Add data normalization mode parameter to QCAlgorithm.History() (#6435)

* Add data normalization mode parameter to big History() methods

* Add C# regression algorithm

* Add Python regression algorithm

Algorithm.Python/HistoryWithDifferentDataNormalizationModeRegressionAlgorithm.py

@@ -0,0 +1,56 @@
+# 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 AlgorithmImports import *
+
+### <summary>
+### Regression algorithm illustrating how to request history data for different data normalization modes.
+### </summary>
+class HistoryWithDifferentDataMappingModeRegressionAlgorithm(QCAlgorithm):
+    def Initialize(self):
+        self.SetStartDate(2013, 10, 7)
+        self.SetEndDate(2014, 1, 1)
+        self.aaplEquitySymbol = self.AddEquity("AAPL", Resolution.Daily).Symbol
+        self.esFutureSymbol = self.AddFuture(Futures.Indices.SP500EMini, Resolution.Daily).Symbol
+
+    def OnEndOfAlgorithm(self):
+        equityDataNormalizationModes = [
+            DataNormalizationMode.Raw,
+            DataNormalizationMode.Adjusted,
+            DataNormalizationMode.SplitAdjusted
+        ]
+        self.CheckHistoryResultsForDataNormalizationModes(self.aaplEquitySymbol, self.StartDate, self.EndDate, Resolution.Daily,
+            equityDataNormalizationModes)
+
+        futureDataNormalizationModes = [
+            DataNormalizationMode.Raw,
+            DataNormalizationMode.BackwardsRatio,
+            DataNormalizationMode.BackwardsPanamaCanal,
+            DataNormalizationMode.ForwardPanamaCanal
+        ]
+        self.CheckHistoryResultsForDataNormalizationModes(self.esFutureSymbol, self.StartDate, self.EndDate, Resolution.Daily,
+            futureDataNormalizationModes)
+
+    def CheckHistoryResultsForDataNormalizationModes(self, symbol, start, end, resolution, dataNormalizationModes):
+        historyResults = [self.History([symbol], start, end, resolution, dataNormalizationMode=x) for x in dataNormalizationModes]
+        historyResults = [x.droplevel(0, axis=0) for x in historyResults] if len(historyResults[0].index.levels) == 3 else historyResults
+        historyResults = [x.loc[symbol].close for x in historyResults]
+
+        if any(x.size == 0 or x.size != historyResults[0].size for x in historyResults):
+            raise Exception(f"History results for {symbol} have different number of bars")
+
+        # Check that, for each history result, close prices at each time are different for these securities (AAPL and ES)
+        for j in range(historyResults[0].size):
+            closePrices = set(historyResults[i][j] for i in range(len(historyResults)))
+            if len(closePrices) != len(dataNormalizationModes):
+                raise Exception(f"History results for {symbol} have different close prices at the same time")