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chore: import upstream snapshot with attribution

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wehub-resource-sync
2026-07-13 13:02:50 +08:00
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Algorithm.CSharp/RegressionTests/CustomData/CustomDataIconicTypesAddDataRegressionAlgorithm.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.Data.Custom.IconicTypes;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Regression algorithm ensures that added data matches expectations
/// </summary>
public class CustomDataIconicTypesAddDataRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _googlEquity;
public override void Initialize()
{
SetStartDate(2013, 10, 7);
SetEndDate(2013, 10, 11);
SetCash(100000);
var twxEquity = AddEquity("TWX", Resolution.Daily).Symbol;
var customTwxSymbol = AddData<LinkedData>(twxEquity, Resolution.Daily).Symbol;
_googlEquity = AddEquity("GOOGL", Resolution.Daily).Symbol;
var customGooglSymbol = AddData<LinkedData>("GOOGL", Resolution.Daily).Symbol;
var unlinkedDataSymbol = AddData<UnlinkedData>("GOOGL", Resolution.Daily).Symbol;
var unlinkedDataSymbolUnderlyingEquity = QuantConnect.Symbol.Create("MSFT", SecurityType.Equity, Market.USA);
var unlinkedDataSymbolUnderlying = AddData<UnlinkedData>(unlinkedDataSymbolUnderlyingEquity, Resolution.Daily).Symbol;
var optionSymbol = AddOption("TWX", Resolution.Minute).Symbol;
var customOptionSymbol = AddData<LinkedData>(optionSymbol, Resolution.Daily).Symbol;
if (customTwxSymbol.Underlying != twxEquity)
{
throw new RegressionTestException($"Underlying symbol for {customTwxSymbol} is not equal to TWX equity. Expected {twxEquity} got {customTwxSymbol.Underlying}");
}
if (customGooglSymbol.Underlying != _googlEquity)
{
throw new RegressionTestException($"Underlying symbol for {customGooglSymbol} is not equal to GOOGL equity. Expected {_googlEquity} got {customGooglSymbol.Underlying}");
}
if (unlinkedDataSymbol.HasUnderlying)
{
throw new RegressionTestException($"Unlinked data type (no underlying) has underlying when it shouldn't. Found {unlinkedDataSymbol.Underlying}");
}
if (!unlinkedDataSymbolUnderlying.HasUnderlying)
{
throw new RegressionTestException("Unlinked data type (with underlying) has no underlying Symbol even though we added with Symbol");
}
if (unlinkedDataSymbolUnderlying.Underlying != unlinkedDataSymbolUnderlyingEquity)
{
throw new RegressionTestException($"Unlinked data type underlying does not equal equity Symbol added. Expected {unlinkedDataSymbolUnderlyingEquity} got {unlinkedDataSymbolUnderlying.Underlying}");
}
if (customOptionSymbol.Underlying != optionSymbol)
{
throw new RegressionTestException("Option symbol not equal to custom underlying symbol. Expected {optionSymbol} got {customOptionSymbol.Underlying}");
}
try
{
var customDataNoCache = AddData<LinkedData>("AAPL", Resolution.Daily);
throw new RegressionTestException("AAPL was found in the SymbolCache, though it should be missing");
}
catch (InvalidOperationException)
{
// This is exactly what we wanted. AAPL shouldn't have been found in the SymbolCache, and because
// LinkedData is mappable, we threw
return;
}
}
public override void OnData(Slice slice)
{
if (!Portfolio.Invested && !Transactions.GetOpenOrders().Any())
{
SetHoldings(_googlEquity, 0.5);
}
}
/// <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, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 49;
/// <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", "1"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "34.800%"},
{"Drawdown", "0.700%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "100382.52"},
{"Net Profit", "0.383%"},
{"Sharpe Ratio", "2.947"},
{"Sortino Ratio", "0"},
{"Probabilistic Sharpe Ratio", "56.505%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.515"},
{"Beta", "0.396"},
{"Annual Standard Deviation", "0.091"},
{"Annual Variance", "0.008"},
{"Information Ratio", "-12.534"},
{"Tracking Error", "0.136"},
{"Treynor Ratio", "0.677"},
{"Total Fees", "$1.00"},
{"Estimated Strategy Capacity", "$130000000.00"},
{"Lowest Capacity Asset", "GOOG T1AZ164W5VTX"},
