fbp_backtest_analyze.py

def backtestStock(stock, pred_df, prices, price_history, interval_width):
    
    # Adding positions to the forecast DF
    positions = pred_df

    # Getting forecast prophet positions 
    positions['fbp_positions'] = positions.apply(
        lambda x: fbpPositions(x, short=True), 
        axis=1
    )

    # Buy and hold position
    positions['buy_hold'] = 1
    
    # Getting daily returns
    log_returns = prices[['ds', 'Close']].set_index(
        'ds'
    ).loc[positions.index].apply(np.log).diff()
    
    # The positions to backtest (shifted ahead by 1 to prevent lookahead bias)
    bt_positions = positions[[
        'buy_hold', 
        'fbp_positions'
    ]].shift(1)

    # The returns during the backtest
    returns = bt_positions.multiply(
        log_returns['Close'], 
        axis=0
    )

    # Inversing the log returns to get daily portfolio balance
    performance = returns.cumsum().apply(
        np.exp
    ).dropna().fillna(
        method='ffill'
    )
    
    # Performing risk analysis
    risk = riskAnalysis(performance, prices, price_history, interval_width)
                
    return risk, performance