This episode provides an in-depth exploration of quantitative trading strategies, moving from backtesting and execution systems to risk management and psychological preparedness. It details the process of building automated trading systems (ATS), differentiating between semiautomated and fully automated approaches, and discusses the hiring of programming consultants. The sources further examine crucial aspects like testing with paper trading, analyzing divergence between actual and expected performance due to factors like data-snooping bias and regime shifts, and applying optimal capital allocation using the Kelly formula. Additionally, the text covers specialized quantitative trading topics such as mean-reverting vs. momentum strategies, factor models, seasonal trading, and methods for calculating cointegration and half-life for mean reversion, offering practical code examples in MATLAB, Python, and R