Multiple Symbol Selection Universe (#7273)

- Add support and example algorithms for multiple symbol selection universe
 including custom data types.

Algorithm.Python/CustomDataUniverseRegressionAlgorithm.py

@@ -0,0 +1,85 @@
+# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
+# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from datetime import datetime
+from AlgorithmImports import *
+
+### <summary>
+### Custom data universe selection regression algorithm asserting it's behavior. See GH issue #6396
+### </summary>
+class CustomDataUniverseRegressionAlgorithm(QCAlgorithm):
+    def Initialize(self):
+        '''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+        self.SetStartDate(2014, 3, 24)
+        self.SetEndDate(2014, 3, 31)
+
+        self.UniverseSettings.Resolution = Resolution.Daily;
+        self.AddUniverse(CoarseFundamental, "custom-data-universe", self.Selection)
+
+        self._selectionTime = [datetime(2014, 3, 24), datetime(2014, 3, 25), datetime(2014, 3, 26),
+                              datetime(2014, 3, 27), datetime(2014, 3, 28), datetime(2014, 3, 29), datetime(2014, 3, 30), datetime(2014, 3, 31)]
+
+    def Selection(self, coarse):
+        self.Debug(f"Universe selection called: {self.Time} Count: {len(coarse)}")
+
+        expectedTime = self._selectionTime.pop(0)
+        if expectedTime != self.Time:
+            raise ValueError(f"Unexpected selection time {self.Time} expected {expectedTime}")
+
+        # sort descending by daily dollar volume
+        sortedByDollarVolume = sorted(coarse, key=lambda x: x.DollarVolume, reverse=True)
+
+        # return the symbol objects of the top entries from our sorted collection
+        underlyingSymbols = [ x.Symbol for x in sortedByDollarVolume[:10] ]
+        customSymbols = []
+        for symbol in underlyingSymbols:
+            customSymbols.append(Symbol.CreateBase(MyPyCustomData, symbol))
+        return underlyingSymbols + customSymbols
+
+    def OnData(self, data):
+        '''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+
+        Arguments:
+            data: Slice object keyed by symbol containing the stock data
+        '''
+        if not self.Portfolio.Invested:
+            customData = data.Get(MyPyCustomData)
+            symbols = [symbol for symbol in data.Keys if symbol.SecurityType is SecurityType.Equity]
+            for symbol in symbols:
+                self.SetHoldings(symbol, 1 / len(symbols))
+
+                if len([x for x in customData.Keys if x.Underlying == symbol]) == 0:
+                    raise ValueError(f"Custom data was not found for symbol {symbol}")
+
+class MyPyCustomData(PythonData):
+
+    def GetSource(self, config, date, isLiveMode):
+        source = f"{Globals.DataFolder}/equity/usa/daily/{LeanData.GenerateZipFileName(config.Symbol, date, config.Resolution, config.TickType)}"
+        return SubscriptionDataSource(source, SubscriptionTransportMedium.LocalFile, FileFormat.Csv)
+
+    def Reader(self, config, line, date, isLiveMode):
+        csv = line.split(',')
+        _scaleFactor = 1 / 10000
+
+        custom = MyPyCustomData()
+        custom.Symbol = config.Symbol
+        custom.Time =  datetime.strptime(csv[0], '%Y%m%d %H:%M')
+        custom.Open = float(csv[1]) * _scaleFactor
+        custom.High = float(csv[2]) * _scaleFactor
+        custom.Low = float(csv[3]) * _scaleFactor
+        custom.Close = float(csv[4]) * _scaleFactor
+        custom.Value = float(csv[4]) * _scaleFactor
+        custom.Period = Time.OneDay
+        custom.EndTime = custom.Time + custom.Period
+
+        return custom