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 QuantConnect.Logging import *
+from enum import Enum
+
+class RsiAlphaModel(AlphaModel):
+    '''Uses Wilder's RSI to create insights.
+    Using default settings, a cross over below 30 or above 70 will trigger a new insight.'''
+
+    def __init__(self,
+                 period = 14,
+                 resolution = Resolution.DAILY):
+        '''Initializes a new instance of the RsiAlphaModel class
+        Args:
+            period: The RSI indicator period'''
+        self.period = period
+        self.resolution = resolution
+        self.insight_period = Time.multiply(Extensions.to_time_span(resolution), period)
+        self.symbol_data_by_symbol ={}
+
+        self.name = '{}({},{})'.format(self.__class__.__name__, 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():
+            rsi = symbol_data.rsi
+            previous_state = symbol_data.state
+            state = self.get_state(rsi, previous_state)
+
+            if state != previous_state and rsi.is_ready:
+                if state == State.TRIPPED_LOW:
+                    insights.append(Insight.price(symbol, self.insight_period, InsightDirection.UP))
+                if state == State.TRIPPED_HIGH:
+                    insights.append(Insight.price(symbol, self.insight_period, InsightDirection.DOWN))
+
+            symbol_data.state = state
+
+        return insights
+
+
+    def on_securities_changed(self, algorithm, changes):
+        '''Cleans out old security data and initializes the RSI for any newly added securities.
+        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'''
+        # clean up data for removed securities
+        for security in changes.removed_securities:
+            symbol_data = self.symbol_data_by_symbol.pop(security.symbol, None)
+            if symbol_data:
+                symbol_data.dispose()
+
+        # initialize data for added securities
+        added_symbols = []
+        for security in changes.added_securities:
+            symbol = security.symbol
+            if symbol not in self.symbol_data_by_symbol:
+                symbol_data = SymbolData(algorithm, symbol, self.period, self.resolution)
+                self.symbol_data_by_symbol[symbol] = symbol_data
+                added_symbols.append(symbol)
+
+        if added_symbols:
+            history = algorithm.history[TradeBar](added_symbols, self.period, self.resolution)
+            for trade_bars in history:
+                for bar in trade_bars.values():
+                    self.symbol_data_by_symbol[bar.symbol].update(bar)
+
+
+    def get_state(self, rsi, previous):
+        ''' Determines the new state. This is basically cross-over detection logic that
+        includes considerations for bouncing using the configured bounce tolerance.'''
+        if rsi.current.value > 70:
+            return State.TRIPPED_HIGH
+        if rsi.current.value < 30:
+            return State.TRIPPED_LOW
+        if previous == State.TRIPPED_LOW:
+            if rsi.current.value > 35:
+                return State.MIDDLE
+        if previous == State.TRIPPED_HIGH:
+            if rsi.current.value < 65:
+                return State.MIDDLE
+
+        return previous
+
+
+class SymbolData:
+    '''Contains data specific to a symbol required by this model'''
+    def __init__(self, algorithm, symbol, period, resolution):
+        self.algorithm = algorithm
+        self.symbol = symbol
+        self.state = State.MIDDLE
+
+        self.rsi = RelativeStrengthIndex(period, MovingAverageType.WILDERS)
+        self.consolidator = algorithm.resolve_consolidator(symbol, resolution)
+        algorithm.register_indicator(symbol, self.rsi, self.consolidator)
+
+    def update(self, bar):
+        self.consolidator.update(bar)
+
+    def dispose(self):
+        self.algorithm.subscription_manager.remove_consolidator(self.symbol, self.consolidator)
+
+
+class State(Enum):
+    '''Defines the state. This is used to prevent signal spamming and aid in bounce detection.'''
+    TRIPPED_LOW = 0
+    MIDDLE = 1
+    TRIPPED_HIGH = 2