/*
* 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
{
///
/// Regression test algorithm simply fetch history on boarder of Daylight Saving Time shift
///
public class DaylightSavingTimeHistoryRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol[] _symbols = new[]
{
QuantConnect.Symbol.Create("EURUSD", SecurityType.Forex, Market.FXCM),
QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA)
};
///
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
///
public override void Initialize()
{
SetStartDate(2011, 11, 10); //Set Start Date
SetEndDate(2011, 11, 11); //Set End Date
SetCash(100000); //Set Strategy Cash
for (int i = 0; i < _symbols.Length; i++)
{
var symbol = _symbols[i];
IEnumerable history;
if (symbol.SecurityType == SecurityType.Equity)
{
try
{
history = History(symbol, 10, Resolution.Daily).Select(bar => bar as BaseData);
throw new RegressionTestException("We were expecting an argument exception to be thrown. Equity does not have daily QuoteBars!");
}
catch (ArgumentException)
{
// expected
}
history = History(symbol, 10, Resolution.Daily).Select(bar => bar as BaseData);
}
else
{
history = History(symbol, 10, Resolution.Daily)
.Select(bar => bar as BaseData);
}
var duplications = history
.GroupBy(k => k.Time)
.Where(g => g.Count() > 1);
if (duplications.Any())
{
var time = duplications.First().Key;
throw new RegressionTestException($"Duplicated bars were issued for time {time}");
}
}
}
///
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
///
public bool CanRunLocally { get; } = true;
///
/// This is used by the regression test system to indicate which languages this algorithm is written in.
///
public List Languages { get; } = new() { Language.CSharp };
///
/// Data Points count of all timeslices of algorithm
///
public long DataPoints => 21;
///
/// Data Points count of the algorithm history
///
public int AlgorithmHistoryDataPoints => 20;
///
/// Final status of the algorithm
///
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
///
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
///
public Dictionary ExpectedStatistics => new Dictionary
{
{"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"}
};
}
}