{"Portfolio Turnover", "10.02%"},
{"Drawdown Recovery", "2"},
{"OrderListHash", "150b29938b60fbc747a3ff8065498bf3"}
};
}
}
Algorithm.CSharp/RegressionTests/CustomData/CustomDataIconicTypesDefaultResolutionRegressionAlgorithm.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.Custom.IconicTypes;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// This regression algorithm tests the performance related GH issue 3772
/// </summary>
public class CustomDataIconicTypesDefaultResolutionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
public override void Initialize()
{
SetStartDate(2013, 10, 11);
SetEndDate(2013, 10, 12);
var spy = AddEquity("SPY").Symbol;
var types = new[]
{
typeof(UnlinkedDataTradeBar),
typeof(DailyUnlinkedData),
typeof(DailyLinkedData)
};
foreach (var type in types)
{
var custom = AddData(type, spy);
if (SubscriptionManager.SubscriptionDataConfigService
.GetSubscriptionDataConfigs(custom.Symbol)
.Any(config => config.Resolution != Resolution.Daily))
{
throw new RegressionTestException("Was expecting resolution to be set to Daily");
}
try
{
AddData(type, spy, Resolution.Tick);
throw new RegressionTestException("Was expecting an ArgumentException to be thrown");
}
catch (ArgumentException)
{
// expected, these custom types don't support tick resolution
}
}
var security = AddData<HourlyDefaultResolutionUnlinkedData>(spy);
if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(security.Symbol)
.Any(config => config.Resolution != Resolution.Hour))
{
throw new RegressionTestException("Was expecting resolution to be set to Hour");
}
var option = AddOption("AAPL");
if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(option.Symbol)
.Any(config => config.Resolution != Resolution.Daily))
{
throw new RegressionTestException("Was expecting resolution to be set to Daily");
}
}
private class DailyUnlinkedData : UnlinkedData
{
public override List<Resolution> SupportedResolutions()
{
return DailyResolution;
}
}
private class DailyLinkedData : LinkedData
{
public override List<Resolution> SupportedResolutions()
{
return DailyResolution;
}
}
private class HourlyDefaultResolutionUnlinkedData : UnlinkedData
{
public override Resolution DefaultResolution()
{
return Resolution.Hour;
}
}
/// <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 => 796;
/// <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", "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"}
};
}
}
Algorithm.CSharp/RegressionTests/CustomData/CustomDataLinkedIconicTypeAddDataCoarseSelectionRegressionAlgorithm.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 QuantConnect.Algorithm.Framework.Selection;
using QuantConnect.Data;
using QuantConnect.Data.Custom.IconicTypes;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Regression algorithm ensures that data added via coarse selection (underlying) is present in ActiveSecurities
/// </summary>
/// <meta name="tag" content="using data" />
/// <meta name="tag" content="custom data" />
/// <meta name="tag" content="regression test" />d
public class CustomDataLinkedIconicTypeAddDataCoarseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private List<Symbol> _customSymbols = new List<Symbol>();
public override void Initialize()
{
SetStartDate(2014, 3, 24);
SetEndDate(2014, 4, 7);
SetCash(100000);
UniverseSettings.Resolution = Resolution.Daily;
AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
}
public IEnumerable<Symbol> CoarseSelector(IEnumerable<CoarseFundamental> coarse)
{
var symbols = new[]
{
QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("GOOGL", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("GOOG", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA),
};
_customSymbols.Clear();
foreach (var symbol in symbols)
{
_customSymbols.Add(AddData<LinkedData>(symbol, Resolution.Daily).Symbol);
}
return symbols;
}
public override void OnData(Slice slice)
{
if (!Portfolio.Invested && Transactions.GetOpenOrders().Count == 0)
{
var aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
SetHoldings(aapl, 0.5);
}
foreach (var customSymbol in _customSymbols)
{
if (!ActiveSecurities.ContainsKey(customSymbol.Underlying))
