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 *
+
+### <summary>
+### Expiry Helper algorithm uses Expiry helper class in an Alpha Model
+### </summary>
+class ExpiryHelperAlphaModelFrameworkAlgorithm(QCAlgorithm):
+    '''Expiry Helper framework algorithm uses Expiry helper class in an Alpha Model'''
+
+    def initialize(self) -> None:
+        ''' Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+
+        # Set requested data resolution
+        self.universe_settings.resolution = Resolution.HOUR
+
+        self.set_start_date(2013,10,7)   #Set Start Date
+        self.set_end_date(2014,1,1)      #Set End Date
+        self.set_cash(100000)           #Set Strategy Cash
+
+        symbols = [ Symbol.create("SPY", SecurityType.EQUITY, Market.USA) ]
+
+        # set algorithm framework models
+        self.set_universe_selection(ManualUniverseSelectionModel(symbols))
+        self.set_alpha(self.ExpiryHelperAlphaModel())
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+        self.set_execution(ImmediateExecutionModel())
+        self.set_risk_management(MaximumDrawdownPercentPerSecurity(0.01))
+
+        self.insights_generated += self.on_insights_generated
+
+    def on_insights_generated(self, s: IAlgorithm, e: GeneratedInsightsCollection) -> None:
+        for insight in e.insights:
+            self.log(f"{e.date_time_utc.isoweekday()}: Close Time {insight.close_time_utc} {insight.close_time_utc.isoweekday()}")
+
+    class ExpiryHelperAlphaModel(AlphaModel):
+        _next_update = None
+        _direction = InsightDirection.UP
+
+        def update(self, algorithm: QCAlgorithm, data: Slice) -> list[Insight]:
+            if self._next_update and self._next_update > algorithm.time:
+                return []
+
+            expiry = Expiry.END_OF_DAY
+
+            # Use the Expiry helper to calculate a date/time in the future
+            self._next_update = expiry(algorithm.time)
+
+            weekday = algorithm.time.isoweekday()
+
+            insights = []
+            for symbol in data.bars.keys():
+                # Expected CloseTime: next month on the same day and time
+                if weekday == 1:
+                    insights.append(Insight.price(symbol, Expiry.ONE_MONTH, self._direction))
+                # Expected CloseTime: next month on the 1st at market open time
+                elif weekday == 2:
+                    insights.append(Insight.price(symbol, Expiry.END_OF_MONTH, self._direction))
+                # Expected CloseTime: next Monday at market open time
+                elif weekday == 3:
+                    insights.append(Insight.price(symbol, Expiry.END_OF_WEEK, self._direction))
+                # Expected CloseTime: next day (Friday) at market open time
+                elif weekday == 4:
+                    insights.append(Insight.price(symbol, Expiry.END_OF_DAY, self._direction))
+
+            return insights