# Backtest Strategy

> Runs backtests on trading strategies using historical market data. Calculates performance metrics including Sharpe ratio, maximum drawdown, win rate, total return, and generates equity curves. Trigger when the user requests backtesting, strategy simulation, or performance evaluation.

- Skill: `lisonevf/backtest-strategy` (Agent Skill)
- Install (CLI): `npx skillmds@latest add lisonevf/backtest-strategy`
- Raw SKILL.md: https://api.skillmd.com/api/skills/lisonevf/backtest-strategy/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: lisonevf (https://skillmd.com/u/lisonevf)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/lisonevf/backtest-strategy

---


# 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