AlexCatarino
111 lines
4.7 KiB
C#
111 lines
4.7 KiB
C#
/*
|
|
* 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.Collections.Generic;
|
|
using QuantConnect.Algorithm.Framework;
|
|
using QuantConnect.Algorithm.Framework.Alphas;
|
|
using QuantConnect.Algorithm.Framework.Execution;
|
|
using QuantConnect.Algorithm.Framework.Portfolio;
|
|
using QuantConnect.Algorithm.Framework.Selection;
|
|
using QuantConnect.Orders;
|
|
using QuantConnect.Interfaces;
|
|
|
|
namespace QuantConnect.Algorithm.CSharp
|
|
{
|
|
/// <summary>
|
|
/// Regression algorithm for the StandardDeviationExecutionModel.
|
|
/// This algorithm shows how the execution model works to split up orders and submit them only when
|
|
/// the price is 2 standard deviations from the 60min mean (default model settings).
|
|
/// </summary>
|
|
public class StandardDeviationExecutionModelRegressionAlgorithm : QCAlgorithmFramework, IRegressionAlgorithmDefinition
|
|
{
|
|
public override void Initialize()
|
|
{
|
|
UniverseSettings.Resolution = Resolution.Minute;
|
|
|
|
SetStartDate(2013, 10, 07);
|
|
SetEndDate(2013, 10, 11);
|
|
SetCash(1000000);
|
|
|
|
SetUniverseSelection(new ManualUniverseSelectionModel(
|
|
QuantConnect.Symbol.Create("AIG", SecurityType.Equity, Market.USA),
|
|
QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA),
|
|
QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA),
|
|
QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)
|
|
));
|
|
|
|
// using hourly rsi to generate more insights
|
|
SetAlpha(new RsiAlphaModel(14, Resolution.Hour));
|
|
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
|
|
SetExecution(new StandardDeviationExecutionModel());
|
|
}
|
|
|
|
public override void OnOrderEvent(OrderEvent orderEvent)
|
|
{
|
|
Log($"{Time}: {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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
|
|
|
/// <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 Trades", "136"},
|
|
{"Average Win", "0.05%"},
|
|
{"Average Loss", "0%"},
|
|
{"Compounding Annual Return", "1982.023%"},
|
|
{"Drawdown", "0.500%"},
|
|
{"Expectancy", "0"},
|
|
{"Net Profit", "3.958%"},
|
|
{"Sharpe Ratio", "11.298"},
|
|
{"Loss Rate", "0%"},
|
|
{"Win Rate", "100%"},
|
|
{"Profit-Loss Ratio", "0"},
|
|
{"Alpha", "0"},
|
|
{"Beta", "178.994"},
|
|
{"Annual Standard Deviation", "0.175"},
|
|
{"Annual Variance", "0.031"},
|
|
{"Information Ratio", "11.244"},
|
|
{"Tracking Error", "0.175"},
|
|
{"Treynor Ratio", "0.011"},
|
|
{"Total Fees", "$176.22"},
|
|
{"Total Insights Generated", "5"},
|
|
{"Total Insights Closed", "3"},
|
|
{"Total Insights Analysis Completed", "0"},
|
|
{"Long Insight Count", "3"},
|
|
{"Short Insight Count", "2"},
|
|
{"Long/Short Ratio", "150.0%"},
|
|
{"Estimated Monthly Alpha Value", "$799818.3566"},
|
|
{"Total Accumulated Estimated Alpha Value", "$128859.6241"},
|
|
{"Mean Population Estimated Insight Value", "$42953.2080"},
|
|
{"Mean Population Direction", "0%"},
|
|
{"Mean Population Magnitude", "0%"},
|
|
{"Rolling Averaged Population Direction", "0%"},
|
|
{"Rolling Averaged Population Magnitude", "0%"}
|
|
};
|
|
}
|
|
}
|