wehub-resource-sync
114 lines
5.6 KiB
Python
114 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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from AlgorithmImports import *
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import tensorflow.compat.v1 as tf
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class TensorFlowNeuralNetworkAlgorithm(QCAlgorithm):
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def initialize(self) -> None:
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self.set_start_date(2013, 10, 7) # Set Start Date
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self.set_end_date(2013, 10, 8) # Set End Date
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self.set_cash(100000) # Set Strategy Cash
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spy = self.add_equity("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.date_rules.every(DayOfWeek.MONDAY), self.time_rules.after_market_open("SPY", 28), self.net_train) # train the neural network 28 mins after market open
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self.schedule.on(self.date_rules.every(DayOfWeek.MONDAY), self.time_rules.after_market_open("SPY", 30), self.trade) # trade 30 mins after market open
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def add_layer(self, inputs: tf.Tensor, in_size: int, out_size: int, activation_function: tf.keras.layers.Activation = None) -> tf.Tensor:
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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 net_train(self) -> None:
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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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tf.disable_v2_behavior()
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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) -> None:
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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 holding.symbol not in self.current_slice.bars:
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return
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if self.current_slice.bars[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.current_slice.bars[holding.symbol].open > self.buy_prices[holding.symbol] and not holding.invested:
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self.set_holdings(holding.symbol, 1 / len(self.symbols))
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