Overall Statistics |

Total Trades 2145 Average Win 0.53% Average Loss -0.38% Compounding Annual Return 2.507% Drawdown 29.400% Expectancy 0.043 Net Profit 13.193% Sharpe Ratio 0.209 Probabilistic Sharpe Ratio 2.578% Loss Rate 56% Win Rate 44% Profit-Loss Ratio 1.39 Alpha 0.046 Beta -0.091 Annual Standard Deviation 0.169 Annual Variance 0.029 Information Ratio -0.317 Tracking Error 0.255 Treynor Ratio -0.39 Total Fees $4725.34 |

class CloseOnCloseExecutionModel(ExecutionModel): """ Provides an implementation of IExecutionModel that immediately submits a market order to achieve the desired portfolio targets and an associated market on close order. """ def __init__(self): self.targetsCollection = PortfolioTargetCollection() self.invested_symbols = [] def Execute(self, algorithm, targets): """ Immediately submits orders for the specified portfolio targets. Input: - algorithm Algorithm instance running the backtest - targets The portfolio targets to be ordered """ # for performance we check count value, OrderByMarginImpact and ClearFulfilled are expensive to call self.targetsCollection.AddRange(targets) if self.targetsCollection.Count > 0: for target in self.targetsCollection.OrderByMarginImpact(algorithm): # calculate remaining quantity to be ordered quantity = OrderSizing.GetUnorderedQuantity(algorithm, target) if quantity == 0: continue algorithm.MarketOrder(target.Symbol, quantity) algorithm.MarketOnCloseOrder(target.Symbol, -quantity) self.targetsCollection.ClearFulfilled(algorithm)

# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. # Lean Algorithmic Trading Engine v2.0. Copyright 2020 QuantConnect Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import tensorflow as tf from tensorflow.keras.layers import Input, Conv1D, Dense, Lambda, Flatten, Concatenate from tensorflow.keras import Model from tensorflow.keras import metrics from tensorflow.keras.losses import CategoricalCrossentropy from tensorflow.keras import utils from sklearn.preprocessing import StandardScaler import numpy as np import math # varibles (aka features in ML lingo) used to make predictions input_vars = ['open', 'high', 'low', 'close', 'volume'] class Direction: '''Constants used for labeling price movements''' # labels must be integers because Keras (and most ML Libraries) # only work with numbers UP = 0 DOWN = 1 STATIONARY = 2 class MyTemporalCNN: '''Temporal Convolutional Neural Network Model built upon Keras''' # the name describes the architecture of the Neural Network model # Temporal refers to the fact the layers are separated temporally into three regions # Convolutional refers to the fact Convolutional layers are used to extract features def __init__(self, n_tsteps = 15): # n_tsteps = number of time steps in time series for one input/prediction self.n_tsteps = n_tsteps self.scaler = StandardScaler() # used for Feature Scaling self.__CreateModel() def __CreateModel(self): '''Creates the neural network model''' inputs = Input(shape=(self.n_tsteps, len(input_vars))) # extract our features using a Convolutional layers, hence "CNN" feature_extraction = Conv1D(30, 4, activation='relu')(inputs) # split layer into three regions based on time, hence "Temporal" long_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[0])(feature_extraction) mid_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[1])(feature_extraction) short_term = Lambda( lambda x: tf.split(x, num_or_size_splits=3, axis=1)[2])(feature_extraction) long_term_conv = Conv1D(1, 1, activation='relu')(long_term) mid_term_conv = Conv1D(1, 1, activation='relu')(mid_term) short_term_conv = Conv1D(1, 1, activation='relu')(short_term) # combine three layers back into one combined = Concatenate(axis=1)([long_term_conv, mid_term_conv, short_term_conv]) # flattening is required since our input is a 2D matrix flattened = Flatten()(combined) # 1 output neuron for each class (Up, Stationary, Down --- see Direction class) outputs = Dense(3, activation='softmax')(flattened) # specify input and output layers of our model self.model = Model(inputs=inputs, outputs=outputs) # compile our model self.model.compile(optimizer='adam', loss=CategoricalCrossentropy(from_logits=True)) def __PrepareData(self, data, rolling_avg_window_size=5, stationary_threshold=.0001): '''Prepares the data for a format friendly for our model''' # rolling_avg_window_size = window size for the future mid prices to average, # this average is what the model wants to predict # stationary_threshold = maximum change of movement to be considered stationary # for the average mid price stated above df = data[input_vars] shift = -(rolling_avg_window_size-1) # function we will use to label our data (used in line ) def label_data(row): if row['close_avg_change_pct'] > stationary_threshold: return Direction.UP elif row['close_avg_change_pct'] < -stationary_threshold: return Direction.DOWN else: return Direction.STATIONARY # compute the % change in the average of the close of the future 5 time steps # at each time step df['close_avg'] = df['close'].rolling(window=rolling_avg_window_size).mean().shift(shift) df['close_avg_change_pct'] = (df['close_avg'] - df['close']) / df['close'] # label data based on direction, # axis=1 signifies a row-wise operation (axis=0 is col-wise) df['movement_labels'] = df.apply(label_data, axis=1) # lists to store each 2D input matrix and the corresponding label data = [] labels = [] for i in range(len(df)-self.n_tsteps+1+shift): label = df['movement_labels'].iloc[i+self.n_tsteps-1] data.append(df[input_vars].iloc[i:i+self.n_tsteps].values) labels.append(label) data = np.array(data) # temporarily reshape data to 2D, # necessary because sklearn only works wtih 2D data dim1, dim2, dim3 = data.shape data = data.reshape(dim1*dim2, dim3) # fit our scaler and transform our data in one method call data = self.scaler.fit_transform(data) # return data to original shape data = data.reshape(dim1, dim2, dim3) # Keras needs dummy matrices for classification problems, # hence the need for to_categorical() # num classes ensures our dummy matrix has 3 columns, # one for each label (Up, Down, Stationary) return data, utils.to_categorical(labels, num_classes=3) def Train(self, data): '''Trains the model''' data, labels = self.__PrepareData(data) self.model.fit(data, labels, epochs=20) def Predict(self, input_data): '''Makes a prediction on the direction of the future stock price''' input_data = self.scaler.transform(input_data.fillna(method='ffill').values) prediction = self.model.predict(input_data[np.newaxis, :])[0] direction = np.argmax(prediction) confidence = prediction[direction] return direction, confidence

# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. # Lean Algorithmic Trading Engine v2.0. Copyright 2020 QuantConnect Corporation. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from MyTemporalCNN import MyTemporalCNN, Direction, input_vars import pandas as pd import math from random import randint from CloseOnCloseExecutionModel import CloseOnCloseExecutionModel class TransdimensionalUncoupledComputer(QCAlgorithm): def Initialize(self): self.SetStartDate(2015, 8, 15) self.SetEndDate(2020, 8, 15) self.SetCash(100000) self.SetBrokerageModel(AlphaStreamsBrokerageModel()) self.SetExecution(CloseOnCloseExecutionModel()) self.SetPortfolioConstruction(InsightWeightingPortfolioConstructionModel()) tickers = ['AAPL', 'FB', 'MSFT'] # subscribe to minute data for our tickers for ticker in tickers: self.AddEquity(ticker, Resolution.Daily) self.models = {} # store our Temporal CNN Models for each symbol self.window = RollingWindow[Slice](500) self.SetWarmup(500) # retrain our model periodically self.Train(self.DateRules.MonthStart('MSFT'), self.TimeRules.Midnight, self.TrainModel) self.months = self.Time.month self.training_complete = False def OnData(self, data): self.window.Add(data) if self.IsWarmingUp: return # this helps limit the number of trades if self.Time.day % 5 == 0: self.Trade() def TrainModel(self): '''Feed in past data to train our models''' self.months += 1 # retrain every six months if self.months % 3 != 1: return self.Debug('Training in progress') try: # since RollingWindow is recent at top, we need to reverse it data = self.PandasConverter.GetDataFrame(self.window).iloc[::-1] except: return # iterate over our symbols and train each model for symbol in self.Securities.Keys: # if model doesn't exist, create one if symbol not in self.models: self.models[symbol] = MyTemporalCNN() # train our model self.models[symbol].Train(data.loc[symbol]) self.training_complete = True self.Debug('Training completed') def Trade(self): '''Emit insights using predictions from models on future prices''' if not self.training_complete: return insights = [] try: # since RollingWindow has the recent data at top and oldest data at the bottom, # we need to reverse it before getting the latest values df = self.PandasConverter.GetDataFrame(self.window).iloc[::-1][input_vars] except: return # use recent data to forecast future price movements, # then emit insights based on predictions for symbol in self.Securities.Keys: symbol_df = df.loc[symbol].tail(15) prediction, confidence = self.models[symbol].Predict(symbol_df) # sometimes we get NaN values in our model prediction, # which means the model is faulty so we don't want to make predictions. if not math.isnan(confidence) and confidence > .55: # since we are prediction the average price of the next five timesteps, choosing a # random value of the future five time steps, over time, pseudo simulates average if prediction == Direction.UP: insights.append(Insight.Price(symbol, timedelta(randint(1,5)), InsightDirection.Up, None, None, None, confidence)) elif prediction == Direction.DOWN: insights.append(Insight.Price(symbol, timedelta(randint(1,5)), InsightDirection.Down, None, None, None, confidence)) # only emit insights if insights isn't empty if insights: self.EmitInsights(insights)