wehub-resource-sync
121 lines
6.4 KiB
Python
121 lines
6.4 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 System.Collections.Generic import Dictionary
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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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most_traded_security_statistic = "Most Traded Security"
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most_traded_security_trade_count_statistic = "Most Traded Security Trade Count"
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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, 11)
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self.set_cash(100000)
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self.spy = self.add_equity("SPY", Resolution.MINUTE).symbol
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self.ibm = self.add_equity("IBM", Resolution.MINUTE).symbol
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self.fast_spy_ema = self.ema(self.spy, 30, Resolution.MINUTE)
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self.slow_spy_ema = self.ema(self.spy, 60, Resolution.MINUTE)
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self.fast_ibm_ema = self.ema(self.spy, 10, Resolution.MINUTE)
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self.slow_ibm_ema = 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 on_data(self, data: Slice):
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if not self.slow_spy_ema.is_ready: return
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if self.fast_spy_ema > self.slow_spy_ema:
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self.set_holdings(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.fast_ibm_ema > self.slow_ibm_ema:
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self.set_holdings(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 on_order_event(self, order_event):
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if order_event.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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statistics_str = "".join([f"{kvp.key}: {kvp.value}" for kvp in statistics])
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self.debug(f"Statistics after fill:{statistics_str}")
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# Access a single statistic
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self.log(f"Total trades so far: {statistics[PerformanceMetrics.TOTAL_ORDERS]}")
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self.log(f"Sharpe Ratio: {statistics[PerformanceMetrics.SHARPE_RATIO]}")
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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.most_traded_security_statistic in statistics:
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raise AssertionError(f"Statistic {StatisticsResultsAlgorithm.most_traded_security_statistic} should not be set yet")
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if StatisticsResultsAlgorithm.most_traded_security_trade_count_statistic in statistics:
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raise AssertionError(f"Statistic {StatisticsResultsAlgorithm.most_traded_security_trade_count_statistic} 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.get_most_trade_security()
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self.check_most_traded_security_statistic(statistics, most_trade_security, most_trade_security_trade_count)
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# Update the trade count
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self.trade_counts[order_event.symbol] += 1
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# Set the most traded security
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most_trade_security, most_trade_security_trade_count = self.get_most_trade_security()
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self.set_summary_statistic(StatisticsResultsAlgorithm.most_traded_security_statistic, str(most_trade_security))
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self.set_summary_statistic(StatisticsResultsAlgorithm.most_traded_security_trade_count_statistic, 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.check_most_traded_security_statistic(statistics, most_trade_security, most_trade_security_trade_count)
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def on_end_of_algorithm(self):
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statistics = self.statistics.summary
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if StatisticsResultsAlgorithm.most_traded_security_statistic not in statistics:
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raise AssertionError(f"Statistic {StatisticsResultsAlgorithm.most_traded_security_statistic} should be in the summary statistics")
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if StatisticsResultsAlgorithm.most_traded_security_trade_count_statistic not in statistics:
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raise AssertionError(f"Statistic {StatisticsResultsAlgorithm.most_traded_security_trade_count_statistic} should be in the summary statistics")
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most_trade_security, most_trade_security_trade_count = self.get_most_trade_security()
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self.check_most_traded_security_statistic(statistics, most_trade_security, most_trade_security_trade_count)
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def check_most_traded_security_statistic(self, statistics: Dictionary[str, str], most_traded_security: Symbol, trade_count: int):
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most_traded_security_statistic = statistics[StatisticsResultsAlgorithm.most_traded_security_statistic]
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most_traded_security_trade_count_statistic = statistics[StatisticsResultsAlgorithm.most_traded_security_trade_count_statistic]
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self.log(f"Most traded security: {most_traded_security_statistic}")
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self.log(f"Most traded security trade count: {most_traded_security_trade_count_statistic}")
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if most_traded_security_statistic != most_traded_security:
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raise AssertionError(f"Most traded security should be {most_traded_security} but it is {most_traded_security_statistic}")
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if most_traded_security_trade_count_statistic != str(trade_count):
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raise AssertionError(f"Most traded security trade count should be {trade_count} but it is {most_traded_security_trade_count_statistic}")
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def get_most_trade_security(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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