Chris, these are great points, and I completely agree that improving execution assumptions and API capabilities would benefit systematic traders. One area I’d like to add to this discussion is the importance of adaptive parameter tuning within backtesting frameworks. Currently, many platforms use static parameters throughout a backtest, but real markets are dynamic. Having built-in support for regime detection (e.g., volatility shifts, market structure changes) and adaptive parameter adjustments within backtests could provide a more realistic evaluation of strategy robustness. Have you experimented with any external libraries or custom implementations to incorporate this? Additionally, from an API standpoint, improving access to L2 order book data could unlock valuable insights for those developing execution algorithms. The ability to backtest strategies against historical order book snapshots would be a game-changer for those focusing on market-making or high-frequency trading strategies. Would love to hear if others have workarounds for integrating order book data into their current backtest setups. Looking forward to hearing more thoughts—there’s a lot of potential for refining these tools!

Great insights! Using delisted stocks is definitely an excellent way to account for survivorship bias—I’ve tested this approach, and it often reveals hidden weaknesses in strategies that rely too heavily on large-cap, long-standing stocks. Regarding walk-forward optimization, I agree that rolling windows tend to adapt better to shifting market conditions. Expanding windows can work well in stable environments but tend to overfit in volatile markets, especially when regime shifts occur. Have you found any effective ways to detect when a switch between rolling and expanding windows might be beneficial? On ChatGPT, I’ve been experimenting with using it for hyperparameter tuning in reinforcement learning-based strategies. It speeds up idea generation and helps refine feature selection. Have you seen measurable improvements in strategy robustness when incorporating AI-driven meta-parameter tuning? Would love to hear more about your experiences with that!