Helix Trading Strategy - BTC Price Action Backtest System

A professional-grade cryptocurrency trading strategy backtesting framework with comprehensive performance analytics, walk-forward validation, and an interactive HTML dashboard.

🎯 Strategy Overview

Helix is an event-driven backtesting system for BTC price action trading using Market Structure and Fair Value Gap (FVG) analysis on 1-hour timeframes.

Performance Highlights (V5)

• Total Return: +949.7%
• Sharpe Ratio: 5.40
• Max Drawdown: 8.55%
• Win Rate: 84.3%
• Profit Factor: 12.46
• Expectancy: $426 per trade
• Walk-Forward: 5/5 folds profitable (avg OOS +48.72%)

⚠️ These are backtest results optimized on in-sample data. See CHANGELOG.md for the full V1→V5 evolution and the strategy's roadmap for holdout OOS validation and paper trading.

✨ Features

Core Backtesting Engine

• Event-driven architecture with realistic order execution
• Multiple indicator systems: Market Structure, Fair Value Gaps, Smart Money Concepts, Classic Price Action
• Advanced risk management: Tiered position sizing, breakeven stops, progressive take-profits
• Comprehensive metrics: 30+ performance indicators including Sharpe, Sortino, Calmar ratios
• Commission & slippage modeling: Realistic trading costs

Validation & Analysis

• ✅ Walk-forward validation with rolling windows
• ✅ Monte Carlo simulation (10,000 iterations)
• ✅ Multi-asset testing (BTC, ETH, SOL)
• ✅ Parameter sensitivity analysis
• ✅ Out-of-sample testing

Interactive Dashboard

• 📊 Real-time equity curves with drawdown visualization
• 📈 Interactive candlestick charts with trade markers (Lightweight Charts)
• 🎨 Monthly returns heatmap
• 📉 Trade distribution & exit reason analysis
• 💰 Treasury simulation with leverage scenarios (1x to 20x)
• 📅 Year-by-year performance breakdown
• 🔍 R-multiple distribution & profit concentration

🚀 Quick Start

Prerequisites

# Python 3.8 or higher
python3 --version

# Install required packages
pip install pandas numpy requests

Installation

git clone https://github.com/ivala2081/helix.git
cd helix-trading-strategy

Run Backtest

# Run strategy with default parameters
python3 strategy.py

# Run with walk-forward validation
python3 strategy.py --validate

# Generate complete dashboard
python3 generate_dashboard.py

The dashboard will be generated at reports/dashboard.html - open it in your browser to view comprehensive results.

📊 Dashboard Preview

The dashboard provides:

• KPI Cards: Quick overview of key metrics
• Price Chart: Interactive OHLCV with trade signals
• Equity Analysis: Strategy vs Buy & Hold comparison
• Risk Analytics: Monte Carlo simulations, drawdown analysis
• Trade Breakdown: PnL distribution, exit reasons, duration analysis
• Treasury Projections: Capital growth scenarios at different leverage levels

🔧 Configuration

Edit strategy.py to customize strategy parameters:

STRATEGY_PARAMS = {
    # Capital & Risk
    "initial_capital": 10_000,
    "risk_pct": 0.03,              # V5: 3% risk per trade
    "max_position_pct": 0.80,      # V5: 80% position cap

    # Indicators
    "use_market_structure": True,
    "use_fvg": True,
    "min_confluence": 0.50,        # Require both MS + FVG

    # Stop Loss & Take Profits
    "sl_atr_mult": 1.0,            # V5: 1x ATR stop loss (tight)
    "tp1_atr_mult": 1.0,           # V5: TP1 at 1x ATR (5% close)
    "tp2_atr_mult": 4.0,           # TP2 at 4x ATR (30% close)
    "tp3_atr_mult": 6.0,           # TP3 at 6x ATR (65% close)

    # Risk Management
    "be_after_tp1": True,          # Move to breakeven after TP1
    "min_bars_before_sl": 50,      # Suppress SL for first 50 bars
    "min_signal_score": 0.60,      # Filter weak signals
    "use_hard_stop": True,         # Catastrophic protection
    "hard_stop_atr_mult": 15.0,    # 15x ATR black-swan floor
}

