGENT = -1, // Intelligent (AI-confidence scaled)
TP_ATR_x1 = 1, // ATR * 1 from entry
TP_ATR_x2 = 2, // ATR * 2 from entry
perf(autotune): replace the genetic search with a filter score - hours to seconds 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>
2026-08-01 10:29:31 -04:00
TP_ATR_x3 = 3, // ATR * 3 from entry
TP_ATR_x4 = 4, // ATR * 4 from entry
TP_ATR_x6 = 6, // ATR * 6 from entry
TP_ATR_x8 = 8, // ATR * 8 from entry
TP_ATR_x10 = 10, // ATR * 10 from entry
};
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>
2026-08-09 14:51:59 -04:00
//--- RISK_REWARD_RATIO removed 2026-08-09 along with its only consumer, the Min_Risk_Reward_Ratio
//--- input. Deleted rather than left dangling: a live enum with no input behind it is exactly the shape
//--- of the 2026-07 incident where a saved .set kept feeding a deleted option's ordinal back in and
//--- trained ~250 eras on the wrong target (MT5 does not validate saved enum inputs). See
//--- Variables\Inputs.mqh for why the ratio itself had to go.
enum MONEY_RISK_PERCENT_PRESET
{
perf(autotune): replace the genetic search with a filter score - hours to seconds 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>
2026-08-01 10:29:31 -04:00
RISK_PCT_1 = 1, // 1
RISK_PCT_2 = 2, // 2
RISK_PCT_3 = 3, // 3
RISK_PCT_4 = 4, // 4
RISK_PCT_5 = 5, // 5
};
enum BARS_EXPIRATION
{
BARS_X1 = 1, // 1 Candle
BARS_X2 = 2, // 2 Candles
BARS_X3 = 3, // 3 Candles
BARS_X5 = 5, // 5 Candles
BARS_X10 = 10, // 10 Candles
BARS_X20 = 20, // 20 Candles
};
//--- Entry order placement. All ATR offsets are measured from the CURRENT price (bid/ask), NOT the
//--- swing - this is the deliberate change for stability. Sign picks the side, magnitude is the ATR
//--- multiple:
//--- MARKET - fill immediately at market.
//--- LIMIT_*xATR - pending LIMIT that many ATR on the favorable side of bid/ask (buy below /
//--- sell above): wait for a pullback into a better price.
//--- STOP_*xATR - pending STOP that many ATR on the breakout side of bid/ask (buy above /
//--- sell below): enter on continuation.
//--- ENTRY_PREV_SWING - pending order anchored at the recent swing (buy at the lookback swing low /
//--- sell at the swing high) - the one swing-anchored option kept as a choice.
//--- ENTRY_INTELLIGENT - AI-confidence-scaled LIMIT pullback from bid/ask: a deep pullback when
//--- confidence is low, collapsing to a market fill as confidence -> 1 (grab
//--- high-conviction setups, demand a better price on marginal ones).
//--- Non-MARKET results that clear the broker's stop-level distance become a pending order that
//--- auto-expires after Signal_Expiration bars; anything closer just fills at market
//--- (CExpertTrade::Buy/Sell handle the market-vs-limit-vs-stop routing off this price natively).
enum ENTRY_MULTIPLIER
{
ENTRY_INTELLIGENT = -100, // Intelligent (AI-confidence scaled limit pullback)
perf(autotune): replace the genetic search with a filter score - hours to seconds 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>
2026-08-01 10:29:31 -04:00
//ENTRY_PREV_SWING = -101, // Pending at previous swing low (buy) / swing high (sell)
LIMIT_3xATR = -3, // Limit 3x ATR from bid/ask
LIMIT_2xATR = -2, // Limit 2x ATR from bid/ask
LIMIT_1xATR = -1, // Limit 1x ATR from bid/ask
MARKET = 0, // Market order
STOP_1xATR = 1, // Stop 1x ATR from bid/ask
STOP_2xATR = 2, // Stop 2x ATR from bid/ask
STOP_3xATR = 3, // Stop 3x ATR from bid/ask
};
enum TRAILING_STRATEGY
{
TRAILING_STRATEGY_NONE, // No Trailing Stop Strategy
TRAILING_STRATEGY_ATR_x1, // ATR * 1 Trailing Strategy
TRAILING_STRATEGY_ATR_x2, // ATR * 2 Trailing Strategy
TRAILING_STRATEGY_ATR_x3, // ATR * 3 Trailing Strategy
//--- Confidence-adaptive ATR trail: widens toward TRAIL_ATR_MAX_MULT when live AI confidence still
//--- backs the position (lets winners run), tightens toward TRAIL_ATR_MIN_MULT as that confidence
//--- weakens or flips against it (locks profit). See Trailing\TrailingIntelligent.mqh.
