Martin Molinero
124 lines
4.9 KiB
C#
124 lines
4.9 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 System.Linq;
|
|
using QuantConnect.Data;
|
|
using QuantConnect.Data.UniverseSelection;
|
|
using QuantConnect.Interfaces;
|
|
|
|
namespace QuantConnect.Algorithm.CSharp
|
|
{
|
|
/// <summary>
|
|
/// Test algorithm that reproduces GH issues 3410 and 3409.
|
|
/// Coarse universe selection should start from the algorithm start date.
|
|
/// Data returned by history requests performed from the selection method should be up to date.
|
|
/// </summary>
|
|
public class CoarseSelectionTimeRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
|
{
|
|
private Symbol _spy;
|
|
private decimal _historyCoarseSpyPrice;
|
|
|
|
/// <summary>
|
|
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
|
/// </summary>
|
|
public override void Initialize()
|
|
{
|
|
SetStartDate(2014, 03, 25);
|
|
SetEndDate(2014, 04, 01);
|
|
|
|
_spy = AddEquity("SPY", Resolution.Daily).Symbol;
|
|
|
|
UniverseSettings.Resolution = Resolution.Daily;
|
|
AddUniverse(CoarseSelectionFunction);
|
|
}
|
|
|
|
public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
|
|
{
|
|
var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume);
|
|
var top = sortedByDollarVolume
|
|
.Where(fundamental => fundamental.Symbol != _spy) // ignore spy
|
|
.Take(1);
|
|
|
|
_historyCoarseSpyPrice = History(_spy, 1).First().Close;
|
|
|
|
return top.Select(x => x.Symbol);
|
|
}
|
|
|
|
/// <summary>
|
|
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
|
|
/// </summary>
|
|
/// <param name="data">Slice object keyed by symbol containing the stock data</param>
|
|
public override void OnData(Slice data)
|
|
{
|
|
if (data.Count != 2)
|
|
{
|
|
throw new Exception($"Unexpected data count: {data.Count}");
|
|
}
|
|
if (ActiveSecurities.Count != 2)
|
|
{
|
|
throw new Exception($"Unexpected ActiveSecurities count: {ActiveSecurities.Count}");
|
|
}
|
|
// the price obtained by the previous coarse selection should be the same as the current price
|
|
if (_historyCoarseSpyPrice != 0 && _historyCoarseSpyPrice != Securities[_spy].Price)
|
|
{
|
|
throw new Exception($"Unexpected SPY price: {_historyCoarseSpyPrice}");
|
|
}
|
|
_historyCoarseSpyPrice = 0;
|
|
if (!Portfolio.Invested)
|
|
{
|
|
SetHoldings(_spy, 1);
|
|
Debug("Purchased Stock");
|
|
}
|
|
}
|
|
|
|
/// <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 };
|
|
|
|
/// <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", "1"},
|
|
{"Average Win", "0%"},
|
|
{"Average Loss", "0%"},
|
|
{"Compounding Annual Return", "57.953%"},
|
|
{"Drawdown", "0.900%"},
|
|
{"Expectancy", "0"},
|
|
{"Net Profit", "1.007%"},
|
|
{"Sharpe Ratio", "4.212"},
|
|
{"Loss Rate", "0%"},
|
|
{"Win Rate", "0%"},
|
|
{"Profit-Loss Ratio", "0"},
|
|
{"Alpha", "-0.143"},
|
|
{"Beta", "0.921"},
|
|
{"Annual Standard Deviation", "0.086"},
|
|
{"Annual Variance", "0.007"},
|
|
{"Information Ratio", "-5.985"},
|
|
{"Tracking Error", "0.031"},
|
|
{"Treynor Ratio", "0.395"},
|
|
{"Total Fees", "$2.91"}
|
|
};
|
|
}
|
|
} |