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
49 lines
2.1 KiB
Python
49 lines
2.1 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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class PEP8StyleBasicAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2013,10, 7)
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self.set_end_date(2013,10,11)
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self.set_cash(100000)
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self.spy = self.add_equity("SPY", Resolution.MINUTE, extended_market_hours=False, fill_forward=True).symbol
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# Test accessing a constant (QCAlgorithm.MaxTagsCount)
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self.debug("MaxTagsCount: " + str(self.MAX_TAGS_COUNT))
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def on_data(self, slice):
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if not self.portfolio.invested:
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self.set_holdings(self.spy, 1)
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self.debug("Purchased Stock")
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def on_order_event(self, order_event):
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self.log(f"{self.time} :: {order_event}")
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def on_end_of_algorithm(self):
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self.log("Algorithm ended!")
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if not self.portfolio.invested:
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raise Exception("Algorithm should have been invested at the end of the algorithm")
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# let's do some logging to do more pep8 style testing
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self.log("-----------------------------------------------------------------------------------------")
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self.log(f"{self.spy.value} last price: {self.securities[self.spy].price}")
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self.log(f"{self.spy.value} holdings: "
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f"{self.securities[self.spy].holdings.quantity}@{self.securities[self.spy].holdings.price}="
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f"{self.securities[self.spy].holdings.holdings_value}")
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self.log("-----------------------------------------------------------------------------------------")
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