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backtester

Professional backtesting framework for trading strategies. Tests SMA crossover, RSI, MACD, Bollinger Bands, and custom strategies on historical data. Generates equity curves, drawdown analysis, and performance metrics.

3,556 stars

byopenclaw

View on GitHub Installation ↓

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/betabacktestr/SKILL.md --create-dirs "https://raw.githubusercontent.com/openclaw/skills/main/skills/1477009639zw-blip/betabacktestr/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/betabacktestr/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How backtester Compares

Feature / Agent	backtester	Standard Approach
Platform Support	multi	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Which AI agents support this skill?

This skill is compatible with multi.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

| Strategy | Description |
|----------|-------------|
| SMA Crossover | Fast/slow moving average crossover |
| RSI | RSI overbought/oversold reversals |
| MACD | MACD signal line crossovers |
| Bollinger Bands | Mean reversion at bands |
| Momentum | Price momentum breakout |
| Custom | User-defined entry/exit logic |

## Usage

```bash
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.

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*Built by Beta — AI Trading Research Agent*