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chore: import upstream snapshot with attribution

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2026-07-13 13:02:50 +08:00
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Algorithm.CSharp/CapacityTests/BeastVsPenny.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Tests capacity by trading SPY (beast) alongside a small cap stock ABUS (penny)
/// </summary>
public class BeastVsPenny : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _spy;
public override void Initialize()
{
SetStartDate(2020, 1, 1);
SetEndDate(2020, 3, 31);
SetCash(10000);
_spy = AddEquity("SPY", Resolution.Hour).Symbol;
var penny = AddEquity("ABUS", Resolution.Hour).Symbol;
Schedule.On(DateRules.EveryDay(_spy), TimeRules.AfterMarketOpen(_spy, 1, false), () =>
{
SetHoldings(_spy, 0.5m);
SetHoldings(penny, 0.5m);
});
}
/// <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; } = false;
/// <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 => 0;
/// <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", "70"},
{"Average Win", "0.07%"},
{"Average Loss", "-0.51%"},
{"Compounding Annual Return", "-89.548%"},
{"Drawdown", "49.900%"},
{"Expectancy", "-0.514"},
{"Net Profit", "-42.920%"},
{"Sharpe Ratio", "-0.797"},
{"Probabilistic Sharpe Ratio", "9.019%"},
{"Loss Rate", "57%"},
{"Win Rate", "43%"},
{"Profit-Loss Ratio", "0.13"},
{"Alpha", "-0.24"},
{"Beta", "1.101"},
{"Annual Standard Deviation", "1.031"},
{"Annual Variance", "1.063"},
{"Information Ratio", "-0.351"},
{"Tracking Error", "0.836"},
{"Treynor Ratio", "-0.747"},
{"Total Fees", "$81.45"},
{"Estimated Strategy Capacity", "$21000.00"},
{"Fitness Score", "0.01"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-1.284"},
{"Return Over Maximum Drawdown", "-1.789"},
{"Portfolio Turnover", "0.038"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "67c9083f604ed16fb68481e7c26878dc"}
};
}
}
Algorithm.CSharp/CapacityTests/CheeseMilkHourlyRebalance.cs
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/*
* 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.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Tests an illiquid asset that has bursts of liquidity around 11:00 A.M. Central Time
/// with an hourly in and out strategy.
/// </summary>
public class CheeseMilkHourlyRebalance : QCAlgorithm, IRegressionAlgorithmDefinition
{
private ExponentialMovingAverage _fast;
private ExponentialMovingAverage _slow;
private Symbol _contract;
private DateTime _lastTrade;
public override void Initialize()
{
SetStartDate(2021, 1, 1);
SetEndDate(2021, 2, 17);
SetTimeZone(TimeZones.Chicago);
SetCash(100000);
SetWarmup(1000);
var dc = AddFuture("DC", Resolution.Minute, Market.CME);
dc.SetFilter(0, 10000);
}
public override void OnData(Slice slice)
{
var contract = slice.FutureChains.Values.SelectMany(c => c.Contracts.Values)
.OrderBy(c => c.Symbol.ID.Date)
.FirstOrDefault()?
