Backtest

Overall Statistics
Total Orders 13 Average Win 0.03% Average Loss -1.13% Compounding Annual Return 27.773% Drawdown 6.300% Expectancy -0.488 Start Equity 1000000 End Equity 1277443.06 Net Profit 27.744% Sharpe Ratio 1.477 Sortino Ratio 1.978 Probabilistic Sharpe Ratio 76.739% Loss Rate 50% Win Rate 50% Profit-Loss Ratio 0.02 Alpha 0.07 Beta 0.506 Annual Standard Deviation 0.111 Annual Variance 0.012 Information Ratio -0.195 Tracking Error 0.111 Treynor Ratio 0.324 Total Fees $122.43 Estimated Strategy Capacity $0 Lowest Capacity Asset AMD R735QTJ8XC9X Portfolio Turnover 0.42%

from AlgorithmImports import *

class MomentumRSIBBAlgorithm(QCAlgorithm):
    def Initialize(self):
        self.SetStartDate(2019, 1, 1)
        self.SetEndDate(2020, 1, 1)
        self.SetCash(1000000)
        
        # Define stocks
        self.symbols = [self.AddEquity("AMD", Resolution.Daily).Symbol, 
                        self.AddEquity("JNJ", Resolution.Daily).Symbol]
        
        # Initialize indicators
        self.indicators = {
            symbol: {
                'rsi': self.RSI(symbol, 14),  ## 14-day lookback
                'bb': self.BB(symbol, 20, 2, MovingAverageType.Simple),  ## 2 standard deviations, 20-day period
                'sma': self.SMA(symbol, 50) ## 50-day SMA
            } for symbol in self.symbols
        }
        
        # Schedule daily rebalancing after market opens
        self.Schedule.On(self.DateRules.EveryDay(), 
                         self.TimeRules.AfterMarketOpen("AMD", 10),  ## when we specify "AMD" here, it's just used as a reference time. The rebalancing happens for both stocks. It's like saying "10 minutes after the market opens based on AMD's trading hours" 
                         self.Rebalance)

    def Rebalance(self):
        for symbol in self.symbols:
            if not all(indicator.IsReady for indicator in self.indicators[symbol].values()):
                continue  # Skip if indicators aren't ready
            
            # Get indicator values
            price = self.Securities[symbol].Price
            rsi = self.indicators[symbol]['rsi'].Current.Value
            upper_bb = self.indicators[symbol]['bb'].UpperBand.Current.Value
            lower_bb = self.indicators[symbol]['bb'].LowerBand.Current.Value
            sma = self.indicators[symbol]['sma'].Current.Value
            
            # Determine trend direction
            trend = "Up" if price > sma else "Down" 
            
            # trend reversing element:
            ## Long when price is below BB lower band, and RSI shows oversold (< 30)
            ## Short when price is above BB Upper band, and RSI shows overbought (> 70)


            # trend-following element with the SMA:
            ## Only goes long if price > SMA (uptrend) and only goes short if price < SMA (downtrend)

            # Trading logic
            if trend == "Up" and rsi < 30 and price < lower_bb:         ## indicate oversold if stock starts passing lower than 30 
                self.SetHoldings(symbol, 0.5)  # Buy signal: 50% long
            elif trend == "Down" and rsi > 70 and price > upper_bb:     ## indicate overbought if stock starts passing higher than 70
                self.SetHoldings(symbol, -0.25)  # Sell signal: 25% short

            # Ensure minimum 25% absolute weight per stock
            current_weight = abs(self.Portfolio[symbol].HoldingsValue / self.Portfolio.TotalPortfolioValue)
            if current_weight < 0.25:
                self.SetHoldings(symbol, 0.25 if trend == "Up" else -0.25)