Descriptions:
The Algovibes creator documents the construction and results of a systematic backtesting infrastructure that ran 131,441 individual strategy configurations across 62 trading strategies and 31 assets — built with substantial AI assistance for the code, optimization pipeline, and database, while relying on years of domain expertise to validate whether the results were economically meaningful. The central methodology is walk-forward testing: strategies are optimized on the first 70% of each time window and then run blind on the remaining 30%, with windows rolling forward sequentially and out-of-sample periods stitched together as the only result that counts.
After walk-forward testing reduced 131,441 runs to 917 promising candidates, six filters were applied: out-of-sample drawdown better than -35%, Sharpe ratio between 0.5 and 2.5, out-of-sample Sharpe no more than 30% above in-sample Sharpe (to catch lucky variance), minimum 30 trades, and positive in-sample Sharpe. These filters cut the field from 358 walk-forward survivors to 65 final strategies. The top result was squeeze momentum on ETH-USD, with an out-of-sample Sharpe of 0.84, maximum drawdown of -6.4% on unseen data, 376 trades, and 58% cumulative return across out-of-sample windows.
The creator is notably transparent about the division of labor between AI tooling and human judgment — AI built the infrastructure at a scale that would not have been feasible otherwise, but domain expertise was required to catch economically nonsensical outputs, compounding artifacts, and filter logic errors. The result is one of the more rigorous public treatments of systematic strategy research for a retail trading audience, with an honest framing of overfitting and survivorship bias that most trading content avoids.
📺 Source: Algovibes · Published May 19, 2026
🏷️ Format: Benchmark Test
