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@@ -117,18 +117,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.700%"},
|
||||
{"Expectancy", "1.781"},
|
||||
{"Net Profit", "1.442%"},
|
||||
{"Sharpe Ratio", "4.86"},
|
||||
{"Probabilistic Sharpe Ratio", "59.497%"},
|
||||
{"Sharpe Ratio", "4.017"},
|
||||
{"Probabilistic Sharpe Ratio", "59.636%"},
|
||||
{"Loss Rate", "33%"},
|
||||
{"Win Rate", "67%"},
|
||||
{"Profit-Loss Ratio", "3.17"},
|
||||
{"Alpha", "4.181"},
|
||||
{"Beta", "-1.322"},
|
||||
{"Annual Standard Deviation", "0.321"},
|
||||
{"Annual Variance", "0.103"},
|
||||
{"Information Ratio", "-0.795"},
|
||||
{"Tracking Error", "0.532"},
|
||||
{"Treynor Ratio", "-1.18"},
|
||||
{"Alpha", "1.53"},
|
||||
{"Beta", "-0.292"},
|
||||
{"Annual Standard Deviation", "0.279"},
|
||||
{"Annual Variance", "0.078"},
|
||||
{"Information Ratio", "-0.743"},
|
||||
{"Tracking Error", "0.372"},
|
||||
{"Treynor Ratio", "-3.845"},
|
||||
{"Total Fees", "$14.78"},
|
||||
{"Estimated Strategy Capacity", "$47000000.00"},
|
||||
{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
|
||||
|
||||
@@ -103,8 +103,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-9.486"},
|
||||
{"Tracking Error", "0.008"},
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
|
||||
@@ -1,131 +0,0 @@
|
||||
/*
|
||||
* 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.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
using System.Collections.Generic;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm reproducing GH issue #5971 where we add and remove an option in the same loop
|
||||
/// </summary>
|
||||
public class AddAndRemoveSecuritySameLoopRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _contract;
|
||||
private bool _hasRemoved;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2014, 06, 06);
|
||||
SetEndDate(2014, 06, 09);
|
||||
|
||||
UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
|
||||
UniverseSettings.MinimumTimeInUniverse = TimeSpan.Zero;
|
||||
|
||||
var aapl = AddEquity("AAPL").Symbol;
|
||||
|
||||
_contract = OptionChainProvider.GetOptionContractList(aapl, Time)
|
||||
.OrderBy(symbol => symbol.ID.Symbol)
|
||||
.FirstOrDefault(optionContract => optionContract.ID.OptionRight == OptionRight.Call
|
||||
&& optionContract.ID.OptionStyle == OptionStyle.American);
|
||||
}
|
||||
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (_hasRemoved)
|
||||
{
|
||||
throw new Exception("Expect a single call to OnData where we removed the option and underlying");
|
||||
}
|
||||
|
||||
_hasRemoved = true;
|
||||
AddOptionContract(_contract);
|
||||
|
||||
// changed my mind!
|
||||
RemoveOptionContract(_contract);
|
||||
RemoveSecurity(_contract.Underlying);
|
||||
|
||||
RemoveSecurity(AddEquity("SPY", Resolution.Daily).Symbol);
|
||||
}
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (!_hasRemoved)
|
||||
{
|
||||
throw new Exception("We did not remove the option contract!");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <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 Trades", "0"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe 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", "-9.486"},
|
||||
{"Tracking Error", "0.008"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", ""},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"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", "d41d8cd98f00b204e9800998ecf8427e"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,151 +0,0 @@
|
||||
/*
|
||||
* 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.Data;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Orders;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression test to explain how Beta indicator works
|
||||
/// </summary>
|
||||
public class AddBetaIndicatorRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Beta _beta;
|
||||
private SimpleMovingAverage _sma;
|
||||
private decimal _lastSMAValue;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 07);
|
||||
SetEndDate(2013, 10, 15);
|
||||
SetCash(10000);
|
||||
|
||||
AddEquity("IBM");
|
||||
AddEquity("SPY");
|
||||
|
||||
