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
@@ -0,0 +1,73 @@
+# 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>
+### In this algorithm we demonstrate how to use the coarse fundamental data to define a universe as the top dollar volume and set the algorithm to use raw prices
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="universes" />
+### <meta name="tag" content="coarse universes" />
+### <meta name="tag" content="fine universes" />
+class RawPricesCoarseUniverseAlgorithm(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.'''
+
+        # what resolution should the data *added* to the universe be?
+        self.universe_settings.resolution = Resolution.DAILY
+
+        self.set_start_date(2014,1,1)    #Set Start Date
+        self.set_end_date(2015,1,1)      #Set End Date
+        self.set_cash(50000)            #Set Strategy Cash
+
+        # Set the security initializer with the characteristics defined in CustomSecurityInitializer
+        self.set_security_initializer(self.custom_security_initializer)
+
+        # this add universe method accepts a single parameter that is a function that
+        # accepts an IEnumerable<CoarseFundamental> and returns IEnumerable<Symbol>
+        self.add_universe(self.coarse_selection_function)
+
+        self.__number_of_symbols = 5
+
+    def custom_security_initializer(self, security):
+        '''Initialize the security with raw prices and zero fees 
+        Args:
+            security: Security which characteristics we want to change'''
+        security.set_data_normalization_mode(DataNormalizationMode.RAW)
+        security.set_fee_model(ConstantFeeModel(0))
+
+    # sort the data by daily dollar volume and take the top 'NumberOfSymbols'
+    def coarse_selection_function(self, coarse):
+        # sort descending by daily dollar volume
+        sorted_by_dollar_volume = sorted(coarse, key=lambda x: x.dollar_volume, reverse=True)
+
+        # return the symbol objects of the top entries from our sorted collection
+        return [ x.symbol for x in sorted_by_dollar_volume[:self.__number_of_symbols] ]
+
+
+    # this event fires whenever we have changes to our universe
+    def on_securities_changed(self, changes):
+        # liquidate removed securities
+        for security in changes.removed_securities:
+            if security.invested:
+                self.liquidate(security.symbol)
+
+        # we want 20% allocation in each security in our universe
+        for security in changes.added_securities:
+            self.set_holdings(security.symbol, 0.2)
+
+    def on_order_event(self, order_event):
+        if order_event.status == OrderStatus.FILLED:
+            self.log(f"OnOrderEvent({self.utc_time}):: {order_event}")