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Magents is an open-source Python framework for simulating and backtesting multiple trading strategies in one system, with isolated strategy pods, an event-driven engine, and central risk controls.
Introduces the open-source Python tool InStock (myhhub/stock) for A-share quantitative analysis, supporting data scraping, technical indicators, stock selection, backtesting, automated trading, etc., and can be deployed via Docker.
A curated list of 10 free GitHub repositories for algorithmic trading and quantitative finance, covering tools for backtesting, data analysis, portfolio optimization, and AI-driven trading strategies.
A user curated a free GitHub repository aggregating numerous open-source quantitative finance tools, including pricing engines, backtesting frameworks, order book simulators, and risk models, making institutional-grade research tools accessible to individuals at minimal cost.
A GitHub repo aggregates free, open-source quantitative finance tools—pricing engines, backtesters, order books, and HFT simulators—typically guarded by firms, including Microsoft's AI quant platform.
The post claims that quant firms' complex mathematical edge is now accessible via a plain-English chat interface that can describe, backtest, and execute trading strategies, democratizing quant finance.
This article details how to build a personal quantitative trading system using free AI open-source tools (such as OpenBB, Qlib, TradingAgents, etc.), covering five major modules: data, research, backtesting, risk control, and execution, and points out common pitfalls and discipline.
Open-source A-share quantitative workstation, based on TickFlow data, implements three functions: stock selection, real-time monitoring, and backtesting. Built-in 20 Polars vectorized strategies, supports AI strategy writing and Feishu push.
A developer demonstrates running Gemma 4 26B MoE model locally on an 8GB RTX 4060 with Hermes agent to fully automate backtesting of trading strategies, highlighting the growing capability of local LLMs as autonomous agents.
A thread introducing Loop Engineering as a solution to the common problem of quant strategies that backtest perfectly but fail in live trading, emphasizing the need for iterative optimization.
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.
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.