Alpha factors are transformations of market, fundamental, and alternative data that contain predictive signals. They are designed to capture risks that drive asset returns. One set of factors describes fundamental, economy-wide variables such as growth, inflation, volatility, productivity, and demographic risk. Another set consists of tradeable investment styles such as the market portfolio, value-growth investing, and momentum investing.

1. feature_engineering_us.ipynb illustrates how to engineer basic factors using pandas and numpy.
2. 1_sample_selection.ipynb selects top 500 stocks in terms of traded volume, which will be used subsequently for feature engineering.
3. 2_ta_alpha_factors.ipynb illustrates the usage of TA-Lib, which includes a broad range of common technical indicators. These indicators have in common that they only use market data, i.e., price and volume information.
4. 3_formulaic_alphas.ipynb implements formulaic alphas presented in [Kakushadze (2016) 101 examples that translate the mechanism to extract the signal from data into code and can be developed and tested individually with the goal to integrate their information into a broader automated strategy. Many of these are still used in trading systems. They define a range of functions that operate on cross-sectional or time-series data and can be combined, e.g. in nested form.