What is Backtesting?

Backtesting is the process of testing a trading strategy on historical market data to evaluate how it would have performed in the past. It's an essential step before deploying any algorithmic strategy with real capital.

Think of backtesting as a flight simulator for trading—it lets you practice and refine your approach without the risk of crashing. However, just like a simulator can't capture every real-world scenario, backtesting has limitations you must understand.

The Backtesting Process

  1. Define Your Strategy

    Clearly specify entry rules, exit rules, position sizing, and any filters or conditions. Be precise—ambiguity leads to look-ahead bias.

  2. Gather Historical Data

    Obtain high-quality, adjusted data for your chosen timeframe. Include dividends, splits, and delisted securities to avoid survivorship bias.

  3. Implement the Strategy

    Code your strategy in a backtesting framework, ensuring you only use information available at each point in time.

  4. Run the Backtest

    Execute the strategy across your historical data, recording all trades, positions, and portfolio values.

  5. Analyze Results

    Evaluate performance metrics, drawdowns, and trade statistics to assess viability.

Key Performance Metrics

Total Return

Overall percentage gain or loss over the backtest period.

CAGR

Compound Annual Growth Rate—annualized return for comparison across timeframes.

Sharpe Ratio

Risk-adjusted return: (Return - Risk-Free Rate) / Standard Deviation. Above 1.0 is good, above 2.0 is excellent.

Max Drawdown

Largest peak-to-trough decline. Critical for understanding worst-case scenarios.

Win Rate

Percentage of trades that are profitable. Consider alongside average win/loss sizes.

Profit Factor

Gross profits divided by gross losses. Above 1.5 is typically desirable.

Realistic Backtesting Requirements

To produce meaningful results, your backtest must account for real-world trading conditions:

  • Transaction costs: Include commissions, fees, and estimated slippage
  • Market impact: Large orders move prices—factor this into entry/exit prices
  • Liquidity constraints: Can you actually execute the trades your strategy signals?
  • Capital requirements: Account for margin requirements and buying power
  • Realistic fill prices: You won't always get the exact price you want
The Reality Check

A backtest showing 15% annual returns can collapse to near-zero after accounting for realistic costs, especially in high-frequency or high-turnover strategies. Always model transaction costs conservatively.