Building Realistic Quantitative Trading Backtests and Reading Their Results
Summary
This tutorial explains how to configure and assess a quantitative strategy backtest, using a platform example and a regime-switching futures strategy. It covers selecting a historical period and bar interval, choosing simulated or closer-to-market data, setting fees and starting capital, and modeling order timing, trade size, and slippage. It also describes common outputs such as trade logs, return and equity curves, Sharpe ratio, annualized volatility, and maximum drawdown. The example strategy switches between oscillating-market and trend-market rules using a market-state measure, moving averages, and volatility bands.
The central advice is to make historical execution assumptions resemble live trading and inspect the implementation when results disappoint. Strong results deserve scrutiny for look-ahead bias, price leakage, overfitting, or omitted slippage; the article recommends out-of-sample data and simulated live trading as checks. It advises spanning bull and bear conditions and having at least 100 trades, while acknowledging that historical success cannot guarantee future performance. Its platform walkthrough and specific thresholds are examples, not universal standards, and the discussion does not establish that the sample strategy is profitable.
Key ideas
- Backtests should model execution timing, fees, position sizes, and slippage realistically.
- The tutorial distinguishes signals evaluated at bar close from signals evaluated on each price update.
- Performance review should include returns, drawdown, volatility, Sharpe ratio, and detailed trade records.
- Unusually strong results can reflect look-ahead bias, overfitting, or missing trading costs.
- Out-of-sample checks and simulated live trading can help assess robustness, but historical results cannot guarantee future performance.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.