EnableAutomaticIndicatorWarmUp = true;
|
||||
_beta = B("IBM", "SPY", 3, Resolution.Daily);
|
||||
_sma = SMA("SPY", 3, Resolution.Daily);
|
||||
_lastSMAValue = 0;
|
||||
|
||||
if (!_beta.IsReady)
|
||||
{
|
||||
throw new Exception("_beta indicator was expected to be ready");
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
var price = data["IBM"].Close;
|
||||
Buy("IBM", 10);
|
||||
LimitOrder("IBM", 10, price * 0.1m);
|
||||
StopMarketOrder("IBM", 10, price / 0.1m);
|
||||
}
|
||||
|
||||
if (_beta.Current.Value < 0m || _beta.Current.Value > 2.80m)
|
||||
{
|
||||
throw new Exception($"_beta value was expected to be between 0 and 2.80 but was {_beta.Current.Value}");
|
||||
}
|
||||
|
||||
Log($"Beta between IBM and SPY is: {_beta.Current.Value}");
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
var order = Transactions.GetOrderById(orderEvent.OrderId);
|
||||
var goUpwards = _lastSMAValue < _sma.Current.Value;
|
||||
_lastSMAValue = _sma.Current.Value;
|
||||
|
||||
if (order.Status == OrderStatus.Filled)
|
||||
{
|
||||
if (order.Type == OrderType.Limit && Math.Abs(_beta.Current.Value - 1) < 0.2m && goUpwards)
|
||||
{
|
||||
Transactions.CancelOpenOrders(order.Symbol);
|
||||
}
|
||||
}
|
||||
|
||||
if (order.Status == OrderStatus.Canceled)
|
||||
{
|
||||
Log(orderEvent.ToString());
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 virtual Language[] Languages { get; } = { Language.CSharp};
|
||||
|
||||
/// <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 Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "12.939%"},
|
||||
{"Drawdown", "0.300%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.289%"},
|
||||
{"Sharpe Ratio", "4.233"},
|
||||
{"Probabilistic Sharpe Ratio", "68.349%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.035"},
|
||||
{"Beta", "0.122"},
|
||||
{"Annual Standard Deviation", "0.024"},
|
||||
{"Annual Variance", "0.001"},
|
||||
{"Information Ratio", "-3.181"},
|
||||
{"Tracking Error", "0.142"},
|
||||
{"Treynor Ratio", "0.842"},
|
||||
{"Total Fees", "$1.00"},
|
||||
{"Estimated Strategy Capacity", "$35000000.00"},
|
||||
{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.022"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "8.508"},
|
||||
{"Return Over Maximum Drawdown", "58.894"},
|
||||
{"Portfolio Turnover", "0.022"},
|
||||
{"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", "bd88c6a0e10c7e146b05377205101a12"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,165 +0,0 @@
|
||||
/*
|
||||
* 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.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Securities.Future;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Continuous Futures Regression algorithm. Asserting and showcasing the behavior of adding a continuous future
|
||||
/// and a future contract at the same time
|
||||
/// </summary>
|
||||
public class AddFutureContractWithContinuousRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _currentMappedSymbol;
|
||||
private Future _continuousContract;
|
||||
private Future _futureContract;
|
||||
private bool _ended;
|
||||
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 6);
|
||||
SetEndDate(2013, 10, 10);
|
||||
|
||||
_continuousContract = AddFuture(Futures.Indices.SP500EMini,
|
||||
dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
|
||||
dataMappingMode: DataMappingMode.LastTradingDay,
|
||||
contractDepthOffset: 0
|
||||
);
|
||||
|
||||
_futureContract = AddFutureContract(FutureChainProvider.GetFutureContractList(_continuousContract.Symbol, Time).First());
|
||||
}
|
||||
|
||||
/// <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 data)
|
||||
{
|
||||
if (_ended)
|
||||
{
|
||||
throw new Exception($"Algorithm should of ended!");
|
||||
}
|
||||
if (data.Keys.Count > 2)
|
||||
{
|
||||
throw new Exception($"Getting data for more than 2 symbols! {string.Join(",", data.Keys.Select(symbol => symbol))}");
|
||||
}
|
||||
if (UniverseManager.Count != 3)
|
||||
{
|
||||
throw new Exception($"Expecting 3 universes (chain, continuous and user defined) but have {UniverseManager.Count}");
|
||||
}
|
||||
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
