Daniel Chen
99 lines
4.2 KiB
Python
99 lines
4.2 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import clr
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clr.AddReference("System")
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clr.AddReference("QuantConnect.Algorithm")
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clr.AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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import numpy as np
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from keras.models import Sequential
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from keras.layers import Dense, Activation
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from keras.optimizers import SGD
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class KerasNeuralNetworkAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2013, 10, 7) # Set Start Date
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self.SetEndDate(2013, 10, 8) # Set End Date
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self.SetCash(100000) # Set Strategy Cash
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spy = self.AddEquity("SPY", Resolution.Minute)
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self.symbols = [spy.Symbol] # This way can be easily extended to multiply symbols
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self.lookback = 30 # day of lookback for historical data
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self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), self.TimeRules.AfterMarketOpen("SPY", 28), self.NetTrain) # train Neural Network
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self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), self.TimeRules.AfterMarketOpen("SPY", 30), self.Trade) # trading
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def NetTrain(self):
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# get daily historical data
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history = self.History(self.symbols, self.lookback + 1, Resolution.Daily)
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# dicts that store prices for training
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self.prices_x = {}
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self.prices_y = {}
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# dicts that store prices for sell and buy
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self.sell_prices = {}
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self.buy_prices = {}
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for symbol in self.symbols:
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if not history.empty:
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# x: pridictors; y: response
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self.prices_x[symbol.Value] = list(history.loc[symbol.Value]['open'])[:-1]
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self.prices_y[symbol.Value] = list(history.loc[symbol.Value]['open'])[1:]
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for symbol in self.symbols:
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if symbol.Value in self.prices_x:
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# convert the original data to np array for fitting the keras NN model
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x_data = np.array(self.prices_x[symbol.Value])
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y_data = np.array(self.prices_y[symbol.Value])
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# build a neural network from the 1st layer to the last layer
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model = Sequential()
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model.add(Dense(10, input_dim = 1))
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model.add(Activation('relu'))
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model.add(Dense(1))
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sgd = SGD(lr = 0.01) # learning rate = 0.01
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# choose loss function and optimizing method
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model.compile(loss='mse', optimizer=sgd)
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# pick an iteration number large enough for convergence
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for step in range(701):
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# training the model
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cost = model.train_on_batch(x_data, y_data)
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# get the final predicted price
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y_pred_final = model.predict(y_data)[0][-1]
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# Follow the trend
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self.buy_prices[symbol.Value] = y_pred_final + np.std(y_data)
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self.sell_prices[symbol.Value] = y_pred_final - np.std(y_data)
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def Trade(self):
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for holding in self.Portfolio.Values:
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# liquidate
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if self.CurrentSlice[holding.Symbol.Value].Open < self.sell_prices[holding.Symbol.Value] and holding.Invested:
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self.Liquidate(i.Symbol)
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# buy
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if self.CurrentSlice[holding.Symbol.Value].Open > self.buy_prices[holding.Symbol.Value] and not holding.Invested:
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self.SetHoldings(holding.Symbol, 1 / len(self.symbols)) |