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 TrailingStopRiskManagementModel(RiskManagementModel):
+    '''Provides an implementation of IRiskManagementModel that limits the maximum possible loss
+    measured from the highest unrealized profit'''
+    def __init__(self, maximum_drawdown_percent = 0.05):
+        '''Initializes a new instance of the TrailingStopRiskManagementModel class
+        Args:
+            maximum_drawdown_percent: The maximum percentage drawdown allowed for algorithm portfolio compared with the highest unrealized profit, defaults to 5% drawdown'''
+        self.maximum_drawdown_percent = abs(maximum_drawdown_percent)
+        self.trailing_absolute_holdings_state = dict()
+
+    def manage_risk(self, algorithm, targets):
+        '''Manages the algorithm's risk at each time step
+        Args:
+            algorithm: The algorithm instance
+            targets: The current portfolio targets to be assessed for risk'''
+        risk_adjusted_targets = list()
+
+        for kvp in algorithm.securities:
+            symbol = kvp.key
+            security = kvp.value
+
+            # Remove if not invested
+            if not security.invested:
+                self.trailing_absolute_holdings_state.pop(symbol, None)
+                continue
+
+            position = PositionSide.LONG if security.holdings.is_long else PositionSide.SHORT
+            absolute_holdings_value = security.holdings.absolute_holdings_value
+            trailing_absolute_holdings_state = self.trailing_absolute_holdings_state.get(symbol)
+
+            # Add newly invested security (if doesn't exist) or reset holdings state (if position changed)
+            if trailing_absolute_holdings_state == None or position != trailing_absolute_holdings_state.position:
+                self.trailing_absolute_holdings_state[symbol] = trailing_absolute_holdings_state = self.HoldingsState(position, security.holdings.absolute_holdings_cost)
+
+            trailing_absolute_holdings_value = trailing_absolute_holdings_state.absolute_holdings_value
+
+            # Check for new max (for long position) or min (for short position) absolute holdings value
+            if ((position == PositionSide.LONG and trailing_absolute_holdings_value < absolute_holdings_value) or
+                (position == PositionSide.SHORT and trailing_absolute_holdings_value > absolute_holdings_value)):
+                self.trailing_absolute_holdings_state[symbol].absolute_holdings_value = absolute_holdings_value
+                continue
+
+            drawdown = abs((trailing_absolute_holdings_value - absolute_holdings_value) / trailing_absolute_holdings_value)
+
+            if self.maximum_drawdown_percent < drawdown:
+                # Cancel insights
+                algorithm.insights.cancel([ symbol ]);
+
+                self.trailing_absolute_holdings_state.pop(symbol, None)
+                # liquidate
+                risk_adjusted_targets.append(PortfolioTarget(symbol, 0))
+
+        return risk_adjusted_targets
+
+    class HoldingsState:
+        def __init__(self, position, absolute_holdings_value):
+            self.position = position
+            self.absolute_holdings_value = absolute_holdings_value