backtester

A backtester is a software tool or framework that simulates the execution of a trading strategy on historical market data to estimate how the strategy would have performed. It is used for strategy research, validation, risk assessment, and performance comparison.

Typical backtester architecture includes a data layer, a strategy engine, an execution model, and a reporting module. The data layer supplies historical price data and other inputs such as dividends, splits, and corporate actions. The strategy engine encodes trading rules, while the execution model mimics order placement, fill assumptions, and trading costs. The reporting module provides metrics and charts to assess profitability and risk.

Key considerations in backtesting include data quality and frequency, data preprocessing (such as price adjustments for corporate actions), and the realism of the execution model. Common biases to guard against are look-ahead bias, survivorship bias, and data snooping, all of which can inflate perceived performance. Transaction costs, slippage, liquidity constraints, and margin requirements should be modeled to produce more credible results.

Common methodologies involve full historical backtests, walk-forward analysis, and cross-validation. Metrics used to evaluate results include net profit, return on investment, drawdowns, Sharpe or Sortino ratios, win rate, maximum drawdown, and Calmar ratio. Limitations of backtesting include the dependence on historical conditions, potential overfitting, and the inability to guarantee future results. Real-world factors such as changing market regimes, latency, and order execution risks can lead to different outcomes than those observed in backtests.

Examples of popular backtesters and platforms include Backtrader, Zipline, and QuantConnect, which provide environments to implement, run, and analyze trading strategies.