📁 Project Structure

helix-trading-strategy/
├── backtester.py           # Core backtesting engine
├── strategy.py             # Main strategy implementation
├── indicators.py           # Technical indicators (MS, FVG, SMC, PA)
├── stake_manager.py        # Position sizing & risk management
├── adaptation.py           # Adaptive strategy methods
├── generate_dashboard.py   # HTML dashboard generator
├── validate_strategy.py    # Multi-asset & sensitivity testing
├── data_fetcher.py         # Binance data download
├── run_tests.py            # Comprehensive test suite
├── data/                   # Historical price data (CSV)
└── reports/                # Generated reports & dashboards
    └── dashboard.html      # Interactive dashboard

🧪 Testing & Validation

Run Comprehensive Tests

# Test all indicator combinations across timeframes
python3 run_tests.py

This will test:

• 3 timeframes (15m, 30m, 1h)
• 8 indicator configurations
• 4 adaptation methods
• = 96 total strategy variants

Validation Suite

# Multi-asset validation
python3 -c "from validate_strategy import test_multi_asset; test_multi_asset()"

# Parameter sensitivity
python3 -c "from validate_strategy import test_sensitivity; test_sensitivity()"

# Out-of-sample testing
python3 -c "from validate_strategy import test_out_of_sample; test_out_of_sample()"

📈 Strategy Evolution

V1: Base Strategy

• Market Structure + FVG on 1H
• Fixed take-profits and stop-loss
• Result: +49.7%, Sharpe 1.81, DD 10.3%

V2: Risk Management

• Suppress SL for first 19 bars (prevent shakeouts)
• Move to breakeven after TP1
• Signal quality filter (min score 0.60)
• Result: +95.5%, Sharpe 3.36, DD 5.9%

V3: Position Sizing

• Rebalanced TP percentages (40% → 20% at TP1)
• Extended SL suppression to 30 bars
• Result: +173.2%, Sharpe 4.66, DD 4.1%

V4: Risk Stabilization

• Fixed risk at 2% per trade (tiered sizing was a no-op)
• Extended SL suppression to 50 bars
• Optimized TP distribution (5%/30%/65%)
• Result: +295.6%, Sharpe 4.96, DD 2.6% (V4)

V5: Final Risk Tuning (Current)

• SL multiplier: 2× → 1× ATR (tighter entry risk)
• TP1 multiplier: 2× → 1× ATR
• Risk per trade: 2% → 3%, max position 80% equity
• Result: +949.7%, Sharpe 5.40, DD 8.55%, WR 84.3%, PF 12.46
• Walk-Forward: 5/5 folds profitable (avg OOS +48.72%)

V5: Return Maximization (Current)

• Tighter stops: SL 2× → 1× ATR (less $ lost per loss)
• Earlier profit lock: TP1 2× → 1× ATR (WR 78.6% → 84.3%)
• Position sizing unlocked: risk 2% → 3%, position cap 50% → 80%
• Result: +949.7%, Sharpe 5.40, DD 8.55%, WR 84.3%, PF 12.46
• Walk-forward: 5/5 folds profitable, avg OOS +48.72%

Note on V5: The monotonic improvement from V1→V5 is a known overfit signal. V5 still needs (a) a true holdout OOS test on data excluded from optimization, (b) a parameter sensitivity matrix to confirm V5 sits on a plateau rather than a lone peak, and (c) paper trading on Binance Testnet before any live capital exposure.

📊 Key Metrics Explained

• Sharpe Ratio: Risk-adjusted return (> 2.0 is excellent)
• Sortino Ratio: Like Sharpe but only considers downside volatility
• Calmar Ratio: Annual return / Max drawdown
• Profit Factor: Gross profit / Gross loss (> 2.0 is strong)
• Expectancy: Average $ per trade
• R-Multiple: Profit/loss relative to initial risk

🔍 Data Sources

Historical data is fetched from Binance API:

• Symbol: BTCUSDT
• Interval: 1H
• Period: Jan 2023 - Feb 2026
• Source: Binance REST API

# Fetch fresh data
python3 -c "from data_fetcher import fetch_klines; fetch_klines('BTCUSDT', '1h', '2023-01-01')"

⚠️ Disclaimer

This is for educational and research purposes only.

• Past performance does not guarantee future results
• Cryptocurrency trading involves substantial risk of loss
• This software is provided "as is" without warranty
• Not financial advice - do your own research
• Use at your own risk

🤝 Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Indicator implementations based on Smart Money Concepts and Price Action principles
• Dashboard powered by Chart.js and Lightweight Charts
• Data provided by Binance API

📧 Contact

For questions or suggestions, please open an issue on GitHub.

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