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

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