# 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 collections import deque ### ### Algorithm asserting that security dynamic properties keep Python references to the Python class they are instances of, ### specifically when this class is a subclass of a C# class. ### class SecurityDynamicPropertyPythonClassAlgorithm(QCAlgorithm): def Initialize(self): self.SetStartDate(2013, 10, 7) self.SetEndDate(2013, 10, 7) self.spy = self.AddEquity("SPY", Resolution.Minute) customSMA = CustomSimpleMovingAverage('custom', 60) self.spy.CustomSMA = customSMA customSMA.Security = self.spy self.RegisterIndicator(self.spy.Symbol, self.spy.CustomSMA, Resolution.Minute) def OnWarmupFinished(self) -> None: if type(self.spy.CustomSMA) != CustomSimpleMovingAverage: raise Exception("spy.CustomSMA is not an instance of CustomSimpleMovingAverage") if self.spy.CustomSMA.Security is None: raise Exception("spy.CustomSMA.Security is None") else: self.Debug(f"spy.CustomSMA.Security.Symbol: {self.spy.CustomSMA.Security.Symbol}") def OnData(self, slice: Slice) -> None: if self.spy.CustomSMA.IsReady: self.Debug(f"CustomSMA: {self.spy.CustomSMA.Current.Value}") class CustomSimpleMovingAverage(PythonIndicator): def __init__(self, name, period): super().__init__() self.Name = name self.Value = 0 self.queue = deque(maxlen=period) def Update(self, input): self.queue.appendleft(input.Value) count = len(self.queue) self.Value = np.sum(self.queue) / count return count == self.queue.maxlen