{
throw new RegressionTestException($"Custom data underlying ({customSymbol.Underlying}) Symbol was not found in active securities");
}
}
}
/// <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, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 78123;
/// <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", "1"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "-33.427%"},
{"Drawdown", "2.000%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "98341.86"},
{"Net Profit", "-1.658%"},
{"Sharpe Ratio", "-4.844"},
{"Sortino Ratio", "-5.768"},
{"Probabilistic Sharpe Ratio", "4.986%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.215"},
{"Beta", "0.503"},
{"Annual Standard Deviation", "0.055"},
{"Annual Variance", "0.003"},
{"Information Ratio", "-3.027"},
{"Tracking Error", "0.054"},
{"Treynor Ratio", "-0.529"},
{"Total Fees", "$14.45"},
{"Estimated Strategy Capacity", "$460000000.00"},
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
{"Portfolio Turnover", "3.33%"},
{"Drawdown Recovery", "0"},
{"OrderListHash", "b5acd2b6fb8c80cdd488ec5a616b07ee"}
};
}
}
Algorithm.CSharp/RegressionTests/CustomData/CustomDataLinkedIconicTypeAddDataOnSecuritiesChangedRegressionAlgorithm.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 QuantConnect.Algorithm.Framework.Selection;
using QuantConnect.Data;
using QuantConnect.Data.Custom.IconicTypes;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Regression algorithm ensures that data added via OnSecuritiesChanged (underlying) is present in ActiveSecurities
/// </summary>
/// <meta name="tag" content="using data" />
/// <meta name="tag" content="custom data" />
/// <meta name="tag" content="regression test" />
public class CustomDataLinkedIconicTypeAddDataOnSecuritiesChangedRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private List<Symbol> _customSymbols = new List<Symbol>();
public override void Initialize()
{
SetStartDate(2014, 3, 24);
SetEndDate(2014, 4, 7);
SetCash(100000);
UniverseSettings.Resolution = Resolution.Daily;
AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
}
public IEnumerable<Symbol> CoarseSelector(IEnumerable<CoarseFundamental> coarse)
{
return new[]
{
QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("BAC", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("FB", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("GOOGL", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("GOOG", SecurityType.Equity, Market.USA),
QuantConnect.Symbol.Create("IBM", SecurityType.Equity, Market.USA),
};
}
public override void OnData(Slice slice)
{
if (!Portfolio.Invested && Transactions.GetOpenOrders().Count == 0)
{
var aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
SetHoldings(aapl, 0.5);
}
foreach (var customSymbol in _customSymbols)
{
if (!ActiveSecurities.ContainsKey(customSymbol.Underlying))
{
throw new RegressionTestException($"Custom data underlying ({customSymbol.Underlying}) Symbol was not found in active securities");
}
}
}
public override void OnSecuritiesChanged(SecurityChanges changes)
{
bool iterated = false;
foreach (var added in changes.AddedSecurities)
{
if (!iterated)
{
_customSymbols.Clear();
iterated = true;
}
_customSymbols.Add(AddData<LinkedData>(added.Symbol, Resolution.Daily).Symbol);
}
}
/// <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, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 78123;
/// <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", "1"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "-33.427%"},
{"Drawdown", "2.000%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "98341.86"},
{"Net Profit", "-1.658%"},
{"Sharpe Ratio", "-4.844"},
{"Sortino Ratio", "-5.768"},
{"Probabilistic Sharpe Ratio", "4.986%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.215"},
{"Beta", "0.503"},
{"Annual Standard Deviation", "0.055"},
{"Annual Variance", "0.003"},
{"Information Ratio", "-3.027"},
{"Tracking Error", "0.054"},
{"Treynor Ratio", "-0.529"},
{"Total Fees", "$14.45"},
{"Estimated Strategy Capacity", "$460000000.00"},
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