.Symbol;
if (contract == null)
{
return;
}
if (_contract != contract || (_fast == null && _slow == null))
{
_fast = EMA(contract, 600);
_slow = EMA(contract, 1200);
_contract = contract;
}
if (!_fast.IsReady || !_slow.IsReady)
{
return;
}
if (Time - _lastTrade <= TimeSpan.FromHours(1) || Time.TimeOfDay <= new TimeSpan(10, 50, 0) || Time.TimeOfDay >= new TimeSpan(12, 30, 0))
{
return;
}
if (!Portfolio.ContainsKey(contract) || (Portfolio[contract].Quantity <= 0 && _fast > _slow))
{
SetHoldings(contract, 0.5);
_lastTrade = Time;
}
else if (Portfolio.ContainsKey(contract) && Portfolio[contract].Quantity >= 0 && _fast < _slow)
{
SetHoldings(contract, -0.5);
_lastTrade = Time;
}
}
/// <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; } = false;
/// <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 => 0;
/// <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", "19"},
{"Average Win", "39.16%"},
{"Average Loss", "-8.81%"},
{"Compounding Annual Return", "-99.857%"},
{"Drawdown", "82.900%"},
{"Expectancy", "-0.359"},
{"Net Profit", "-57.725%"},
{"Sharpe Ratio", "-0.555"},
{"Probabilistic Sharpe Ratio", "10.606%"},
{"Loss Rate", "88%"},
{"Win Rate", "12%"},
{"Profit-Loss Ratio", "4.45"},
{"Alpha", "-1.188"},
{"Beta", "0.603"},
{"Annual Standard Deviation", "1.754"},
{"Annual Variance", "3.075"},
{"Information Ratio", "-0.759"},
{"Tracking Error", "1.753"},
{"Treynor Ratio", "-1.612"},
{"Total Fees", "$2558.55"},
{"Estimated Strategy Capacity", "$20000.00"},
{"Fitness Score", "0.351"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-0.602"},
{"Return Over Maximum Drawdown", "-1.415"},
{"Portfolio Turnover", "14.226"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "4f5fd2fb25e957bd0cb7cb6d275ddb97"}
};
}
}
Algorithm.CSharp/CapacityTests/EmaPortfolioRebalance100.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Tests a wide variety of liquid and illiquid stocks together, with bins
/// of 20 ranging from micro-cap to mega-cap stocks.
/// </summary>
public class EmaPortfolioRebalance100 : QCAlgorithm, IRegressionAlgorithmDefinition
{
private List<SymbolData> _data;
public override void Initialize()
{
SetStartDate(2020, 1, 1);
SetEndDate(2020, 2, 5);
SetWarmup(1000);
SetCash(100000);
_data = new List<SymbolData> {
new SymbolData(this, AddEquity("AADR", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AAMC", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AAU", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ABDC", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ABIO", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ABUS", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AC", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACER", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACES", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACGLO", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACH", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACHV", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACIO", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACIU", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACNB", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACRS", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACSI", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACT", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACT", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACTG", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZYNE", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZYME", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZUO", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZUMZ", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZTR", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZSL", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZSAN", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZROZ", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZLAB", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZIXI", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZIV", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZIOP", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZGNX", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZG", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZEUS", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZAGG", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("YYY", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("YRD", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("YRCW", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("YPF", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AA", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AAN", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AAP", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AAXN", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ABB", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ABC", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACAD", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACC", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACGL", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACIW", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACM", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACWV", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ACWX", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ADM", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ADPT", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ADS", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ADUS", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AEM", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AEO", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AEP", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ZTS", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("YUM", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XLY", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XLV", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XLRE", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XLP", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XLNX", