chore: import upstream snapshot with attribution

2026-07-13 13:02:50 +08:00
commit 0fc60fdcb1
5008 changed files with 910633 additions and 0 deletions
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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 *
+from CustomDataRegressionAlgorithm import Bitcoin
+
+### <summary>
+### Regression algorithm reproducing data type bugs in the Consolidate API. Related to GH 4205.
+### </summary>
+class ConsolidateRegressionAlgorithm(QCAlgorithm):
+
+    # Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
+    def initialize(self):
+        self.set_start_date(2020, 1, 5)
+        self.set_end_date(2020, 1, 20)
+
+        SP500 = Symbol.create(Futures.Indices.SP_500_E_MINI, SecurityType.FUTURE, Market.CME)
+        symbol = list(sorted(self.futures_chain(SP500).contracts.keys(), key=lambda symbol: symbol.id.date))[0]
+        self._future = self.add_future_contract(symbol)
+
+        tradable_dates_count = len(list(Time.each_tradeable_day_in_time_zone(self._future.exchange.hours,
+                                                                             self.start_date,
+                                                                             self.end_date,
+                                                                             self._future.exchange.time_zone,
+                                                                             False)))
+        self._expected_consolidation_counts = []
+
+        self.consolidate(symbol, Calendar.MONTHLY, lambda bar: self.update_monthly_consolidator(bar)) # shouldn't consolidate
+
+        self.consolidate(symbol, Calendar.WEEKLY, TickType.TRADE, lambda bar: self.update_weekly_consolidator(bar))
+
+        self.consolidate(symbol, Resolution.DAILY, lambda bar: self.update_trade_bar(bar, 0))
+        self._expected_consolidation_counts.append(tradable_dates_count)
+
+        self.consolidate(symbol, Resolution.DAILY, TickType.QUOTE, lambda bar: self.update_quote_bar(bar, 1))
+        self._expected_consolidation_counts.append(tradable_dates_count)
+
+        self.consolidate(symbol, timedelta(1), lambda bar: self.update_trade_bar(bar, 2))
+        self._expected_consolidation_counts.append(tradable_dates_count - 1)
+
+        self.consolidate(symbol, timedelta(1), TickType.QUOTE, lambda bar: self.update_quote_bar(bar, 3))
+        self._expected_consolidation_counts.append(tradable_dates_count - 1)
+
+        # sending None tick type
+        self.consolidate(symbol, timedelta(1), None, lambda bar: self.update_trade_bar(bar, 4))
+        self._expected_consolidation_counts.append(tradable_dates_count - 1)
+
+        self.consolidate(symbol, Resolution.DAILY, None, lambda bar: self.update_trade_bar(bar, 5))
+        self._expected_consolidation_counts.append(tradable_dates_count)
+
+        self._consolidation_counts = [0] * len(self._expected_consolidation_counts)
+        self._smas = [SimpleMovingAverage(10) for x in self._consolidation_counts]
+        self._last_sma_updates = [datetime.min for x in self._consolidation_counts]
+        self._monthly_consolidator_sma = SimpleMovingAverage(10)
+        self._monthly_consolidation_count = 0
+        self._weekly_consolidator_sma = SimpleMovingAverage(10)
+        self._weekly_consolidation_count = 0
+        self._last_weekly_sma_update = datetime.min
+
+        # custom data
+        self._custom_data_consolidator = 0
+        custom_symbol = self.add_data(Bitcoin, "BTC", Resolution.MINUTE).symbol
+        self.consolidate(custom_symbol, timedelta(1), lambda bar: self.increment_counter(1))
+
+    def increment_counter(self, id):
+        if id == 1:
+            self._custom_data_consolidator += 1
+
+    def update_trade_bar(self, bar, position):
+        self._smas[position].update(bar.end_time, bar.volume)
+        self._last_sma_updates[position] = bar.end_time
+        self._consolidation_counts[position] += 1
+
+    def update_quote_bar(self, bar, position):
+        self._smas[position].update(bar.end_time, bar.ask.high)
+        self._last_sma_updates[position] = bar.end_time
+        self._consolidation_counts[position] += 1
+
+    def update_monthly_consolidator(self, bar):
+        self._monthly_consolidator_sma.update(bar.end_time, bar.volume)
+        self._monthly_consolidation_count += 1
+
+    def update_weekly_consolidator(self, bar):
+        self._weekly_consolidator_sma.update(bar.end_time, bar.volume)
+        self._last_weekly_sma_update = bar.end_time
+        self._weekly_consolidation_count += 1
+
+    def on_end_of_algorithm(self):
+        for i, expected_consolidation_count in enumerate(self._expected_consolidation_counts):
+            consolidation_count = self._consolidation_counts[i]
+            if consolidation_count != expected_consolidation_count:
+                raise ValueError(f"Unexpected consolidation count for index {i}: expected {expected_consolidation_count} but was {consolidation_count}")
+
+        expected_weekly_consolidations = (self.end_date - self.start_date).days // 7
+        if self._weekly_consolidation_count != expected_weekly_consolidations:
+            raise ValueError(f"Expected {expected_weekly_consolidations} weekly consolidations but found {self._weekly_consolidation_count}")
+
+        if self._custom_data_consolidator == 0:
+            raise ValueError("Custom data consolidator did not consolidate any data")
+
+        for i, sma in enumerate(self._smas):
+            if sma.samples != self._expected_consolidation_counts[i]:
+                raise AssertionError(f"Expected {self._expected_consolidation_counts[i]} samples in each SMA but found {sma.samples} in SMA in index {i}")
+
+            last_update = self._last_sma_updates[i]
+            if sma.current.time != last_update:
+                raise AssertionError(f"Expected SMA in index {i} to have been last updated at {last_update} but was {sma.current.time}")
+
+        if self._monthly_consolidation_count != 0 or self._monthly_consolidator_sma.samples != 0:
+            raise AssertionError("Expected monthly consolidator to not have consolidated any data")
+
+        if self._weekly_consolidator_sma.samples != expected_weekly_consolidations:
+            raise AssertionError(f"Expected {expected_weekly_consolidations} samples in the weekly consolidator SMA but found {self._weekly_consolidator_sma.samples}")
+
+        if self._weekly_consolidator_sma.current.time != self._last_weekly_sma_update:
+            raise AssertionError(f"Expected weekly consolidator SMA to have been last updated at {self._last_weekly_sma_update} but was {self._weekly_consolidator_sma.current.time}")
+
+    # on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.
+    def on_data(self, data):
+        if not self.portfolio.invested and self._future.has_data:
+           self.set_holdings(self._future.symbol, 0.5)