TRAILING_STRATEGY_INTELLIGENT, // Intelligent (AI-confidence adaptive ATR) Trailing Strategy
};
enum MONEY_MANAGEMENT_STRATEGY
{
FIXED_RISK, // Fixed risk Percent of Account
INTELLIGENT, // Intelligent lot size
FIXED_LOT, // Fixed lot size
};
enum CLOSE_HOUR_OF_DAY
{
CLOSE_HOUR_DISABLED = -1, // Disabled
CH_0 = 0, // 00Hxx
CH_1 = 1, // 1Hxx
CH_2 = 2, // 2Hxx
CH_3 = 3, // 3Hxx
CH_4 = 4, // 4Hxx
CH_5 = 5, // 5Hxx
CH_6 = 6, // 6Hxx
CH_7 = 7, // 7Hxx
CH_8 = 8, // 8Hxx
CH_9 = 9, // 9Hxx
CH_10 = 10, // 10Hxx
CH_11 = 11, // 11Hxx
CH_12 = 12, // 12Hxx
CH_13 = 13, // 13Hxx
CH_14 = 14, // 14Hxx
CH_15 = 15, // 15Hxx
CH_16 = 16, // 16Hxx
CH_17 = 17, // 17Hxx
CH_18 = 18, // 18Hxx
CH_19 = 19, // 19Hxx
CH_20 = 20, // 20Hxx
CH_21 = 21, // 21Hxx
CH_22 = 22, // 22Hxx
CH_23 = 23, // 23Hxx
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>
2026-08-19 11:34:27 -04:00
//--- Resolves per day from the SYMBOL'S OWN trading-session table (SymbolInfoSessionTrade),
//--- so it follows the broker through DST and per-symbol schedules with nothing to retune:
//--- the close-all fires "Close-all minute" minutes BEFORE that day's last session close
//--- (e.g. minute = xxH05 -> 5 minutes before the close). The label walk resolves the same
//--- value (Expert\AIBase\Labels.mqh), so training and the live book share one definition of
//--- "the day ends". Explicit 24: impossible as a literal hour, appended (values are saved,
//--- never validated - members are only ever added at the end).
CH_MARKET_CLOSE = 24, // Market close (minus Close-all minute)
};
enum CLOSE_MINUTE_OF_HOUR
{
CLOSE_MINUTE_DISABLED = -1,// Disabled
CM_0 = 0, // xxH00
CM_5 = 5, // xxH05
CM_10 = 10, // xxH10
CM_15 = 15, // xxH15
CM_20 = 20, // xxH20
CM_25 = 25, // xxH25
CM_30 = 30, // xxH30
CM_35 = 35, // xxH35
CM_40 = 40, // xxH40
CM_45 = 45, // xxH45
CM_50 = 50, // xxH50
CM_55 = 55, // xxH55
CM_60 = 60, // xxH60
};
enum CLOSE_DAY_OF_WEEK
{
CLOSE_DAY_DISABLED = -1, // Disabled
CLOSE_MONDAY = 1, // Monday
CLOSE_TUESDAY = 2, // Tuesday
CLOSE_WEDNESDAY = 3, // Wednesday
CLOSE_THURSDAY = 4, // Thursday
CLOSE_FRIDAY = 5, // Friday
CLOSE_EVERYDAY, // Every Day
};
enum NF_LOOKBACK_PRESETS
{
NF_DISABLED = -1, // Disabled
M5 = 5, // 5 Minutes
M15 = 15, // 15 Minutes
M30 = 30, // 30 Minutes
M45 = 45, // 45 Minutes
M60 = 60, // 1 Hour
M120 = 120, // 2 Hours
M240 = 240, // 4 Hours
};
enum NF_IMPACT_PRESETS
{
HOLIDAYS = 0, //Holidays
LOW = 1, // Low Impact News
MEDIUM = 2, // Medium Impact News
HIGH = 3, // High Impact News
};
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>
2026-08-19 11:10:05 -04:00
//--- INTELLIGENT added 2026-08-19 (user request, SQX EdgeFinder precedent: "adjust for the drift
//--- to increase success rate"). It resolves to LONG_ONLY / SHORT_ONLY / BOTH at runtime from the
//--- label cache's measured per-side win rates at the REAL geometry (the verdict block in
//--- Expert\AIBase\Labels.mqh): a side is dropped only when the drift gap clears 2 combined SEs
//--- on the overlap-deflated sample AND that side sits below cost-adjusted break-even. BOTH until
//--- measured; classic-only charts (no label cache) never resolve past BOTH. Appended with an
//--- explicit value - MT5 saves enum VALUES and never validates them, so members are only ever
//--- added at the end, never renumbered.
