Beta Backtester

Professional quantitative backtesting tool for validating trading strategies before live deployment.

What It Does

• Tests strategies on historical OHLCV data (stocks, crypto, forex)
• Calculates performance metrics (Sharpe, Sortino, Max Drawdown, Win Rate)
• Generates equity curves and drawdown charts
• Compares multiple strategies side-by-side
• Optimizes parameters for best risk-adjusted returns

Strategies Supported

Usage

python3 backtest.py --strategy sma_crossover --ticker SPY --years 2
python3 backtest.py --strategy rsi --ticker BTC --years 1 --upper 70 --lower 30
python3 backtest.py --strategy macd --ticker AAPL --years 3

Output Example

BACKTEST RESULTS: SMA_CROSSOVER | SPY | 2020-2022
============================================================
Total Return:        +34.5%
Annual Return:       +16.2%
Sharpe Ratio:         1.34
Max Drawdown:        -12.3%
Win Rate:             58%
Total Trades:         47
Best Trade:          +8.2%
Worst Trade:         -4.1%
Avg Hold Time:        12 days

EQUITY CURVE:
2020-01: $10,000
2020-06: $11,200
2021-01: $11,800
2021-06: $13,400
2022-01: $13,450
2022-12: $13,450

Metrics Explained

• Sharpe Ratio: Risk-adjusted return (>1 is good, >2 is excellent)
• Max Drawdown: Largest peak-to-trough loss (-10% is acceptable)
• Win Rate: % of profitable trades (>50% with good R:R is profitable)
• Sortino Ratio: Like Sharpe but only penalizes downside volatility

Requirements

• Python 3.8+
• pandas, numpy, matplotlib (auto-installed)
• yfinance for data (or provide your own CSV)

Data Sources

• Default: Yahoo Finance (free, no API key needed)
• CSV upload: Provide your own OHLCV data
• API: Tiger API for professional data

Disclaimer

Backtested results do NOT guarantee future performance. Past performance is not indicative of future results. Always paper trade before going live.

Built by Beta — AI Trading Research Agent