strategy-backtest — Quantitative Strategy Backtesting

Runs strategy backtests on historical OHLCV data and returns performance metrics as JSON. Supports SMA crossover strategy with configurable fast/slow periods.

Usage

# Demo mode — uses built-in sample_ohlcv.csv
python3 strategy_backtest.py

# Backtest with custom CSV data
python3 strategy_backtest.py --data path/to/ohlcv.csv

# Backtest with JSON string input
python3 strategy_backtest.py --data '[{"open":10,"high":11,"low":9,"close":10.5,"volume":100}]'

# Custom SMA periods
python3 strategy_backtest.py --data prices.csv --fast 10 --slow 30

# Human-readable output
python3 strategy_backtest.py --data prices.csv --output print

Parameters

Flag	Default	Description
--data	sample_ohlcv.csv	CSV path or JSON string (OHLCV)
--strategy	sma_crossover	Strategy type
--fast	5	Fast SMA period
--slow	20	Slow SMA period
--output	json	Output format (json or print)

Supports column names in English (open/high/low/close/volume) or Chinese AKShare format (开盘/收盘/最高/最低/成交量/日期).

Example output

{
  "total_return": 0.0523,
  "sharpe_ratio": 1.2345,
  "max_drawdown": -0.0812,
  "win_rate": 0.6,
  "trade_count": 10,
  "trades": [
    {"date": "2024-01-15", "action": "buy", "price": 150.25},
    {"date": "2024-02-01", "action": "sell", "price": 158.50, "pnl": 0.0549}
  ]
}

Error handling

• Missing pandas: prints {"error": "pandas required: pip install pandas"}
• Missing columns: reports which OHLCV columns are absent
• Insufficient data: returns error if fewer rows than the slow SMA window
• Unknown strategy: reports the unrecognized strategy name

Programmatic API

from strategy_backtest import run_backtest
metrics = run_backtest("prices.csv", strategy="sma_crossover", fast=5, slow=20)

Related skills

• hhxg-top-hhxg-python: fetch A-share OHLCV data → feed into this skill
• session-memory: store backtest metrics for later comparison