fix(build): four compile faults - one was a SILENT enum collision that inverted the direction policy Reported by the user's MetaEditor compile of f64e0f8 (26 errors, 4 warnings). The four warnings mattered more than the errors. 1. INTELLIGENT WAS TWO ENUMS. MONEY_MANAGEMENT_STRATEGY::INTELLIGENT (=1) is declared BEFORE TRADING_DIRECTION::INTELLIGENT (=3) in InputEnums.mqh, so MQL5 resolved every 'tradingdirection == INTELLIGENT' to the MM member and converted it to value 1 = TRADING_DIRECTION::LONG_ONLY. Wrong in both directions at once: selecting Intelligent (3) matched NOTHING and silently traded both sides, while selecting Long only (1) matched and handed the decision to the measured drift verdict - which can answer SHORT_ONLY, so the one setting that must never go short could have. Reported by the compiler as a WARNING only, never an error. Renamed to DIRECTION_INTELLIGENT; the VALUE stays 3, so saved .set files are unaffected. Swept every enum in the repo for sibling collisions (38 enums, detector validated against the pre-fix source, which it flags): none remain. 2. g_warriorMetaGate sits above the class it points at - added the forward declaration, the same pattern g_warriorEnsemble already uses in ExpertSignalAIBase.mqh. 3. The broker-time rename (b63e39f) never reached BufferNewTickSignal's PARAMETER or its two call sites: the local became brokerTime, the parameter stayed gmtTime, and the body was rewritten to read brokerTime. All five sites now agree. 4. ConfigureAISignal calls IsMetaTarget() from a free function - moved it to the public section (identity, not an implementation seam); the other meta seams stay protected. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-19 13:16:45 -04:00
//--- NAMED DIRECTION_INTELLIGENT, NOT the bare `INTELLIGENT` (compiler-caught 2026-08-19, before
//--- this ever ran): MONEY_MANAGEMENT_STRATEGY already owns that name and is declared FIRST in
//--- this file, so MQL5 resolved every `tradingdirection == INTELLIGENT` to THAT enum's value 1 -
//--- which is TRADING_DIRECTION::LONG_ONLY. The behaviour was wrong in both directions at once:
//--- selecting "Intelligent" (3) matched nothing and silently traded BOTH sides, while selecting
//--- "Long only" (1) matched and handed the decision to the measured drift verdict - which can
//--- answer SHORT_ONLY, i.e. the one setting that must never go short could. MQL5 reports this as
//--- a WARNING only ("implicit conversion ... LONG_ONLY will be used instead"), never an error, so
//--- the NAME is the thing that has to stay unique - a cross-enum collision cannot be caught by
//--- reading either enum alone. The VALUE stays 3: saved .set files are unaffected by a rename.
