Hands-On Machine Learning for Algorithmic Trading

Separating signal and noise – how to use alphalens

Quantopian has open sourced the Python library, alphalens, for the performance analysis of predictive stock factors that integrates well with the backtesting library zipline and the portfolio performance and risk analysis library pyfolio that we will explore in the next chapter.

alphalens facilitates the analysis of the predictive power of alpha factors concerning the:

• Correlation of the signals with subsequent returns
• Profitability of an equal or factor-weighted portfolio based on a (subset of) the signals
• Turnover of factors to indicate the potential trading costs
• Factor-performance during specific events
• Breakdowns of the preceding by sector

The analysis can be conducted using tearsheets ...