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XLF", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XLC", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XLB", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XEL", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("XBI", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("X", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("WYNN", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("WW", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("WORK", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("WMB", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("WM", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("WELL", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("WEC", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AAPL", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("ADBE", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AGG", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AMD", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("AMZN", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("BA", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("BABA", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("BAC", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("BMY", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("C", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("CMCSA", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("CRM", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("CSCO", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("DIS", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("EEM", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("EFA", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("FB", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("GDX", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("GE", Resolution.Minute).Symbol),
new SymbolData(this, AddEquity("SPY", Resolution.Minute).Symbol)
};
}
public override void OnData(Slice slice)
{
var fastFactor = 0.005m;
foreach (var sd in _data)
{
if (!Portfolio.Invested && sd.Fast * (1 + fastFactor) > sd.Slow)
{
SetHoldings(sd.Symbol, 0.01);
}
else if (Portfolio.Invested && sd.Fast * (1 - fastFactor) < sd.Slow)
{
Liquidate(sd.Symbol);
}
}
}
public class SymbolData
{
public Symbol Symbol { get; set; }
public ExponentialMovingAverage Fast { get; set; }
public ExponentialMovingAverage Slow { get; set; }
public bool IsCrossed => Fast > Slow;
public SymbolData(QCAlgorithm algorithm, Symbol symbol) {
Symbol = symbol;
Fast = algorithm.EMA(symbol, 20);
Slow = algorithm.EMA(symbol, 300);
}
}
/// <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; } = false;
/// <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 => 0;
/// <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", "1015"},
{"Average Win", "0.01%"},
{"Average Loss", "0.00%"},
{"Compounding Annual Return", "-12.674%"},
{"Drawdown", "1.400%"},
{"Expectancy", "-0.761"},
{"Net Profit", "-1.328%"},
{"Sharpe Ratio", "-12.258"},
{"Probabilistic Sharpe Ratio", "0.000%"},
{"Loss Rate", "95%"},
{"Win Rate", "5%"},
{"Profit-Loss Ratio", "3.67"},
{"Alpha", "-0.142"},
{"Beta", "0.038"},
{"Annual Standard Deviation", "0.01"},
{"Annual Variance", "0"},
{"Information Ratio", "-4.389"},
{"Tracking Error", "0.123"},
{"Treynor Ratio", "-3.359"},
{"Total Fees", "$1125.52"},
{"Estimated Strategy Capacity", "$300.00"},
{"Fitness Score", "0.007"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-14.315"},
{"Return Over Maximum Drawdown", "-9.589"},
{"Portfolio Turnover", "0.406"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "4c165e8d648d54a85bb7b564050a6f85"}
};
}
}
Algorithm.CSharp/CapacityTests/IntradayMinuteScalping.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Scalps SPY using an EMA cross strategy at minute resolution.
/// This tests equity strategies that trade at a higher frequency, which
/// should have a reduced capacity estimate as a result.
/// </summary>
public class IntradayMinuteScalping : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _spy;
private ExponentialMovingAverage _fast;
private ExponentialMovingAverage _slow;
public override void Initialize()
{
SetStartDate(2020, 1, 1);
SetEndDate(2020, 1, 30);
SetCash(100000);
SetWarmup(100);
_spy = AddEquity("SPY", Resolution.Minute).Symbol;
_fast = EMA(_spy, 20);
_slow = EMA(_spy, 40);
}
public override void OnData(Slice slice)
{
if (Portfolio[_spy].Quantity <= 0 && _fast > _slow)
{
SetHoldings(_spy, 1);
}
else if (Portfolio[_spy].Quantity >= 0 && _fast < _slow)
{
SetHoldings(_spy, -1);
}
}
/// <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; } = false;
/// <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 => 0;
/// <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", "150"},
{"Average Win", "0.16%"},
{"Average Loss", "-0.11%"},
{"Compounding Annual Return", "-19.320%"},
{"Drawdown", "3.900%"},
{"Expectancy", "-0.193"},
{"Net Profit", "-1.730%"},
{"Sharpe Ratio", "-1.606"},
{"Probabilistic Sharpe Ratio", "21.397%"},
{"Loss Rate", "67%"},
{"Win Rate", "33%"},
{"Profit-Loss Ratio", "1.45"},
{"Alpha", "-0.357"},
{"Beta", "0.635"},
{"Annual Standard Deviation", "0.119"},
{"Annual Variance", "0.014"},
{"Information Ratio", "-4.249"},
{"Tracking Error", "0.106"},
{"Treynor Ratio", "-0.302"},
{"Total Fees", "$449.14"},
{"Estimated Strategy Capacity", "$27000000.00"},
{"Fitness Score", "0.088"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-3.259"},
{"Return Over Maximum Drawdown", "-7.992"},
{"Portfolio Turnover", "14.605"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "f5a0e9547f7455004fa6c3eb136534e9"}
};
}
}
Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingBTCETH.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
using QuantConnect.Securities;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Scalps BTCETH using an EMA cross strategy at minute resolution.
/// This tests crypto strategies that trade at a higher frequency, which
/// should have a reduced capacity estimate as a result. This also tests
/// that currency conversions are handled properly in the strategy capacity
/// calculation class.