enum TRADING_DIRECTION
{
fix(build): four compile faults - one was a SILENT enum collision that inverted the direction policy Reported by the user's MetaEditor compile of f64e0f8 (26 errors, 4 warnings). The four warnings mattered more than the errors. 1. INTELLIGENT WAS TWO ENUMS. MONEY_MANAGEMENT_STRATEGY::INTELLIGENT (=1) is declared BEFORE TRADING_DIRECTION::INTELLIGENT (=3) in InputEnums.mqh, so MQL5 resolved every 'tradingdirection == INTELLIGENT' to the MM member and converted it to value 1 = TRADING_DIRECTION::LONG_ONLY. Wrong in both directions at once: selecting Intelligent (3) matched NOTHING and silently traded both sides, while selecting Long only (1) matched and handed the decision to the measured drift verdict - which can answer SHORT_ONLY, so the one setting that must never go short could have. Reported by the compiler as a WARNING only, never an error. Renamed to DIRECTION_INTELLIGENT; the VALUE stays 3, so saved .set files are unaffected. Swept every enum in the repo for sibling collisions (38 enums, detector validated against the pre-fix source, which it flags): none remain. 2. g_warriorMetaGate sits above the class it points at - added the forward declaration, the same pattern g_warriorEnsemble already uses in ExpertSignalAIBase.mqh. 3. The broker-time rename (b63e39f) never reached BufferNewTickSignal's PARAMETER or its two call sites: the local became brokerTime, the parameter stayed gmtTime, and the body was rewritten to read brokerTime. All five sites now agree. 4. ConfigureAISignal calls IsMetaTarget() from a free function - moved it to the public section (identity, not an implementation seam); the other meta seams stay protected. NOT COMPILED - user compiles in MetaEditor. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-08-19 13:16:45 -04:00
BOTH, // Allow both long and short trades
LONG_ONLY, // Allow only long (buy) trades
SHORT_ONLY, // Allow only short (sell) trades
DIRECTION_INTELLIGENT = 3, // Intelligent - measured drift picks the side(s)
};
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 38a12a2) described as live and still firing every bar; - the m_lastNonNeutralSignal alternation gate (removed 2026-08-01) described as consuming the AI's vote; - 16 lines of VOTE_CLOSE_PRESETS documentation orphaned by that enum's deletion, ending with "see that enum's note directly above" pointing at nothing. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-20 08:39:01 -04:00
//--- 5-POINT STEPS BELOW 50, 10-POINT ABOVE (2026-08-19). This is Signal_ThresholdOpen's scale, and under
//--- CONSENSUS arithmetic the votes it must separate are quantized by AGREEMENT: with four members
//--- whose tiers self-rank to pooled win rates ~29, unanimity reads ~29, 3-of-4 ~22, 2-of-4 ~14.5.
//--- The old 10-point grid straddled every rung the ensemble can express - 20 admitted 3-of-4 and
//--- 30 admitted nothing - so the thresholds an operator actually wants, which sit BETWEEN rungs,
//--- did not exist on the dropdown. Steps stay coarse above 50 because nothing reachable lives up
//--- there until pooled skill does. Members are ADDED, never removed or renumbered: MT5 saves the
//--- VALUE and does not validate it against the current enum (see the RISK_LIMIT_PCT_PRESET
//--- removal note at the bottom of this file), so adding explicit-valued members is .set-safe
//--- while deleting one is the trained-250-eras-on-the-wrong-target failure.
