Files
quantconnect--lean/Algorithm.CSharp/RegressionAlgorithm.cs
T
Juan José D'Ambrosio 951e89314a Update Regression tests
2020-03-11 14:34:16 -03:00

132 lines
5.1 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;
using System.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Algorithm used for regression tests purposes
/// </summary>
/// <meta name="tag" content="regression test" />
public class RegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
public override void Initialize()
{
SetStartDate(2013, 10, 07);
SetEndDate(2013, 10, 11);
SetCash(10000000);
// Find more symbols here: http://quantconnect.com/data
AddSecurity(SecurityType.Equity, "SPY", Resolution.Tick);
AddSecurity(SecurityType.Equity, "BAC", Resolution.Minute);
AddSecurity(SecurityType.Equity, "AIG", Resolution.Hour);
AddSecurity(SecurityType.Equity, "IBM", Resolution.Daily);
}
private DateTime lastTradeTradeBars;
private DateTime lastTradeTicks;
private TimeSpan tradeEvery = TimeSpan.FromMinutes(1);
public void OnData(Slice data)
{
if (Time - lastTradeTradeBars < tradeEvery) return;
lastTradeTradeBars = Time;
foreach (var kvp in data.Bars)
{
var symbol = kvp.Key;
var bar = kvp.Value;
if (bar.Time.RoundDown(bar.Period) != bar.Time)
{
// only trade on new data
continue;
}
var holdings = Portfolio[symbol];
if (!holdings.Invested)
{
MarketOrder(symbol, 10);
}
else
{
MarketOrder(symbol, -holdings.Quantity);
}
}
}
/// <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", "1638"},
{"Average Win", "0.00%"},
{"Average Loss", "0.00%"},
{"Compounding Annual Return", "-1.204%"},
{"Drawdown", "0.000%"},
{"Expectancy", "-0.972"},
{"Net Profit", "-0.017%"},
{"Sharpe Ratio", "-8.771"},
{"Probabilistic Sharpe Ratio", "0.002%"},
{"Loss Rate", "99%"},
{"Win Rate", "1%"},
{"Profit-Loss Ratio", "3.51"},
{"Alpha", "-0.006"},
{"Beta", "-0.001"},
{"Annual Standard Deviation", "0.001"},
{"Annual Variance", "0"},
{"Information Ratio", "-4.5"},
{"Tracking Error", "0.193"},
{"Treynor Ratio", "7.606"},
{"Total Fees", "$1638.00"},
{"Fitness Score", "0"},
{"Kelly Criterion Estimate", "60.719"},
{"Kelly Criterion Probability Value", "0.026"},
{"Sortino Ratio", "-15.512"},
{"Return Over Maximum Drawdown", "-72.565"},
{"Portfolio Turnover", "0.021"},
{"Total Insights Generated", "1638"},
{"Total Insights Closed", "1635"},
{"Total Insights Analysis Completed", "1635"},
{"Long Insight Count", "818"},
{"Short Insight Count", "3"},
{"Long/Short Ratio", "27266.67%"},
{"Estimated Monthly Alpha Value", "$1435679.3912"},
{"Total Accumulated Estimated Alpha Value", "$247255.8952"},
{"Mean Population Estimated Insight Value", "$151.2268"},
{"Mean Population Direction", "7.0894%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "2.5872%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "-2129865458"}
};
}
}