Jhonathan Abreu
0a9dc2c71c
* Fix pandas converter to handle list of data with different symbols * Properly convert list of data into dataframe Take into consideration data for multiple symbols in the same list * Cleanup * Index dataframes by symbol object instead of SID string * Add symbol equality operator to compare against object * Exclude "ID" from option chain dataframe * Minor fix * Add greeks columns directly in option chain dataframe. Also add pass-through properties for greek values in OptionUniverse * Some cleanup * Minor fix * Add new QCAlgorithm.OptionChains() method - Use OptionChains as output - Add DataFrame to OptionChain and OptionChains - Rename Greeks classes - Add ISymbolProvider for classes that have a symbol (IBaseData, OptionContract) * Unify QCAlgorithmOptionChain API Also refactor OptionContract to handle: (1) Actual market data and option price model data, and (2) OptionUniverse data * Pass symbol properties to OptionUniverse option chain from algorithm * Format OptionContract for dataframe * Minor fix * Add multiple option chains api regression algorithms and other minor changes * Address peer review Add NullGreeks class: keep ModeledGreeks as internal as possible * Minor fix and add PandasConverter unit tests * Peer review: Non-thread-safe Lazy for Python * Handle Greeks unwrapping by PandasData * PandasData cleanup * Add data and other minor changes * Unit test fix * Update Pythonnet to 2.0.39 * Cleanup * PandasData handling children class members Address peer review * Fix: indexing symbol conversion in pandas mapper * Fix pandas mapper to convert string keys to symbol only when necessary * Cleanup * Cleanup * Add PandasColumn python class to handle proper indexing This allows propery hash and equality between Symbols, C# strings and Python strings * Minor fixes * Symbol cache improvements * Minor fix for cache miss * Revert PandasMapper reserved names and improvements * Minor fix * Revert reserved names * Minor fix for Symbol equality operators --------- Co-authored-by: Martin Molinero <martin.molinero1@gmail.com>
90 lines
2.8 KiB
C#
90 lines
2.8 KiB
C#
/*
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* 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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*
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*/
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using System;
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using NUnit.Framework;
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using Python.Runtime;
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using static QLNet.NumericHaganPricer;
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namespace QuantConnect.Tests.Python
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{
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[TestFixture]
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// TODO: Rename to PandasPythonTests, dedicate class to python tests under ./PandasTests directory
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public class PandasIndexingTests
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{
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private dynamic _module;
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private dynamic _pandasIndexingTests;
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private dynamic _pandasDataFrameTests;
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[SetUp]
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public void Setup()
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{
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using (Py.GIL())
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{
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_module = Py.Import("PandasIndexingTests");
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_pandasIndexingTests = _module.PandasIndexingTests();
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_pandasDataFrameTests = _module.PandasDataFrameTests();
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}
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}
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[Test]
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public void IndexingDataFrameWithList()
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{
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using (Py.GIL())
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{
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Assert.DoesNotThrow((() => _pandasIndexingTests.test_indexing_dataframe_with_list()));
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}
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}
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[Test]
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public void ContainsUserMappedTickers()
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{
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using (Py.GIL())
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{
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PyObject result = _pandasDataFrameTests.test_contains_user_mapped_ticker();
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var test = result.As<bool>();
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Assert.IsTrue(test);
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}
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}
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[TestCase("SPY WhatEver")]
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[TestCase("Sharpe ratio")]
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public void ContainsUserDefinedColumnsWithSpaces(string columnName)
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{
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using (Py.GIL())
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{
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PyObject result = _pandasDataFrameTests.test_contains_user_defined_columns_with_spaces(columnName);
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var test = result.As<bool>();
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Assert.IsTrue(test);
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}
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}
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[Test]
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public void ExpectedException()
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{
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using (Py.GIL())
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{
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PyObject result = _pandasDataFrameTests.test_expected_exception();
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var exception = result.As<string>();
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Assert.IsTrue(exception.Contains("No key found for either mapped or original key.", StringComparison.InvariantCulture), exception);
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}
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}
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}
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}
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