enum PERCENTAGE_PRESETS
{
PCT_5 = 5, // 5%
PCT_10 = 10, // 10%
PCT_15 = 15, // 15%
PCT_20 = 20, // 20%
PCT_25 = 25, // 25%
PCT_30 = 30, // 30%
PCT_35 = 35, // 35%
PCT_40 = 40, // 40%
PCT_45 = 45, // 45%
PCT_50 = 50, // 50%
PCT_60 = 60, // 60%
PCT_70 = 70, // 70%
PCT_80 = 80, // 80%
PCT_90 = 90, // 90%
PCT_100 = 100, // 100%
};
feat(ui): thresholds pick from a dropdown, and the finder arrows are back beside the level lines Two UX changes the operator asked for. THRESHOLDS. Signal_ThresholdOpen/Close were raw ints with the legal range written in the label ("[0...100, 101 = never]") - the one input style this codebase converted away from everywhere else. Open now takes the existing PERCENTAGE_PRESETS, whose comment already declared itself to be "Signal_ ThresholdOpen's scale" but was never wired to it; Close takes a new SIGNAL_CLOSE_PRESETS carrying the same rungs plus CLOSE_DISABLED = 101, which is why it cannot just reuse the other enum. Member names are prefixed because MQL5 enum members share ONE flat namespace - a bare PCT_25 in the second enum would silently resolve to the first one's, warning only. Values are unchanged, so existing .set files keep their settings. Both call sites now cast explicitly at the CExpertSignal boundary rather than leaning on an implicit enum-to-int conversion that only warns. ARROWS. 2026-08-19 replaced the low/high arrows WITH trigger-price lines; that was a swap where it should have been an addition, and it cost the zoomed-out view. A mark is now both objects: the line is the precise entry/exit level, the arrow off the candle's extreme is the finder that says there is something here to zoom into. The arrow's name is the line's plus a suffix, so it stays inside SIG_ARROW_PREFIX and every prefix-scoped purge already reaches it. The two type-filtered sweeps had to widen or they would clear one half and leave the other: the Hide/Show visibility loop and the pre-rescan scoped delete both walked OBJ_TREND only. Both are typed-blind and prefix-scoped now - the same widening this file's 2026-08-09 note describes, for the same reason it gives. Deletes go through one WarriorDeleteSignalMark() so an arrow cannot outlive the line it belongs to, and the sidecar deliberately still records one row per mark off the line (the half carrying the price), with the restore redrawing the pair. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-20 13:50:33 -04:00
//--- Signal_ThresholdClose's scale. Same rungs as PERCENTAGE_PRESETS plus the 101 that disables the
//--- vote exit by arithmetic (a weighted mean of 0-100 weights cannot reach it), which is why this
//--- cannot simply reuse that enum. Names are prefixed because MQL5 enum members share ONE FLAT
//--- namespace across the whole build - a bare PCT_25 here would collide with the one above and
//--- resolve to whichever enum was declared first, with only a warning (see TRADING_DIRECTION).
enum SIGNAL_CLOSE_PRESETS
{
CLOSE_PCT_5 = 5, // 5%
CLOSE_PCT_10 = 10, // 10%
CLOSE_PCT_15 = 15, // 15%
CLOSE_PCT_20 = 20, // 20%
CLOSE_PCT_25 = 25, // 25%
CLOSE_PCT_30 = 30, // 30%
CLOSE_PCT_35 = 35, // 35%
CLOSE_PCT_40 = 40, // 40%
CLOSE_PCT_45 = 45, // 45%
CLOSE_PCT_50 = 50, // 50%
CLOSE_PCT_60 = 60, // 60%
CLOSE_PCT_70 = 70, // 70%
CLOSE_PCT_80 = 80, // 80%
CLOSE_PCT_90 = 90, // 90%
CLOSE_PCT_100 = 100, // 100%
CLOSE_DISABLED = 101, // Disabled (hold to the barrier)
};
//--- Strength (tau) of the post-hoc logit adjustment / prior correction applied to the AI's 3-class
//--- decision at inference (see AdjustedSignalFromSoftmax in ExpertSignalAIBase.mqh). The network is
//--- trained on class-balance-oversampled data, so its raw softmax over-calls the rare Buy/Sell classes;
//--- re-weighting each class by its measured true base rate (prior^tau) pulls the decision back toward the
//--- real distribution. 0 = Off (raw argmax, may over-call), 100 = full Bayesian calibration to the true
//--- base rate. Stored as a percent; divided by 100 to get tau.
