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
107 lines
4.8 KiB
Python
107 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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import torch
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import torch.nn.functional as F
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class PytorchNeuralNetworkAlgorithm(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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# add symbol
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spy = self.AddEquity("SPY", Resolution.Minute)
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self.symbols = [spy.Symbol] # using a list can extend to condition for multiple symbols
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self.lookback = 30 # days of historical data (look back)
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self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 28), self.NetTrain) # train the NN
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self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.AfterMarketOpen("SPY", 30), self.Trade)
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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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# 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: preditors; y: response
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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 this symbol has historical data
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if symbol in self.prices_x:
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net = Net(n_feature=1, n_hidden=10, n_output=1) # define the network
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optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
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loss_func = torch.nn.MSELoss() # this is for regression mean squared loss
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for t in range(200):
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# Get data and do preprocessing
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x = torch.from_numpy(np.array(self.prices_x[symbol])).float()
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y = torch.from_numpy(np.array(self.prices_y[symbol])).float()
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# unsqueeze data (see pytorch doc for details)
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x = x.unsqueeze(1)
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y = y.unsqueeze(1)
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prediction = net(x) # input x and predict based on x
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loss = loss_func(prediction, y) # must be (1. nn output, 2. target)
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optimizer.zero_grad() # clear gradients for next train
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loss.backward() # backpropagation, compute gradients
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optimizer.step() # apply gradients
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# Follow the trend
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self.buy_prices[symbol] = net(y)[-1] + np.std(y.data.numpy())
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self.sell_prices[symbol] = net(y)[-1] - np.std(y.data.numpy())
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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))
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# class for Pytorch NN model
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class Net(torch.nn.Module):
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def __init__(self, n_feature, n_hidden, n_output):
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super(Net, self).__init__()
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self.hidden = torch.nn.Linear(n_feature, n_hidden) # hidden layer
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self.predict = torch.nn.Linear(n_hidden, n_output) # output layer
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def forward(self, x):
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x = F.relu(self.hidden(x)) # activation function for hidden layer
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x = self.predict(x) # linear output
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return x
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