With the rise of financial technology and quantitative trading, using APIs to obtain financial data has become a must for investors and traders. Github, as the world’s largest open source community, contains numerous high-quality investment APIs that provide access to real-time and historical data for stocks, options, forex, cryptocurrencies and more. This article will provide a comprehensive overview of the most useful Github repositories for financial data acquisition and analysis using Python. Whether you are looking to develop trading algorithms, backtest strategies, conduct market research or build trading applications, these Github investment APIs have got you covered.
Comprehensive Python Repositories for Financial Data Acquisition
Yahoo Finance, Google Finance and Quandl are among the most popular sources for free financial data. Libraries like yfinance, pandas-datareader, finnhub-python simplify fetching OHLCV data. For Chinese stocks, akshare, ricequant and tushare offer convenient APIs. More specialized data providers like IEX Cloud, Tiingo, Polygon, Alpha Vantage are also wrapped into Python packages (iexfinance, tiingo, polygon-api-client, alpha_vantage). For people looking for a one-stop solution, findatapy integrates 30+ data sources. These libraries taken together enable individual investors to access institutional quality data for quantitative analysis.
Abundant Python Packages for Technical Indicators
Technical indicators are essential for algorithmic trading and backtesting. Packages like Tulipy, TA-Lib, pandas-ta, finta provide a Python interface to 200+ commonly used indicators. They have optimized vectorized implementations that are efficient even for large datasets. More specialized indicators can be found in libraries like lppls (LPPLS bubbles model), alphalens (factor analysis), pygtrends (Google Trends). For drawing candlestick charts and visualization, python-tradingview-ta, plotly, matplotlib offer ample options.
Powerful Python Backtesting Frameworks
Backtesting trading strategies is key to quantitative analysis. Frameworks like zipline, backtrader, pyalgotrade and freqtrade allow rapid strategy prototyping and testing on historical data. More advanced platforms like QuantConnect enable cloud-based live trading and simulation across multiple asset classes. For Chinese A-shares specifically, libraries like zvt, fooltrader, ricequant provide data sources, analytics and backtesting tailored for local markets. Whether you need minute-level ticks or end-of-day OHLCV, python has your backtesting needs covered.
Handy Python Tools for Trading System Development
From live trading and paper trading (alpaca-trade-api, Robinhood) to performance analytics (pyfolio, empyrical) to algorithmic trading (zipline, vnpy) to visualization (plotly, bokeh), Python offers a rich set of libraries for developing systematic trading systems. Tools like jupyterlab, quantdom and D-Tale also facilitate interactive data analysis and strategy exploration. For machine learning, pytorch, tensorflow and sklearn enable techniques like sentiment analysis, pattern recognition and time series forecasting.
Github hosts awesome Python packages that provide access to financial data, indicators, backtesting capabilities and trading infrastructure for both professionals and retail investors. With the power of open source, anyone can leverage these tools to conduct quantitative analysis, develop trading strategies and automate their investing process.