Martin-Molinero
03f56481d4
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Python research import improvements - Improve start.py for research env - Remove unrequired imports * Centralize algorithm imports * Add regression test GH action * Unit test python import clean up * Join research and main imports * More python import clean up * Fix failing skipped regression algorithm
93 lines
3.8 KiB
Python
93 lines
3.8 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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### <summary>
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### In this algortihm, we fetch a list of tickers with corresponding dates from a file on Dropbox.
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### We then create a fine fundamental universe which contains those symbols on their respective dates.###
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### </summary>
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### <meta name="tag" content="download" />
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### <meta name="tag" content="universes" />
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### <meta name="tag" content="custom data" />
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class DropboxCoarseFineAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.SetStartDate(2019, 9, 23) # Set Start Date
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self.SetEndDate(2019, 9, 30) # Set End Date
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self.SetCash(100000) # Set Strategy Cash
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self.AddUniverse(self.SelectCoarse, self.SelectFine)
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self.universeData = None
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self.nextUpdate = datetime(1, 1, 1) # Minimum datetime
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self.url = "https://www.dropbox.com/s/x2sb9gaiicc6hm3/tickers_with_dates.csv?dl=1"
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def OnEndOfDay(self):
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for security in self.ActiveSecurities.Values:
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self.Debug(f"{self.Time.date()} {security.Symbol.Value} with Market Cap: ${security.Fundamentals.MarketCap}")
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def SelectCoarse(self, coarse):
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return self.GetSymbols()
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def SelectFine(self, fine):
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symbols = self.GetSymbols()
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# Return symbols from our list which have a market capitalization of at least 10B
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return [f.Symbol for f in fine if f.MarketCap > 1e10 and f.Symbol in symbols]
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def GetSymbols(self):
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# In live trading update every 12 hours
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if self.LiveMode:
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if self.Time < self.nextUpdate:
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# Return today's row
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return self.universeData[self.Time.date()]
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# When updating set the new reset time.
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self.nextUpdate = self.Time + timedelta(hours=12)
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self.universeData = self.Parse(self.url)
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# In backtest load once if not set, then just use the dates.
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if self.universeData is None:
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self.universeData = self.Parse(self.url)
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# Check if contains the row we need
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if self.Time.date() not in self.universeData:
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return Universe.Unchanged
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return self.universeData[self.Time.date()]
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def Parse(self, url):
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# Download file from url as string
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file = self.Download(url).split("\n")
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# # Remove formatting characters
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data = [x.replace("\r", "").replace(" ", "") for x in file]
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# # Split data by date and symbol
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split_data = [x.split(",") for x in data]
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# Dictionary to hold list of active symbols for each date, keyed by date
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symbolsByDate = {}
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# Parse data into dictionary
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for arr in split_data:
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date = datetime.strptime(arr[0], "%Y%m%d").date()
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symbols = [Symbol.Create(ticker, SecurityType.Equity, Market.USA) for ticker in arr[1:]]
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symbolsByDate[date] = symbols
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return symbolsByDate
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def OnSecuritiesChanged(self, changes):
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self.Log(f"Added Securities: {[security.Symbol.Value for security in changes.AddedSecurities]}")
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