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 *
+
+class EmaCrossAlphaModel(AlphaModel):
+    '''Alpha model that uses an EMA cross to create insights'''
+
+    def __init__(self,
+                 fast_period = 12,
+                 slow_period = 26,
+                 resolution = Resolution.DAILY):
+        '''Initializes a new instance of the EmaCrossAlphaModel class
+        Args:
+            fast_period: The fast EMA period
+            slow_period: The slow EMA period'''
+        self.fast_period = fast_period
+        self.slow_period = slow_period
+        self.resolution = resolution
+        self.prediction_interval = Time.multiply(Extensions.to_time_span(resolution), fast_period)
+        self.symbol_data_by_symbol = {}
+
+        self.name = '{}({},{},{})'.format(self.__class__.__name__, fast_period, slow_period, resolution)
+
+
+    def update(self, algorithm, data):
+        '''Updates this alpha model with the latest data from the algorithm.
+        This is called each time the algorithm receives data for subscribed securities
+        Args:
+            algorithm: The algorithm instance
+            data: The new data available
+        Returns:
+            The new insights generated'''
+        insights = []
+        for symbol, symbol_data in self.symbol_data_by_symbol.items():
+            if symbol_data.fast.is_ready and symbol_data.slow.is_ready:
+
+                if symbol_data.fast_is_over_slow:
+                    if symbol_data.slow > symbol_data.fast:
+                        insights.append(Insight.price(symbol_data.symbol, self.prediction_interval, InsightDirection.DOWN))
+
+                elif symbol_data.slow_is_over_fast:
+                    if symbol_data.fast > symbol_data.slow:
+                        insights.append(Insight.price(symbol_data.symbol, self.prediction_interval, InsightDirection.UP))
+
+            symbol_data.fast_is_over_slow = symbol_data.fast > symbol_data.slow
+
+        return insights
+
+    def on_securities_changed(self, algorithm, changes):
+        '''Event fired each time the we add/remove securities from the data feed
+        Args:
+            algorithm: The algorithm instance that experienced the change in securities
+            changes: The security additions and removals from the algorithm'''
+        for added in changes.added_securities:
+            symbol_data = self.symbol_data_by_symbol.get(added.symbol)
+            if symbol_data is None:
+                symbol_data = SymbolData(added, self.fast_period, self.slow_period, algorithm, self.resolution)
+                self.symbol_data_by_symbol[added.symbol] = symbol_data
+            else:
+                # a security that was already initialized was re-added, reset the indicators
+                symbol_data.fast.reset()
+                symbol_data.slow.reset()
+
+        for removed in changes.removed_securities:
+            data = self.symbol_data_by_symbol.pop(removed.symbol, None)
+            if data is not None:
+                # clean up our consolidators
+                data.remove_consolidators()
+
+
+class SymbolData:
+    '''Contains data specific to a symbol required by this model'''
+    def __init__(self, security, fast_period, slow_period, algorithm, resolution):
+        self.security = security
+        self.symbol = security.symbol
+        self.algorithm = algorithm
+
+        self.fast_consolidator = algorithm.resolve_consolidator(security.symbol, resolution)
+        self.slow_consolidator = algorithm.resolve_consolidator(security.symbol, resolution)
+
+        algorithm.subscription_manager.add_consolidator(security.symbol, self.fast_consolidator)
+        algorithm.subscription_manager.add_consolidator(security.symbol, self.slow_consolidator)
+
+        # create fast/slow EMAs
+        self.fast = ExponentialMovingAverage(security.symbol, fast_period, ExponentialMovingAverage.smoothing_factor_default(fast_period))
+        self.slow = ExponentialMovingAverage(security.symbol, slow_period, ExponentialMovingAverage.smoothing_factor_default(slow_period))
+
+        algorithm.register_indicator(security.symbol, self.fast, self.fast_consolidator);
+        algorithm.register_indicator(security.symbol, self.slow, self.slow_consolidator);
+
+        algorithm.warm_up_indicator(security.symbol, self.fast, resolution);
+        algorithm.warm_up_indicator(security.symbol, self.slow, resolution);
+
+        # True if the fast is above the slow, otherwise false.
+        # This is used to prevent emitting the same signal repeatedly
+        self.fast_is_over_slow = False
+
+    def remove_consolidators(self):
+        self.algorithm.subscription_manager.remove_consolidator(self.security.symbol, self.fast_consolidator)
+        self.algorithm.subscription_manager.remove_consolidator(self.security.symbol, self.slow_consolidator)
+
+    @property
+    def slow_is_over_fast(self):
+        return not self.fast_is_over_slow