wehub-resource-sync
93 lines
3.4 KiB
Python
93 lines
3.4 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 sklearn.linear_model import LinearRegression
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class ScikitLearnLinearRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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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.lookback = 30 # number of previous days for training
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self.set_cash(100000) # Set Strategy Cash
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spy = self.add_equity("SPY", Resolution.MINUTE)
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self.symbols = [ spy.symbol ] # In the future, we can include more symbols to the list in this way
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self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.after_market_open("SPY", 28), self.regression)
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self.schedule.on(self.date_rules.every_day("SPY"), self.time_rules.after_market_open("SPY", 30), self.trade)
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def regression(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, Resolution.DAILY)
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# price dictionary: key: symbol; value: historical price
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self.prices = {}
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# slope dictionary: key: symbol; value: slope
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self.slopes = {}
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for symbol in self.symbols:
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if not history.empty:
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# get historical open price
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self.prices[symbol] = list(history.loc[symbol.value]['open'])
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# A is the design matrix
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A = range(self.lookback + 1)
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for symbol in self.symbols:
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if symbol in self.prices:
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# response
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Y = self.prices[symbol]
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# features
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X = np.column_stack([np.ones(len(A)), A])
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# data preparation
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length = min(len(X), len(Y))
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X = X[-length:]
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Y = Y[-length:]
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A = A[-length:]
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# fit the linear regression
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reg = LinearRegression().fit(X, Y)
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# run linear regression y = ax + b
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b = reg.intercept_
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a = reg.coef_[1]
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# store slopes for symbols
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self.slopes[symbol] = a/b
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def trade(self):
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# if there is no open price
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if not self.prices:
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return
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thod_buy = 0.001 # threshold of slope to buy
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thod_liquidate = -0.001 # threshold of slope to liquidate
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for holding in self.portfolio.values():
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slope = self.slopes[holding.symbol]
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# liquidate when slope smaller than thod_liquidate
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if holding.invested and slope < thod_liquidate:
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self.liquidate(holding.symbol)
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for symbol in self.symbols:
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# buy when slope larger than thod_buy
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if self.slopes[symbol] > thod_buy:
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self.set_holdings(symbol, 1 / len(self.symbols))
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