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 *
+from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
+
+class EmaCrossUniverseSelectionModel(FundamentalUniverseSelectionModel):
+    '''Provides an implementation of FundamentalUniverseSelectionModel that subscribes to
+    symbols with the larger delta by percentage between the two exponential moving average'''
+
+    def __init__(self,
+                 fastPeriod = 100,
+                 slowPeriod = 300,
+                 universeCount = 500,
+                 universeSettings = None):
+        '''Initializes a new instance of the EmaCrossUniverseSelectionModel class
+        Args:
+            fastPeriod: Fast EMA period
+            slowPeriod: Slow EMA period
+            universeCount: Maximum number of members of this universe selection
+            universeSettings: The settings used when adding symbols to the algorithm, specify null to use algorithm.UniverseSettings'''
+        super().__init__(False, universeSettings)
+        self.fast_period = fastPeriod
+        self.slow_period = slowPeriod
+        self.universe_count = universeCount
+        self.tolerance = 0.01
+        # holds our coarse fundamental indicators by symbol
+        self.averages = {}
+
+    def select_coarse(self, algorithm: QCAlgorithm, fundamental: list[Fundamental]) -> list[Symbol]:
+        '''Defines the coarse fundamental selection function.
+        Args:
+            algorithm: The algorithm instance
+            fundamental: The coarse fundamental data used to perform filtering</param>
+        Returns:
+            An enumerable of symbols passing the filter'''
+        filtered = []
+
+        for cf in fundamental:
+            if cf.symbol not in self.averages:
+                self.averages[cf.symbol] = self.SelectionData(cf.symbol, self.fast_period, self.slow_period)
+
+            # grab th SelectionData instance for this symbol
+            avg = self.averages.get(cf.symbol)
+
+            # Update returns true when the indicators are ready, so don't accept until they are
+            # and only pick symbols who have their fastPeriod-day ema over their slowPeriod-day ema
+            if avg.update(cf.end_time, cf.adjusted_price) and avg.fast > avg.slow * (1 + self.tolerance):
+                filtered.append(avg)
+
+        # prefer symbols with a larger delta by percentage between the two averages
+        filtered = sorted(filtered, key=lambda avg: avg.scaled_delta, reverse = True)
+
+        # we only need to return the symbol and return 'universeCount' symbols
+        return [x.symbol for x in filtered[:self.universe_count]]
+
+    # class used to improve readability of the coarse selection function
+    class SelectionData:
+        def __init__(self, symbol, fast_period, slow_period):
+            self.symbol = symbol
+            self.fast_ema = ExponentialMovingAverage(fast_period)
+            self.slow_ema = ExponentialMovingAverage(slow_period)
+
+        @property
+        def fast(self):
+            return float(self.fast_ema.current.value)
+
+        @property
+        def slow(self):
+            return float(self.slow_ema.current.value)
+
+        # computes an object score of how much large the fast is than the slow
+        @property
+        def scaled_delta(self):
+            return (self.fast - self.slow) / ((self.fast + self.slow) / 2)
+
+        # updates the EMAFast and EMASlow indicators, returning true when they're both ready
+        def update(self, time, value):
+            return self.slow_ema.update(time, value) & self.fast_ema.update(time, value)