What is alphalens?

Alphalens is a Python tool for testing whether a stock-picking signal actually works before you risk real money on it. If you've come up with a formula or data signal that you think predicts which stocks will outperform, this library helps you check if you're right by generating a detailed report, called a "tear sheet", full of statistics and charts. At a high level, you feed the tool two things: your signal data and historical stock prices. The library then crunches the numbers to see how well your signal correlated with actual future returns. It breaks stocks into groups (like top performers vs. bottom performers based on your signal) and shows you whether the ranking held true over time. The output covers returns analysis, how predictive your signal actually was, how often you'd need to buy and sell, and performance broken down by sector. This is designed for quantitative researchers, hedge fund analysts, or hobbyist traders building systematic stock strategies. For example, if you hypothesize that stocks with high social media mentions tend to rise, you'd use this tool to test that theory against years of market data. It's part of a broader ecosystem, it pairs naturally with Zipline for backtesting full strategies and Pyfolio for analyzing portfolio-level risk once you're trading. The tool is built to run in Jupyter notebooks, which means you work through your analysis interactively, seeing charts update as you go. It relies on standard Python data science libraries. The project originated at Quantopian, a now-defunct community platform for quant research, so while it's open source and installable via standard package managers, the organizational context behind it has changed.