Value Line investment analyzer is a popular proprietary investment research tool used by many investors to analyze stocks. However, the high subscription cost has led many to look for open source and free alternatives on github. Here we explore some of the open source python libraries and tools available on github that offer similar functionalities as Value Line for fundamental analysis, forecasting models and data analytics.
Fundamental data and financial modeling libraries
On github, there are several open source libraries focused on gathering fundamental data and building financial models for stocks analysis and valuation. These include financialmodelingprep, fundamentals, finDatasets, fmpsdk etc. They provide APIs to get fundamentals like financial statements, valuation ratios, growth rates, forecasts etc on equities. One can use these libraries to screen stocks based on fundamental criteria just like Value Line.
Technical analysis and charting libraries
For technical analysis, libraries like TA-lib, finta, stocktrends etc offer a wide range of indicators that can be used to analyze price trends and patterns. These include momentum, volatility, volume, moving averages etc. One can use these libraries to identify buying and selling opportunities in stocks. The python-tradingview library integrates TradingView charts into Jupyter notebooks for technical analysis.
Backtesting and trading strategy libraries
To backtest various trading strategies, python libraries like backtrader, zipline, pyalgotrade provide backtesting capabilities with data handling functionalities. These allow developing and testing different trading strategies with historical data. Along with fundamental factors, technical indicators from TA-Lib can also be incorporated into the strategy for entries and exits.
Macroeconomic data libraries
Value Line also factors in macroeconomic trends while forecasting individual stocks. Python libraries like pandas-datareader, quandl, fredapi provide easy access to various economic datasets like GDP, inflation, interest rates, unemployment etc. These datasets can be incorporated into forecasting models for equities analysis.
Alternative data libraries
In recent years, hedge funds and asset managers have started using alternative data like web scrapping, satellite imagery, geolocation data etc for investing. Python libraries like beautifulsoup, selenium, scrapy help in structured and unstructured web data extraction. Other libraries like pytrends, twitterscraper offer sentiment analysis which can be useful in estimating market mood.
While not a full replacement, the wide range of open source python libraries on github offer many capabilities to build your own investment research and analysis workflow for stocks as an alternative to proprietary tools like Value Line.