Hands-On Machine Learning for Algorithmic Trading

Eigen portfolios

Another application of PCA involves the covariance matrix of the normalized returns. The principal components of the correlation matrix capture most of the covariation among assets in descending order and are mutually uncorrelated. Moreover, we can use standardized principal components as portfolio weights.

Let's use the 30 largest stocks with data for the 2010-2018 period to facilitate the exposition:

idx = pd.IndexSlicewith pd.HDFStore('../../data/assets.h5') as store:stocks = store['us_equities/stocks'].marketcap.nlargest(30)returns = (store['quandl/wiki/prices'].loc[idx['2010': '2018', stocks.index], 'adj_close'].unstack('ticker').pct_change())

We again winsorize and also normalize the returns:

normed_returns = scale(returns ...