Backtest

Overall Statistics
Total Trades 45282 Average Win 0.01% Average Loss 0.00% Compounding Annual Return 0.013% Drawdown 12.700% Expectancy -0.011 Net Profit 0.118% Sharpe Ratio 0.015 Probabilistic Sharpe Ratio 0.018% Loss Rate 69% Win Rate 31% Profit-Loss Ratio 2.16 Alpha 0.003 Beta -0.035 Annual Standard Deviation 0.021 Annual Variance 0 Information Ratio -0.559 Tracking Error 0.155 Treynor Ratio -0.009 Total Fees $48708.25 Estimated Strategy Capacity $1100000.00 Lowest Capacity Asset BIL TT1EBZ21QWKL

#region imports
from AlgorithmImports import *
from sklearn.ensemble import RandomForestRegressor
#endregion

class RandomForestRegressionDemo(QCAlgorithm):

    def Initialize(self):
        #1. Required: Five years of backtest history
        self.SetStartDate(2014, 1, 1)
    
        #2. Required: Alpha Streams Models:
        self.SetBrokerageModel(BrokerageName.AlphaStreams)
    
        #3. Required: Significant AUM Capacity
        self.SetCash(1000000)
    
        #4. Required: Benchmark to SPY
        self.SetBenchmark("SPY")
        
        self.SetPortfolioConstruction(MeanVarianceOptimizationPortfolioConstructionModel(portfolioBias = PortfolioBias.Long,
                                                                                        period=5))
        self.SetExecution(ImmediateExecutionModel())
    
        self.assets = ["SHY", "TLT", "IEI", "SHV", "TLH", "EDV", "BIL",
                        "SPTL", "TBT", "TMF", "TMV", "TBF", "VGSH", "VGIT",
                        "VGLT", "SCHO", "SCHR", "SPTS", "GOVT"]
        
        # Add Equity ------------------------------------------------ 
        for i in range(len(self.assets)):
            self.AddEquity(self.assets[i], Resolution.Minute)
            
        # Initialize the timer to train the Machine Learning model
        self.time = datetime.min
        
        # Set Scheduled Event Method For Our Model
        self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.BeforeMarketClose("SHY", 5), self.EveryDayBeforeMarketClose)
        
        
    def BuildModel(self):
        # Initialize the Random Forest Regressor
        self.regressor = RandomForestRegressor(n_estimators=100, min_samples_split=5, random_state = 1990)
        
        # Get historical data
        history = self.History(self.Securities.Keys, 360, Resolution.Daily)
        
        # Select the close column and then call the unstack method.
        df = history['close'].unstack(level=0)
        
        # Feature engineer the data for input.
        input_ = df.diff() * 0.5 + df * 0.5
        input_ = input_.iloc[1:].ffill().fillna(0)
        
        # Shift the data for 1-step backward as training output result.
        output = df.shift(-1).iloc[:-1].ffill().fillna(0)
        
        # Fit the regressor
        self.regressor.fit(input_, output)
        
        
    def EveryDayBeforeMarketClose(self):
        # Retrain the regressor every week
        if self.time < self.Time:
            self.BuildModel()
            self.time = Expiry.EndOfWeek(self.Time)
        
        qb = self
        # Fetch history on our universe
        df = qb.History(qb.Securities.Keys, 2, Resolution.Daily)
        if df.empty: return
    
        # Make all of them into a single time index.
        df = df.close.unstack(level=0)
    
        # Feature engineer the data for input
        input_ = df.diff() * 0.5 + df * 0.5
        input_ = input_.iloc[-1].fillna(0).values.reshape(1, -1)
        
        # Predict the expected price
        predictions = self.regressor.predict(input_)
        
        # Get the expected return
        predictions = (predictions - df.iloc[-1].values) / df.iloc[-1].values
        predictions = predictions.flatten()
    
        # ==============================
        
        insights = []
        
        for i in range(len(predictions)):
            insights.append( Insight.Price(self.assets[i], timedelta(days=1), InsightDirection.Up, predictions[i]) )
    
        self.EmitInsights(insights)