This episode offer a comprehensive guide to fitting trading rules and allocating capital within systematic trading systems. They emphasize the perils of over-fitting, such as selecting rules that appear profitable in back-tests but fail in real-world trading due to relying on chance occurrences in historical data. The sources discuss methods for effective fitting, including various out-of-sample back-testing techniques like expanding and rolling windows, while highlighting the extensive historical data required to statistically validate a trading rule or differentiate between rules. Furthermore, the chapters address portfolio allocation, explaining how traditional optimization can lead to unstable, extreme weights and introducing alternative strategies like bootstrapping and a handcrafting method that prioritizes diversification and incorporates Sharpe ratios more cautiously to achieve robust, balanced portfolios