113 lines
4.3 KiB
C#
113 lines
4.3 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.Market;
|
|
using QuantConnect.Interfaces;
|
|
|
|
namespace QuantConnect.Algorithm.CSharp
|
|
{
|
|
public class MissingTickDataAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
|
{
|
|
public override void Initialize()
|
|
{
|
|
SetStartDate(2013, 10, 08);
|
|
SetEndDate(2013, 10, 08);
|
|
|
|
var spy = AddEquity("SPY", Resolution.Tick).Symbol;
|
|
|
|
var start = new DateTime(2013, 10, 07, 15, 0, 0);
|
|
|
|
var slices = History(new[] { spy }, start, start.AddSeconds(10), Resolution.Tick).ToList();
|
|
var tickCountInSliceHistoryCall = slices.Sum(x => x.Ticks[spy].Count);
|
|
|
|
var ticks = History<Tick>(spy, start, start.AddSeconds(10), Resolution.Tick).ToList();
|
|
var tickCountInTickHistoryCall = ticks.Count;
|
|
|
|
if (tickCountInSliceHistoryCall != tickCountInTickHistoryCall)
|
|
{
|
|
throw new RegressionTestException($@"Tick count mismatch in Slice and Tick history calls: History() returned {
|
|
tickCountInSliceHistoryCall} ticks, while History<Tick>() returned {tickCountInTickHistoryCall} ticks");
|
|
}
|
|
|
|
// Early quit, we already tested what we wanted
|
|
Quit();
|
|
}
|
|
|
|
/// <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 };
|
|
|
|
/// <summary>
|
|
/// Data Points count of all timeslices of algorithm
|
|
/// </summary>
|
|
public long DataPoints => 0;
|
|
|
|
/// <summary>
|
|
/// Data Points count of the algorithm history
|
|
/// </summary>
|
|
public int AlgorithmHistoryDataPoints => 1610;
|
|
|
|
/// <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", "0"},
|
|
{"Average Win", "0%"},
|
|
{"Average Loss", "0%"},
|
|
{"Compounding Annual Return", "0%"},
|
|
{"Drawdown", "0%"},
|
|
{"Expectancy", "0"},
|
|
{"Start Equity", "100000"},
|
|
{"End Equity", "100000"},
|
|
{"Net Profit", "0%"},
|
|
{"Sharpe Ratio", "0"},
|
|
{"Sortino Ratio", "0"},
|
|
{"Probabilistic Sharpe Ratio", "0%"},
|
|
{"Loss Rate", "0%"},
|
|
{"Win Rate", "0%"},
|
|
{"Profit-Loss Ratio", "0"},
|
|
{"Alpha", "0"},
|
|
{"Beta", "0"},
|
|
{"Annual Standard Deviation", "0"},
|
|
{"Annual Variance", "0"},
|
|
{"Information Ratio", "0"},
|
|
{"Tracking Error", "0"},
|
|
{"Treynor Ratio", "0"},
|
|
{"Total Fees", "$0.00"},
|
|
{"Estimated Strategy Capacity", "$0"},
|
|
{"Lowest Capacity Asset", ""},
|
|
{"Portfolio Turnover", "0%"},
|
|
{"Drawdown Recovery", "0"},
|
|
{"OrderListHash", "d41d8cd98f00b204e9800998ecf8427e"}
|
|
};
|
|
}
|
|
}
|