/// </summary>
public class IntradayMinuteScalpingBTCETH : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _ethbtc;
private ExponentialMovingAverage _fast;
private ExponentialMovingAverage _slow;
public override void Initialize()
{
SetStartDate(2021, 1, 1);
SetEndDate(2021, 1, 30);
SetCash(100000);
SetWarmup(100);
var ethbtc = AddCrypto("ETHBTC", Resolution.Minute, Market.GDAX);
ethbtc.BuyingPowerModel = new BuyingPowerModel();
_ethbtc = ethbtc.Symbol;
_fast = EMA(_ethbtc, 20);
_slow = EMA(_ethbtc, 40);
}
public override void OnData(Slice slice)
{
if (Portfolio[_ethbtc].Quantity <= 0 && _fast > _slow)
{
SetHoldings(_ethbtc, 1);
}
else if (Portfolio[_ethbtc].Quantity >= 0 && _fast < _slow)
{
SetHoldings(_ethbtc, -1);
}
}
/// <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; } = false;
/// <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 => 0;
/// <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", "1005"},
{"Average Win", "0.96%"},
{"Average Loss", "-0.33%"},
{"Compounding Annual Return", "76.267%"},
{"Drawdown", "77.100%"},
{"Expectancy", "-0.012"},
{"Net Profit", "4.768%"},
{"Sharpe Ratio", "1.01909630017278E+24"},
{"Probabilistic Sharpe Ratio", "93.814%"},
{"Loss Rate", "75%"},
{"Win Rate", "25%"},
{"Profit-Loss Ratio", "2.95"},
{"Alpha", "1.3466330963256E+25"},
{"Beta", "25.59"},
{"Annual Standard Deviation", "13.214"},
{"Annual Variance", "174.61"},
{"Information Ratio", "1.02164274756513E+24"},
{"Tracking Error", "13.181"},
{"Treynor Ratio", "5.2622435344112E+23"},
{"Total Fees", "$0.00"},
{"Estimated Strategy Capacity", "$1300000.00"},
{"Fitness Score", "0.38"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-0.239"},
{"Return Over Maximum Drawdown", "-1.385"},
{"Portfolio Turnover", "81.433"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "6a779e7a8d12b4808845c75b88d43b3a"}
};
}
}
Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingEURUSD.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Scalps EURUSD using an EMA cross strategy at minute resolution.
/// This tests FOREX strategies that trade at a higher frequency, which
/// should have a reduced capacity estimate as a result.
/// </summary>
public class IntradayMinuteScalpingEURUSD : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _eurusd;
private ExponentialMovingAverage _fast;
private ExponentialMovingAverage _slow;
public override void Initialize()
{
SetStartDate(2021, 1, 1);
SetEndDate(2021, 1, 30);
SetCash(100000);
SetWarmup(100);
_eurusd = AddForex("EURUSD", Resolution.Minute, Market.Oanda).Symbol;
_fast = EMA(_eurusd, 20);
_slow = EMA(_eurusd, 40);
}
public override void OnData(Slice slice)
{
if (Portfolio[_eurusd].Quantity <= 0 && _fast > _slow)
{
SetHoldings(_eurusd, 1);
}
else if (Portfolio[_eurusd].Quantity >= 0 && _fast < _slow)
{
SetHoldings(_eurusd, -1);
}
}
/// <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; } = false;
/// <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 => 0;
/// <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", "671"},
{"Average Win", "0.07%"},
{"Average Loss", "-0.04%"},
{"Compounding Annual Return", "-80.820%"},
{"Drawdown", "12.200%"},
{"Expectancy", "-0.447"},
{"Net Profit", "-12.180%"},
{"Sharpe Ratio", "-13.121"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "79%"},
{"Win Rate", "21%"},
{"Profit-Loss Ratio", "1.61"},
{"Alpha", "-0.746"},
{"Beta", "-0.02"},
{"Annual Standard Deviation", "0.057"},
{"Annual Variance", "0.003"},
{"Information Ratio", "-4.046"},
{"Tracking Error", "0.161"},
{"Treynor Ratio", "37.346"},
{"Total Fees", "$0.00"},
{"Estimated Strategy Capacity", "$44000000.00"},
{"Fitness Score", "0.025"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-16.609"},
{"Return Over Maximum Drawdown", "-7.115"},
{"Portfolio Turnover", "52.476"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "74ee44736b9300c0262dc75c0cd140e1"}
};
}
}
Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingFuturesES.cs
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/*
* 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.Collections.Generic;
using System.Linq;
using QuantConnect.Data;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Scalps ES futures contracts (E-mini SP500) using an EMA cross strategy at minute resolution.
