To use lagged values as input variables or features associated with the current observations, we use the .shift() method to move historical returns up to the current period:

for t in range(1, 7):
    data[f'return_1m_t-{t}'] = data.groupby(level='ticker').return_1m.shift(t)

Similarly, to compute returns for various holding periods, we use the normalized period returns computed previously and shift them back to align them with the current financial features:

for t in [1,2,3,6,12]:
    data[f'target_{t}m'] = data.groupby(level='ticker')[f'return_{t}m'].shift(-t)