Buy(_futureContract.Symbol, 1);
|
||||
Buy(_continuousContract.Mapped, 1);
|
||||
|
||||
RemoveSecurity(_futureContract.Symbol);
|
||||
RemoveSecurity(_continuousContract.Symbol);
|
||||
|
||||
_ended = true;
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
if (orderEvent.Status == OrderStatus.Filled)
|
||||
{
|
||||
Log($"{orderEvent}");
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
Debug($"{Time}-{changes}");
|
||||
|
||||
if (changes.AddedSecurities.Any(security => security.Symbol != _continuousContract.Symbol && security.Symbol != _futureContract.Symbol)
|
||||
|| changes.RemovedSecurities.Any(security => security.Symbol != _continuousContract.Symbol && security.Symbol != _futureContract.Symbol))
|
||||
{
|
||||
throw new Exception($"We got an unexpected security changes {changes}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <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 Trades", "3"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.03%"},
|
||||
{"Compounding Annual Return", "-2.503%"},
|
||||
{"Drawdown", "0.000%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.032%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-0.678"},
|
||||
{"Tracking Error", "0.243"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$7.40"},
|
||||
{"Estimated Strategy Capacity", "$2100000.00"},
|
||||
{"Lowest Capacity Asset", "ES VMKLFZIH2MTD"},
|
||||
{"Fitness Score", "0.419"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-81.557"},
|
||||
{"Portfolio Turnover", "0.837"},
|
||||
{"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", "68775c18eb40c1bde212653faec4016e"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -40,7 +40,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2020, 1, 4);
|
||||
SetStartDate(2020, 1, 5);
|
||||
SetEndDate(2020, 1, 6);
|
||||
|
||||
_es20h20 = AddFutureContract(
|
||||
@@ -51,7 +51,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
QuantConnect.Symbol.CreateFuture(Futures.Indices.SP500EMini, Market.CME, new DateTime(2020, 6, 19)),
|
||||
Resolution.Minute).Symbol;
|
||||
|
||||
var optionChains = OptionChainProvider.GetOptionContractList(_es20h20, Time.AddDays(1))
|
||||
var optionChains = OptionChainProvider.GetOptionContractList(_es20h20, Time)
|
||||
.Concat(OptionChainProvider.GetOptionContractList(_es19m20, Time));
|
||||
|
||||
foreach (var optionContract in optionChains)
|
||||
@@ -168,31 +168,31 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "116.059%"},
|
||||
{"Compounding Annual Return", "217.585%"},
|
||||
{"Drawdown", "0.600%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.635%"},
|
||||
{"Sharpe Ratio", "17.16"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "2.25"},
|
||||
{"Beta", "-1.665"},
|
||||
{"Annual Standard Deviation", "0.071"},
|
||||
{"Annual Variance", "0.005"},
|
||||
{"Information Ratio", "5.319"},
|
||||
{"Tracking Error", "0.114"},
|
||||
{"Treynor Ratio", "-0.735"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-14.395"},
|
||||
{"Tracking Error", "0.043"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$7.40"},
|
||||
{"Estimated Strategy Capacity", "$24000000.00"},
|
||||
{"Estimated Strategy Capacity", "$28000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "1"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "2.133"},
|
||||
{"Portfolio Turnover", "3.199"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
|
||||
@@ -42,7 +42,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2020, 1, 4);
|
||||
SetStartDate(2020, 1, 5);
|
||||
SetEndDate(2020, 1, 6);
|
||||
|
||||
_es = AddFuture(Futures.Indices.SP500EMini, Resolution.Minute, Market.CME);
|
||||
@@ -227,31 +227,31 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "-10.708%"},
|
||||
{"Compounding Annual Return", "-15.625%"},
|
||||
{"Drawdown", "0.200%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-0.093%"},
|
||||
{"Sharpe Ratio", "-10.594"},
|
||||
{"Sharpe Ratio", "-11.181"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.261"},
|
||||
{"Beta", "0.244"},
|
||||
{"Annual Standard Deviation", "0.01"},
|
||||
{"Alpha", "0.002"},
|
||||
{"Beta", "-0.016"},
