wehub-resource-sync
78 lines
3.9 KiB
Python
78 lines
3.9 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 datetime import timedelta
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from AlgorithmImports import *
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### <summary>
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### Regression algorithm that asserts Stochastic indicator, registered with a different resolution consolidator,
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### is warmed up properly by calling QCAlgorithm.WarmUpIndicator
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### </summary>
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class StochasticIndicatorWarmsUpProperlyRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2020, 1, 1) # monday = holiday..
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self.set_end_date(2020, 2, 1)
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self.set_cash(100000)
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self.data_points_received = False
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self.spy = self.add_equity("SPY", Resolution.HOUR).symbol
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self.daily_consolidator = TradeBarConsolidator(timedelta(days=1))
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self._rsi = RelativeStrengthIndex(14, MovingAverageType.WILDERS)
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self._sto = Stochastic("FIRST", 10, 3, 3)
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self.register_indicator(self.spy, self._rsi, self.daily_consolidator)
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self.register_indicator(self.spy, self._sto, self.daily_consolidator)
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# warm_up indicator
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self.warm_up_indicator(self.spy, self._rsi, timedelta(days=1))
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self.warm_up_indicator(self.spy, self._sto, timedelta(days=1))
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self._rsi_history = RelativeStrengthIndex(14, MovingAverageType.WILDERS)
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self._sto_history = Stochastic("SECOND", 10, 3, 3)
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self.register_indicator(self.spy, self._rsi_history, self.daily_consolidator)
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self.register_indicator(self.spy, self._sto_history, self.daily_consolidator)
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# history warm up
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history = self.history[TradeBar](self.spy, max(self._rsi_history.warm_up_period, self._sto_history.warm_up_period), Resolution.DAILY)
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for bar in history:
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self._rsi_history.update(bar.end_time, bar.close)
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if self._rsi_history.samples == 1:
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continue
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self._sto_history.update(bar)
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indicators = [self._rsi, self._sto, self._rsi_history, self._sto_history]
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for indicator in indicators:
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if not indicator.is_ready:
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raise AssertionError(f"{indicator.name} should be ready, but it is not. Number of samples: {indicator.samples}")
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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):
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self.data_points_received = True
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if self._rsi.current.value != self._rsi_history.current.value:
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raise AssertionError(f"Values of indicators differ: {self._rsi.name}: {self._rsi.current.value} | {self._rsi_history.name}: {self._rsi_history.current.value}")
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if self._sto.stoch_k.current.value != self._sto_history.stoch_k.current.value:
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raise AssertionError(f"Stoch K values of indicators differ: {self._sto.name}.StochK: {self._sto.stoch_k.current.value} | {self._sto_history.name}.StochK: {self._sto_history.stoch_k.current.value}")
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if self._sto.stoch_d.current.value != self._sto_history.stoch_d.current.value:
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raise AssertionError(f"Stoch D values of indicators differ: {self._sto.name}.StochD: {self._sto.stoch_d.current.value} | {self._sto_history.name}.StochD: {self._sto_history.stoch_d.current.value}")
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def on_end_of_algorithm(self):
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if not self.data_points_received:
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raise AssertionError("No data points received")
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