Free quant tools and datasets
Microsoft open‑sourced an AI‑powered quant investment platform and shared a beginner roadmap for systematic strategy building, and a public post surfaced a massive finance database with roughly 300,000 tickers useful for backtesting. (x.com) (x.com). A curated list of 185 Python data‑science projects — many focused on quantitative trading and dashboards — was also circulated as practical hands‑on material. (x.com).
Most people who try quantitative trading get stuck before they write a single strategy. They need three things at once: a research engine, a clean map of what securities exist, and small projects they can copy apart and rebuild. (github.com 1) (github.com 2) (pyquantnews.com) Microsoft’s answer is Qlib, an open-source quantitative investment platform that bundles data handling, model training, backtesting, portfolio optimization, and order execution in one Python framework. Microsoft Research described it as a way to bridge artificial intelligence tools and quantitative investing, and the GitHub repository says it supports supervised learning, market dynamics modeling, and reinforcement learning. (github.com) (microsoft.com) Backtesting is the part beginners usually underestimate. It means taking a rule like “buy the strongest stocks in the last 20 days” and running it on old market data to see how it would have behaved before risking real money, and Qlib is built around that full research loop. (github.com 1) (github.com 2) A platform like that is only useful if you know what to test on. FinanceDatabase fills that gap with a public catalog of more than 300,000 symbols covering equities, exchange-traded funds, mutual funds, indices, currencies, cryptocurrencies, and money markets. (github.com) That database is not a live price feed. Its own documentation says the point is to organize what products exist by country, sector, industry, and exchange, which makes it useful for screening universes before you pull price history from somewhere else. (github.com 1) (github.com 2) That sounds mundane until you try to build a strategy across markets. A backtest can break if your stock universe is missing delisted names, mislabeled exchanges, or entire categories like exchange-traded funds, so a giant symbol map is like having a complete card catalog before you start running experiments. (github.com) The third piece is practice material. PyQuant News keeps a free resource hub with hands-on Python articles on algorithmic trading, backtesting, options, dashboards, machine learning, and data analysis, and its contributor page on Interactive Brokers says it offers hundreds of code tutorials aimed at both beginners and practitioners. (pyquantnews.com) (interactivebrokers.com) Those tutorials are concrete enough to build from. The free library includes guides on walk-forward analysis, Backtrader, technical indicators, real-time financial data, and trading dashboards with Plotly and Dash, which means a learner can move from a notebook to a working interface instead of stopping at a chart. (pyquantnews.com) (pyquantnews.com) There is also a public PyQuant Newsletter GitHub repository with notebooks for pairs trading, risk parity, implied volatility surfaces, drawdown analysis, portfolio metrics, and automated data pipelines. That matters because many people learn quantitative finance faster from a 100-line notebook they can run than from a 1,000-page textbook they never finish. (github.com) Put together, the stack is unusually complete for free tools. Qlib gives you the lab bench, FinanceDatabase gives you the inventory list, and the PyQuant material gives you starter experiments you can actually run this weekend. (github.com) (github.com) (pyquantnews.com)
