prophet_backtest.py

def fbpBacktest(df, short=True):
    """
    Performs the final backtest using log returns and the positions function.
    Returns the performance.
    """
    # Getting positions
    df['positions'] = df.apply(lambda x: get_prophet_positions(x, short=short), axis=1)

    # Compensating for lookahead bias
    df['positions'] = df['positions'].shift(1)
    
    # Getting log returns
    df['log_returns'] = df['Open'].apply(np.log).diff()

    # Dropping any Nans
    df.dropna(inplace=True)
    
    # Performing the backtest
    returns = df['positions'] * df['log_returns']

    # Inversing the log returns and getting daily portfolio balance
    performance = returns.cumsum().apply(np.exp)
    
    return performance