|
||||
{"Annual Standard Deviation", "0.001"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-22.456"},
|
||||
{"Tracking Error", "0.032"},
|
||||
{"Treynor Ratio", "-0.454"},
|
||||
{"Information Ratio", "-14.343"},
|
||||
{"Tracking Error", "0.044"},
|
||||
{"Treynor Ratio", "0.479"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$41000.00"},
|
||||
{"Lowest Capacity Asset", "ES 31C3JQTOYO9T0|ES XCZJLC9NOB29"},
|
||||
{"Fitness Score", "0.273"},
|
||||
{"Fitness Score", "0.41"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-123.159"},
|
||||
{"Portfolio Turnover", "0.547"},
|
||||
{"Return Over Maximum Drawdown", "-185.654"},
|
||||
{"Portfolio Turnover", "0.821"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
|
||||
@@ -126,21 +126,21 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "0.300%"},
|
||||
{"Expectancy", "-0.042"},
|
||||
{"Net Profit", "-0.332%"},
|
||||
{"Sharpe Ratio", "-3.149"},
|
||||
{"Probabilistic Sharpe Ratio", "0.427%"},
|
||||
{"Sharpe Ratio", "-3.7"},
|
||||
{"Probabilistic Sharpe Ratio", "0.563%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "0.92"},
|
||||
{"Alpha", "-0.015"},
|
||||
{"Beta", "-0.012"},
|
||||
{"Annual Standard Deviation", "0.005"},
|
||||
{"Alpha", "-0.021"},
|
||||
{"Beta", "-0.011"},
|
||||
{"Annual Standard Deviation", "0.006"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-2.823"},
|
||||
{"Tracking Error", "0.049"},
|
||||
{"Treynor Ratio", "1.372"},
|
||||
{"Information Ratio", "-3.385"},
|
||||
{"Tracking Error", "0.058"},
|
||||
{"Treynor Ratio", "2.117"},
|
||||
{"Total Fees", "$2.00"},
|
||||
{"Estimated Strategy Capacity", "$67000000.00"},
|
||||
{"Lowest Capacity Asset", "AOL VRKS95ENLBYE|AOL R735QTJ8XC9X"},
|
||||
{"Estimated Strategy Capacity", "$45000000.00"},
|
||||
{"Lowest Capacity Asset", "AOL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
@@ -160,7 +160,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "4f50b8360ea317ef974801649088bd06"}
|
||||
{"OrderListHash", "b006bb7864c0b2f1a6552fb2aa7f03b8"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,165 +0,0 @@
|
||||
/*
|
||||
* 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.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
using System.Collections.Generic;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression algorithm reproducing GH issue #6073 where we remove and re add an option and expect it to work
|
||||
/// </summary>
|
||||
public class AddOptionContractTwiceRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _contract;
|
||||
private bool _hasRemoved;
|
||||
private bool _reAdded;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2014, 06, 06);
|
||||
SetEndDate(2014, 06, 09);
|
||||
|
||||
UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
|
||||
UniverseSettings.MinimumTimeInUniverse = TimeSpan.Zero;
|
||||
UniverseSettings.FillForward = false;
|
||||
|
||||
AddEquity("SPY", Resolution.Daily);
|
||||
|
||||
var aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
|
||||
|
||||
_contract = OptionChainProvider.GetOptionContractList(aapl, Time)
|
||||
.OrderBy(symbol => symbol.ID.Symbol)
|
||||
.FirstOrDefault(optionContract => optionContract.ID.OptionRight == OptionRight.Call
|
||||
&& optionContract.ID.OptionStyle == OptionStyle.American);
|
||||
AddOptionContract(_contract);
|
||||
}
|
||||
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (_hasRemoved)
|
||||
{
|
||||
if (!_reAdded && slice.ContainsKey(_contract) && slice.ContainsKey(_contract.Underlying))
|
||||
{
|
||||
throw new Exception("Getting data for removed option and underlying!");
|
||||
}
|
||||
|
||||
if (!Portfolio.Invested && _reAdded)
|
||||
{
|
||||
var option = Securities[_contract];
|
||||
var optionUnderlying = Securities[_contract.Underlying];
|
||||
if (option.IsTradable && optionUnderlying.IsTradable
|
||||
&& slice.ContainsKey(_contract) && slice.ContainsKey(_contract.Underlying))
|
||||
{
|
||||
Buy(_contract, 1);
|
||||
}
|
||||
}
|
||||
|
||||
if (!Securities[_contract].IsTradable
|
||||
&& !Securities[_contract.Underlying].IsTradable
|
||||
&& !_reAdded)
|
||||
{
|
||||
// ha changed my mind!
