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>
+### Demonstration of how to define a universe as a combination of use the coarse fundamental data and fine fundamental data
+### </summary>
+### <meta name="tag" content="using data" />
+### <meta name="tag" content="universes" />
+### <meta name="tag" content="coarse universes" />
+### <meta name="tag" content="regression test" />
+class CoarseFineFundamentalRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2014,3,24)   #Set Start Date
+        self.set_end_date(2014,4,7)      #Set End Date
+        self.set_cash(50000)            #Set Strategy Cash
+
+        self.universe_settings.resolution = Resolution.DAILY
+
+        # this add universe method accepts two parameters:
+        # - coarse selection function: accepts an List[CoarseFundamental] and returns an List[Symbol]
+        # - fine selection function: accepts an List[FineFundamental] and returns an List[Symbol]
+        self.add_universe(self.coarse_selection_function, self.fine_selection_function)
+
+        self.changes = None
+        self.number_of_symbols_fine = 2
+
+    # return a list of three fixed symbol objects
+    def coarse_selection_function(self, coarse):
+        tickers = [ "GOOG", "BAC", "SPY" ]
+
+        if self.time.date() < date(2014, 4, 1):
+            tickers = [ "AAPL", "AIG", "IBM" ]
+
+        return [ Symbol.create(x, SecurityType.EQUITY, Market.USA) for x in tickers ]
+
+
+    # sort the data by market capitalization and take the top 'number_of_symbols_fine'
+    def fine_selection_function(self, fine):
+        # sort descending by market capitalization
+        sorted_by_market_cap = sorted(fine, key=lambda x: x.market_cap, reverse=True)
+
+        # take the top entries from our sorted collection
+        return [ x.symbol for x in sorted_by_market_cap[:self.number_of_symbols_fine] ]
+
+    def on_data(self, data):
+        # if we have no changes, do nothing
+        if self.changes is None: return
+
+        # liquidate removed securities
+        for security in self.changes.removed_securities:
+            if security.invested:
+                self.liquidate(security.symbol)
+                self.debug("Liquidated Stock: " + str(security.symbol.value))
+
+        # we want 50% allocation in each security in our universe
+        for security in self.changes.added_securities:
+            if (security.fundamentals.earning_ratios.equity_per_share_growth.one_year > 0.25):
+                self.set_holdings(security.symbol, 0.5)
+                self.debug("Purchased Stock: " + str(security.symbol.value))
+
+        self.changes = None
+
+    # this event fires whenever we have changes to our universe
+    def on_securities_changed(self, changes):
+        self.changes = changes