GP FX Trading Strategy

An autonomous Genetic Programming (GP) system that evolves high win-rate FX trading strategies using multi-pair OHLCV data, vectorbt backtesting, and an AI advisor loop (Kimi-K2.5 / deepseek-r1).

Results

Phase 1 — Best Round

Metric	Value
Win Rate	98.71%
Sharpe Ratio	9.635
Total Return	+21.29%
Total Trades	545
Test Period	Nov 2025 – Mar 2026

Phase 2 — In Progress

Rule	Value
Position size	$1,000/trade (training)
Stop loss	1x ATR
Trail trigger	3x ATR
Trail distance	15% behind current price
Max live trades	3 × $300 = $900 exposure

Quick Start (New Machine)

# 1. Clone
git clone https://github.com/Anwar-Projects/gp-fx-strategy.git
cd gp-fx-strategy

# 2. Setup environment
make setup

# 3. Download and prepare data
make data

# 4. Run Phase 2 training
make train

# 5. Monitor
make monitor

Architecture

HistData.com (1M bars)
 ↓
scripts/resample_to_5m.py
 ↓
data/EURUSD_5M.csv
data/GBPUSD_5M.csv
data/USDJPY_5M.csv
 ↓
src/run_until_good_phase2.py ←── loops until 80% win rate
 ↓
src/gp_strategy_progress_vectorbt_phase2.py
 ↓ ↓
 DEAP GP Evolution vectorbt simulation
 (34 workers) (SL + trailing stop)
 ↓
 best_individual.dill
 ↓
src/kimi_advisor_phase2.py ←── Kimi-K2.5 / deepseek-r1
 ↓
 Fitness function tuned
 ↓
 Telegram notification when target hit

Directory Structure

gp-fx-strategy/
├── data/               # Downloaded + resampled OHLCV data
├── docs/               # Documentation and notes
├── logs/               # Training logs and summaries
├── models/             # Saved GP strategies (.dill files)
├── scripts/            # Setup and data download helpers
│   ├── download_data.sh
│   ├── resample_to_5m.py
│   └── setup.sh
└── src/                # Training + inference source code
    ├── gp_strategy_progress_vectorbt_phase2.py
    ├── run_until_good_phase2.py
    └── kimi_advisor_phase2.py

Credits

Developed as part of autonomous trading research.