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+## Engineering alpha factors
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+Based on a conceptual understanding of key factor categories, their rationale and popular metrics, a key task is to identify new factors that may better capture the risks embodied by the return drivers laid out previously, or to find new ones.
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+### Useful pandas and NumPy methods
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+NumPy and pandas are the key tools for custom factor computations. The Notebook [feature_engineering](feature_engineering.ipynb) contains examples of how to create various factors. 
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+The notebook uses data generated by the notebook [create_datasets](../../data/create_datasets.ipynb) script in the data folder in the root directory of this GitHub repo and stored in HDF5 format for faster access. 
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+See the notebook [storage_benchmarks](../../02_market_and_fundamental_data/04_storage_benchmark/storage_benchmark.ipynb) in the directory for Chapter 2, Market and Fundamental Data for a comparison of parquet, HDF5, and csv storage formats for pandas DataFrames.