/// This tests futures strategies that trade at a higher frequency, which
/// should have a reduced capacity estimate as a result.
/// </summary>
/// <remarks>
/// The insanely high capacity estimate of this strategy is realistic.
/// ES notional contract value traded is around $600 Billion USD per day (!!!), which
/// is what the capacity is set to.
/// </remarks>
public class IntradayMinuteScalpingFuturesES : QCAlgorithm, IRegressionAlgorithmDefinition
{
private ExponentialMovingAverage _fast;
private ExponentialMovingAverage _slow;
private Symbol _contract;
public override void Initialize()
{
SetStartDate(2021, 1, 1);
SetEndDate(2021, 1, 31);
SetCash(100000);
SetWarmup(1000);
var a = AddFuture("ES", Resolution.Minute, Market.CME);
a.SetFilter(0, 10000);
}
public override void OnData(Slice slice)
{
var contract = slice.FutureChains.Values.SelectMany(c => c.Contracts.Values)
.OrderBy(c => c.Symbol.ID.Date)
.FirstOrDefault()?
.Symbol;
if (contract == null)
{
return;
}
if (_contract != contract || (_fast == null && _slow == null))
{
_fast = EMA(contract, 10);
_slow = EMA(contract, 20);
_contract = contract;
}
if (!_fast.IsReady || !_slow.IsReady)
{
return;
}
if (!Portfolio.ContainsKey(contract) || (Portfolio[contract].Quantity <= 0 && _fast > _slow))
{
SetHoldings(contract, 1);
}
else if (Portfolio.ContainsKey(contract) && Portfolio[contract].Quantity >= 0 && _fast < _slow)
{
SetHoldings(contract, -1);
}
}
/// <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; } = false;
/// <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 => 0;
/// <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", "1217"},
{"Average Win", "2.69%"},
{"Average Loss", "-0.93%"},
{"Compounding Annual Return", "-99.756%"},
{"Drawdown", "77.200%"},
{"Expectancy", "-0.047"},
{"Net Profit", "-40.013%"},
{"Sharpe Ratio", "-0.52"},
{"Probabilistic Sharpe Ratio", "19.865%"},
{"Loss Rate", "75%"},
{"Win Rate", "25%"},
{"Profit-Loss Ratio", "2.88"},
{"Alpha", "-1.279"},
{"Beta", "-3.686"},
{"Annual Standard Deviation", "1.85"},
{"Annual Variance", "3.422"},
{"Information Ratio", "-0.463"},
{"Tracking Error", "1.895"},
{"Treynor Ratio", "0.261"},
{"Total Fees", "$19843.10"},
{"Estimated Strategy Capacity", "$560000000.00"},
{"Fitness Score", "0.334"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-0.837"},
{"Return Over Maximum Drawdown", "-1.402"},
{"Portfolio Turnover", "1174.125"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "f353843132df7b0604eff3a37b134ca2"}
};
}
}
Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingGBPJPY.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Scalps GBPJPY using an EMA cross strategy at minute resolution.
/// This tests FOREX strategies that trade at a higher frequency, which
/// should have a reduced capacity estimate as a result. This test also
/// tests that currency conversion rates are applied and calculated correctly.