|
||||
AddOptionContract(_contract);
|
||||
_reAdded = true;
|
||||
}
|
||||
}
|
||||
|
||||
if (slice.ContainsKey(_contract) && slice.ContainsKey(_contract.Underlying))
|
||||
{
|
||||
if (!_hasRemoved)
|
||||
{
|
||||
RemoveOptionContract(_contract);
|
||||
RemoveSecurity(_contract.Underlying);
|
||||
_hasRemoved = true;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (!_hasRemoved)
|
||||
{
|
||||
throw new Exception("We did not remove the option contract!");
|
||||
}
|
||||
if (!_reAdded)
|
||||
{
|
||||
throw new Exception("We did not re add the option contract!");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <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 Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.05%"},
|
||||
{"Compounding Annual Return", "-4.548%"},
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.051%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-9.486"},
|
||||
{"Tracking Error", "0.008"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$2.00"},
|
||||
{"Estimated Strategy Capacity", "$30000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL VXBK4Q9ZIFD2|AAPL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-89.181"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"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", "7fbcd12db40304d50b3a34d7878eb3cf"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -90,18 +90,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "3.300%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.168%"},
|
||||
{"Sharpe Ratio", "62.513"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Sharpe Ratio", "-0.126"},
|
||||
{"Probabilistic Sharpe Ratio", "45.081%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "1.118"},
|
||||
{"Beta", "1.19"},
|
||||
{"Annual Standard Deviation", "0.213"},
|
||||
{"Annual Variance", "0.046"},
|
||||
{"Information Ratio", "70.862"},
|
||||
{"Tracking Error", "0.043"},
|
||||
{"Treynor Ratio", "11.209"},
|
||||
{"Alpha", "-2.896"},
|
||||
{"Beta", "0.551"},
|
||||
{"Annual Standard Deviation", "0.385"},
|
||||
{"Annual Variance", "0.148"},
|
||||
{"Information Ratio", "-13.66"},
|
||||
{"Tracking Error", "0.382"},
|
||||
{"Treynor Ratio", "-0.088"},
|
||||
{"Total Fees", "$23.21"},
|
||||
{"Estimated Strategy Capacity", "$340000000.00"},
|
||||
{"Lowest Capacity Asset", "FB V6OIPNZEM8V9"},
|
||||
|
||||
@@ -101,18 +101,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "5.800%"},
|
||||
{"Expectancy", "-0.731"},
|
||||
{"Net Profit", "-5.588%"},
|
||||
{"Sharpe Ratio", "-3.252"},
|
||||
{"Probabilistic Sharpe Ratio", "5.526%"},
|
||||
{"Sharpe Ratio", "-3.272"},
|
||||
{"Probabilistic Sharpe Ratio", "5.825%"},
|
||||
{"Loss Rate", "86%"},
|
||||
{"Win Rate", "14%"},
|
||||
{"Profit-Loss Ratio", "0.89"},
|
||||
{"Alpha", "-0.499"},
|
||||
{"Beta", "1.483"},
|
||||
{"Annual Standard Deviation", "0.196"},
|
||||
{"Annual Variance", "0.039"},
|
||||
{"Information Ratio", "-3.844"},
|
||||
{"Tracking Error", "0.142"},
|
||||
{"Treynor Ratio", "-0.43"},
|
||||
{"Alpha", "-0.594"},
|
||||
{"Beta", "0.707"},
|
||||
{"Annual Standard Deviation", "0.203"},
|
||||
{"Annual Variance", "0.041"},
|
||||
{"Information Ratio", "-2.929"},
|
||||
{"Tracking Error", "0.193"},
|
||||
{"Treynor Ratio", "-0.942"},
|
||||
{"Total Fees", "$37.25"},
|
||||
{"Estimated Strategy Capacity", "$520000000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
|
||||
@@ -30,7 +30,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
private Symbol _aapl;
|
||||
private const string Ticker = "AAPL";
|
||||
private CorporateFactorProvider _factorFile;
|
||||
private FactorFile _factorFile;
|
||||
private readonly IEnumerator<decimal> _expectedAdjustedVolume = new List<decimal> { 6164842, 3044047, 3680347, 3468303, 2169943, 2652523,
|
||||
1499707, 1518215, 1655219, 1510487 }.GetEnumerator();
|
||||
