| 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}")