wehub-resource-sync
70 lines
3.7 KiB
Python
70 lines
3.7 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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### This regression algorithm asserts the consolidated US equity daily bars from the hour bars exactly matches
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### the daily bars returned from the database
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### </summary>
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class ConsolidateHourBarsIntoDailyBarsRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2020, 5, 1)
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self.set_end_date(2020, 6, 5)
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self.spy = self.add_equity("SPY", Resolution.HOUR).symbol
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# We will use these two indicators to compare the daily consolidated bars equals
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# the ones returned from the database. We use this specific type of indicator as
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# it depends on its previous values. Thus, if at some point the bars received by
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# the indicators differ, so will their final values
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self._rsi = RelativeStrengthIndex("First", 15, MovingAverageType.WILDERS)
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self.register_indicator(self.spy, self._rsi, Resolution.DAILY, selector= lambda bar: (bar.close + bar.open) / 2)
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# We won't register this indicator as we will update it manually at the end of the
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# month, so that we can compare the values of the indicator that received consolidated
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# bars and the values of this one
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self._rsi_timedelta = RelativeStrengthIndex("Second", 15, MovingAverageType.WILDERS)
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self._values = {}
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self.count = 0
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self._indicators_compared = False
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def on_data(self, data: Slice):
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if self.is_warming_up:
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return
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if data.contains_key(self.spy) and data[self.spy] != None:
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if self.time.month == self.end_date.month:
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history = self.history[TradeBar](self.spy, self.count, Resolution.DAILY)
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for bar in history:
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time = bar.end_time.strftime('%Y-%m-%d')
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average = (bar.close + bar.open) / 2
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self._rsi_timedelta.update(bar.end_time, average)
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if self._rsi_timedelta.current.value != self._values[time]:
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raise AssertionError(f"Both {self._rsi.name} and {self._rsi_timedelta.name} should have the same values, but they differ. {self._rsi.name}: {self._values[time]} | {self._rsi_timedelta.name}: {self._rsi_timedelta.current.value}")
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self._indicators_compared = True
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self.quit()
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else:
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time = self.time.strftime('%Y-%m-%d')
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self._values[time] = self._rsi.current.value
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# Since the symbol resolution is hour and the symbol is equity, we know the last bar received in a day will
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# be at the market close, this is 16h. We need to count how many daily bars were consolidated in order to know
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# how many we need to request from the history
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if self.time.hour == 16:
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self.count += 1
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def on_end_of_algorithm(self):
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if not self._indicators_compared:
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raise AssertionError(f"Indicators {self._rsi.name} and {self._rsi_timedelta.name} should have been compared, but they were not. Please make sure the indicators are getting SPY data")
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