Signal diagnostics, statistical validation, and backtest evaluation for quantitative trading workflows.
Fortran Statistics and Machine Learning Library
AI-powered trading research platform. Test any idea on stocks, futures, and crypto with event studies, backtesting, and statistical validation. MCP server with 8 tools. pip install varrd.
Statistical evaluation framework for AI agents
High-performance statistical testing and regression for Polars DataFrames, powered by Rust.
AI Firewall and guardrails for LLM-based Elixir applications
A modern Fortran statistical library.
Statistical tests in Rust
Pairing-based value betting system for SportyBet Instant Virtual Soccer — 31,500+ matches analyzed, per-fixture EV evaluation against real odds
Statistically sound A/B tests for prompts and models: paired tests, confidence intervals, sample-size planning and a CI regression gate. Type-safe prompts with Pydantic.
Significant Network Interval Mining
Quantitative research tool analyzing stock performance around US Thanksgiving. 354 stocks, 8,293 observations (2000-2024). Statistical significance testing included.
Zero-error LLM execution via SPRT voting. Rust library and MCP server implementing the MAKER algorithm for mathematically-grounded error correction in long-horizon AI agent tasks. Research experiment based on arXiv:2511.09030
Customer base analysis is concerned with using the observed past purchase behavior of customers to understand their current and likely future purchase patterns. More specifically, as developed in Schmittlein et al. (1987), customer base analysis uses data on the frequency, timing, and dollar value of each customer's past purchases
可插拔的大模型能力评测框架：三层判定 × 偏差控制 × 裁判校准 × 统计显著性检验 × 数据飞轮。290+ 题 13 维度评测集，唯一依赖 PyYAML，离线可跑 | Pluggable LLM evaluation framework with three-tier judging & judge calibration
🪞 Catch false positives/negatives in AI eval claims — pre-registration, fair-baseline, small-sample CI. Zero training, zero deps.
End-to-end Python regression pipeline for Jane Street market data forecasting with Optuna tuning, chronological cross-validation, custom weighted R2 evaluation, model comparison, plots, SHAP explainability, and Docker support.
This repository is a fork of a repository originally created by Lucas Descause. It is the codebase used for my Master's dissertation "Reinforcement Learning with Function Approximation in Continuing Tasks: Discounted Return or Average Reward?" which was also an extension of Luca's work.
Compare old and new ML models under a fixed evaluation budget, with paired tests and reproducible regression reports.
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