chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,198 @@
|
||||
/*
|
||||
* 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 QuantConnect.Algorithm.Framework.Alphas;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
using QuantConnect.Algorithm.Framework.Risk;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm demonstrating how to get order events in custom execution models
|
||||
/// and asserting that they match the algorithm's order events.
|
||||
/// </summary>
|
||||
public class ExecutionModelOrderEventsRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private readonly List<OrderEvent> _orderEvents = new();
|
||||
private CustomImmediateExecutionModel _executionModel;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
UniverseSettings.Resolution = Resolution.Minute;
|
||||
|
||||
SetStartDate(2013, 10, 07);
|
||||
SetEndDate(2013, 10, 11);
|
||||
SetCash(100000);
|
||||
|
||||
SetUniverseSelection(new ManualUniverseSelectionModel(QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)));
|
||||
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromMinutes(20), 0.025, null));
|
||||
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel(Resolution.Daily));
|
||||
|
||||
_executionModel = new CustomImmediateExecutionModel();
|
||||
SetExecution(_executionModel);
|
||||
SetRiskManagement(new MaximumDrawdownPercentPerSecurity(0.01m));
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
_orderEvents.Add(orderEvent);
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (_executionModel.OrderEvents.Count != _orderEvents.Count)
|
||||
{
|
||||
throw new RegressionTestException($"Order events count mismatch. Execution model: {_executionModel.OrderEvents.Count}, Algorithm: {_orderEvents.Count}");
|
||||
}
|
||||
|
||||
for (int i = 0; i < _orderEvents.Count; i++)
|
||||
{
|
||||
var modelEvent = _executionModel.OrderEvents[i];
|
||||
var algoEvent = _orderEvents[i];
|
||||
|
||||
if (modelEvent.Id != algoEvent.Id ||
|
||||
modelEvent.OrderId != algoEvent.OrderId ||
|
||||
modelEvent.Status != algoEvent.Status)
|
||||
{
|
||||
throw new RegressionTestException($"Order event mismatch at index {i}. Execution model: {_executionModel.OrderEvents[i]}, Algorithm: {_orderEvents[i]}");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private class CustomImmediateExecutionModel : ExecutionModel
|
||||
{
|
||||
private readonly PortfolioTargetCollection _targetsCollection = new PortfolioTargetCollection();
|
||||
|
||||
private readonly Dictionary<int, OrderTicket> _orderTickets = new();
|
||||
|
||||
public List<OrderEvent> OrderEvents { get; } = new();
|
||||
|
||||
public override void Execute(QCAlgorithm algorithm, IPortfolioTarget[] targets)
|
||||
{
|
||||
_targetsCollection.AddRange(targets);
|
||||
if (!_targetsCollection.IsEmpty)
|
||||
{
|
||||
foreach (var target in _targetsCollection.OrderByMarginImpact(algorithm))
|
||||
{
|
||||
var security = algorithm.Securities[target.Symbol];
|
||||
|
||||
// calculate remaining quantity to be ordered
|
||||
var quantity = OrderSizing.GetUnorderedQuantity(algorithm, target, security, true);
|
||||
|
||||
if (quantity != 0 &&
|
||||
security.BuyingPowerModel.AboveMinimumOrderMarginPortfolioPercentage(security, quantity,
|
||||
algorithm.Portfolio, algorithm.Settings.MinimumOrderMarginPortfolioPercentage))
|
||||
{
|
||||
var ticket = algorithm.MarketOrder(security, quantity, asynchronous: true, tag: target.Tag);
|
||||
_orderTickets[ticket.OrderId] = ticket;
|
||||
}
|
||||
}
|
||||
|
||||
_targetsCollection.ClearFulfilled(algorithm);
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(QCAlgorithm algorithm, OrderEvent orderEvent)
|
||||
{
|
||||
algorithm.Log($"{algorithm.Time} - Order event received: {orderEvent}");
|
||||
|
||||
// This method will get events for all orders, but if we save the tickets in Execute we can filter
|
||||
// to process events for orders placed by this model
|
||||
if (_orderTickets.TryGetValue(orderEvent.OrderId, out var ticket))
|
||||
{
|
||||
if (orderEvent.Status.IsFill())
|
||||
{
|
||||
algorithm.Debug($"Purchased Stock: {orderEvent.Symbol}");
|
||||
}
|
||||
|
||||
if (orderEvent.Status.IsClosed())
|
||||
{
|
||||
// Once the order is closed we can remove it from our tracking dictionary
|
||||
_orderTickets.Remove(orderEvent.OrderId);
|
||||
}
|
||||
}
|
||||
|
||||
OrderEvents.Add(orderEvent);
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 virtual List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// Data Points count of all timeslices of algorithm
|
||||
/// </summary>
|
||||
public long DataPoints => 3943;
|
||||
|
||||
/// <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", "3"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-1.01%"},
|
||||
{"Compounding Annual Return", "261.134%"},
|
||||
{"Drawdown", "2.200%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Start Equity", "100000"},
|
||||
{"End Equity", "101655.30"},
|
||||
{"Net Profit", "1.655%"},
|
||||
{"Sharpe Ratio", "8.472"},
|
||||
{"Sortino Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "66.693%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.091"},
|
||||
{"Beta", "1.006"},
|
||||
{"Annual Standard Deviation", "0.224"},
|
||||
{"Annual Variance", "0.05"},
|
||||
{"Information Ratio", "-33.445"},
|
||||
{"Tracking Error", "0.002"},
|
||||
{"Treynor Ratio", "1.885"},
|
||||
{"Total Fees", "$10.32"},
|
||||
{"Estimated Strategy Capacity", "$27000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
{"Portfolio Turnover", "59.86%"},
|
||||
{"Drawdown Recovery", "3"},
|
||||
{"OrderListHash", "f209ed42701b0419858e0100595b40c0"}
|
||||
};
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user