This article surveys the leading open-source GitHub projects for acquiring quantitative trading data, covering stocks, crypto, futures, and forex. It compares six major projects: AKShare (16.3k+ stars, free MIT-licensed aggregator of 30+ Chinese financial data sources covering A-shares, US stocks, HK stocks, and crypto), CCXT (41k+ stars, unified API for 107+ cryptocurrency exchanges with WebSocket support via CCXT Pro), Tushare (14.4k+ stars, professional A-share data with credit-based access), yfinance (~12k stars, Yahoo Finance historical data for US equities), Microsoft's Qlib (37.5k+ stars, industrial AI quant platform with a full ML pipeline), and FinRL-Meta (~3k stars, multi-source aggregation for reinforcement learning). The guide includes Python code examples for each library, a comparison of real-time versus historical data requirements, market-by-market and use-case selection tables, and best practices such as data quality checks, cross-source validation, and parquet-based caching strategies.
Quantitative Trading Data Acquisition Guide: A Deep Dive into Open-Source GitHub Projects
Summary
This article surveys the leading open-source GitHub projects for acquiring quantitative trading data, covering stocks, crypto, futures, and forex. It compares six major projects: AKShare (16.3k+ stars, free MIT-licensed aggregator of 30+ Chinese financial data sources covering A-shares, US stocks, HK stocks, and crypto), CCXT (41k+ stars, unified API for 107+ cryptocurrency exchanges with WebSocket support via CCXT Pro), Tushare (14.4k+ stars, professional A-share data with credit-based access), yfinance (~12k stars, Yahoo Finance historical data for US equities), Microsoft's Qlib (37.5k+ stars, industrial AI quant platform with a full ML pipeline), and FinRL-Meta (~3k stars, multi-source aggregation for reinforcement learning). The guide includes Python code examples for each library, a comparison of real-time versus historical data requirements, market-by-market and use-case selection tables, and best practices such as data quality checks, cross-source validation, and parquet-based caching strategies.
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