enum LOGIT_PRIOR_STRENGTH_PRESETS
{
LOGIT_PRIOR_OFF = 0, // Off (raw argmax - may over-call Buy/Sell)
LOGIT_PRIOR_25 = 25, // 25% (light correction)
LOGIT_PRIOR_50 = 50, // 50% (moderate)
LOGIT_PRIOR_75 = 75, // 75% (strong)
LOGIT_PRIOR_100 = 100, // 100% (full calibration to true base rate)
};
refactor(ai): derive the first dense layer's width instead of asking for it InitialNeurons was an input whose only defensible value depends on two things the user cannot see when picking from a dropdown: how wide the input vector ended up after feature selection, and how much in-sample data the study period actually yields. Left to a hand-picked constant it was badly wrong - 500 units against a 420-wide input is 210,500 weights, 72% of a 292,583-weight model, against ~36,500 training bars of which only ~2,236 are directional. That is 6.6 weights per training bar, and it EXPANDS a set of highly correlated inputs rather than compressing them. The symptom was already in the logs and had been read as a depth problem: the shallowest topology consistently beat the deepest (perceptron 52.7% balanced, hybrid 41.3%). Over-parameterization predicts that ordering just as well as covariate shift does, and only one of the two had been addressed. ComputeFirstLayerWidth() budgets roughly one first-layer weight per in-sample bar. Measured across the configurations in use: M15 10y -> 256 units, 129,071 weights, 0.73 per bar H1 10y -> 64 units, 28,727 weights, 0.65 per bar H4 10y -> 16 units, 7,559 weights, 0.68 per bar Two design points that matter: - It estimates in-sample bars from the STUDY PERIOD and timeframe, not from Bars(). What is downloaded grows over a terminal's lifetime, and a topology that widened as history filled in would re-key its own weights file and discard a trained model. - The result is snapped down to a coarse power-of-two ladder, so the estimate would have to be wrong by ~2x to change the answer. Every field it reads is already part of the weights-filename fingerprint, so the derived value needs no fingerprint entry of its own. The public setter is removed - it could only have been called after construction, and would either be ignored or silently re-key the model mid-run. Where the data cannot support even the floor (D1 over 10 years is under 2,000 bars) it now says so and names the fixes, rather than quietly training a model with more weights than examples. The DB config fingerprint drops the term too, which re-keys existing pattern databases once - correct, since a model an order of magnitude smaller should not inherit the old one's win-rate history. Compiles 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 13:01:16 -04:00
//--- FIRST_LAYER_NEURONS removed 2026-07-29. The first dense layer dominates the parameter count -
//--- it is (inputWidth+1) x width - so its only defensible value is a function of the input width and
//--- the amount of in-sample data, neither of which the user can see when picking from a dropdown. It
//--- is now derived: see CExpertSignalAIBase::ComputeFirstLayerWidth().
feat(ensemble): per-NN inputs replace the preset selector - the meta head becomes the vote's gate 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>
2026-08-19 13:01:02 -04:00
//--- Architecture-aware dense-topology presets were then folded into the (since-removed) AI_CHOICE
//--- selector; today the front-end choice is the per-NN Use_* toggles and the dense taper is fully
//--- derived - see ComputeHiddenLayerCount.
//--- LSTM's own recurrent hidden-unit count - previously silently piggybacked on HiddenLayersCount
//--- (an unrelated dense-taper-depth setting), which meant it could never be tuned independently and
//--- defaulted to a value (4) nobody actually chose on purpose. Decoupled into its own input.
enum LSTM_HIDDEN_SIZE_PRESET
{
LSTM_HIDDEN_8 = 8, // 8 Units
LSTM_HIDDEN_16 = 16, // 16 Units
LSTM_HIDDEN_32 = 32, // 32 Units
LSTM_HIDDEN_64 = 64, // 64 Units
LSTM_HIDDEN_128 = 128, // 128 Units
};
//--- CONV's own output-filter count for its convolutional layer - previously silently piggybacked on
//--- HiddenLayersCount too (same bug class as LstmHiddenSize above), defaulting to a bottleneck of 4
//--- filters/bar. Decoupled into its own input.
enum CONV_FILTER_COUNT_PRESET
{
CONV_FILTERS_8 = 8, // 8 Filters
CONV_FILTERS_16 = 16, // 16 Filters
CONV_FILTERS_32 = 32, // 32 Filters
CONV_FILTERS_64 = 64, // 64 Filters
CONV_FILTERS_128 = 128, // 128 Filters
};
//--- Shared pooling shape for the Conv front-end used by both CONV and HYBRID. Keeping this
//--- separate from ConvFilterCount lets the filter-bank width and the downsampling span be tuned
//--- independently, instead of smuggling one into the other.