{"Portfolio Turnover", "3.33%"},
{"Drawdown Recovery", "0"},
{"OrderListHash", "b5acd2b6fb8c80cdd488ec5a616b07ee"}
};
}
}
Algorithm.CSharp/RegressionTests/CustomData/CustomDataUnlinkedTradeBarIconicTypeConsolidationRegressionAlgorithm.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.IO;
using QuantConnect.Data;
using QuantConnect.Data.Custom.IconicTypes;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Tests the consolidation of custom data with random data
/// </summary>
public class CustomDataUnlinkedTradeBarIconicTypeConsolidationRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _vix;
private BollingerBands _bb;
private bool _invested;
/// <summary>
/// Initializes the algorithm with fake "VIX" data
/// </summary>
public override void Initialize()
{
SetStartDate(2013, 10, 7);
SetEndDate(2013, 10, 11);
SetCash(100000);
_vix = AddData<IncrementallyGeneratedCustomData>("VIX", Resolution.Daily).Symbol;
_bb = BB(_vix, 30, 2, MovingAverageType.Simple, Resolution.Daily);
}
/// <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 slice)
{
if (_bb.Current.Value == 0)
{
throw new RegressionTestException("Bollinger Band value is zero when we expect non-zero value.");
}
if (!_invested && _bb.Current.Value > 0.05m)
{
MarketOrder(_vix, 1);
_invested = true;
}
}
/// <summary>
/// Incrementally updating data
/// </summary>
public class IncrementallyGeneratedCustomData : UnlinkedDataTradeBar
{
private const decimal _start = 10.01m;
private static decimal _step;
/// <summary>
/// Gets the source of the subscription. In this case, we set it to existing
/// equity data so that we can pass fake data from Reader
/// </summary>
/// <param name="config">Subscription configuration</param>
/// <param name="date">Date we're making this request</param>
/// <param name="isLiveMode">Is live mode</param>
/// <returns>Source of subscription</returns>
public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
{
return new SubscriptionDataSource(Path.Combine(Globals.DataFolder, "equity", "usa", "minute", "spy", $"{date:yyyyMMdd}_trade.zip#{date:yyyyMMdd}_spy_minute_trade.csv"), SubscriptionTransportMedium.LocalFile, FileFormat.Csv);
}
/// <summary>
/// Reads the data, which in this case is fake incremental data
/// </summary>
/// <param name="config">Subscription configuration</param>
/// <param name="line">Line of data</param>
/// <param name="date">Date of the request</param>
/// <param name="isLiveMode">Is live mode</param>
/// <returns>Incremental BaseData instance</returns>
public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
{
var unlinkedBar = new UnlinkedDataTradeBar();
_step += 0.10m;
var open = _start + _step;
var close = _start + _step + 0.02m;
var high = close;
var low = open;
return new IncrementallyGeneratedCustomData
{
Open = open,
High = high,
Low = low,
Close = close,
Time = date,
Symbol = new Symbol(
SecurityIdentifier.GenerateBase(typeof(IncrementallyGeneratedCustomData), "VIX", Market.USA, false),
"VIX"),
Period = unlinkedBar.Period,
DataType = unlinkedBar.DataType
};
}
}
/// <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>
/// <remarks>
/// Unable to be tested in Python, due to pythonnet not supporting overriding of methods from Python
/// </remarks>
public List<Language> Languages { get; } = new() { Language.CSharp };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 4171;
/// <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", "1"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "28.248%"},
{"Drawdown", "0%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "100330"},
{"Net Profit", "0.330%"},
{"Sharpe Ratio", "315.406"},
{"Sortino Ratio", "0"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0.22"},
{"Beta", "0.002"},
{"Annual Standard Deviation", "0.001"},
{"Annual Variance", "0"},
{"Information Ratio", "-7.886"},
{"Tracking Error", "0.222"},
{"Treynor Ratio", "144.512"},
{"Total Fees", "$0.00"},
{"Estimated Strategy Capacity", "$0"},
{"Lowest Capacity Asset", "VIX.IncrementallyGeneratedCustomData 2S"},
{"Portfolio Turnover", "0.02%"},
{"Drawdown Recovery", "0"},
{"OrderListHash", "a3abee8c47244710f63c596af48a7951"}
};
}
}