Files
quantconnect--lean/Algorithm.Python/UniverseUnchangedRegressionAlgorithm.py
T
Martin Molinero 1983f36792 Allow Python selection to return unchanged
- We will now check if python selection method returned `Universe.Unchanged`
- Removing `ToList()` call on fine and coarse data before sending it to
the python algorithm
- Adding regression algorithms
2019-07-31 15:36:07 -03:00

70 lines
3.1 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 clr import AddReference
AddReference("System.Core")
AddReference("QuantConnect.Common")
AddReference("QuantConnect.Algorithm")
from System import *
from QuantConnect import *
from QuantConnect.Algorithm import QCAlgorithm
from QuantConnect.Data.UniverseSelection import Universe
from QuantConnect.Algorithm.Framework.Alphas import *
from QuantConnect.Algorithm.Framework.Portfolio import *
from datetime import date, timedelta
### <summary>
### Regression algorithm used to test a fine and coarse selection methods returning Universe.Unchanged
### </summary>
class UniverseUnchangedRegressionAlgorithm(QCAlgorithm):
def Initialize(self):
self.UniverseSettings.Resolution = Resolution.Daily
self.SetStartDate(2014,3,24)
self.SetEndDate(2014,4,7)
self.SetAlpha(ConstantAlphaModel(InsightType.Price, InsightDirection.Up, timedelta(days = 1), 0.025, None))
self.SetPortfolioConstruction(EqualWeightingPortfolioConstructionModel())
self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
self.numberOfSymbolsFine = 2
def CoarseSelectionFunction(self, coarse):
# the first and second selection
if self.Time.date() <= date(2014, 3, 25):
tickers = [ "AAPL", "AIG", "IBM" ]
return [ Symbol.Create(x, SecurityType.Equity, Market.USA) for x in tickers ]
# will skip fine selection
return Universe.Unchanged
def FineSelectionFunction(self, fine):
if self.Time.date() == date(2014, 3, 24):
sortedByPeRatio = sorted(fine, key=lambda x: x.ValuationRatios.PERatio, reverse=True)
return [ x.Symbol for x in sortedByPeRatio[:self.numberOfSymbolsFine] ]
# the second selection will return unchanged, in the following fine selection will be skipped
return Universe.Unchanged
# assert security changes, throw if called more than once
def OnSecuritiesChanged(self, changes):
addedSymbols = [ x.Symbol for x in changes.AddedSecurities ]
if (len(changes.AddedSecurities) != 2
or self.Time.date() != date(2014, 3, 25)
or Symbol.Create("AAPL", SecurityType.Equity, Market.USA) not in addedSymbols
or Symbol.Create("IBM", SecurityType.Equity, Market.USA) not in addedSymbols):
raise ValueError("Unexpected security changes")
self.Log(f"OnSecuritiesChanged({self.Time}):: {changes}")