private readonly IEnumerator<decimal> _expectedAdjustedAskSize = new List<decimal> { 215600, 5600, 25200, 8400, 5600, 5600, 2800,
|
||||
@@ -56,7 +56,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
factorFileProvider.Initialize(mapFileProvider, dataProvider);
|
||||
|
||||
|
||||
_factorFile = factorFileProvider.Get(_aapl) as CorporateFactorProvider;
|
||||
_factorFile = factorFileProvider.Get(_aapl);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
@@ -83,7 +83,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
if (_expectedAdjustedVolume.MoveNext() && _expectedAdjustedVolume.Current != aaplData.Volume)
|
||||
{
|
||||
// Our values don't match lets try and give a reason why
|
||||
var dayFactor = _factorFile.GetPriceScale(aaplData.Time, DataNormalizationMode.SplitAdjusted);
|
||||
var dayFactor = _factorFile.GetSplitFactor(aaplData.Time);
|
||||
var probableAdjustedVolume = aaplData.Volume / dayFactor;
|
||||
|
||||
if (_expectedAdjustedVolume.Current == probableAdjustedVolume)
|
||||
@@ -107,7 +107,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
if (_expectedAdjustedAskSize.MoveNext() && _expectedAdjustedAskSize.Current != aaplQuoteData.LastAskSize)
|
||||
{
|
||||
// Our values don't match lets try and give a reason why
|
||||
var dayFactor = _factorFile.GetPriceScale(aaplQuoteData.Time, DataNormalizationMode.SplitAdjusted);
|
||||
var dayFactor = _factorFile.GetSplitFactor(aaplQuoteData.Time);
|
||||
var probableAdjustedAskSize = aaplQuoteData.LastAskSize / dayFactor;
|
||||
|
||||
if (_expectedAdjustedAskSize.Current == probableAdjustedAskSize)
|
||||
@@ -126,7 +126,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
if (_expectedAdjustedBidSize.MoveNext() && _expectedAdjustedBidSize.Current != aaplQuoteData.LastBidSize)
|
||||
{
|
||||
// Our values don't match lets try and give a reason why
|
||||
var dayFactor = _factorFile.GetPriceScale(aaplQuoteData.Time, DataNormalizationMode.SplitAdjusted);
|
||||
var dayFactor = _factorFile.GetSplitFactor(aaplQuoteData.Time);
|
||||
var probableAdjustedBidSize = aaplQuoteData.LastBidSize / dayFactor;
|
||||
|
||||
if (_expectedAdjustedBidSize.Current == probableAdjustedBidSize)
|
||||
|
||||
@@ -198,18 +198,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.192%"},
|
||||
{"Sharpe Ratio", "231.673"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Sharpe Ratio", "31.331"},
|
||||
{"Probabilistic Sharpe Ratio", "88.448%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.163"},
|
||||
{"Beta", "-0.007"},
|
||||
{"Annual Standard Deviation", "0.001"},
|
||||
{"Alpha", "0.138"},
|
||||
{"Beta", "0.04"},
|
||||
{"Annual Standard Deviation", "0.004"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "4.804"},
|
||||
{"Tracking Error", "0.098"},
|
||||
{"Treynor Ratio", "-22.526"},
|
||||
{"Information Ratio", "4.767"},
|
||||
{"Tracking Error", "0.077"},
|
||||
{"Treynor Ratio", "3.223"},
|
||||
{"Total Fees", "$307.50"},
|
||||
{"Estimated Strategy Capacity", "$2600000.00"},
|
||||
{"Lowest Capacity Asset", "GOOCV VP83T1ZUHROL"},
|
||||
|
||||
@@ -13,12 +13,15 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Brokerages;
|
||||
using QuantConnect.Securities;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Data.Custom.AlphaStreams;
|
||||
using QuantConnect.Algorithm.Framework.Alphas;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
|
||||
@@ -29,6 +32,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
public class AlphaStreamsBasicTemplateAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Dictionary<Symbol, HashSet<Symbol>> _symbolsPerAlpha = new Dictionary<Symbol, HashSet<Symbol>>();
|
||||
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
@@ -37,11 +42,11 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetStartDate(2018, 04, 04);
|
||||
SetEndDate(2018, 04, 06);
|
||||
|
||||
SetAlpha(new AlphaStreamAlphaModule());
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