fix(ai): drop the conv pooling stage - it reduced across filters, not time FeedForwardConv emits POSITION-MAJOR output, matrix_o[out + window_out * i], so one bar's window_out filter responses are contiguous and consecutive bars sit window_out apart. Both pooling implementations (FeedForwardProof and CPU_FeedForwardProof) slide FLAT over that buffer - pos = i * step, reducing `window` CONSECUTIVE elements. On a position-major layout those neighbours are different FILTERS of the same bar, never one filter across time. At the shipped 3/2 the pool computed max(bar0_f0, bar0_f1, bar0_f2), then max(bar0_f2, bar0_f3, bar0_f4), with every 8th window straddling a bar boundary. So it collapsed unrelated feature detectors into whichever fired hardest, passed gradient to that winner only, and halved the feature map while doing it - all below every learnable layer, where nothing above can recover it. The removed inputs' own labels ("3 Bars") show time-axis pooling was the intent throughout. Measured cost: CONV sat pinned at ~40% balanced accuracy for 510 eras with Sell recall 0%, while plain MLPs on the same data reached 57-61%. HYBRID, which also carried this stage, came second-worst of the batch-norm group. Not fixable in the topology: pooling one filter across time needs a stride of window_out BETWEEN samples within a window, which a consecutive-window kernel cannot express at any window/step. That needs a stride-aware kernel in Network.cl + WarriorCPU.cpp + WarriorDML.cpp and a DLL rebuild, and is only worth doing if a conv front-end earns its place without downsampling first - with 20 sliding positions there is little to gain by halving them. ConvPoolWindow/ConvPoolStep and their enums are removed with it, along with the |CP: fingerprint term added earlier today. Both builds compile 0 errors, 0 warnings. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
2026-07-29 19:28:44 -04:00
//--- CONV_POOL_WINDOW_PRESET / CONV_POOL_STEP_PRESET removed 2026-07-29 along with the pooling
//--- stage itself - their "N Bars" labels described time-axis pooling the implementation could
//--- never perform. See AddConvStage() in Expert\ExpertSignalAIBase.mqh.
enum MIN_NEURONS_COUNT
{
MIN_NEURONS_10 = 10, // Min. 10 Neurons per layer
MIN_NEURONS_20 = 20, // Min. 20 Neurons per layer
MIN_NEURONS_30 = 30, // Min. 30 Neurons per layer
MIN_NEURONS_40 = 40, // Min. 40 Neurons per layer
MIN_NEURONS_50 = 50, // Min. 50 Neurons per layer
};
// Value IS the reduction percentage applied per hidden layer (retention = 100-value), consumed
// via BuildFreshTopology()'s n = n*((100-value)*0.01) taper - e.g. RF_70 keeps 30% of the previous
// layer's neurons, i.e. a genuine 70% reduction per layer, matching the label at face value.
enum NEURONS_REDUCTION_FACTOR
{
RF_10 = 10, // 10 % Neurons Reduction Per Layer
RF_20 = 20, // 20 % Neurons Reduction Per Layer
RF_30 = 30, // 30 % Neurons Reduction Per Layer
RF_40 = 40, // 40 % Neurons Reduction Per Layer
RF_50 = 50, // 50 % Neurons Reduction Per Layer
RF_60 = 60, // 60 % Neurons Reduction Per Layer
RF_70 = 70, // 70 % Neurons Reduction Per Layer
RF_80 = 80, // 80 % Neurons Reduction Per Layer
RF_90 = 90, // 90 % Neurons Reduction Per Layer
};
enum OUTPUT_NEURONS_COUNT
{
OUTPUT_REGRESSION = 1, // Regression Algorithm
OUTPUT_CLASSIFICATION = 3, // Classification Algorithm
};
feat(ensemble): per-NN inputs replace the preset selector - the meta head becomes the vote's gate 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>
2026-08-19 13:01:02 -04:00
//--- AI_CHOICE REMOVED 2026-08-19 (user request: "remove the enum menu that selects neural networks,
//--- add individual inputs for every NN just like classic signals"). The preset selector could only
//--- express solo-or-all (no 2-3 member subsets) and made the META head mutually exclusive with the
//--- direction NNs. Replaced by the per-NN bools in Variables\Inputs.mqh (Use_MLP/Use_CONV/Use_LSTM/
//--- Use_CONVLSTM) plus Use_MetaLabeling; the ensemble machinery keys off "two or more direction NNs
//--- enabled" (ConfigureAISignal), which reproduces the old AI_HYBRID fingerprints exactly, and the
//--- pattern-DB filename keeps its first slot via DbLegacyAiSlot() (Warrior_EA.mq5) so no existing
//--- database re-keys. Deleted rather than left dangling, same doctrine as RISK_LIMIT_PCT_PRESET at
//--- the bottom of this file: a live enum with no input behind it is exactly the stale-.set trap
//--- shape. Stale "AIType=..." lines in saved .set files are ignored by name, harmlessly.
