Michael Handschuh
fdc866fda0
We didn't experience the expected performance improvements. Locally under unit test there was aboout an order of magnitude throughput increase, but when run against the history benchmark, this new approach was 60% slower. We're reverting this for now to perform further analysis and better understand the performance profiling of the python history stack.
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 |