Jhonathan Abreu
6bfe45dbcf
* PEP8 algorithms conversion * PEP8 unit tests algorithms conversion * Minor fixes
117 lines
5.8 KiB
Python
117 lines
5.8 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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from CustomDataRegressionAlgorithm import Bitcoin
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### <summary>
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### Regression algorithm reproducing data type bugs in the RegisterIndicator API. Related to GH 4205.
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### </summary>
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class RegisterIndicatorRegressionAlgorithm(QCAlgorithm):
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# Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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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(2013, 10, 9)
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SP500 = Symbol.create(Futures.Indices.SP_500_E_MINI, SecurityType.FUTURE, Market.CME)
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self._symbol = _symbol = self.future_chain_provider.get_future_contract_list(SP500, (self.start_date + timedelta(days=1)))[0]
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self.add_future_contract(_symbol)
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# this collection will hold all indicators and at the end of the algorithm we will assert that all of them are ready
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self._indicators = []
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# this collection will be used to determine if the Selectors were called, we will assert so at the end of algorithm
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self._selector_called = [ False, False, False, False, False, False ]
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# First we will test that we can register our custom indicator using a QuoteBar consolidator
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indicator = CustomIndicator()
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consolidator = self.resolve_consolidator(_symbol, Resolution.MINUTE, QuoteBar)
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self.register_indicator(_symbol, indicator, consolidator)
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self._indicators.append(indicator)
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indicator2 = CustomIndicator()
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# We use the TimeDelta overload to fetch the consolidator
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consolidator = self.resolve_consolidator(_symbol, timedelta(minutes=1), QuoteBar)
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# We specify a custom selector to be used
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self.register_indicator(_symbol, indicator2, consolidator, lambda bar: self.set_selector_called(0) and bar)
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self._indicators.append(indicator2)
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# We use a IndicatorBase<IndicatorDataPoint> with QuoteBar data and a custom selector
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indicator3 = SimpleMovingAverage(10)
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consolidator = self.resolve_consolidator(_symbol, timedelta(minutes=1), QuoteBar)
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self.register_indicator(_symbol, indicator3, consolidator, lambda bar: self.set_selector_called(1) and (bar.ask.high - bar.bid.low))
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self._indicators.append(indicator3)
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# We test default consolidator resolution works correctly
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moving_average = SimpleMovingAverage(10)
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# Using Resolution, specifying custom selector and explicitly using TradeBar.volume
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self.register_indicator(_symbol, moving_average, Resolution.MINUTE, lambda bar: self.set_selector_called(2) and bar.volume)
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self._indicators.append(moving_average)
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moving_average2 = SimpleMovingAverage(10)
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# Using Resolution
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self.register_indicator(_symbol, moving_average2, Resolution.MINUTE)
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self._indicators.append(moving_average2)
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moving_average3 = SimpleMovingAverage(10)
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# Using timedelta
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self.register_indicator(_symbol, moving_average3, timedelta(minutes=1))
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self._indicators.append(moving_average3)
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moving_average4 = SimpleMovingAverage(10)
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# Using time_delta, specifying custom selector and explicitly using TradeBar.volume
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self.register_indicator(_symbol, moving_average4, timedelta(minutes=1), lambda bar: self.set_selector_called(3) and bar.volume)
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self._indicators.append(moving_average4)
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# Test custom data is able to register correctly and indicators updated
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symbol_custom = self.add_data(Bitcoin, "BTC", Resolution.MINUTE).symbol
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sma_custom_data = SimpleMovingAverage(1)
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self.register_indicator(symbol_custom, sma_custom_data, timedelta(minutes=1), lambda bar: self.set_selector_called(4) and bar.volume)
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self._indicators.append(sma_custom_data)
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sma_custom_data2 = SimpleMovingAverage(1)
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self.register_indicator(symbol_custom, sma_custom_data2, Resolution.MINUTE)
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self._indicators.append(sma_custom_data2)
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sma_custom_data3 = SimpleMovingAverage(1)
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consolidator = self.resolve_consolidator(symbol_custom, timedelta(minutes=1))
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self.register_indicator(symbol_custom, sma_custom_data3, consolidator, lambda bar: self.set_selector_called(5) and bar.volume)
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self._indicators.append(sma_custom_data3)
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def set_selector_called(self, position):
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self._selector_called[position] = True
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return True
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# OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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def on_data(self, data):
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if not self.portfolio.invested:
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self.set_holdings(self._symbol, 0.5)
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def on_end_of_algorithm(self):
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if any(not was_called for was_called in self._selector_called):
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raise ValueError("All selectors should of been called")
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if any(not indicator.is_ready for indicator in self._indicators):
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raise ValueError("All indicators should be ready")
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self.log(f'Total of {len(self._indicators)} are ready')
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class CustomIndicator(PythonIndicator):
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def __init__(self):
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super().__init__()
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self.name = "Jose"
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self.value = 0
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def update(self, input):
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self.value = input.ask.high
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return True
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