chore: import upstream snapshot with attribution

Tests/RegressionAlgorithms/Test_CustomDataAlgorithm.py

@@ -0,0 +1,54 @@
+# 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 *
+
+from custom_data import *
+
+class Test_CustomDataAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.add_data(Nifty, "NIFTY")
+        self.add_data(CustomPythonData, "IBM", Resolution.DAILY)
+
+class Nifty(PythonData):
+    '''NIFTY Custom Data Class'''
+    def get_source(self, config, date, is_live_mode):
+        return SubscriptionDataSource("https://www.dropbox.com/s/rsmg44jr6wexn2h/CNXNIFTY.csv?dl=1", SubscriptionTransportMedium.REMOTE_FILE)
+
+
+    def reader(self, config, line, date, is_live_mode):
+        if not (line.strip() and line[0].isdigit()): return None
+
+        # New Nifty object
+        index = Nifty()
+        index.symbol = config.symbol
+
+        try:
+            # Example File Format:
+            # Date,       Open       High        Low       Close     Volume      Turnover
+            # 2011-09-13  7792.9    7799.9     7722.65    7748.7    116534670    6107.78
+            data = line.split(',')
+            index.time = datetime.strptime(data[0], "%Y-%m-%d")
+            index.value = decimal.decimal(data[4])
+            index["Open"] = float(data[1])
+            index["High"] = float(data[2])
+            index["Low"] = float(data[3])
+            index["Close"] = float(data[4])
+
+
+        except ValueError:
+                # Do nothing
+                return None
+
+        return index