Most trading ideas look convincing until they meet bad data, trading costs, and the next market regime.

Quantitative Trading Strategies with Python is a practical guide for Python-literate investors who want to turn market ideas into transparent, testable research. Rather than promising a shortcut to profits, it teaches the process that credible quantitative work requires: write the rule, validate the data, backtest without obvious bias, measure the risk, and decide whether the evidence is strong enough to justify further paper trading.

Inside, you will learn how to:

• build a lean, reproducible Python research environment;
• avoid look-ahead bias, survivorship bias, and misleading backtests;
• implement trend following, cross-sectional momentum, mean reversion, and pairs-research frameworks;
• model signal timing, portfolio weights, turnover, and transaction costs;
• evaluate CAGR, drawdown, volatility, exposure, and robustness together;
• use walk-forward testing and holdout periods to challenge an encouraging idea; and
• design risk budgets and a paper-trading operating process.

Every strategy chapter combines plain-English intuition with Python examples, tables, diagrams, failure modes, and a practical exercise. The approach is deliberately transparent: learn not only how to construct a rule, but also how to recognise when a historical result does not deserve trust.

This book is for educational purposes only. It is not investment advice and does not promise trading results.