Settings.MinimumOrderMarginPortfolioPercentage = 0.01m;
|
||||
SetPortfolioConstruction(new EqualWeightingAlphaStreamsPortfolioConstructionModel());
|
||||
SetSecurityInitializer(new BrokerageModelSecurityInitializer(BrokerageModel,
|
||||
|
||||
SetSecurityInitializer(new BrokerageModelSecurityInitializer(new DefaultBrokerageModel(),
|
||||
new FuncSecuritySeeder(GetLastKnownPrices)));
|
||||
|
||||
foreach (var alphaId in new [] { "623b06b231eb1cc1aa3643a46", "9fc8ef73792331b11dbd5429a" })
|
||||
@@ -50,11 +55,77 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 data)
|
||||
{
|
||||
foreach (var portfolioState in data.Get<AlphaStreamsPortfolioState>().Values)
|
||||
{
|
||||
ProcessPortfolioState(portfolioState);
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
Log($"OnOrderEvent: {orderEvent}");
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
changes.FilterCustomSecurities = false;
|
||||
foreach (var addedSecurity in changes.AddedSecurities)
|
||||
{
|
||||
if (addedSecurity.Symbol.IsCustomDataType<AlphaStreamsPortfolioState>())
|
||||
{
|
||||
if (!_symbolsPerAlpha.ContainsKey(addedSecurity.Symbol))
|
||||
{
|
||||
_symbolsPerAlpha[addedSecurity.Symbol] = new HashSet<Symbol>();
|
||||
}
|
||||
// warmup alpha state, adding target securities
|
||||
ProcessPortfolioState(addedSecurity.Cache.GetData<AlphaStreamsPortfolioState>());
|
||||
}
|
||||
}
|
||||
|
||||
Log($"OnSecuritiesChanged: {changes}");
|
||||
}
|
||||
|
||||
private bool UsedBySomeAlpha(Symbol asset)
|
||||
{
|
||||
return _symbolsPerAlpha.Any(pair => pair.Value.Contains(asset));
|
||||
}
|
||||
|
||||
private void ProcessPortfolioState(AlphaStreamsPortfolioState portfolioState)
|
||||
{
|
||||
if (portfolioState == null)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
var alphaId = portfolioState.Symbol;
|
||||
if (!_symbolsPerAlpha.TryGetValue(alphaId, out var currentSymbols))
|
||||
{
|
||||
_symbolsPerAlpha[alphaId] = currentSymbols = new HashSet<Symbol>();
|
||||
}
|
||||
|
||||
var newSymbols = new HashSet<Symbol>(currentSymbols.Count);
|
||||
foreach (var symbol in portfolioState.PositionGroups?.SelectMany(positionGroup => positionGroup.Positions).Select(state => state.Symbol) ?? Enumerable.Empty<Symbol>())
|
||||
{
|
||||
// only add it if it's not used by any alpha (already added check)
|
||||
if (newSymbols.Add(symbol) && !UsedBySomeAlpha(symbol))
|
||||
{
|
||||
AddSecurity(symbol, resolution: UniverseSettings.Resolution, extendedMarketHours: UniverseSettings.ExtendedMarketHours);
|
||||
}
|
||||
}
|
||||
_symbolsPerAlpha[alphaId] = newSymbols;
|
||||
|
||||
foreach (var symbol in currentSymbols.Where(symbol => !UsedBySomeAlpha(symbol)))
|
||||
{
|
||||
RemoveSecurity(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>
|
||||
|
||||
@@ -58,13 +58,13 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "16.59"},
|
||||
{"Alpha", "0.006"},
|
||||
{"Beta", "1.011"},
|
||||
{"Alpha", "0.008"},
|
||||
{"Beta", "1.012"},
|
||||
{"Annual Standard Deviation", "0.343"},
|
||||
{"Annual Variance", "0.117"},
|
||||
{"Information Ratio", "-0.859"},
|
||||
{"Information Ratio", "-0.57"},
|
||||
{"Tracking Error", "0.004"},
|
||||
{"Treynor Ratio", "-0.832"},
|
||||
{"Treynor Ratio", "-0.831"},
|
||||
{"Total Fees", "$2.89"},
|
||||
{"Estimated Strategy Capacity", "$8900000000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
|
||||
@@ -14,11 +14,9 @@
|
||||
*/
|
||||
|
||||
using System;
|
||||
using QuantConnect.Interfaces;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Data.Custom.AlphaStreams;
|
||||
using QuantConnect.Algorithm.Framework.Alphas;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
@@ -28,7 +26,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <summary>
|
||||
/// Example algorithm consuming an alpha streams portfolio state and trading based on it
|
||||
/// </summary>
|
||||
