chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
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+# 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>
+### This regression algorithm asserts the consolidated US equity daily bars from the hour bars exactly matches
+### the daily bars returned from the database
+### </summary>
+class ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm(QCAlgorithm):
+    def initialize(self):
+        self.set_start_date(2020, 5, 1)
+        self.set_end_date(2020, 6, 5)
+
+        self.spy = self.add_equity("SPY", Resolution.HOUR).symbol
+
+        # We will use these two indicators to compare the daily consolidated bars equals
+        # the ones returned from the database. We use this specific type of indicator as
+        # it depends on its previous values. Thus, if at some point the bars received by
+        # the indicators differ, so will their final values
+        self._rsi = RelativeStrengthIndex("First", 15, MovingAverageType.WILDERS)
+        self.register_indicator(self.spy, self._rsi, Resolution.DAILY, selector= lambda bar: (bar.close + bar.open) / 2)
+
+        # We won't register this indicator as we will update it manually at the end of the
+        # month, so that we can compare the values of the indicator that received consolidated
+        # bars and the values of this one
+        self._rsi_timedelta = RelativeStrengthIndex("Second", 15, MovingAverageType.WILDERS)
+        self._values = {}
+        self.count = 0
+        self._indicators_compared = False
+
+    def on_data(self, data: Slice):
+        if self.is_warming_up:
+            return
+
+        if data.contains_key(self.spy) and data[self.spy] != None:
+            if self.time.month == self.end_date.month:
+                history = self.history[TradeBar](self.spy, self.count, Resolution.DAILY)
+                for bar in history:
+                    time = bar.end_time.strftime('%Y-%m-%d')
+                    average = (bar.close + bar.open) / 2
+                    self._rsi_timedelta.update(bar.end_time, average)
+                    if self._rsi_timedelta.current.value != self._values[time]:
+                        raise AssertionError(f"Both {self._rsi.name} and {self._rsi_timedelta.name} should have the same values, but they differ. {self._rsi.name}: {self._values[time]} | {self._rsi_timedelta.name}: {self._rsi_timedelta.current.value}")
+                self._indicators_compared = True
+                self.quit()
+            else:
+                time = self.time.strftime('%Y-%m-%d')
+                self._values[time] = self._rsi.current.value
+
+                # Since the symbol resolution is hour and the symbol is equity, we know the last bar received in a day will
+                # be at the market close, this is 16h. We need to count how many daily bars were consolidated in order to know
+                # how many we need to request from the history
+                if self.time.hour == 16:
+                    self.count += 1
+
+    def on_end_of_algorithm(self):
+        if not self._indicators_compared:
+            raise AssertionError(f"Indicators {self._rsi.name} and {self._rsi_timedelta.name} should have been compared, but they were not. Please make sure the indicators are getting SPY data")