Jhonathan Abreu
2ddf40b8e9
* feat: support snake-case style Python QCAlgorithm implementations * feat: add unit tests and minor fixes * feat: implement new BasePythonWrapper class for python wrappers. Used to cache methods and contains invoke functionality * feat: make python wrappers implement the new base class for pep8 style support * feat: keep overriden methods in Algorithm Python Wrapper * feat: add unit tests for custom models algorithms with PEP8 style * Bump pythonnet version to 2.0.30 * fix bugs and address peer review * Address peer review * Minor revert * feat: StubsIgnoreAttribute for ignoring members or classes by the stubs generator * Minor fixes * Minor fix * Minor fix * Bump pythonnet version to 2.0.31 * Added Greeks.Lambda_ alias of Lambda for python compatibility. Remove unused method
57 lines
2.3 KiB
Python
57 lines
2.3 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### Regression algorithm asserting that universe symbols selection can be done by returning the symbol IDs in the selection function
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### </summary>
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class SelectUniverseSymbolsFromIDRegressionAlgorithm(QCAlgorithm):
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'''
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Regression algorithm asserting that universe symbols selection can be done by returning the symbol IDs in the selection function
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'''
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def Initialize(self):
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self.SetStartDate(2014, 3, 24)
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self.SetEndDate(2014, 3, 26)
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self.SetCash(100000)
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self._securities = []
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self.UniverseSettings.Resolution = Resolution.Daily
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self.AddUniverse(self.select_symbol)
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def select_symbol(self, fundamental):
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symbols = [x.Symbol for x in fundamental]
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if not symbols:
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return []
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self.Log(f"Symbols: {', '.join([str(s) for s in symbols])}")
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# Just for testing, but more filtering could be done here as shown below:
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#symbols = [x.Symbol for x in fundamental if x.AssetClassification.MorningstarSectorCode == MorningstarSectorCode.Technology]
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history = self.History(symbols, datetime(1998, 1, 1), self.Time, Resolution.Daily)
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all_time_highs = history['high'].unstack(0).max()
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last_closes = history['close'].unstack(0).iloc[-1]
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security_ids = (last_closes / all_time_highs).sort_values().index[-5:]
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return security_ids
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def OnSecuritiesChanged(self, changes):
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self._securities.extend(changes.AddedSecurities)
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def OnEndOfAlgorithm(self):
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if not self._securities:
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raise Exception("No securities were selected")
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