/*
* 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 QuantConnect.Interfaces;
using QuantConnect.Data.Market;
using System.Collections.Generic;
namespace QuantConnect.Algorithm.CSharp
{
///
/// Regression algorithm asserting consolidation happing flushed due to scan calls
///
public class ConsolidateScanRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private readonly Queue _consolidationDaily = new();
private readonly Queue _consolidationHourly = new();
private readonly Queue _consolidation2Days = new();
///
/// 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(2013, 10, 07);
SetEndDate(2013, 10, 10);
AddEquity("SPY", Resolution.Hour);
Consolidate("SPY", Resolution.Daily, (TradeBar bar) =>
{
Debug($"Consolidated.Daily: {Time} {bar}");
var expectedTime = _consolidationDaily.Dequeue();
if (expectedTime != Time)
{
throw new RegressionTestException($"Unexpected consolidation time {expectedTime} != {Time}");
}
if (!Portfolio.Invested)
{
SetHoldings("SPY", 1);
}
});
_consolidationDaily.Enqueue(new DateTime(2013, 10, 7, 16, 0, 0));
_consolidationDaily.Enqueue(new DateTime(2013, 10, 8, 16, 0, 0));
_consolidationDaily.Enqueue(new DateTime(2013, 10, 9, 16, 0, 0));
_consolidationDaily.Enqueue(new DateTime(2013, 10, 10, 16, 0, 0));
Consolidate("SPY", TimeSpan.FromHours(3), (TradeBar bar) =>
{
Debug($"Consolidated.FromHours(3): {Time} {bar}");
var expectedTime = _consolidationHourly.Dequeue();
if (expectedTime != Time)
{
throw new RegressionTestException($"Unexpected consolidation time {expectedTime} != {Time} 3 hours");
}
});
_consolidationHourly.Enqueue(new DateTime(2013, 10, 7, 12, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 7, 15, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 7, 18, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 8, 12, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 8, 15, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 8, 18, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 9, 12, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 9, 15, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 9, 18, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 10, 12, 0, 0));
_consolidationHourly.Enqueue(new DateTime(2013, 10, 10, 15, 0, 0));
Consolidate("SPY", TimeSpan.FromDays(2), (TradeBar bar) =>
{
Debug($"Consolidated.2Days: {Time} {bar}");
var expectedTime = _consolidation2Days.Dequeue();
if (expectedTime != Time)
{
throw new RegressionTestException($"Unexpected consolidation time {expectedTime} != {Time} 2 days");
}
});
_consolidation2Days.Enqueue(new DateTime(2013, 10, 9, 9, 0, 0));
}
public override void OnEndOfAlgorithm()
{
if (_consolidationDaily.Count != 0 || _consolidationHourly.Count != 0 || _consolidation2Days.Count != 0)
{
throw new RegressionTestException($"Unexpected consolidation count");
}
}
///
/// 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 => 64;
///
/// Data Points count of the algorithm history
///
public int AlgorithmHistoryDataPoints => 0;
///
/// 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", "1"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "186.478%"},
{"Drawdown", "1.500%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "101062.91"},
{"Net Profit", "1.063%"},
{"Sharpe Ratio", "5.448"},
{"Sortino Ratio", "0"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.055"},
{"Beta", "1.003"},
{"Annual Standard Deviation", "0.272"},
{"Annual Variance", "0.074"},
{"Information Ratio", "-33.89"},
{"Tracking Error", "0.001"},
{"Treynor Ratio", "1.479"},
{"Total Fees", "$3.45"},
{"Estimated Strategy Capacity", "$130000000.00"},
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
{"Portfolio Turnover", "25.24%"},
{"Drawdown Recovery", "2"},
{"OrderListHash", "bbda6d0a04ae0b87b2fa10e036296cbb"}
};
}
}