Martin-Molinero
03f56481d4
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Python research import improvements - Improve start.py for research env - Remove unrequired imports * Centralize algorithm imports * Add regression test GH action * Unit test python import clean up * Join research and main imports * More python import clean up * Fix failing skipped regression algorithm
128 lines
4.8 KiB
Python
128 lines
4.8 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 io import StringIO
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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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from keras.utils.generic_utils import serialize_keras_object
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class KerasNeuralNetworkAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2019, 1, 1) # Set Start Date
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self.SetEndDate(2020, 4, 1) # Set End Date
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self.SetCash(100000) # Set Strategy Cash
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self.modelBySymbol = {}
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for ticker in ["SPY", "QQQ", "TLT"]:
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symbol = self.AddEquity(ticker).Symbol
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# Read the model saved in the ObjectStore
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if self.ObjectStore.ContainsKey(f'{symbol}_model'):
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modelStr = self.ObjectStore.Read(f'{symbol}_model')
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config = json.loads(modelStr)['config']
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self.modelBySymbol[symbol] = Sequential.from_config(config)
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self.Debug(f'Model for {symbol} sucessfully retrieved from the ObjectStore')
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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.DateRules.Every(DayOfWeek.Monday),
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self.TimeRules.AfterMarketOpen("SPY"),
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self.NeuralNetworkTraining)
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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.DateRules.EveryDay("SPY"),
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self.TimeRules.AfterMarketOpen("SPY", 30),
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self.Trade)
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def OnEndOfAlgorithm(self):
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''' Save the data and the mode using the ObjectStore '''
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for symbol, model in self.modelBySymbol.items():
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modelStr = json.dumps(serialize_keras_object(model))
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self.ObjectStore.Save(f'{symbol}_model', modelStr)
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self.Debug(f'Model for {symbol} sucessfully saved in the ObjectStore')
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def NeuralNetworkTraining(self):
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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(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(200):
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# training the model
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cost = model.train_on_batch(predictor, predictand)
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self.modelBySymbol[symbol] = model
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def Trade(self):
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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.modelBySymbol.items():
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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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historyStd = np.std(history)
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holding = self.Portfolio[symbol]
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openPrice = self.CurrentSlice[symbol].Open
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# Follow the trend
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if holding.Invested:
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if openPrice < prediction - historyStd:
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self.Liquidate(symbol)
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else:
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if openPrice > prediction + historyStd:
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self.SetHoldings(symbol, target)
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