AlexCatarino
6121236f20
This algorithm is an example on how to extend the `BasePairsTradingAlphaModel` to select a pair to trade based on pearson correlation.
102 lines
4.7 KiB
Python
102 lines
4.7 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 clr import AddReference
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Common")
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from QuantConnect import *
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from QuantConnect.Algorithm import *
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from QuantConnect.Algorithm.Framework import *
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from QuantConnect.Algorithm.Framework.Alphas import *
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from QuantConnect.Algorithm.Framework.Selection import *
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from Portfolio.EqualWeightingPortfolioConstructionModel import EqualWeightingPortfolioConstructionModel
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from Alphas.BasePairsTradingAlphaModel import BasePairsTradingAlphaModel
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from Execution.ImmediateExecutionModel import ImmediateExecutionModel
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from Risk.NullRiskManagementModel import NullRiskManagementModel
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from datetime import timedelta
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from scipy.stats import pearsonr
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import numpy as np
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### <summary>
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### Framework algorithm that uses the PearsonCorrelationPairsTradingAlphaModel.
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### This model extendes BasePairsTradingAlphaModel and uses Pearson correlation
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### to rank the pairs trading candidates and use the best candidate to trade.
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### </summary>
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class PearsonCorrelationPairsTradingAlphaModelFrameworkAlgorithm(QCAlgorithmFramework):
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'''Framework algorithm that uses the PearsonCorrelationPairsTradingAlphaModel.
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This model extendes BasePairsTradingAlphaModel and uses Pearson correlation
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to rank the pairs trading candidates and use the best candidate to trade.'''
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def Initialize(self):
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self.SetStartDate(2013,10,7)
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self.SetEndDate(2013,10,11)
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self.SetUniverseSelection(ManualUniverseSelectionModel(
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Symbol.Create('AIG', SecurityType.Equity, Market.USA),
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Symbol.Create('BAC', SecurityType.Equity, Market.USA),
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Symbol.Create('IBM', SecurityType.Equity, Market.USA),
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Symbol.Create('SPY', SecurityType.Equity, Market.USA)))
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self.SetAlpha(self.PearsonCorrelationPairsTradingAlphaModel(360, timedelta(minutes = 15)))
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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self.SetExecution(ImmediateExecutionModel())
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self.SetRiskManagement(NullRiskManagementModel())
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class PearsonCorrelationPairsTradingAlphaModel(BasePairsTradingAlphaModel):
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''' This alpha model is designed to rank every pair combination by its pearson correlation
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and trade the pair with the hightest correlation
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This model generates alternating long ratio/short ratio insights emitted as a group'''
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def __init__(self, lookback, period, threshold = 1):
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'''Initializes a new instance of the PearsonCorrelationPairsTradingAlphaModel class
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Args:
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lookback: lookback period to evaluate the historical correlation
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period: Period over which this insight is expected to come to fruition
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threshold: The percent [0, 100] deviation of the ratio from the mean before emitting an insight'''
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super().__init__(period, threshold)
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self.lookback = lookback
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self.best_pair = ()
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def OnSecuritiesChanged(self, algorithm, changes):
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for security in changes.AddedSecurities:
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self.Securities.append(security)
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for security in changes.RemovedSecurities:
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if security in self.Securities:
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self.Securities.remove(security)
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symbols = [ x.Symbol for x in self.Securities ]
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history = algorithm.History(symbols, self.lookback, Resolution.Daily).close.unstack(level=0)
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df = (np.log(history) - np.log(history.shift(1))).dropna()
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stop = len(df.columns)
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corr = dict()
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for i in range(0, stop):
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for j in range(i+1, stop):
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if (j, i) not in corr:
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corr[(i, j)] = pearsonr(df.iloc[:,i], df.iloc[:,j])[0]
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corr = sorted(corr.items(), key = lambda kv: kv[1])
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self.best_pair = (symbols[corr[-1][0][0]], symbols[corr[-1][0][1]])
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super().OnSecuritiesChanged(algorithm, changes)
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def HasPassedTest(self, algorithm, asset1, asset2):
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return self.best_pair == (asset1, asset2) |