Martin-Molinero
03f56481d4
Regression Tests / build (push) Has been cancelled
Build & Test Lean / build (push) Has been cancelled
* Python research import improvements - Improve start.py for research env - Remove unrequired imports * Centralize algorithm imports * Add regression test GH action * Unit test python import clean up * Join research and main imports * More python import clean up * Fix failing skipped regression algorithm
70 lines
2.8 KiB
Python
70 lines
2.8 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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### <summary>
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### Algorithm used for regression tests purposes
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### </summary>
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### <meta name="tag" content="regression test" />
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class RegressionAlgorithm(QCAlgorithm):
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def Initialize(self):
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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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self.SetStartDate(2013,10,7) #Set Start Date
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self.SetEndDate(2013,10,11) #Set End Date
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self.SetCash(10000000) #Set Strategy Cash
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# Find more symbols here: http://quantconnect.com/data
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self.AddEquity("SPY", Resolution.Tick)
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self.AddEquity("BAC", Resolution.Minute)
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self.AddEquity("AIG", Resolution.Hour)
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self.AddEquity("IBM", Resolution.Daily)
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self.__lastTradeTicks = self.StartDate
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self.__lastTradeTradeBars = self.__lastTradeTicks
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self.__tradeEvery = timedelta(minutes=1)
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def OnData(self, data):
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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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if self.Time - self.__lastTradeTradeBars < self.__tradeEvery:
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return
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self.__lastTradeTradeBars = self.Time
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for kvp in data.Bars:
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period = kvp.Value.Period.total_seconds()
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if self.roundTime(self.Time, period) != self.Time:
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pass
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symbol = kvp.Key
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holdings = self.Portfolio[symbol]
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if not holdings.Invested:
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self.MarketOrder(symbol, 10)
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else:
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self.MarketOrder(symbol, -holdings.Quantity)
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def roundTime(self, dt=None, roundTo=60):
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"""Round a datetime object to any time laps in seconds
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dt : datetime object, default now.
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roundTo : Closest number of seconds to round to, default 1 minute.
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"""
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if dt is None : dt = datetime.now()
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seconds = (dt - dt.min).seconds
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# // is a floor division, not a comment on following line:
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rounding = (seconds+roundTo/2) // roundTo * roundTo
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return dt + timedelta(0,rounding-seconds,-dt.microsecond)
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