AlexCatarino
113 lines
5.3 KiB
Python
113 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.Core")
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AddReference("System.Collections")
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AddReference("QuantConnect.Common")
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AddReference("QuantConnect.Algorithm")
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from System import *
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from System.Collections.Generic import List
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from QuantConnect import *
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from QuantConnect.Algorithm import QCAlgorithm
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from QuantConnect.Data.UniverseSelection import *
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from math import ceil
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import numpy as np
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import pandas as pd
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import scipy as sp
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### <summary>
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### Demonstration of how to estimate constituents of QC500 index based on the company fundamentals
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### The algorithm creates a default tradable and liquid universe containing 500 US equities
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### which are chosen at the first trading day of each month.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="universes" />
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### <meta name="tag" content="coarse universes" />
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### <meta name="tag" content="fine universes" />
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class ConstituentsQC500GeneratorAlgorithm(QCAlgorithm):
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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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self.SetStartDate(2018, 1, 1) #Set Start Date
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self.SetEndDate(2018, 1, 3) #Set End Date
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self.SetCash(50000) #Set Strategy Cash
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self.UniverseSettings.Resolution = Resolution.Daily
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# this add universe method accepts two parameters:
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# - coarse selection function: accepts an IEnumerable<CoarseFundamental> and returns an IEnumerable<Symbol>
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# - fine selection function: accepts an IEnumerable<FineFundamental> and returns an IEnumerable<Symbol>
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self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
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self.spy = self.AddEquity("SPY", Resolution.Daily)
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self.Schedule.On(self.DateRules.MonthStart("SPY"), self.TimeRules.At(0, 0), self.monthly_rebalance)
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self.num_coarse = 1000
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self.num_fine = 500
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self.dollar_volume = {}
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self.rebalance = True
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def CoarseSelectionFunction(self, coarse):
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if not self.rebalance: return []
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# The stocks must have fundamental data
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# The stock must have positive previous-day close price
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# The stock must have positive volume on the previous trading day
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filtered = [x for x in coarse if x.HasFundamentalData
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and x.Volume > 0
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and x.Price > 0]
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# sort the stocks by dollar volume and take the top 1000
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sort_filtered = sorted(filtered, key=lambda x: x.DollarVolume, reverse=True)[:self.num_coarse]
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for i in sort_filtered:
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self.dollar_volume[i.Symbol.Value] = i.DollarVolume
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# return the symbol objects our sorted collection
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return [x.Symbol for x in sort_filtered]
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def FineSelectionFunction(self, fine):
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if not self.rebalance: return []
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self.rebalance = False
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# The company's headquarter must in the U.S.
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# The stock must be traded on either the NYSE or NASDAQ
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# At least half a year since its initial public offering
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# The stock's market cap must be greater than 500 million
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filtered_fine = [x for x in fine if (x.CompanyReference.CountryId == "USA")
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and (x.CompanyReference.PrimaryExchangeID == "NYS" or x.CompanyReference.PrimaryExchangeID == "NAS")
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and ((self.Time - x.SecurityReference.IPODate).days > 180)
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and x.EarningReports.BasicAverageShares.ThreeMonths * (x.EarningReports.BasicEPS.TwelveMonths*x.ValuationRatios.PERatio) > 5e8]
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count = len(filtered_fine)
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if count == 0: return []
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# select stocks with top dollar volume in every single sector
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for i in filtered_fine:
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i.DollarVolume = self.dollar_volume[i.Symbol.Value]
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percent = float(self.num_fine/count)
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group_by_code = {}
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top_list = []
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for code in ["N", "M", "U", "T", "B", "I"]:
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group_by_code[code] = list(filter(lambda x: x.CompanyReference.IndustryTemplateCode == code, filtered_fine))
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top = sorted(group_by_code[code], key=lambda x: x.DollarVolume, reverse = True)[:ceil(len(group_by_code[code])*percent)]
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top_list.append(top)
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joined_list = top_list[0]
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for ls in top_list[1:]:
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joined_list += ls
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self.symbols = [x.Symbol for x in joined_list][:self.num_fine]
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self.Log(",".join(sorted(i.Value for i in self.symbols)))
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return self.symbols
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def OnData(self, data):
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pass
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def monthly_rebalance(self):
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self.rebalance = True |