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chore: import upstream snapshot with attribution

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2026-07-13 13:02:50 +08:00
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Algorithm.CSharp/StatisticsResultsAlgorithm.cs
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/*
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
using System;
using System.Collections.Generic;
using System.Linq;
using QuantConnect.Data;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
using QuantConnect.Orders;
using QuantConnect.Statistics;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Demonstration of how to access the statistics results from within an algorithm through the <see cref="Statistics"/> property.
/// </summary>
public class StatisticsResultsAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private const string MostTradedSecurityStatistic = "Most Traded Security";
private const string MostTradedSecurityTradeCountStatistic = "Most Traded Security Trade Count";
private Symbol _spy;
private Symbol _ibm;
private ExponentialMovingAverage _fastSpyEma;
private ExponentialMovingAverage _slowSpyEma;
private ExponentialMovingAverage _fastIbmEma;
private ExponentialMovingAverage _slowIbmEma;
private Dictionary<Symbol, int> _tradeCounts = new();
public override void Initialize()
{
SetStartDate(2013, 10, 07);
SetEndDate(2013, 10, 11);
SetCash(100000);
_spy = AddEquity("SPY", Resolution.Minute).Symbol;
_ibm = AddEquity("IBM", Resolution.Minute).Symbol;
_fastSpyEma = EMA(_spy, 30, Resolution.Minute);
_slowSpyEma = EMA(_spy, 60, Resolution.Minute);
_fastIbmEma = EMA(_spy, 10, Resolution.Minute);
_slowIbmEma = EMA(_spy, 30, Resolution.Minute);
}
public override void OnData(Slice slice)
{
if (!_slowSpyEma.IsReady) return;
if (_fastSpyEma > _slowSpyEma)
{
SetHoldings(_spy, 0.5);
}
else if (Securities[_spy].Invested)
{
Liquidate(_spy);
}
if (_fastIbmEma > _slowIbmEma)
{
SetHoldings(_ibm, 0.2);
}
else if (Securities[_ibm].Invested)
{
Liquidate(_ibm);
}
}
public override void OnOrderEvent(OrderEvent orderEvent)
{
if (orderEvent.Status == OrderStatus.Filled)
{
// We can access the statistics summary at runtime
var statistics = Statistics.Summary;
var statisticsStr = string.Join("\n\t", statistics.Select(kvp => $"{kvp.Key}: {kvp.Value}"));
Debug($"\nStatistics after fill:\n\t{statisticsStr}");
// Access a single statistic
Log($"Total trades so far: {statistics[PerformanceMetrics.TotalOrders]}");
Log($"Sharpe Ratio: {statistics[PerformanceMetrics.SharpeRatio]}");
// --------
// We can also set custom summary statistics:
KeyValuePair<Symbol, int> mostTradeSecurityKvp;
// Before the first fill event, our custom statistics should not be set in the summary
if (_tradeCounts.All(kvp => kvp.Value == 0))
{
if (statistics.ContainsKey(MostTradedSecurityStatistic))
{
throw new RegressionTestException($"Statistic {MostTradedSecurityStatistic} should not be set yet");
}
if (statistics.ContainsKey(MostTradedSecurityTradeCountStatistic))
{
throw new RegressionTestException($"Statistic {MostTradedSecurityTradeCountStatistic} should not be set yet");
}
}
else
{
// The current most traded security should be set in the summary
mostTradeSecurityKvp = _tradeCounts.MaxBy(kvp => kvp.Value);
CheckMostTradedSecurityStatistic(statistics, mostTradeSecurityKvp.Key, mostTradeSecurityKvp.Value);
}
// Update the trade count
var tradeCount = _tradeCounts.GetValueOrDefault(orderEvent.Symbol);
_tradeCounts[orderEvent.Symbol] = tradeCount + 1;
// Set the most traded security
