Overall Statistics
Total Trades
1267
Average Win
0.08%
Average Loss
-0.04%
Compounding Annual Return
7.764%
Drawdown
6.500%
Expectancy
0.308
Net Profit
7.126%
Sharpe Ratio
0.772
Probabilistic Sharpe Ratio
40.699%
Loss Rate
59%
Win Rate
41%
Profit-Loss Ratio
2.17
Alpha
0.078
Beta
-0.058
Annual Standard Deviation
0.084
Annual Variance
0.007
Information Ratio
-1.175
Tracking Error
0.146
Treynor Ratio
-1.112
Total Fees
$1426.42
import numpy as np
import pandas as pd

from keras.layers import LSTM
from keras.layers import Dense
from keras.layers import Dropout
from keras.models import Sequential
from sklearn.preprocessing import MinMaxScaler

class MyLSTM:
    
    def __init__(self):
        self.model = None
        self.scaler = MinMaxScaler(feature_range = (0, 1))

    def ProcessData(self, data, n = 60):
        # Split the data
        training_data = data[:1260]
        test_data = data[1260:]
        
        # Transform data
        training_data_array = np.array(training_data).reshape((len(training_data), 1))
    
        training_data_scaled = self.scaler.fit_transform(training_data_array)
        
        # Get features and labels
        features_set = []
        labels = []
        for i in range(60, 1260):
            features_set.append(training_data_scaled[i-60:i, 0])
            labels.append(training_data_scaled[i, 0])
            
        features_set, labels = np.array(features_set), np.array(labels)
        features_set = np.reshape(features_set, (features_set.shape[0], features_set.shape[1], 1))
        return features_set, labels, training_data, test_data
    
    
    def CreateModel(self, features_set, labels):
    
        # Create Model
        self.model = Sequential()
        self.model.add(LSTM(units = 50, return_sequences=True, input_shape=(features_set.shape[1], 1)))
        self.model.add(Dropout(0.2))
        self.model.add(LSTM(units=50, return_sequences=True))
        self.model.add(Dropout(0.2))
        self.model.add(LSTM(units=50, return_sequences=True))
        self.model.add(Dropout(0.2))
        self.model.add(LSTM(units=50))
        self.model.add(Dropout(0.2))
        self.model.add(Dense(units = 1))
        self.model.compile(optimizer = 'adam', loss = 'mean_squared_error')
        
    def FitModel(self, features_set, labels):
        self.model.fit(features_set, labels, epochs = 50, batch_size = 32)
    
        
    def PredictFromModel(self, test_data):
        test_inputs = test_data[-80:].values
        test_inputs = test_inputs.reshape(-1,1)
        test_inputs = self.scaler.transform(test_inputs)
        test_features = []
        for i in range(60, 80):
            test_features.append(test_inputs[i-60:i, 0])
        test_features = np.array(test_features)
        test_features = np.reshape(test_features, (test_features.shape[0], test_features.shape[1], 1))
    
        predictions = self.model.predict(test_features)
        predictions = self.scaler.inverse_transform(predictions)
        
        return predictions
from MyLSTM import MyLSTM

class MultidimensionalHorizontalFlange(QCAlgorithm):

    def Initialize(self):
        self.SetStartDate(2019, 1, 4)  # Set Start Date
        self.SetCash(100000)  # Set Strategy Cash
        
        self.SetBrokerageModel(AlphaStreamsBrokerageModel())
        
        self.SetExecution(ImmediateExecutionModel())

        self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())

        self.UniverseSettings.Resolution = Resolution.Minute
        self.SetUniverseSelection(LiquidETFUniverse())
        
        # Helper dictionaries
        self.macro_symbols = {'Bull' : Symbol.Create('SPY', SecurityType.Equity, Market.USA)}
        self.models = {'Bull': None, 'Bear': None}

        # Use Train() method to avoid runtime error
        self.Train(self.TrainMyModel)
        self.Train(self.DateRules.MonthEnd(), self.TimeRules.At(8,0), self.TrainMyModel)
        
        # Schedule prediction and plotting
        self.AddEquity('SPY')
        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.AfterMarketOpen('SPY', 5), self.Predict)
        self.Schedule.On(self.DateRules.EveryDay('SPY'), self.TimeRules.AfterMarketOpen('SPY', 6), self.PlotMe)
        
        # Create custom charts
        prediction = Chart('Prediction Plot')
        prediction.AddSeries(Series('Actual Bull', SeriesType.Line, 0))
        prediction.AddSeries(Series('Predicted Bull', SeriesType.Line, 0))
        
        prediction.AddSeries(Series('Actual Bear', SeriesType.Line, 1))
        prediction.AddSeries(Series('Predicted Bear', SeriesType.Line, 1))
        
    def TrainMyModel(self):
        qb = self
        
        # Fetch history
        history = qb.History([symbol for key, symbol in self.macro_symbols.items()], 1280, Resolution.Daily)
        
        # Iterate over macro symbols
        for key, symbol in self.macro_symbols.items():
            # Initialize LSTM class instance
            lstm = MyLSTM()
            # Prepare data
            features_set, labels, training_data, test_data = lstm.ProcessData(history.loc[symbol].close)
            # Build model layers
            lstm.CreateModel(features_set, labels)
            # Fit model
            lstm.FitModel(features_set, labels)
            # Add LSTM class to dictionary to store later
            self.models[key] = lstm

    def Predict(self):
        delta = {}
        qb = self
        for key, symbol in self.macro_symbols.items():
            # Fetch LSTM class
            lstm = self.models[key]
            # Fetch history
            history = qb.History([symbol for key, symbol in self.macro_symbols.items()], 80, Resolution.Daily)
            # Predict
            predictions = lstm.PredictFromModel(history.loc[symbol].close)
            # Grab latest prediction and calculate if predict symbol to go up or down
            delta[key] = ( predictions[-1] / self.Securities[symbol].Price ) - 1
            # Plot prediction
            self.Plot('Prediction Plot', f'Predicted {key}', predictions[-1])
        
        insights = []
        # Iterate over macro symbols
        for key, change in delta.items():
            if key == 'Bull':
                insights += [Insight.Price(symbol, timedelta(1), InsightDirection.Up if change > 0 else InsightDirection.Flat) for symbol in LiquidETFUniverse.SP500Sectors.Long if self.Securities.ContainsKey(symbol)]
                insights += [Insight.Price(symbol, timedelta(1), InsightDirection.Up if change > 0 else InsightDirection.Flat) for symbol in LiquidETFUniverse.Treasuries.Inverse if self.Securities.ContainsKey(symbol)]
                insights += [Insight.Price(symbol, timedelta(1), InsightDirection.Flat if change > 0 else InsightDirection.Up) for symbol in LiquidETFUniverse.Treasuries.Long if self.Securities.ContainsKey(symbol)]
        self.EmitInsights(insights)
        
    def PlotMe(self):
        # Plot current price of symbols to match against prediction
        for key, symbol in self.macro_symbols.items():
            self.Plot('Prediction Plot', f'Actual {key}', self.Securities[symbol].Price)