Jhonathan Abreu
bc5d51806d
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* Normalize universe data frames Universe and (generically BaseDataCollection) data frames are not normalize and unpacked into a data frame, instead of just creating data frames with the universe lists within it * Fix unit tests and algorithms to expecte new universe dataframe format * Fixes * Add PandasConverter.DataFrameGenerator class * Pandas data frame generator class fixes * Add comments * Housekeeping * Add attributes to mark classes and properties for pandas processing * Improve pandas properties expanding Allow and handle duplicate names * Use PandasData generalization for Lean common data types * Add points time as column when converting base data collections to data frames * Cleanup and minor changes * Minor change * Pandas data to get type members on demand * Move Pandas helper classes to their own files * Minor changes * Add flatten argument to python history api This allows users to decide whether they want fully expanded dataframes for universe and other collection data types. Else, master behavior is kept * Adding missing changes to last commit * Update Pythonnet version to 2.0.40 * Add flattent argument to algorithm's OptionChain api * Minor changes * Housekeeping * Minor changes * Bug fix skipping data collection data points * Add comment * Set correct exchange time to OptionUniverse instances * Address peer review and cleanup * Cleanup * Minor changes
48 lines
2.3 KiB
Python
48 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 testing history requests for <see cref="OptionUniverse"/> type work as expected
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### and return the same data as the option chain provider.
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### </summary>
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class OptionUniverseHistoryRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2015, 12, 25)
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self.set_end_date(2015, 12, 25)
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option = self.add_option("GOOG").symbol
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historical_options_data_df = self.history(option, 3, flatten=True)
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# Level 0 of the multi-index is the date, we expect 3 dates, 3 option chains
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if historical_options_data_df.index.levshape[0] != 3:
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raise RegressionTestException(f"Expected 3 option chains from history request, but got {historical_options_data_df.index.levshape[1]}")
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for date in historical_options_data_df.index.levels[0]:
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expected_chain = list(self.option_chain_provider.get_option_contract_list(option, date))
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expected_chain_count = len(expected_chain)
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actual_chain = historical_options_data_df.loc[date]
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actual_chain_count = len(actual_chain)
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if expected_chain_count != actual_chain_count:
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raise RegressionTestException(f"Expected {expected_chain_count} options in chain on {date}, but got {actual_chain_count}")
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for i, symbol in enumerate(actual_chain.index):
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expected_symbol = expected_chain[i]
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if symbol != expected_symbol:
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raise RegressionTestException(f"Expected symbol {expected_symbol} at index {i} on {date}, but got {symbol}")
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