mostTradeSecurityKvp = _tradeCounts.MaxBy(kvp => kvp.Value);
SetSummaryStatistic(MostTradedSecurityStatistic, mostTradeSecurityKvp.Key);
SetSummaryStatistic(MostTradedSecurityTradeCountStatistic, mostTradeSecurityKvp.Value);
// Re-calculate statistics:
statistics = Statistics.Summary;
// Let's keep track of our custom summary statistics after the update
CheckMostTradedSecurityStatistic(statistics, mostTradeSecurityKvp.Key, mostTradeSecurityKvp.Value);
}
}
public override void OnEndOfAlgorithm()
{
var statistics = Statistics.Summary;
if (!statistics.ContainsKey(MostTradedSecurityStatistic))
{
throw new RegressionTestException($"Statistic {MostTradedSecurityStatistic} should be in the summary statistics");
}
if (!statistics.ContainsKey(MostTradedSecurityTradeCountStatistic))
{
throw new RegressionTestException($"Statistic {MostTradedSecurityTradeCountStatistic} should be in the summary statistics");
}
var mostTradeSecurityKvp = _tradeCounts.MaxBy(kvp => kvp.Value);
CheckMostTradedSecurityStatistic(statistics, mostTradeSecurityKvp.Key, mostTradeSecurityKvp.Value);
}
private void CheckMostTradedSecurityStatistic(Dictionary<string, string> statistics, Symbol mostTradedSecurity, int tradeCount)
{
var mostTradedSecurityStatistic = statistics[MostTradedSecurityStatistic];
var mostTradedSecurityTradeCountStatistic = statistics[MostTradedSecurityTradeCountStatistic];
Log($"Most traded security: {mostTradedSecurityStatistic}");
Log($"Most traded security trade count: {mostTradedSecurityTradeCountStatistic}");
if (mostTradedSecurityStatistic != mostTradedSecurity)
{
throw new RegressionTestException($"Most traded security should be {mostTradedSecurity} but it is {mostTradedSecurityStatistic}");
}
if (mostTradedSecurityTradeCountStatistic != tradeCount.ToStringInvariant())
{
throw new RegressionTestException($"Most traded security trade count should be {tradeCount} but it is {mostTradedSecurityTradeCountStatistic}");
}
}
/// <summary>
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
/// </summary>
public bool CanRunLocally { get; } = true;
/// <summary>
/// This is used by the regression test system to indicate which languages this algorithm is written in.
/// </summary>
public List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 7843;
/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public int AlgorithmHistoryDataPoints => 0;
/// <summary>
/// Final status of the algorithm
/// </summary>
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
/// <summary>
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
/// </summary>
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
{
{"Total Orders", "94"},
{"Average Win", "0.09%"},
{"Average Loss", "-0.03%"},
{"Compounding Annual Return", "18.903%"},
{"Drawdown", "0.800%"},
{"Expectancy", "0.135"},
{"Start Equity", "100000"},
{"End Equity", "100221.61"},
{"Net Profit", "0.222%"},
{"Sharpe Ratio", "6.406"},
{"Sortino Ratio", "0"},
{"Probabilistic Sharpe Ratio", "68.575%"},
{"Loss Rate", "70%"},
{"Win Rate", "30%"},
{"Profit-Loss Ratio", "2.73"},
{"Alpha", "-0.144"},
{"Beta", "0.264"},
{"Annual Standard Deviation", "0.059"},
{"Annual Variance", "0.003"},
{"Information Ratio", "-9.751"},
{"Tracking Error", "0.164"},
{"Treynor Ratio", "1.43"},
{"Total Fees", "$114.39"},
{"Estimated Strategy Capacity", "$1100000.00"},
{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
{"Portfolio Turnover", "549.26%"},
{"Drawdown Recovery", "3"},
{"Most Traded Security", "IBM"},
{"Most Traded Security Trade Count", "63"},
{"OrderListHash", "8dd77e35338a81410a5b68dc8345f402"}
};
}
}