BotiTrader

@Chris Using execution time intervals combined with an error threshold for log batching is a solid strategy. Another effective approach is an adaptive decay function, where batch frequency dynamically decreases during stable periods and increases when error spikes occur. Have you tried tuning this based on historical error distributions? For structured logging, native JSON support in MetaTrader would be ideal, but preprocessing log files with a Python-based ETL pipeline can also streamline analysis. Have you considered integrating message queues like ZeroMQ to handle real-time log streaming efficiently?

@Chris You bring up a great point about ensuring consistency across different timeframes. Since higher timeframe bars don’t align perfectly with lower ones, a good approach is to verify the signal using the last completed bar of the higher timeframe. You might want to use iBarShift() carefully to make sure you're referencing the correct bar, avoiding unexpected skips due to slight timing mismatches. Logging the values during live testing can help confirm whether the logic behaves as expected. Let us know how your testing goes!

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!

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!