backtest-strategy

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• SKILL.md

SKILL.md (first lines)

# Backtest Strategy

Runs backtests on trading strategies and provides performance analysis.

## Real Code Reference

- `tradinglearn/backtest/backtester.py` — `Backtester` class: `run_backtest(data, strategy_class, strategy_params)`, `get_performance()`, `plot_results()`
- `tradinglearn/strategies/macd_strategy.py` — `MACDStrategy` with `generate_signals(data)` → positions
- `tradinglearn/pytdx2/backtest.py` — `BacktestEngine` + `BacktestConfig` + `BaseStrategy`
- `tradinglearn/utils/parameter_optimizer.py` — `ParameterOptimizer.optimize_macd_parameters()`

## Architecture

```
DataLoader → Strategy signals → Portfolio tracking → Metrics calculation → Report
```

1. **DataLoader** — fetch historical K-line via `data_fetcher.fetch_stock_data(ticker, start, end)`
2. **Strategy** — generate buy/sell signals per bar (`MACDStrategy(fast, slow, signal)`)
3. **Backtester** — `run_backtest(data, MACDStrategy, params)` iterates bars, tracks positions
4. **Metrics** — `get_performance()` returns Sharpe, max drawdown, win rate, total return, CAGR
5. **Plot** — `plot_results()` shows price vs portfolio value overlay

## Usage

```python
from backtest.backtester import Backtester
from strategies.macd_strategy import MACDStrategy
from utils.data_fetcher import fetch_stock_data

data = fetch_stock_data("000001", start_date="2024-01-01", end_date="2025-01-01")
bt = Backtester(initial_capital=100000.0, transaction_cost=0.001)
bt.run_backtest(data, MACDStrategy, {"fast_period": 12, "slow_period": 26, "signal_period": 9})
bt.generate_detailed_report()
bt.plot_results()
```

## Key Checks

- No lookahead bias — signal at bar `t` uses only data up to bar `t`
- Out-of-sample validation separate from parameter optimization
- Account for transaction costs (commission + slippage)
- Handle corporate actions (splits, dividends) in price data

From LisonEvf/finance-skills, MIT.

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