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>
+### This algorithm shows how to set a custom security initializer.
+### A security initializer is run immediately after a new security object
+### has been created and can be used to security models and other settings,
+### such as data normalization mode
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="securities and portfolio" />
+### <meta name="tag" content="trading and orders" />
+class CustomSecurityInitializerAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        
+        # set our initializer to our custom type
+        self.set_brokerage_model(BrokerageName.INTERACTIVE_BROKERS_BROKERAGE)
+        
+        func_security_seeder = FuncSecuritySeeder(self.custom_seed_function)
+        self.set_security_initializer(CustomSecurityInitializer(self.brokerage_model, func_security_seeder, DataNormalizationMode.RAW))
+        
+        self.set_start_date(2013,10,1)
+        self.set_end_date(2013,11,1)
+
+        self.add_equity("SPY", Resolution.HOUR)
+
+    def on_data(self, data):
+        if not self.portfolio.invested:
+            self.set_holdings("SPY", 1)
+
+    def custom_seed_function(self, security):
+
+        resolution = Resolution.HOUR
+
+        df = self.history(security.symbol, 1, resolution)
+        if df.empty:
+            return None
+
+        last_bar = df.unstack(level=0).iloc[-1]
+        date_time = last_bar.name.to_pydatetime()
+        open = last_bar.open.values[0]
+        high = last_bar.high.values[0]
+        low = last_bar.low.values[0]
+        close = last_bar.close.values[0]
+        volume = last_bar.volume.values[0]
+        return TradeBar(date_time, security.symbol, open, high, low, close, volume, Extensions.to_time_span(resolution))
+
+
+class CustomSecurityInitializer(BrokerageModelSecurityInitializer):
+    '''Our custom initializer that will set the data normalization mode.
+    We sub-class the BrokerageModelSecurityInitializer so we can also
+    take advantage of the default model/leverage setting behaviors'''
+
+    def __init__(self, brokerage_model, security_seeder, data_normalization_mode):
+        '''Initializes a new instance of the CustomSecurityInitializer class with the specified normalization mode
+        brokerage_model -- The brokerage model used to get fill/fee/slippage/settlement models
+        security_seeder -- The security seeder to be used
+        data_normalization_mode -- The desired data normalization mode'''
+        self.base = BrokerageModelSecurityInitializer(brokerage_model, security_seeder)
+        self.data_normalization_mode = data_normalization_mode
+
+    def initialize(self, security):
+        '''Initializes the specified security by setting up the models
+        security -- The security to be initialized
+        seed_security -- True to seed the security, false otherwise'''
+        # first call the default implementation
+        self.base.initialize(security)
+
+        # now apply our data normalization mode
+        security.set_data_normalization_mode(self.data_normalization_mode)