/// </summary>
public class IntradayMinuteScalpingGBPJPY : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _gbpjpy;
private ExponentialMovingAverage _fast;
private ExponentialMovingAverage _slow;
public override void Initialize()
{
SetStartDate(2021, 1, 1);
SetEndDate(2021, 1, 30);
SetCash(100000);
SetWarmup(100);
_gbpjpy = AddForex("GBPJPY", Resolution.Minute, Market.Oanda).Symbol;
_fast = EMA(_gbpjpy, 20);
_slow = EMA(_gbpjpy, 40);
}
public override void OnData(Slice slice)
{
if (Portfolio[_gbpjpy].Quantity <= 0 && _fast > _slow)
{
SetHoldings(_gbpjpy, 1);
}
else if (Portfolio[_gbpjpy].Quantity >= 0 && _fast < _slow)
{
SetHoldings(_gbpjpy, -1);
}
}
/// <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; } = false;
/// <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 => 0;
/// <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", "735"},
{"Average Win", "0.08%"},
{"Average Loss", "-0.05%"},
{"Compounding Annual Return", "-93.946%"},
{"Drawdown", "19.900%"},
{"Expectancy", "-0.592"},
{"Net Profit", "-19.794%"},
{"Sharpe Ratio", "-10.054"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "84%"},
{"Win Rate", "16%"},
{"Profit-Loss Ratio", "1.56"},
{"Alpha", "-0.895"},
{"Beta", "0.068"},
{"Annual Standard Deviation", "0.09"},
{"Annual Variance", "0.008"},
{"Information Ratio", "-4.929"},
{"Tracking Error", "0.164"},
{"Treynor Ratio", "-13.276"},
{"Total Fees", "$0.00"},
{"Estimated Strategy Capacity", "$49000000.00"},
{"Fitness Score", "0.049"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-10.846"},
{"Return Over Maximum Drawdown", "-4.904"},
{"Portfolio Turnover", "58.921"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "66f04c9622ab242993c8ce951418e6d9"}
};
}
}
Algorithm.CSharp/CapacityTests/IntradayMinuteScalpingTRYJPY.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Scalps TRYJPY using an EMA cross strategy at minute resolution.
/// This tests FOREX strategies that trade at a higher frequency, which
/// should have a reduced capacity estimate as a result. This tests that
/// currency conversions are applied properly to the capacity estimate calculation.
/// </summary>
public class IntradayMinuteScalpingTRYJPY : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _tryjpy;
private ExponentialMovingAverage _fast;
private ExponentialMovingAverage _slow;
public override void Initialize()
{
SetStartDate(2021, 1, 1);
SetEndDate(2021, 1, 30);
SetCash(100000);
SetWarmup(100);
_tryjpy = AddForex("TRYJPY", Resolution.Minute, Market.Oanda).Symbol;
_fast = EMA(_tryjpy, 20);
_slow = EMA(_tryjpy, 40);
}
public override void OnData(Slice slice)
{
if (Portfolio[_tryjpy].Quantity <= 0 && _fast > _slow)
{
SetHoldings(_tryjpy, 1);
}
else if (Portfolio[_tryjpy].Quantity >= 0 && _fast < _slow)
{
SetHoldings(_tryjpy, -1);
}
}
/// <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; } = false;
/// <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 => 0;
/// <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", "603"},
{"Average Win", "0.20%"},
{"Average Loss", "-0.26%"},
{"Compounding Annual Return", "-100.000%"},
{"Drawdown", "73.200%"},
{"Expectancy", "-0.849"},
{"Net Profit", "-73.118%"},
{"Sharpe Ratio", "-2.046"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "91%"},
{"Win Rate", "9%"},
{"Profit-Loss Ratio", "0.75"},
{"Alpha", "-0.95"},
{"Beta", "0.541"},
{"Annual Standard Deviation", "0.489"},
{"Annual Variance", "0.239"},
{"Information Ratio", "-1.863"},
{"Tracking Error", "0.487"},
{"Treynor Ratio", "-1.849"},
{"Total Fees", "$0.00"},
{"Estimated Strategy Capacity", "$4400000.00"},
{"Fitness Score", "0.259"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-2.135"},
{"Return Over Maximum Drawdown", "-1.389"},
{"Portfolio Turnover", "49.501"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "4eb4d703a9f200b6bb3d8b0ebbc9db7f"}
};
}
}
Algorithm.CSharp/CapacityTests/MonthlyRebalanceDaily.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Rebalances ultra-liquid stocks monthly, testing
/// bursts of orders centered around the start of the month at Daily resolution
/// </summary>
public class MonthlyRebalanceDaily : QCAlgorithm, IRegressionAlgorithmDefinition
