Lookahead bias in backtesting?

Strategy Backtesting
#1

In the below code for chapter 8/03_ml4t_with_zipline.ipynb.

returns are shifted forward, which makes sense to me since we are trying to anticipate the next day returns with prior data.

but why are the features shifted back? doesn’t shifting the features back allow look ahead bias to creep in?

shift_by = N_FORWARD_DAYS + 1
outcome = returns[shift_by:].flatten()
features = np.dstack(inputs)[:-shift_by]

class LinearModel(CustomFactor):
“”“Obtain model predictions”“”
train_on_weekday = [0, 2, 4]

def __init__(self, *args, **kwargs):
    super().__init__(self, *args, **kwargs)

    self._scaler = StandardScaler()
    self._model = SGDRegressor(penalty='L2')
    self._trained = False

def _train_model(self, today, returns, inputs):

    scaler = self._scaler
    model = self._model

    shift_by = N_FORWARD_DAYS + 1
    outcome = returns[shift_by:].flatten()
    features = np.dstack(inputs)[:-shift_by]
#2

Hey,

I am new here and this topic is also new to me. But I have interest in learning new things. So, I searched about this topic and found this :point_right: Look-Ahead Bias In Backtests And How To Detect It | by Michael Harris | Medium

It seems really helpful in resolving the issue. If anyone have knowledge about then provide a right guidance.