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Stone141319 released a local cryptocurrency quantitative backtesting tool called Hermes Backtest Lab, supporting spot, perpetual swap, and on-chain token backtesting without requiring an API key, and offers various presets and custom parameters.
A Chinese quantitative finance tutorial for absolute beginners, using Jupyter Notebook format, containing 4 chapters (quantitative cognition, return analysis, dual moving average strategy, and strategy backtesting). Uses yfinance to fetch real data, each chapter can be run through in about 30 minutes.
Introducing QuantDinger, an open-source AI quantitative trading platform that supports local deployment, full-chain connectivity for crypto, US stocks, and forex, integrating AI analysis, strategy generation, backtesting, and live trading integration.
ai-quant-lab is an open-source Python framework that uses Claude to generate algorithmic trading strategies and rigorously validates them with statistical tests to avoid overfitting.
Proposes TEMPO, a policy optimization method that trains LLMs to reason exclusively from pre-cutoff information by using a two-mode reward and GRPO-based training, reducing knowledge leakage by 2–13% while improving task performance by 6–13%.
Mobius is a tool that lets users describe a trade and automatically builds, backtests, and runs it.
A local quantitative engine for A-share stock and ETF minute/day K-line data, with incremental sync, local cache, batch query, backtesting, and indicator calculation.
NautilusTrader is an open-source, Rust-native algorithmic trading engine for multi-asset, multi-venue systems, providing a single event-driven architecture for research, simulation, and live execution with Python or Rust strategy development.