Backtesting
Testing a strategy on past data to assess its performance.
Beginner-friendly explanation
Backtesting lets you see if a trading strategy would have worked in the past. You apply the strategy’s rules to old data to check if it would have made or lost money. It helps decide if a plan is worth using before risking real money. Example: You want to buy Bitcoin each time it drops 5%. You check what would’ve happened if you did this last year. That’s backtesting.
Intermediate-level insight
A backtest applies a strategy to historical data, using the same rules as in real conditions: signals, stop loss, take profit, etc. It helps measure success rate, returns, and drawdown. It’s essential to use clean, representative data and avoid overfitting the strategy. Example: An RSI + EMA crossover strategy is tested on 3 years of BTC/USDT hourly data. Result: 62% win rate, 1.4 R/R ratio, max drawdown -8%.
Advanced perspective
Advanced backtesting includes realistic constraints: latency, fees, slippage, liquidity, dynamic position sizing. It can be coded in Python, R, or run on platforms like TradingView or QuantConnect. Results are analyzed with statistical metrics (Sharpe, Sortino, Expectancy) to assess strategy robustness and consistency. Walk-forward testing and cross-validation help avoid biases. Example: A multi-asset backtest on 100 crypto pairs (daily) using trailing stops, ATR-based sizing, 0.075% fees, validated via walk-forward across 6 sub-periods.
Trading Strategies
backtest, historical data, performance, drawdown, strategy, overfitting, validation, slippage, walk-forward, statistics