Overall Statistics |
Total Orders 0 Average Win 0% Average Loss 0% Compounding Annual Return 0% Drawdown 0% Expectancy 0 Start Equity 100000 End Equity 100000 Net Profit 0% Sharpe Ratio 0 Sortino Ratio 0 Probabilistic Sharpe Ratio 0% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha 0 Beta 0 Annual Standard Deviation 0 Annual Variance 0 Information Ratio 0 Tracking Error 0 Treynor Ratio 0 Total Fees $0.00 Estimated Strategy Capacity $0 Lowest Capacity Asset Portfolio Turnover 0% |
# 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 Alphas.PearsonCorrelationPairsTradingAlphaModel import PearsonCorrelationPairsTradingAlphaModel ### <summary> ### Framework algorithm that uses the PearsonCorrelationPairsTradingAlphaModel. ### This model extendes BasePairsTradingAlphaModel and uses Pearson correlation ### to rank the pairs trading candidates and use the best candidate to trade. ### </summary> class PearsonCorrelationPairsTradingAlphaModelFrameworkAlgorithm(QCAlgorithm): '''Framework algorithm that uses the PearsonCorrelationPairsTradingAlphaModel. This model extendes BasePairsTradingAlphaModel and uses Pearson correlation to rank the pairs trading candidates and use the best candidate to trade.''' def initialize(self): self.set_start_date(2013, 10, 7) self.set_end_date(2013, 10, 11) self.spy = self.add_equity("SPY").symbol self.consolidator = self.resolve_consolidator(self.spy, Resolution.MINUTE) name = self.create_indicator_name(self.spy, "close", Resolution.MINUTE) identity = Identity(name) self.indicator = self.register_indicator(self.spy, identity, self.consolidator) self.schedule.on(self.date_rules.today, self.time_rules.before_market_close(self.spy), self.remove_consolidator) def remove_consolidator(self): self.subscription_manager.remove_consolidator(self.spy, self.consolidator) consolidator_count = sum(s.consolidators.count for s in self.subscription_manager.subscriptions) if consolidator_count > 0: raise Exception(f"The number of consolidator should be zero. Actual: {consolidator_count}") def on_end_of_algorithm(self) -> None: # We have removed all securities from the universe. The Alpha Model should remove the consolidator consolidator_count = sum(s.consolidators.count for s in self.subscription_manager.subscriptions) if consolidator_count > 0: raise Exception(f"The number of consolidator should be zero. Actual: {consolidator_count}")