{
public override void Initialize()
{
SetStartDate(2019, 12, 31);
SetEndDate(2020, 4, 5);
SetCash(100000);
var spy = AddEquity("SPY", Resolution.Daily).Symbol;
AddEquity("GE", Resolution.Daily);
AddEquity("FB", Resolution.Daily);
AddEquity("DIS", Resolution.Daily);
AddEquity("CSCO", Resolution.Daily);
AddEquity("CRM", Resolution.Daily);
AddEquity("C", Resolution.Daily);
AddEquity("BAC", Resolution.Daily);
AddEquity("BABA", Resolution.Daily);
AddEquity("AAPL", Resolution.Daily);
Schedule.On(DateRules.MonthStart(spy), TimeRules.Noon, () =>
{
foreach (var symbol in Securities.Keys)
{
SetHoldings(symbol, 0.10);
}
});
}
/// <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; } = false;
/// <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 => 0;
/// <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", "35"},
{"Average Win", "0.07%"},
{"Average Loss", "-0.07%"},
{"Compounding Annual Return", "-68.407%"},
{"Drawdown", "32.400%"},
{"Expectancy", "-0.309"},
{"Net Profit", "-25.901%"},
{"Sharpe Ratio", "-1.503"},
{"Probabilistic Sharpe Ratio", "2.878%"},
{"Loss Rate", "64%"},
{"Win Rate", "36%"},
{"Profit-Loss Ratio", "0.90"},
{"Alpha", "-0.7"},
{"Beta", "-0.238"},
{"Annual Standard Deviation", "0.386"},
{"Annual Variance", "0.149"},
{"Information Ratio", "-0.11"},
{"Tracking Error", "0.712"},
{"Treynor Ratio", "2.442"},
{"Total Fees", "$38.99"},
{"Estimated Strategy Capacity", "$19000000.00"},
{"Fitness Score", "0.003"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-2.021"},
{"Return Over Maximum Drawdown", "-2.113"},
{"Portfolio Turnover", "0.014"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "76d8164a3c0d4a7d45e94367c4ba5be1"}
};
}
}
Algorithm.CSharp/CapacityTests/MonthlyRebalanceHourly.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Rebalances ultra-liquid stocks monthly, testing
/// bursts of orders centered around the start of the month at Hourly resolution
/// </summary>
public class MonthlyRebalanceHourly : QCAlgorithm, IRegressionAlgorithmDefinition
{
public override void Initialize()
{
SetStartDate(2019, 12, 31);
SetEndDate(2020, 4, 5);
SetCash(100000);
var spy = AddEquity("SPY", Resolution.Hour).Symbol;
AddEquity("GE", Resolution.Hour);
AddEquity("FB", Resolution.Hour);
AddEquity("DIS", Resolution.Hour);
AddEquity("CSCO", Resolution.Hour);
AddEquity("CRM", Resolution.Hour);
AddEquity("C", Resolution.Hour);
AddEquity("BAC", Resolution.Hour);
AddEquity("BABA", Resolution.Hour);
AddEquity("AAPL", Resolution.Hour);
Schedule.On(DateRules.MonthStart(spy), TimeRules.Noon, () =>
{
foreach (var symbol in Securities.Keys)
{
SetHoldings(symbol, 0.10);
}
});
}
/// <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; } = false;
/// <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 => 0;
/// <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", "35"},
{"Average Win", "0.05%"},
{"Average Loss", "-0.10%"},
{"Compounding Annual Return", "-72.444%"},
{"Drawdown", "36.500%"},
{"Expectancy", "-0.449"},
{"Net Profit", "-28.406%"},
{"Sharpe Ratio", "-1.369"},
{"Probabilistic Sharpe Ratio", "4.398%"},
{"Loss Rate", "64%"},
{"Win Rate", "36%"},
{"Profit-Loss Ratio", "0.51"},
{"Alpha", "-0.175"},
{"Beta", "0.892"},
{"Annual Standard Deviation", "0.503"},
{"Annual Variance", "0.253"},
{"Information Ratio", "-0.822"},
{"Tracking Error", "0.138"},
{"Treynor Ratio", "-0.772"},
{"Total Fees", "$38.83"},
{"Estimated Strategy Capacity", "$6000000.00"},
{"Fitness Score", "0.004"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-2.033"},
{"Return Over Maximum Drawdown", "-2.079"},
{"Portfolio Turnover", "0.018"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "1de9bcf6cda0945af6ba1f74c4dcb22c"}
};
}
}
Algorithm.CSharp/CapacityTests/SplitTestingStrategy.cs
+127
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/*
* 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.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Tests that splits do not cause the algorithm to report capacity estimates
/// above or below the actual capacity due to splits. The stock HTGM is illiquid,
/// trading only $1.2 Million per day on average with sparse trade frequencies.
