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 +1,102 @@
+# 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 ConstantAlphaModel(AlphaModel):
+    ''' Provides an implementation of IAlphaModel that always returns the same insight for each security'''
+
+    def __init__(self, type, direction, period, magnitude = None, confidence = None, weight = None):
+        '''Initializes a new instance of the ConstantAlphaModel class
+        Args:
+            type: The type of insight
+            direction: The direction of the insight
+            period: The period over which the insight with come to fruition
+            magnitude: The predicted change in magnitude as a +- percentage
+            confidence: The confidence in the insight
+            weight: The portfolio weight of the insights'''
+        self.type = type
+        self.direction = direction
+        self.period = period
+        self.magnitude = magnitude
+        self.confidence = confidence
+        self.weight = weight
+        self.securities = []
+        self.insights_time_by_symbol = {}
+
+        self.Name = '{}({},{},{}'.format(self.__class__.__name__, type, direction, strfdelta(period))
+        if magnitude is not None:
+            self.Name += ',{}'.format(magnitude)
+        if confidence is not None:
+            self.Name += ',{}'.format(confidence)
+
+        self.Name += ')'
+
+
+    def update(self, algorithm, data):
+        ''' Creates a constant insight for each security as specified via the constructor
+        Args:
+            algorithm: The algorithm instance
+            data: The new data available
+        Returns:
+            The new insights generated'''
+        insights = []
+
+        for security in self.securities:
+            # security price could be zero until we get the first data point. e.g. this could happen
+            # when adding both forex and equities, we will first get a forex data point
+            if security.price != 0 and self.should_emit_insight(algorithm.utc_time, security.symbol):
+                insights.append(Insight(security.symbol, self.period, self.type, self.direction, self.magnitude, self.confidence, weight = self.weight))
+
+        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:
+            self.securities.append(added)
+
+        # this will allow the insight to be re-sent when the security re-joins the universe
+        for removed in changes.removed_securities:
+            if removed in self.securities:
+                self.securities.remove(removed)
+            if removed.symbol in self.insights_time_by_symbol:
+                self.insights_time_by_symbol.pop(removed.symbol)
+
+
+    def should_emit_insight(self, utc_time, symbol):
+        if symbol.is_canonical():
+            # canonical futures & options are none tradable
+            return False
+
+        generated_time_utc = self.insights_time_by_symbol.get(symbol)
+
+        if generated_time_utc is not None:
+            # we previously emitted a insight for this symbol, check it's period to see
+            # if we should emit another insight
+            if utc_time - generated_time_utc < self.period:
+                return False
+
+        # we either haven't emitted a insight for this symbol or the previous
+        # insight's period has expired, so emit a new insight now for this symbol
+        self.insights_time_by_symbol[symbol] = utc_time
+        return True
+
+def strfdelta(tdelta):
+    d = tdelta.days
+    h, rem = divmod(tdelta.seconds, 3600)
+    m, s = divmod(rem, 60)
+    return "{}.{:02d}:{:02d}:{:02d}".format(d,h,m,s)