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 MaximumDrawdownPercentPortfolio(RiskManagementModel):
+    '''Provides an implementation of IRiskManagementModel that limits the drawdown of the portfolio to the specified percentage.'''
+
+    def __init__(self, maximum_drawdown_percent = 0.05, is_trailing = False):
+        '''Initializes a new instance of the MaximumDrawdownPercentPortfolio class
+        Args:
+            maximum_drawdown_percent: The maximum percentage drawdown allowed for algorithm portfolio compared with starting value, defaults to 5% drawdown</param>
+            is_trailing: If "false", the drawdown will be relative to the starting value of the portfolio.
+                        If "true", the drawdown will be relative the last maximum portfolio value'''
+        self.maximum_drawdown_percent = -abs(maximum_drawdown_percent)
+        self.is_trailing = is_trailing
+        self.initialised = False
+        self.portfolio_high = 0
+
+    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'''
+        current_value = algorithm.portfolio.total_portfolio_value
+
+        if not self.initialised:
+            self.portfolio_high = current_value   # Set initial portfolio value
+            self.initialised = True
+
+        # Update trailing high value if in trailing mode
+        if self.is_trailing and self.portfolio_high < current_value:
+            self.portfolio_high = current_value
+            return []   # return if new high reached
+
+        pnl = self.get_total_drawdown_percent(current_value)
+        if pnl < self.maximum_drawdown_percent and len(targets) != 0:
+            self.initialised = False # reset the trailing high value for restart investing on next rebalcing period
+
+            risk_adjusted_targets = []
+            for target in targets:
+                symbol = target.symbol
+
+                # Cancel insights
+                algorithm.insights.cancel([symbol])
+
+                # liquidate
+                risk_adjusted_targets.append(PortfolioTarget(symbol, 0))
+            return risk_adjusted_targets
+
+        return []
+
+    def get_total_drawdown_percent(self, current_value):
+        return (float(current_value) / float(self.portfolio_high)) - 1.0