| Overall Statistics |
|
Total Orders 299 Average Win 0.16% Average Loss -0.14% Compounding Annual Return 23.826% Drawdown 6.300% Expectancy 0.576 Start Equity 1000000 End Equity 1238014.63 Net Profit 23.801% Sharpe Ratio 1.123 Sortino Ratio 1.616 Probabilistic Sharpe Ratio 62.126% Loss Rate 25% Win Rate 75% Profit-Loss Ratio 1.09 Alpha 0.003 Beta 0.741 Annual Standard Deviation 0.125 Annual Variance 0.016 Information Ratio -0.444 Tracking Error 0.102 Treynor Ratio 0.19 Total Fees $1100.72 Estimated Strategy Capacity $89000000.00 Lowest Capacity Asset JNJ R735QTJ8XC9X Portfolio Turnover 2.14% |
from AlgorithmImports import * class MomentumTrendStrategy(QCAlgorithm): def Initialize(self): self.SetStartDate(2019, 1, 1) self.SetEndDate(2020, 1, 1) self.SetCash(1000000) # Add AMD and JNJ (daily resolution) self.symbols = [] self.symbols.append(self.AddEquity("AMD", Resolution.Daily).Symbol) self.symbols.append(self.AddEquity("JNJ", Resolution.Daily).Symbol) # Dictionary to hold indicators for each stock self.indicators = {} for symbol in self.symbols: self.indicators[symbol] = { "sma50": self.SMA(symbol, 50, Resolution.Daily), "sma200": self.SMA(symbol, 200, Resolution.Daily), "rsi": self.RSI(symbol, 14, MovingAverageType.Simple, Resolution.Daily), "bb": self.BB(symbol, 20, 2, Resolution.Daily) } # Warm up indicators self.SetWarmUp(200) def OnData(self, data): if self.IsWarmingUp: return for symbol in self.symbols: # Ensure the indicators are ready ind = self.indicators[symbol] if not all([ind["sma50"].IsReady, ind["sma200"].IsReady, ind["rsi"].IsReady, ind["bb"].IsReady]): continue # Get the current price from the security object instead of the data slice price = self.Securities[symbol].Price sma50 = ind["sma50"].Current.Value sma200 = ind["sma200"].Current.Value rsi = ind["rsi"].Current.Value bb = ind["bb"] upperBB = bb.UpperBand.Current.Value middleBB = bb.MiddleBand.Current.Value lowerBB = bb.LowerBand.Current.Value # Define base position (trend-following component) baseWeight = 0.25 if sma50 > sma200 else -0.25 # Adjust exposure based on momentum signals bullishSignal = price > upperBB and rsi > 50 bearishSignal = price < lowerBB and rsi < 50 targetWeight = baseWeight if bullishSignal and baseWeight > 0: targetWeight = 0.5 elif bearishSignal and baseWeight < 0: targetWeight = -0.5 # Scale back position if momentum weakens if self.Portfolio[symbol].IsLong and (rsi >= 70 or price < middleBB): targetWeight = 0.25 if self.Portfolio[symbol].IsShort and (rsi <= 30 or price > middleBB): targetWeight = -0.25 # Ensure minimum absolute exposure is 25% if abs(targetWeight) < 0.25: targetWeight = 0.25 if baseWeight > 0 else -0.25 self.SetHoldings(symbol, targetWeight) # Debugging output self.Debug(f"{self.Time} {symbol.Value}: Price={price:.2f}, SMA50={sma50:.2f}, " f"SMA200={sma200:.2f}, RSI={rsi:.2f}, UpperBB={upperBB:.2f}, " f"MiddleBB={middleBB:.2f}, LowerBB={lowerBB:.2f}, Target Weight={targetWeight:.2f}")
