Martin-Molinero
17ca8a743f
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* Refactor universe historical data source - Add new universe history API methods - Refactor QuantBook UniverseHistory to use the universe selection itself instead of a given func - Refactor and rename fundamental types - Refactor AddUniverse API to handle universe collection data which holds another type internally, like fundamental * Fix minor bug causing ApiDataProvider not to serve Bitfinex universe data * Further improvements to add universe API * Handle no selection function
95 lines
3.5 KiB
Python
95 lines
3.5 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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from System.Collections.Generic import List
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### <summary>
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### In this algorithm we show how you can easily use the universe selection feature to fetch symbols
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### to be traded using the BaseData custom data system in combination with the AddUniverse{T} method.
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### AddUniverse{T} requires a function that will return the symbols to be traded.
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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="custom universes" />
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class DropboxBaseDataUniverseSelectionAlgorithm(QCAlgorithm):
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def Initialize(self):
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self.UniverseSettings.Resolution = Resolution.Daily
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# Order margin value has to have a minimum of 0.5% of Portfolio value, allows filtering out small trades and reduce fees.
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# Commented so regression algorithm is more sensitive
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#self.Settings.MinimumOrderMarginPortfolioPercentage = 0.005
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self.SetStartDate(2017, 7, 6)
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self.SetEndDate(2018, 7, 4)
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universe = self.AddUniverse(StockDataSource, self.stockDataSource)
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historicalSelectionData = self.History(universe, 3)
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if len(historicalSelectionData) != 3:
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raise ValueError(f"Unexpected universe data count {len(historicalSelectionData)}")
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for universeData in historicalSelectionData["symbols"]:
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if len(universeData) != 5:
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raise ValueError(f"Unexpected universe data receieved")
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def stockDataSource(self, data):
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list = []
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for item in data:
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for symbol in item["Symbols"]:
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list.append(symbol)
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return list
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def OnData(self, slice):
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if slice.Bars.Count == 0: return
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if self._changes is None: return
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# start fresh
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self.Liquidate()
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percentage = 1 / slice.Bars.Count
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for tradeBar in slice.Bars.Values:
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self.SetHoldings(tradeBar.Symbol, percentage)
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# reset changes
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self._changes = None
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def OnSecuritiesChanged(self, changes):
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self._changes = changes
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class StockDataSource(PythonData):
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def GetSource(self, config, date, isLiveMode):
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url = "https://www.dropbox.com/s/2l73mu97gcehmh7/daily-stock-picker-live.csv?dl=1" if isLiveMode else \
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"https://www.dropbox.com/s/ae1couew5ir3z9y/daily-stock-picker-backtest.csv?dl=1"
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return SubscriptionDataSource(url, SubscriptionTransportMedium.RemoteFile)
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def Reader(self, config, line, date, isLiveMode):
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if not (line.strip() and line[0].isdigit()): return None
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stocks = StockDataSource()
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stocks.Symbol = config.Symbol
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csv = line.split(',')
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if isLiveMode:
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stocks.Time = date
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stocks["Symbols"] = csv
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else:
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stocks.Time = datetime.strptime(csv[0], "%Y%m%d")
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stocks["Symbols"] = csv[1:]
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return stocks
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