Backtesting refers to the process of testing a trading strategy on historical data to evaluate its performance before deploying it in real markets. It's a critical step in algorithmic trading and helps traders understand the viability and robustness of their strategies. A good backtest should mimic real market conditions, include realistic assumptions like slippage and commissions, and analyze metrics such as win rate, average return, drawdown, Sharpe ratio, and more. To begin with backtesting, you need a clean historical dataset and a strategy coded in a backtesting framework like Backtrader, QuantConnect, or a custom Python script using pandas. The strategy logic—buy/sell rules, entry/exit triggers, stop losses, etc.—must be accurately translated into code. A well-executed backtest provides insight into how your strategy would have performed over time. It can also reveal weaknesses, overfitting, or false assumptions. However, traders should avoid relying only on past performance as a guarantee for future results. Instead, treat backtesting as a validation tool to refine strategies and test multiple scenarios. The next step after backtesting is paper trading (live testing without real capital), followed by actual trading with strict risk management.