/// </summary>
public class SplitTestingStrategy : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _htgm;
public override void Initialize()
{
SetStartDate(2020, 11, 1);
SetEndDate(2020, 12, 5);
SetCash(100000);
var htgm = AddEquity("HTGM", Resolution.Hour);
htgm.SetDataNormalizationMode(DataNormalizationMode.Raw);
_htgm = htgm.Symbol;
}
public override void OnData(Slice slice)
{
if (!Portfolio.Invested)
{
SetHoldings(_htgm, 1);
}
else
{
SetHoldings(_htgm, -1);
}
}
/// <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; } = false;
/// <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 => 0;
/// <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", "162"},
{"Average Win", "0.10%"},
{"Average Loss", "-0.35%"},
{"Compounding Annual Return", "-94.432%"},
{"Drawdown", "30.400%"},
{"Expectancy", "-0.564"},
{"Net Profit", "-23.412%"},
{"Sharpe Ratio", "-1.041"},
{"Probabilistic Sharpe Ratio", "12.971%"},
{"Loss Rate", "66%"},
{"Win Rate", "34%"},
{"Profit-Loss Ratio", "0.29"},
{"Alpha", "-4.827"},
{"Beta", "1.43"},
{"Annual Standard Deviation", "0.876"},
{"Annual Variance", "0.767"},
{"Information Ratio", "-4.288"},
{"Tracking Error", "0.851"},
{"Treynor Ratio", "-0.637"},
{"Total Fees", "$2655.91"},
{"Estimated Strategy Capacity", "$11000.00"},
{"Fitness Score", "0.052"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-2.2"},
{"Return Over Maximum Drawdown", "-3.481"},
{"Portfolio Turnover", "0.307"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "54f571c11525656e9b383e235e77002e"}
};
}
}
Algorithm.CSharp/CapacityTests/SpyBondPortfolioRebalance.cs
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/*
* 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.Collections.Generic;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Rebalances between SPY and BND. Tests capacity of the weakest link, which in this
/// case is BND, dragging down the capacity estimate.
/// </summary>
public class SpyBondPortfolioRebalance : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _spy;
public override void Initialize()
{
SetStartDate(2020, 1, 1);
SetEndDate(2020, 3, 31);
SetCash(10000);
_spy = AddEquity("SPY", Resolution.Hour).Symbol;
var bnd = AddEquity("BND", Resolution.Hour).Symbol;
Schedule.On(DateRules.EveryDay(_spy), TimeRules.AfterMarketOpen(_spy, 1, false), () =>
{
SetHoldings(_spy, 0.5m);
SetHoldings(bnd, 0.5m);
});
}
/// <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; } = false;
/// <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 => 0;
/// <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", "21"},
{"Average Win", "0.02%"},
{"Average Loss", "-0.03%"},
{"Compounding Annual Return", "-33.564%"},
{"Drawdown", "19.700%"},
{"Expectancy", "-0.140"},
{"Net Profit", "-9.655%"},
{"Sharpe Ratio", "-0.99"},
{"Probabilistic Sharpe Ratio", "13.754%"},
{"Loss Rate", "50%"},
{"Win Rate", "50%"},
{"Profit-Loss Ratio", "0.72"},
{"Alpha", "-0.022"},
{"Beta", "0.538"},
{"Annual Standard Deviation", "0.309"},
{"Annual Variance", "0.096"},
{"Information Ratio", "0.826"},
{"Tracking Error", "0.269"},
{"Treynor Ratio", "-0.569"},
{"Total Fees", "$21.00"},
{"Estimated Strategy Capacity", "$1100000.00"},
{"Fitness Score", "0.005"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-1.524"},
{"Return Over Maximum Drawdown", "-1.688"},
{"Portfolio Turnover", "0.02"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "95a130426900aaf227a08a5d1c617b2b"}
};
}
}