Hands-On Machine Learning for Algorithmic Trading

Creating a stock return series to predict asset price movement

We will use the last 24 monthly returns and dummy variables for the month and the year to predict whether the price will go up or down the following month. We use the daily Quandl stock price dataset (see GitHub for instructions on how to source the data). Run the following code:

prices = (pd.read_hdf('../data/assets.h5', 'quandl/wiki/prices')        .adj_close        .unstack().loc['2007':])DatetimeIndex: 4706 entries, 2000-01-03 to 2018-03-27Columns: 3199 entries, A to ZUMZdtypes: float64(3199)

We will work with monthly returns to keep the size of the dataset manageable and remove some of the noise contained in daily returns, which leaves us with almost 2,500 stocks with 120 monthly returns ...