Daniel Chen
119 lines
5.6 KiB
Python
119 lines
5.6 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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import tensorflow as tf
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class TensorFlowNeuralNetworkAlgorithm(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) # Add Equity
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self.symbols = [spy.Symbol] # potential trading symbols pool (in this algorithm there is only 1).
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self.lookback = 30 # number of previous days for training
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self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), self.TimeRules.AfterMarketOpen("SPY", 28), self.NetTrain) # train the neural network 28 mins after market open
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self.Schedule.On(self.DateRules.Every(DayOfWeek.Monday), self.TimeRules.AfterMarketOpen("SPY", 30), self.Trade) # trade 30 mins after market open
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def add_layer(self, inputs, in_size, out_size, activation_function=None):
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# add one more layer and return the output of this layer
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# this is one NN with only one hidden layer
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Weights = tf.Variable(tf.random_normal([in_size, out_size]))
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biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
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Wx_plus_b = tf.matmul(inputs, Weights) + biases
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if activation_function is None:
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outputs = Wx_plus_b
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else:
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outputs = activation_function(Wx_plus_b)
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return outputs
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def NetTrain(self):
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# Daily historical data is used to train the machine learning model
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history = self.History(self.symbols, self.lookback + 1, Resolution.Daily)
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# model: use prices_x to fit prices_y; key: symbol; value: according price
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self.prices_x, self.prices_y = {}, {}
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# key: symbol; values: prices for sell or buy
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self.sell_prices, self.buy_prices = {}, {}
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for symbol in self.symbols:
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if not history.empty:
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# Daily historical data is used to train the machine learning model
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# use open prices to predict the next days'
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self.prices_x[symbol] = list(history.loc[symbol.Value]['open'][:-1])
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self.prices_y[symbol] = list(history.loc[symbol.Value]['open'][1:])
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for symbol in self.symbols:
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if symbol in self.prices_x:
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# create numpy array
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x_data = np.array(self.prices_x[symbol]).astype(np.float32).reshape((-1,1))
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y_data = np.array(self.prices_y[symbol]).astype(np.float32).reshape((-1,1))
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# define placeholder for inputs to network
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xs = tf.placeholder(tf.float32, [None, 1])
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ys = tf.placeholder(tf.float32, [None, 1])
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# add hidden layer
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l1 = self.add_layer(xs, 1, 10, activation_function=tf.nn.relu)
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# add output layer
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prediction = self.add_layer(l1, 10, 1, activation_function=None)
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# the error between prediciton and real data
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loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
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reduction_indices=[1]))
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# use gradient descent and square error
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train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
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# the following is precedure for tensorflow
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sess = tf.Session()
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init = tf.global_variables_initializer()
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sess.run(init)
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for i in range(200):
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# training
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sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
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# predict today's price
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y_pred_final = sess.run(prediction, feed_dict = {xs: y_data})[0][-1]
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# get sell prices and buy prices as trading signals
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self.sell_prices[symbol] = y_pred_final - np.std(y_data)
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self.buy_prices[symbol] = y_pred_final + np.std(y_data)
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def Trade(self):
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'''
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Enter or exit positions based on relationship of the open price of the current bar and the prices defined by the machine learning model.
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Liquidate if the open price is below the sell price and buy if the open price is above the buy price
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'''
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for holding in self.Portfolio.Values:
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if self.CurrentSlice[holding.Symbol].Open < self.sell_prices[holding.Symbol] and holding.Invested:
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self.Liquidate(holding.Symbol)
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if self.CurrentSlice[holding.Symbol].Open > self.buy_prices[holding.Symbol] and not holding.Invested:
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self.SetHoldings(holding.Symbol, 1 / len(self.symbols)) |