wehub-resource-sync
58 lines
3.1 KiB
Python
58 lines
3.1 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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### <summary>
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### Regression algorithm illustrating how to request history data for different data normalization modes.
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### </summary>
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class HistoryWithDifferentDataNormalizationModeRegressionAlgorithm(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(2014, 1, 1)
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self.aapl_equity_symbol = self.add_equity("AAPL", Resolution.DAILY).symbol
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self.es_future_symbol = self.add_future(Futures.Indices.SP_500_E_MINI, Resolution.DAILY).symbol
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def on_end_of_algorithm(self):
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equity_data_normalization_modes = [
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DataNormalizationMode.RAW,
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DataNormalizationMode.ADJUSTED,
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DataNormalizationMode.SPLIT_ADJUSTED
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]
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self.check_history_results_for_data_normalization_modes(self.aapl_equity_symbol, self.start_date, self.end_date, Resolution.DAILY,
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equity_data_normalization_modes)
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future_data_normalization_modes = [
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DataNormalizationMode.RAW,
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DataNormalizationMode.BACKWARDS_RATIO,
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DataNormalizationMode.BACKWARDS_PANAMA_CANAL,
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DataNormalizationMode.FORWARD_PANAMA_CANAL
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]
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self.check_history_results_for_data_normalization_modes(self.es_future_symbol, self.start_date, self.end_date, Resolution.DAILY,
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future_data_normalization_modes)
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def check_history_results_for_data_normalization_modes(self, symbol, start, end, resolution, data_normalization_modes):
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history_results = [self.history([symbol], start, end, resolution, data_normalization_mode=x) for x in data_normalization_modes]
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history_results = [x.droplevel(0, axis=0) for x in history_results] if len(history_results[0].index.levels) == 3 else history_results
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history_results = [x.loc[symbol].close for x in history_results]
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if any(x.size == 0 or x.size != history_results[0].size for x in history_results):
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raise AssertionError(f"History results for {symbol} have different number of bars")
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# Check that, for each history result, close prices at each time are different for these securities (AAPL and ES)
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for j in range(history_results[0].size):
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close_prices = set(history_results[i][j] for i in range(len(history_results)))
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if len(close_prices) != len(data_normalization_modes):
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raise AssertionError(f"History results for {symbol} have different close prices at the same time")
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