fbp_risk_analysis.py

def riskAnalysis(performance, prices, price_history, interval_width):
    """
    Analyzes the performance DataFrame to calculate various
    evaluation metrics on the backtest to determine if
    the backtest performance was favorable.
    """
    ### Hypothesis testing average returns
    
    # Weekly returns for fb prophet
    rets = performance['fbp_positions'].pct_change(5).dropna()

    # Buy and hold for about the last two years for the stock
    hold = price_history['Close'].tail(500).apply(np.log).diff().cumsum().apply(np.exp).dropna()

    # Weekly returns in those years
    hold_ret = hold.pct_change(5).mean()

    # Average returns
    if len(performance)<=30:
        # T-testing
        stat_test = stats.ttest_1samp(
            rets, 
            popmean=hold_ret
        )
    else:
        # Z-testing
        stat_test = ztest(
            rets, 
            value=hold_ret
        )

    # Ending portfolio balance
    bal = performance.tail(1)
    
    # Moving Average returns
    ma_ret = performance.rolling(window=5).mean().dropna()
    
    # How often fbp beats holding
    ma_ret['diff'] = ma_ret['fbp_positions'] > ma_ret['buy_hold']
    
    diff = ma_ret['diff'].mean()

    # How often the fbp portfolio had a balance greater than its initial balance
    ma_ret['beat_bal'] = ma_ret['fbp_positions'] > 1
    
    beat_bal = ma_ret['beat_bal'].mean()
    
    # How often fbp MA returns were positive
    ma_ret['uptrend'] = ma_ret['fbp_positions'].diff().dropna()>=0
    
    uptrend = ma_ret['uptrend'].mean()
    
    # Performance score
    score = 0
    
    # P-value check
    if stat_test[1]<0.05:
        score += 1
    
    # Checking ending portfolio balance
    if bal['fbp_positions'][0]>bal['buy_hold'][0] and bal['fbp_positions'][0]>1: 
        score += 1
    
    # How often fbp outperformed buy and hold
    if diff>.8: 
        score += 1
        
    # How often fbp had returns greater than the initial portfolio balance
    if beat_bal>.6: 
        score += 1
        
    # How often fbp had positive upward trend
    if uptrend>.55:
        score += 1
        
    # Dictionary containing values
    score_res = {
        "result": True,
        "score": score,
        "endingBalance": {
            "prophet": bal['fbp_positions'][0],
            "buyHold": bal['buy_hold'][0]
        },
        "betterThanBuyHold": diff,
        "greaterPortfolioBalance": beat_bal,
        "upwardTrend": uptrend,
        "pValue": stat_test[1],
        "interval_width": interval_width
    }
    
    if score>=5:
                
        return score_res
    
    else:
        # Backtest result is bad
        score_res['result'] = False
        
        return score_res