Jhonathan Abreu
5d6110f38c
* Make statistics available at runtime to algorithms * Re-calculate statistics on every call * Housekeeping * Add regression algorithms * Address peer review * Support for custom summary statistics at runtime * Minor changes * Address peer review * Address peer review * Minor changes * Minor changes
119 lines
6.2 KiB
Python
119 lines
6.2 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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### Demonstration of how to access the statistics results from within an algorithm through the `Statistics` property.
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### </summary>
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class StatisticsResultsAlgorithm(QCAlgorithm):
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MostTradedSecurityStatistic = "Most Traded Security"
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MostTradedSecurityTradeCountStatistic = "Most Traded Security Trade Count"
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def Initialize(self):
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self.SetStartDate(2013, 10, 7)
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self.SetEndDate(2013, 10, 11)
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self.SetCash(100000)
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self.spy = self.AddEquity("SPY", Resolution.Minute).Symbol
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self.ibm = self.AddEquity("IBM", Resolution.Minute).Symbol
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self.fastSpyEma = self.EMA(self.spy, 30, Resolution.Minute)
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self.slowSpyEma = self.EMA(self.spy, 60, Resolution.Minute)
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self.fastIbmEma = self.EMA(self.spy, 10, Resolution.Minute)
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self.slowIbmEma = self.EMA(self.spy, 30, Resolution.Minute)
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self.trade_counts = {self.spy: 0, self.ibm: 0}
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def OnData(self, data: Slice):
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if not self.slowSpyEma.IsReady: return
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if self.fastSpyEma > self.slowSpyEma:
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self.SetHoldings(self.spy, 0.5)
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elif self.Securities[self.spy].Invested:
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self.Liquidate(self.spy)
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if self.fastIbmEma > self.slowIbmEma:
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self.SetHoldings(self.ibm, 0.2)
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elif self.Securities[self.ibm].Invested:
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self.Liquidate(self.ibm)
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def OnOrderEvent(self, orderEvent):
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if orderEvent.Status == OrderStatus.Filled:
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# We can access the statistics summary at runtime
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statistics = self.Statistics.Summary
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statisticsStr = "\n\t".join([f"{kvp.Key}: {kvp.Value}" for kvp in statistics])
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self.Debug(f"\nStatistics after fill:\n\t{statisticsStr}")
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# Access a single statistic
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self.Log(f"Total trades so far: {statistics[PerformanceMetrics.TotalTrades]}")
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self.Log(f"Sharpe Ratio: {statistics[PerformanceMetrics.SharpeRatio]}")
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# --------
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# We can also set custom summary statistics:
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if all(count == 0 for count in self.trade_counts.values()):
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if StatisticsResultsAlgorithm.MostTradedSecurityStatistic in statistics:
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raise Exception(f"Statistic {StatisticsResultsAlgorithm.MostTradedSecurityStatistic} should not be set yet")
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if StatisticsResultsAlgorithm.MostTradedSecurityTradeCountStatistic in statistics:
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raise Exception(f"Statistic {StatisticsResultsAlgorithm.MostTradedSecurityTradeCountStatistic} should not be set yet")
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else:
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# The current most traded security should be set in the summary
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most_trade_security, most_trade_security_trade_count = self.GetMostTradeSecurity()
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self.CheckMostTradedSecurityStatistic(statistics, most_trade_security, most_trade_security_trade_count)
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# Update the trade count
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self.trade_counts[orderEvent.Symbol] += 1
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# Set the most traded security
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most_trade_security, most_trade_security_trade_count = self.GetMostTradeSecurity()
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self.SetSummaryStatistic(StatisticsResultsAlgorithm.MostTradedSecurityStatistic, most_trade_security)
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self.SetSummaryStatistic(StatisticsResultsAlgorithm.MostTradedSecurityTradeCountStatistic, most_trade_security_trade_count)
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# Re-calculate statistics:
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statistics = self.Statistics.Summary
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# Let's keep track of our custom summary statistics after the update
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self.CheckMostTradedSecurityStatistic(statistics, most_trade_security, most_trade_security_trade_count)
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def OnEndOfAlgorithm(self):
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statistics = self.Statistics.Summary
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if StatisticsResultsAlgorithm.MostTradedSecurityStatistic not in statistics:
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raise Exception(f"Statistic {StatisticsResultsAlgorithm.MostTradedSecurityStatistic} should be in the summary statistics")
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if StatisticsResultsAlgorithm.MostTradedSecurityTradeCountStatistic not in statistics:
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raise Exception(f"Statistic {StatisticsResultsAlgorithm.MostTradedSecurityTradeCountStatistic} should be in the summary statistics")
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most_trade_security, most_trade_security_trade_count = self.GetMostTradeSecurity()
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self.CheckMostTradedSecurityStatistic(statistics, most_trade_security, most_trade_security_trade_count)
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def CheckMostTradedSecurityStatistic(self, statistics: Dict[str, str], mostTradedSecurity: Symbol, tradeCount: int):
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mostTradedSecurityStatistic = statistics[StatisticsResultsAlgorithm.MostTradedSecurityStatistic]
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mostTradedSecurityTradeCountStatistic = statistics[StatisticsResultsAlgorithm.MostTradedSecurityTradeCountStatistic]
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self.Log(f"Most traded security: {mostTradedSecurityStatistic}")
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self.Log(f"Most traded security trade count: {mostTradedSecurityTradeCountStatistic}")
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if mostTradedSecurityStatistic != mostTradedSecurity:
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raise Exception(f"Most traded security should be {mostTradedSecurity} but it is {mostTradedSecurityStatistic}")
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if mostTradedSecurityTradeCountStatistic != str(tradeCount):
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raise Exception(f"Most traded security trade count should be {tradeCount} but it is {mostTradedSecurityTradeCountStatistic}")
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def GetMostTradeSecurity(self) -> Tuple[Symbol, int]:
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most_trade_security = max(self.trade_counts, key=lambda symbol: self.trade_counts[symbol])
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most_trade_security_trade_count = self.trade_counts[most_trade_security]
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return most_trade_security, most_trade_security_trade_count
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