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Filename Latest commit message Latest commit date
AnimateDread e9c562b39f Add Feature Scaling and Regime Math Classes; Implement Mind Trading Logic 
- Introduced `FeatureScale.mqh` with `FeatSquash` function for stateless feature scaling.
- Added `RegimeMath.mqh` class for regime arithmetic, including efficiency and variance calculations.
- Documented the Mind trading logic in `MIND.md`, detailing the trading process and modes.
- Created `VOLNORM_PLAN.md` and `VOLNORM_RESULTS.md` for tick-volume normalization testing.
- Implemented `read_book.py` for analyzing trade book data and correlations.
- Developed `volnorm.py` for testing tick-volume normalization with new and old methods.
2026-09-30 18:36:33 -04:00
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README.md feat: restructure input enums with intelligent SL/TP and AI exit 2026-07-22 13:33:56 -04:00
WarriorMoney.mqh Add Feature Scaling and Regime Math Classes; Implement Mind Trading Logic 2026-09-30 18:36:33 -04:00

README.md

Money Management Subsystem (Money/)

Overview

The Money/ directory contains all money management logic for the Warrior EA. It provides multiple strategies for position sizing, ranging from simple fixed lots to adaptive, streak-based approaches. Each strategy is encapsulated in its own class and can be selected/configured as needed.

Components

Money.mqh

  • Aggregates all money management strategies.
  • Includes: MoneyFixedRisk, MoneyFixedLot, MoneyIntelligent.
  • Entry point for money management logic selection.

MoneyFixedLot.mqh

  • Class: CMoneyFixedLot
  • Purpose: Fixed lot size per trade.
  • Key Features:
    • User-defined lot size (m_lots).
    • Validates lot size against symbol min/max/step constraints.
    • Simple, robust, suitable for static position sizing.

MoneyFixedRisk.mqh

  • Class: CMoneyFixedRisk
  • Purpose: Risk-based position sizing.
  • Key Features:
    • Calculates lot size based on account balance and risk percentage (m_percent).
    • Ensures risk per trade is controlled.
    • Handles both long and short positions.
    • Validates margin and volume constraints.

MoneyIntelligent.mqh

  • Class: CMoneyIntelligent
  • Purpose: Edge-based, AI-confidence-driven money management (selected via MM_STRATEGY = INTELLIGENT; AI lot scaling is always on).
  • Key Features:
    • Scales risk% via a quarter-Kelly criterion: uses the empirically calibrated AI/DB confidence magnitude as the win-probability estimate (p) and the specific trade's real reward:risk ratio (bridged from OpenParams()) as the payoff ratio (b).
    • Only ever scales the configured Money_Risk_Percent down from its input ceiling, never above it.
    • Suitable for advanced, AI/ML-driven strategies.

Integration Notes

  • All strategies derive from a common base (CExpertMoneyCustom).
  • Designed for modularity and easy extension.
  • Can be further enhanced with AI/ML-driven logic for dynamic risk and position sizing.

Documented April 2026. For modernization and AI/ML integration, see AI_NETWORK.md and project roadmap.