wehub-resource-sync
131 lines
5.1 KiB
Python
131 lines
5.1 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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from keras.models import *
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from tensorflow import keras
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from keras 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) -> None:
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self.set_start_date(2019, 1, 1) # Set Start Date
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self.set_end_date(2020, 4, 1) # Set End Date
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self.set_cash(100000) # Set Strategy Cash
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self._model_by_symbol = {}
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for ticker in ["SPY", "QQQ", "TLT"]:
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symbol = self.add_equity(ticker).symbol
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# Read the model saved in the ObjectStore
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for kvp in self.object_store:
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key = f'{symbol}_model'
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if not (key == kvp.key and kvp.value):
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continue
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file_path = self.object_store.get_file_path(kvp.key)
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self._model_by_symbol[symbol] = keras.models.load_model(file_path)
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self.debug(f'Model for {symbol} sucessfully retrieved. File {file_path}. Size {len(kvp.value)}. Weights {self._model_by_symbol[symbol].get_weights()}')
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# Look-back period for training set
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self._lookback = 30
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# Train Neural Network every monday
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self.train(
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self.date_rules.every(DayOfWeek.MONDAY),
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self.time_rules.after_market_open("SPY"),
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self.neural_network_training)
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# Place trades on Monday, 30 minutes after the market is open
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self.schedule.on(
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self.date_rules.every_day("SPY"),
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self.time_rules.after_market_open("SPY", 30),
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self.trade)
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def on_end_of_algorithm(self) -> None:
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''' Save the data and the mode using the ObjectStore '''
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for symbol, model in self._model_by_symbol.items():
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key = f'{symbol}_model.keras'
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file = self.object_store.get_file_path(key)
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model.save(file)
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self.object_store.save(key)
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self.debug(f'Model for {symbol} sucessfully saved in the ObjectStore')
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def neural_network_training(self) -> None:
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'''Train the Neural Network and save the model in the ObjectStore'''
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symbols = self.securities.keys()
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# Daily historical data is used to train the machine learning model
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history = self.history(symbols, self._lookback + 1, Resolution.DAILY)
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history = history.open.unstack(0)
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for symbol in symbols:
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if symbol not in history:
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continue
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predictor = history[symbol][:-1]
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predictand = history[symbol][1:]
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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(learning_rate = 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(200):
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# training the model
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cost = model.train_on_batch(predictor, predictand)
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self._model_by_symbol[symbol] = model
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def trade(self) -> None:
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'''
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Predict the price using the trained model and out-of-sample data
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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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target = 1 / len(self.securities)
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for symbol, model in self._model_by_symbol.items():
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if symbol not in self.current_slice.bars:
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continue
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# Get the out-of-sample history
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history = self.history(symbol, self._lookback, Resolution.DAILY)
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history = history.open.unstack(0)[symbol]
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# Get the final predicted price
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prediction = model.predict(history)[0][-1]
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history_std = np.std(history)
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holding = self.portfolio[symbol]
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open_price = self.current_slice.bars[symbol].open
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# Follow the trend
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if holding.invested:
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if open_price < prediction - history_std:
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self.liquidate(symbol)
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else:
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if open_price > prediction + history_std:
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self.set_holdings(symbol, target)
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