Open-sourced a systematic strategy research pipeline to reduce backtest false positives - looking for critique

Built and open-sourced a systematic strategy research pipeline for crypto strategy testing. Main goal is to reduce false positives from naive backtests. This came out of getting burned by unreliable backtest results and deciding to build a stricter validation workflow instead of trusting pretty equity curves. Current design: 1. A 3-vault structure: in-sample, out-of-sample, and final holdout 2. Walk-forward optimization for adaptive testing instead of one-shot fitting 3. Chart permutation testing on the early stages to check whether apparent edge is stronger than randomized market noise 4. Modular “indicator cartridges” so different signal components can be combined without rewriting the engine 5. Default multi-asset crypto basket currently includes BTC, ETH, LTC, and XRP A lot of the work is aimed at one question: does a strategy still look real after stricter validation, or was the original result just backtest noise? It’s open source and I’d genuinely like critique on: * failure modes I may still be missing * whether the validation stack is sensible * where the pipeline could still fool me Repo: [https://github.com/chinloong0/Strategy-Factory](https://github.com/chinloong0/Strategy-Factory)