Chris Auld
d8772051b2
File had hard-coded references and calls to Python Tools for Visual Studio. This PR removes these. Algorithm tested and runs in online QuantConnect deployment of LEAN.
126 lines
5.3 KiB
Python
126 lines
5.3 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("System")
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AddReference("QuantConnect.Algorithm")
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AddReference("QuantConnect.Indicators")
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AddReference("QuantConnect.Algorithm.Framework")
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AddReference("QuantConnect.Common")
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from System import *
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from QuantConnect import *
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from QuantConnect.Indicators 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.Execution import *
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from QuantConnect.Algorithm.Framework.Portfolio import *
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from QuantConnect.Algorithm.Framework.Risk import *
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from QuantConnect.Algorithm.Framework.Selection import *
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from datetime import timedelta
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import numpy as np
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import pandas as pd
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### <summary>
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### CapmAlphaRankingFrameworkAlgorithm: example of custom scheduled universe selection model
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### Universe Selection inspired by https://www.quantconnect.com/tutorials/strategy-library/capm-alpha-ranking-strategy-on-dow-30-companies
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### </summary>
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class CapmAlphaRankingFrameworkAlgorithm(QCAlgorithm):
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'''CapmAlphaRankingFrameworkAlgorithm: example of custom scheduled universe selection model'''
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def Initialize(self):
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''' Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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# Set requested data resolution
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self.UniverseSettings.Resolution = Resolution.Minute
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self.SetStartDate(2016, 1, 1) #Set Start Date
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self.SetEndDate(2017, 1, 1) #Set End Date
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self.SetCash(100000) #Set Strategy Cash
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# set algorithm framework models
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self.SetUniverseSelection(CapmAlphaRankingUniverseSelectionModel())
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self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(1), 0.025, None))
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self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
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self.SetExecution(ImmediateExecutionModel())
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self.SetRiskManagement(MaximumDrawdownPercentPerSecurity(0.01))
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from QuantConnect.Data.UniverseSelection import ScheduledUniverse
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from Selection.UniverseSelectionModel import UniverseSelectionModel
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class CapmAlphaRankingUniverseSelectionModel(UniverseSelectionModel):
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'''This universe selection model picks stocks with the highest alpha: interception of the linear regression against a benchmark.'''
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period = 21;
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benchmark = "SPY"
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# Symbols of Dow 30 companies.
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symbols = [Symbol.Create(x, SecurityType.Equity, Market.USA)
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for x in ["AAPL", "AXP", "BA", "CAT", "CSCO", "CVX", "DD", "DIS", "GE", "GS",
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"HD", "IBM", "INTC", "JPM", "KO", "MCD", "MMM", "MRK", "MSFT",
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"NKE","PFE", "PG", "TRV", "UNH", "UTX", "V", "VZ", "WMT", "XOM"]]
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def CreateUniverses(self, algorithm):
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# Adds the benchmark to the user defined universe
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benchmark = algorithm.AddEquity(self.benchmark, Resolution.Daily)
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# Defines a schedule universe that fires after market open when the month starts
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return [ ScheduledUniverse(
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benchmark.Exchange.TimeZone,
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algorithm.DateRules.MonthStart(self.benchmark),
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algorithm.TimeRules.AfterMarketOpen(self.benchmark),
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lambda datetime: self.SelectPair(algorithm, datetime),
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algorithm.UniverseSettings,
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algorithm.SecurityInitializer)]
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def SelectPair(self, algorithm, date):
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'''Selects the pair (two stocks) with the highest alpha'''
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dictionary = dict()
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benchmark = self._getReturns(algorithm, self.benchmark)
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ones = np.ones(len(benchmark))
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for symbol in self.symbols:
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prices = self._getReturns(algorithm, symbol)
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if prices is None: continue
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A = np.vstack([prices, ones]).T
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# Calculate the Least-Square fitting to the returns of a given symbol and the benchmark
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ols = np.linalg.lstsq(A, benchmark)[0]
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dictionary[symbol] = ols[1]
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# Returns the top 2 highest alphas
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orderedDictionary = sorted(dictionary.items(), key= lambda x: x[1], reverse=True)
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return [x[0] for x in orderedDictionary[:2]]
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def _getReturns(self, algorithm, symbol):
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history = algorithm.History([symbol], self.period, Resolution.Daily)
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if history.empty: return None
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window = RollingWindow[float](self.period)
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rateOfChange = RateOfChange(1)
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def roc_updated(s, item):
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window.Add(item.Value)
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rateOfChange.Updated += roc_updated
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history = history.close.reset_index(level=0, drop=True).iteritems()
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for time, value in history:
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rateOfChange.Update(time, value);
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return [ x for x in window]
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