1. The expectancy stop was stone dead at shipped defaults. Its only feed -
RecordTradeResult inside CTradeJournalManager::Update() - ran solely under
UseDatabaseRanking, which ships false, so the da54639 halt was armed
(ExpectancyMinTrades=40) and never received a single closed trade. A risk
rule must not be a side effect of an analytics toggle: the journal gains
InitTrackingOnly(), Update() runs unconditionally from OnTick and skips
only the DB insert when no DB was initialized.
2. Below-minimum lots were silently bumped UP to SYMBOL_VOLUME_MIN by
TCNormalizeVolume - correct for a user-entered fixed lot, but in the
risk-sizing path it turned a budget-capped 0.05 into 0.10 on min-0.10/
step-0.01 symbols: double the intended risk, after CapRiskAmount already
clamped, exactly the routine-stop-out-breaches-the-daily-limit scenario
the budget exists to close. CMoneyRiskBase now refuses the trade when the
risk-derived lot is below the broker minimum.
3. All trading was async fire-and-forget (SetAsyncMode(true)) with no
OnTradeTransaction handler and no retry: server retcodes were never
observed. Fail-safe for entries, not for closes - a silently rejected
close rode the position until the next bar (or next day for the timed
close window). Now synchronous, matching the risk-budget flatten's own
already-synchronous CTrade; on an H1 EA the latency is irrelevant.
4. FIXED_LOT bypassed the budget entirely (no CapRiskAmount, no
OpenRiskAtStops) - pre-halt it could commit more than the remaining daily
allowance. A fixed lot cannot be scaled, so the rule is binary: its
loss-to-stop fits the remaining allowance whole or the trade is refused;
unpriceable risk (no SL) is refused while the budget is enabled.
Compile: 0 errors, 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
The barrier geometry is derived from the instrument's own excursion
distribution (stop at q75 of adverse travel, target at q50 of favourable),
and then a 1:2 floor was applied on top, raising the target to twice whatever
the stop happened to be. On SP500 H1 that pushed the target to 6.66*ATR,
reached on 3.3% of bars inside the horizon - so the label became "almost
never a win" and every topology was trained to predict an event that
essentially does not occur. A measured target has to stay measured.
The ratio never bought what it was believed to buy. A reward:risk floor does
not create expectancy; it trades hit rate against payoff at a break-even the
geometry already fixes - which this project has separately MEASURED (payoff
0.92 -> 5.72 with expectancy flat). What it did buy was two outages: four
consecutive Market validation rejections for "no trading operations" when it
rejected 100% of setups, and the label corruption above.
Removed:
- the input and the RISK_REWARD_RATIO enum (deleted, not left dangling - a
live enum with no input behind it is the shape of the stale-.set incident
that trained ~250 eras on the wrong target)
- the forced target raise in the label geometry
- the rrOK eligibility gate in the barrier-geometry scan, so every unclamped
pairing now competes on the measurement alone. Clamping stays disqualifying
for its own unrelated reason.
- the reward < minRR*risk veto in OpenParams
Kept: g_TradeRewardRiskRatio still computed and still bridged to Kelly sizing
in MoneyIntelligent - the ratio as a SIZING input was always the sound use.
Risk stays bounded where it actually is - account risk % and CRiskBudget.
The low-reachability warning survives but is re-aimed: with nothing inflating
the target, a target the market rarely reaches can only mean the horizon is
truncating the excursions the geometry is derived from.
Both build variants compile 0 errors / 0 warnings.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
- Implemented sqx_audit.py to audit StrategyQuant X trade lists, focusing on performance metrics and cost analysis.
- Created sqx_portfolio.py to evaluate portfolio performance based on uncorrelated components and their impact on risk and return.
- Developed swing.py to analyze cost ratios across different holding periods and assess swing trading structures.
- Introduced test_management.py to investigate the effectiveness of exit rules on random entries and their impact on expectancy.
- Moves CLayer neuron construction to AI/Impl/Layer.mqh to keep Network.mqh clean
- Unifies four previously duplicated architecture initialisation blocks (MLP/CONV/LSTM/HYBRID) into a single shared function
- Eliminates risk of behavioural drift where one architecture missed a setter, causing mismatched feature sets or targets
Add freeze-level checks, no-change modification skipping, entry price routing, and per-tick/memory budget monitoring. Override trade actions (Open, Close, Reverse, TrailingStop, TrailingOrder) to validate at the final gate before sending orders.
Replace old ATR_MULTIPLIER, THRESHOLDS_PRESET enums with new
STOP_LOSS_MODE, TAKE_PROFIT_MODE, AI_EXIT_MODE enums that support
ATR-based, intelligent confidence-scaled, and swing-anchored modes.
Also fix LSTM signal identity string.
Optimize() scaled lot size off account trade-history streaks with no Magic-number
filter (picked up other EAs'/manual trades) and an unconfigurable m_factor stuck at
1.0 (Factor() was never wired from an input), so a 3-trade streak could triple lot
size or send it negative. It was also entirely disconnected from what the AI model
actually knows about the current setup.
Replaced both AdjustRiskAmount()'s linear confidence-only scale and Optimize()'s
streak multiplier with one edge-based model: p from the empirically calibrated
AI/DB confidence magnitude, b from the trade's real reward:risk ratio (newly
bridged from OpenParams() via g_TradeRewardRiskRatio), quarter-Kelly applied and
clamped so risk% can only ever scale down from its configured ceiling, never above it.
CMoneyFixedRisk and CMoneyIntelligent both derived independently from
CExpertMoneyCustom and carried near-identical CalculatePotentialLoss()/
CheckOpenLong()/CheckOpenShort()/CalculateLotSize() bodies, maintained
in two places. New Money/MoneyRiskBase.mqh (CMoneyRiskBase) houses the
shared core, with two virtual hooks - AdjustRiskAmount()/AdjustLotSize()
- at the exact two points CMoneyIntelligent's confidence-scaling and
trade-history Optimize() step used to diverge. CMoneyFixedRisk now has
an empty body (the base's defaults are exactly its old behavior);
CMoneyIntelligent overrides only the two hooks. CMoneyFixedLot is
untouched - it doesn't do risk-based sizing.
Pure reorganization, no behavior change; compiled clean (MetaEditor,
0 errors/0 warnings).
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
- Define MAX_WEIGHT constant (1.0e6) for weight limits in clusters
- Remove redundant barrier from FeedForward kernel (prevents sync issues)
- Port FeedForwardProof and CalcInputGradientProof kernels for max-pooling (no weights, sliding max)
- Port FeedForwardConv kernel for convolution layers (shared weights, multiple output channels)
- Remove unused code and refactor signal condition logic (CSignalPAI)
- Updated README.md with project overview, key features, directory structure, getting started guide, and modernization roadmap.
- Added AI_NETWORK.md detailing the neural network and AI/ML infrastructure, including architecture, components, usage patterns, and next steps.
- Introduced DATABASE.md for the Database module, outlining key components, design highlights, usage patterns, and future enhancements.
- Created README.md files for Enumerations, Expert, Money, Signals, Structures, System, Trailing, Variables directories, detailing their purpose, key components, and integration notes.
- Documented the Signals subsystem, emphasizing modularity, extensibility, and AI/ML readiness.
- Added comprehensive descriptions for individual signal modules in Signals/ directory.
- Established clear integration notes and recommendations for future improvements across all modules.
