backtesting-trading-strategies

Files (30)

• origin.json · .clawhub/
• SKILL.md
• _meta.json
• settings.yaml · config/
• errors.md · references/
• examples.md · references/
• implementation.md · references/
• optimization_rsi_reversal_20260417_083012.txt · reports/
• rsi_reversal_510300.SS_20260417_064322_chart.png · reports/
• rsi_reversal_510300.SS_20260417_064322_summary.txt · reports/
• rsi_reversal_510300.SS_20260417_065003_chart.png · reports/
• rsi_reversal_510300.SS_20260417_065003_summary.txt · reports/
• rsi_reversal_510300.SS_20260417_082915_chart.png · reports/
• rsi_reversal_510300.SS_20260417_082915_summary.txt · reports/
• rsi_reversal_510300_20260417_083032_chart.png · reports/
• rsi_reversal_510300_20260417_083032_summary.txt · reports/
• rsi_reversal_BTC-USD_20260417_082919_chart.png · reports/
• rsi_reversal_BTC-USD_20260417_082919_summary.txt · reports/
• rsi_reversal_BTC-USD_20260417_083026_chart.png · reports/
• rsi_reversal_BTC-USD_20260417_083026_summary.txt · reports/
• rsi_reversal_ETH-USD_20260417_083034_chart.png · reports/
• rsi_reversal_ETH-USD_20260417_083034_summary.txt · reports/
• backtest.py · scripts/
• china_stock_loader.py · scripts/
• fetch_data.py · scripts/
• metrics.py · scripts/
• optimize.py · scripts/
• skill_settings.py · scripts/
• strategies.py · scripts/
• skill.json

SKILL.md (first lines)

# Backtesting Trading Strategies

## Overview

Validate trading strategies against historical data before risking real capital. This skill provides a complete backtesting framework with 8 built-in strategies, comprehensive performance metrics, and parameter optimization.

**Key Features:**
- 8 pre-built trading strategies (SMA, EMA, RSI, MACD, Bollinger, Breakout, Mean Reversion, Momentum)
- Full performance metrics (Sharpe, Sortino, Calmar, VaR, max drawdown)
- Parameter grid search optimization
- Equity curve visualization
- Trade-by-trade analysis

## Prerequisites

Install required dependencies:

```bash
pip install pandas numpy yfinance matplotlib
```

For **A-share / ETF** backtests (6-digit codes such as `510300` or `510300.SS`), the scripts load history via this repo’s `plugins/data_collection` stack (AkShare, mootdx, project cache — same strategy as `openclaw-data-china-stock`). Install:

```bash
pip install akshare
```

Optional for advanced features:
```bash
pip install ta-lib scipy scikit-learn
```

## Instructions

### Step 1: Fetch Historical Data

Crypto / US (Yahoo):

```bash
python {baseDir}/scripts/fetch_data.py --symbol BTC-USD --period 2y --interval 1d --source yfinance
```

China ETF (plugin-aligned pipeline, default for `--source auto` when symbol is a 6-digit code):

```bash
python {baseDir}/scripts/fetch_data.py --symbol 510300.SS --period 2y --interval 1d --source auto
```

Data is cached to `{baseDir}/data/{symbol}_{interval}.csv` for reuse.

### Step 2: Run Backtest

Basic backtest with default parameters:

```bash
python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol BTC-USD --period 1y --data-source yfinance
```

China ETF (uses `plugins/data_collection` automatically; no Yahoo):

```bash
python {baseDir}/scripts/backtest.py --strategy sma_crossover --symbol 510300.SS --period 1y
```

Advanced backtest with custom parameters:

```bash
# Example: backtest with specific date range
python {baseDir}/scripts/backtest.py \
  --strategy rsi_reversal \
  --symbol ETH-USD \
  --period 1y \
  --capital 10000 \
  --params '{"period": 14, "overbought": 70, "oversold": 30}'
```

### Step 3: Analyze Results

Results are saved to `{baseDir}/reports/` including:
- `*_summary.txt` - Performance metrics
- `*_trades.csv` - Trade log
- `*_equity.csv` - Equity curve data
- `*_chart.png` - Visual equity curve

### Step 4: Optimize Parameters

Find optimal parameters via grid search:

```bash
python {baseDir}/scripts/optimize.py \
  --strategy sma_crossover \
  --symbol BTC-USD \
  --period 1y \
  --param-grid '{"fast_period": [10, 20, 30], "slow_period": [50, 100, 200]}'
```

## Output

### Performance Metrics

| Metric | Description |
|--------|-------------|
| Total Return | Overall percentage gain/loss |
| CAGR | Compound annual growth rate |
| Sharpe Ratio | Risk-adjusted return (target: >1.5) |
| Sortino Ratio | Downside risk-adjusted return |
| Calmar Ratio | Return divided by max drawdown |

### Risk Metrics

| Metric | Description |
|--------|-------------|
| Max Drawdown | Largest peak-to-trough decline |
| VaR (95%) | Value at Risk at 95% confidence |
| CVaR (95%) | Expected loss beyond VaR |
| Volatility | Annualized standard deviation |

### Trade Statistics

From shaoxing-xie/etf-options-ai-assistant, MIT.

More from shaoxing-xie/etf-options-ai-assistant