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>
+### Test algorithm using 'QCAlgorithm.add_alpha_model()'
+### </summary>
+class AddAlphaModelAlgorithm(QCAlgorithm):
+    def initialize(self):
+        ''' Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
+
+        self.set_start_date(2013,10,7)   #Set Start Date
+        self.set_end_date(2013,10,11)    #Set End Date
+        self.set_cash(100000)           #Set Strategy Cash
+
+        self.universe_settings.resolution = Resolution.DAILY
+
+        spy = Symbol.create("SPY", SecurityType.EQUITY, Market.USA)
+        fb = Symbol.create("FB", SecurityType.EQUITY, Market.USA)
+        ibm = Symbol.create("IBM", SecurityType.EQUITY, Market.USA)
+
+        # set algorithm framework models
+        self.set_universe_selection(ManualUniverseSelectionModel([ spy, fb, ibm ]))
+        self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
+        self.set_execution(ImmediateExecutionModel())
+
+        self.add_alpha(OneTimeAlphaModel(spy))
+        self.add_alpha(OneTimeAlphaModel(fb))
+        self.add_alpha(OneTimeAlphaModel(ibm))
+
+class OneTimeAlphaModel(AlphaModel):
+    def __init__(self, symbol):
+        self.symbol = symbol
+        self.triggered = False
+
+    def update(self, algorithm, data):
+        insights = []
+        if not self.triggered:
+            self.triggered = True
+            insights.append(Insight.price(
+                self.symbol,
+                Resolution.DAILY,
+                1,
+                InsightDirection.DOWN))
+        return insights