Open‑source pairs + AI quants
An open‑source Python pairs‑trading repo is circulating that tests cointegration and signal generation for mean‑reversion strategies—useful base code for systematic traders . Separately, a group report claims AI agent ensembles and P2P evolutionary stacks (135 quants in one example) have tripled returns and cut drawdowns to ~5.5% in backtests, a striking improvement if robust out‑of‑sample [](https://x.com/i/status/2032787731676307901).
A handful of public GitHub projects implement end‑to‑end cointegration pipelines with signal generation and backtesting; for example, MisbahAN/cointegration‑pairs‑trading includes OLS residual modeling, ADF filtering, rolling z‑score signals and an intraday backtest script. (github.com) The 'cointegration‑analysis' repository by gustavlan adds explicit walk‑forward cross‑validation and models 20 basis‑point round‑trip costs, illustrating an example WTI vs Brent experiment that posts an 8.9% return and −5.8% max drawdown in its walk‑forward notebook. (github.com) Multiple public implementations rely on Engle–Granger or Kalman hedge‑ratio estimation but often omit granular microstructure slippage and order‑routing assumptions; QuantConnect’s robust‑backtesting guide and Hyper‑Quant’s methodology paper both stress walk‑forward validation and explicit slippage modeling. (github.com) Recent AI multi‑agent work shows clear scaling behavior: Google Research’s January 28, 2026 study evaluated 180 agent configurations and derived quantitative principles where multi‑agent coordination improves performance on parallelizable tasks. (research.google) Financial‑specific ensemble experiments also exist: an ensemble reinforcement‑learning paper (arXiv, 23 Feb 2025) reports that combining classifier and RL families can materially improve risk‑return tradeoffs in trading simulations. (arxiv.org) Crowdsourced quant platforms already operationalize large ensembles; Numerai documents “target ensembles” and a model‑aggregation workflow that routinely combines dozens to thousands of submissions from external contributors. (numer.ai) Methodological safeguards are well established in the literature: researchers recommend measuring Probability of Backtest Overfitting (PBO), Probabilistic Sharpe Ratio (PSR) and Deflated Sharpe Ratio (DSR) to quantify false‑discovery risk in historical searches. (quinfer.github.io) Practical next steps for any group claiming cross‑model gains are therefore reproducible walk‑forward notebooks, published transaction‑cost and capacity assumptions, and independent replication; recent proposals such as the GT‑Score and SSRN walk‑forward studies (2024–2026) codify those reporting standards. (arxiv.org)
