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quantconnect--lean/Algorithm.Python/ContinuousFutureRegressionAlgorithm.py
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Louis Szeto cece811cce
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Remove extra ; at Python files (#6248)
Co-authored-by: LouisSzeto <hke0073@hotmail.com>
2022-03-10 11:09:33 -03:00

82 lines
3.9 KiB
Python

# 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 *
### <summary>
### Continuous Futures Regression algorithm. Asserting and showcasing the behavior of adding a continuous future
### </summary>
class ContinuousFutureRegressionAlgorithm(QCAlgorithm):
'''Basic template algorithm simply initializes the date range and cash'''
def Initialize(self):
'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
self.SetStartDate(2013, 7, 1)
self.SetEndDate(2014, 1, 1)
self._mappings = []
self._lastDateLog = -1
self._continuousContract = self.AddFuture(Futures.Indices.SP500EMini,
dataNormalizationMode = DataNormalizationMode.BackwardsRatio,
dataMappingMode = DataMappingMode.LastTradingDay,
contractDepthOffset= 0)
self._currentMappedSymbol = self._continuousContract.Symbol
def OnData(self, data):
'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
Arguments:
data: Slice object keyed by symbol containing the stock data
'''
currentlyMappedSecurity = self.Securities[self._continuousContract.Mapped]
if len(data.Keys) != 1:
raise ValueError(f"We are getting data for more than one symbols! {','.join(data.Keys)}")
for changedEvent in data.SymbolChangedEvents.Values:
if changedEvent.Symbol == self._continuousContract.Symbol:
self._mappings.append(changedEvent)
self.Log(f"SymbolChanged event: {changedEvent}")
if self._currentMappedSymbol == self._continuousContract.Mapped:
raise ValueError(f"Continuous contract current symbol did not change! {self._continuousContract.Mapped}")
if self._lastDateLog != self.Time.month and currentlyMappedSecurity.HasData:
self._lastDateLog = self.Time.month
self.Log(f"{self.Time}- {currentlyMappedSecurity.GetLastData()}")
if self.Portfolio.Invested:
self.Liquidate()
else:
# This works because we set this contract as tradable, even if it's a canonical security
self.Buy(currentlyMappedSecurity.Symbol, 1)
if self.Time.month == 1 and self.Time.year == 2013:
response = self.History( [ self._continuousContract.Symbol ], 60 * 24 * 90)
if response.empty:
raise ValueError("Unexpected empty history response")
self._currentMappedSymbol = self._continuousContract.Mapped
def OnOrderEvent(self, orderEvent):
if orderEvent.Status == OrderStatus.Filled:
self.Debug("Purchased Stock: {0}".format(orderEvent.Symbol))
def OnSecuritiesChanged(self, changes):
self.Debug(f"{self.Time}-{changes}")
def OnEndOfAlgorithm(self):
expectedMappingCounts = 2
if len(self._mappings) != expectedMappingCounts:
raise ValueError(f"Unexpected symbol changed events: {self._mappings.count()}, was expecting {expectedMappingCounts}")