Jhonathan Abreu
159fa5a182
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* Fix ConsolidateRegressionAlgorithm - Fix Bitcoin custom data time range. Historical data goes from April 2014 - Fix expected consolidated bar counts - Fix python Consolidate implementations to respect daily strict times for Daily resolution * Housekeeping
140 lines
7.5 KiB
Python
140 lines
7.5 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 Consolidate API. Related to GH 4205.
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### </summary>
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class ConsolidateRegressionAlgorithm(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(2020, 1, 5)
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self.set_end_date(2020, 1, 20)
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SP500 = Symbol.create(Futures.Indices.SP_500_E_MINI, SecurityType.FUTURE, Market.CME)
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symbol = self.future_chain_provider.get_future_contract_list(SP500, self.start_date)[0]
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self._future = self.add_future_contract(symbol)
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tradable_dates_count = len(list(Time.each_tradeable_day_in_time_zone(self._future.exchange.hours,
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self.start_date,
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self.end_date,
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self._future.exchange.time_zone,
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False)));
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self._expected_consolidation_counts = [];
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self.consolidate(symbol, Calendar.MONTHLY, lambda bar: self.update_monthly_consolidator(bar, -1)) # shouldn't consolidate
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self.consolidate(symbol, Calendar.WEEKLY, TickType.TRADE, lambda bar: self.update_weekly_consolidator(bar))
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self.consolidate(symbol, Resolution.DAILY, lambda bar: self.update_trade_bar(bar, 0))
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self._expected_consolidation_counts.append(tradable_dates_count)
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self.consolidate(symbol, Resolution.DAILY, TickType.QUOTE, lambda bar: self.update_quote_bar(bar, 1))
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self._expected_consolidation_counts.append(tradable_dates_count)
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self.consolidate(symbol, timedelta(1), lambda bar: self.update_trade_bar(bar, 2))
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self._expected_consolidation_counts.append(tradable_dates_count - 1)
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self.consolidate(symbol, timedelta(1), TickType.QUOTE, lambda bar: self.update_quote_bar(bar, 3))
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self._expected_consolidation_counts.append(tradable_dates_count - 1)
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# sending None tick type
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self.consolidate(symbol, timedelta(1), None, lambda bar: self.update_trade_bar(bar, 4))
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self._expected_consolidation_counts.append(tradable_dates_count - 1)
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self.consolidate(symbol, Resolution.DAILY, None, lambda bar: self.update_trade_bar(bar, 5))
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self._expected_consolidation_counts.append(tradable_dates_count)
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self._consolidation_counts = [0] * len(self._expected_consolidation_counts)
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self._smas = [SimpleMovingAverage(10) for x in self._consolidation_counts]
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self._last_sma_updates = [datetime.min for x in self._consolidation_counts]
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self._monthly_consolidator_sma = SimpleMovingAverage(10)
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self._monthly_consolidation_count = 0
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self._weekly_consolidator_sma = SimpleMovingAverage(10)
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self._weekly_consolidation_count = 0
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self._last_weekly_sma_update = datetime.min
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# custom data
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self._custom_data_consolidator = 0
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custom_symbol = self.add_data(Bitcoin, "BTC", Resolution.DAILY).symbol
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self.consolidate(custom_symbol, timedelta(1), lambda bar: self.increment_counter(1))
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self._custom_data_consolidator2 = 0
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self.consolidate(custom_symbol, Resolution.DAILY, lambda bar: self.increment_counter(2))
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def increment_counter(self, id):
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if id == 1:
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self._custom_data_consolidator += 1
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if id == 2:
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self._custom_data_consolidator2 += 1
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def update_trade_bar(self, bar, position):
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self._smas[position].update(bar.end_time, bar.volume)
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self._last_sma_updates[position] = bar.end_time
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self._consolidation_counts[position] += 1
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def update_quote_bar(self, bar, position):
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self._smas[position].update(bar.end_time, bar.ask.high)
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self._last_sma_updates[position] = bar.end_time
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self._consolidation_counts[position] += 1
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def update_monthly_consolidator(self, bar):
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self._monthly_consolidator_sma.update(bar.end_time, bar.volume)
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self._monthly_consolidation_count += 1
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def update_weekly_consolidator(self, bar):
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self._weekly_consolidator_sma.update(bar.end_time, bar.volume)
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self._last_weekly_sma_update = bar.end_time
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self._weekly_consolidation_count += 1
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def on_end_of_algorithm(self):
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for i, expected_consolidation_count in enumerate(self._expected_consolidation_counts):
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consolidation_count = self._consolidation_counts[i]
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if consolidation_count != expected_consolidation_count:
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raise ValueError(f"Unexpected consolidation count for index {i}: expected {expected_consolidation_count} but was {consolidation_count}")
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expected_weekly_consolidations = (self.end_date - self.start_date).days // 7
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if self._weekly_consolidation_count != expected_weekly_consolidations:
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raise ValueError(f"Expected {expected_weekly_consolidations} weekly consolidations but found {self._weekly_consolidation_count}")
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if self._custom_data_consolidator == 0:
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raise ValueError("Custom data consolidator did not consolidate any data")
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if self._custom_data_consolidator2 == 0:
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raise ValueError("Custom data consolidator 2 did not consolidate any data")
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for i, sma in enumerate(self._smas):
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if sma.samples != self._expected_consolidation_counts[i]:
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raise Exception(f"Expected {self._expected_consolidation_counts[i]} samples in each SMA but found {sma.samples} in SMA in index {i}")
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last_update = self._last_sma_updates[i]
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if sma.current.time != last_update:
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raise Exception(f"Expected SMA in index {i} to have been last updated at {last_update} but was {sma.current.time}")
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if self._monthly_consolidation_count != 0 or self._monthly_consolidator_sma.samples != 0:
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raise Exception("Expected monthly consolidator to not have consolidated any data")
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if self._weekly_consolidator_sma.samples != expected_weekly_consolidations:
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raise Exception(f"Expected {expected_weekly_consolidations} samples in the weekly consolidator SMA but found {self._weekly_consolidator_sma.samples}")
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if self._weekly_consolidator_sma.current.time != self._last_weekly_sma_update:
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raise Exception(f"Expected weekly consolidator SMA to have been last updated at {self._last_weekly_sma_update} but was {self._weekly_consolidator_sma.current.time}")
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# on_data 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 and self._future.has_data:
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self.set_holdings(self._future.symbol, 0.5)
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