//--- (Historical: value 4 was renamed AI_CONVLSTM 2026-08-15; State\HYBRID\ folder names were kept
//--- across that rename and remain the CONVLSTM instance's identity - see CSignalHYBRID.)
//--- What the direction models (MLP/CONV/LSTM/CONVLSTM) train toward. Ignored by the META head,
//--- whose candidate-quality target is baked into its own class. Part of the model fingerprint (|TGT:FRA1),
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>
2026-08-15 04:44:10 -04:00
//--- so switching it trains a separate model rather than silently relabelling an existing one.
enum TRAINING_TARGET
{
TARGET_BARRIER = 0, // Triple barrier (does a trade here reach target before stop)
TARGET_FRACTAL = 1, // Next fractal direction (which way is the next confirmed swing extreme)
};
// Confidence used to scale SL/TP, gate early AI exits, and (Intelligent MM) scale lot size.
// AI confidence comes from the signal filter's live prediction (0..1); DB confidence comes
// from the historical time-based win rate of the currently traded patterns (0..1). Blended
// averages both, so a pattern is only sized up when both the model and its track record agree.
enum CONFIDENCE_SOURCE
{
CONF_AI = 0, // AI signal confidence only
CONF_DB = 1, // Database win-rate confidence only
CONF_BLENDED = 2, // Average of AI and database confidence
};
// How many bars to wait, after a candidate ZigZag reversal bar, before trusting the real ADZigZag
// indicator's verdict on it as a training label - see CExpertSignalAIBase's m_swingConfirmationBars
// declaration comment. A ZigZag's most recent 1-3 legs can still repaint as new bars arrive, so this
// must be generous enough to let a leg fully settle (bumped from the old fractal-based system's
// default of 20 to 100 for exactly that reason). A value of 0 is clamped up to a 1-bar minimum
// internally, never used to mean "no delay".
enum SWING_CONFIRMATION_PRESET
{
SC_10 = 10, // 10 Bars
SC_20 = 20, // 20 Bars
SC_30 = 30, // 30 Bars
SC_50 = 50, // 50 Bars
SC_100 = 100, // 100 Bars
SC_200 = 200, // 200 Bars
};
enum MAX_ERAS_PRESET
{
ME_100 = 100, // 100 Eras
ME_200 = 200, // 200 Eras
ME_300 = 300, // 300 Eras
ME_500 = 500, // 500 Eras
ME_1000 = 1000, // 1000 Eras
ME_2000 = 2000, // 2000 Eras
ME_3000 = 3000, // 3000 Eras
ME_5000 = 5000, // 5000 Eras
ME_10000 = 10000, // 10000 Eras
};
//--- percentage of the study period held back as out-of-sample data never trained on;
//--- value is the OOS share, in-sample share is the remainder (e.g. OOS_30 -> 70% IS / 30% OOS)
enum OOS_SPLIT_PRESET
{
OOS_10 = 10, // 90% IS / 10% OOS
OOS_20 = 20, // 80% IS / 20% OOS
OOS_30 = 30, // 70% IS / 30% OOS
OOS_40 = 40, // 60% IS / 40% OOS
OOS_50 = 50, // 50% IS / 50% OOS
};
//--- RISK_LIMIT_PCT_PRESET REMOVED 2026-08-02. It backed MaxDailyLossPct/MaxDrawdownPct as a dropdown
//--- of eight fixed percentages, which no funded-account programme is obliged to match - 4.5% or 3.75%
//--- were unreachable. Both inputs are now free-entry doubles (Variables\Inputs.mqh) validated at init.
//--- Note for anyone reinstating an enum input here: MT5 does NOT validate a saved enum value against
//--- the current enum, so a .set file holding a deleted member loads as a silent out-of-range int - the
//--- failure mode that trained four topologies on the wrong barrier (project memory: stale enum wrong
//--- target). Changing these two to doubles removes that exposure rather than renaming it.
//+------------------------------------------------------------------+