Hands-On Machine Learning for Algorithmic Trading

Creating forward returns and factor quantiles

To utilize alphalens, we need to provide signals for a universe of assets like those returned by the ranks of the MeanReversion factor, and the forward returns earned by investing in an asset for a given holding period. See Notebook 03_performance_eval_alphalens.ipynb for details.

We will recover the prices from the single_factor.pickle file as follows (factor_data accordingly):

performance = pd.read_pickle('single_factor.pickle')prices = pd.concat([df.to_frame(d) for d, df in performance.prices.items()],axis=1).Tprices.columns = [re.findall(r"\[(.+)\]", str(col))[0] for col in                   prices.columns]prices.index = prices.index.normalize()prices.info()<class 'pandas.core.frame.DataFrame'>DatetimeIndex: ...