public class AlphaStreamsUniverseSelectionTemplateAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
public class AlphaStreamsUniverseSelectionTemplateAlgorithm : AlphaStreamsBasicTemplateAlgorithm
|
||||
{
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
@@ -38,7 +36,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetStartDate(2018, 04, 04);
|
||||
SetEndDate(2018, 04, 06);
|
||||
|
||||
SetAlpha(new AlphaStreamAlphaModule());
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
Settings.MinimumOrderMarginPortfolioPercentage = 0.01m;
|
||||
SetPortfolioConstruction(new EqualWeightingAlphaStreamsPortfolioConstructionModel());
|
||||
@@ -68,63 +65,23 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
public override Dictionary<string, string> ExpectedStatistics
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.12%"},
|
||||
{"Compounding Annual Return", "-13.200%"},
|
||||
{"Drawdown", "0.200%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.116%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "2.474"},
|
||||
{"Tracking Error", "0.339"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$83000.00"},
|
||||
{"Lowest Capacity Asset", "BTCUSD XJ"},
|
||||
{"Fitness Score", "0.011"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-113.513"},
|
||||
{"Portfolio Turnover", "0.023"},
|
||||
{"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", "2b94bc50a74caebe06c075cdab1bc6da"}
|
||||
};
|
||||
get
|
||||
{
|
||||
var result = base.ExpectedStatistics;
|
||||
result["Compounding Annual Return"] = "-13.200%";
|
||||
result["Information Ratio"] = "2.827";
|
||||
result["Tracking Error"] = "0.248";
|
||||
result["Fitness Score"] = "0.011";
|
||||
result["Return Over Maximum Drawdown"] = "-113.513";
|
||||
result["Portfolio Turnover"] = "0.023";
|
||||
return result;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -85,18 +85,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Expectancy", "3.321"},
|
||||
{"Net Profit", "0.089%"},
|
||||
{"Sharpe Ratio", "0.798"},
|
||||
{"Probabilistic Sharpe Ratio", "40.893%"},
|
||||
{"Sharpe Ratio", "0.868"},
|
||||
{"Probabilistic Sharpe Ratio", "44.482%"},
|
||||
{"Loss Rate", "24%"},
|
||||
{"Win Rate", "76%"},
|
||||
{"Profit-Loss Ratio", "4.67"},
|
||||
{"Alpha", "-0.001"},
|
||||
{"Beta", "0.008"},
|
||||
{"Alpha", "0.001"},
|
||||
{"Beta", "-0"},
|
||||
{"Annual Standard Deviation", "0.001"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-1.961"},
|
||||
{"Tracking Error", "0.092"},
|
||||
{"Treynor Ratio", "0.08"},
|
||||
{"Information Ratio", "-2.148"},
|
||||
{"Tracking Error", "0.101"},
|
||||
{"Treynor Ratio", "-4.168"},
|
||||
{"Total Fees", "$52.00"},
|
||||
{"Estimated Strategy Capacity", "$32000000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -34,7 +34,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
UniverseSettings.DataNormalizationMode = DataNormalizationMode.Raw;
|
||||
EnableAutomaticIndicatorWarmUp = true;
|
||||
SetStartDate(2013, 10, 07);
|
||||
SetStartDate(2013, 10, 08);
|
||||
SetEndDate(2013, 10, 09);
|
||||
|
||||
var SP500 = QuantConnect.Symbol.Create(Futures.Indices.SP500EMini, SecurityType.Future, Market.CME);
|
||||
@@ -151,31 +151,31 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "-100.000%"},
|
||||
{"Drawdown", "19.800%"},
|
||||
{"Compounding Annual Return", "-99.999%"},
|
||||
{"Drawdown", "16.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-10.353%"},
|
||||
{"Sharpe Ratio", "-1.379"},
|
||||
{"Net Profit", "-6.366%"},
|
||||
{"Sharpe Ratio", "1.194"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "3.004"},
|
||||
{"Beta", "5.322"},
|
||||
{"Annual Standard Deviation", "0.725"},
|
||||
{"Annual Variance", "0.525"},
|
||||
{"Information Ratio", "-0.42"},
|
||||
{"Tracking Error", "0.589"},
|
||||
{"Treynor Ratio", "-0.188"},
|
||||
{"Alpha", "5.56"},
|
||||
{"Beta", "-71.105"},
|
||||
{"Annual Standard Deviation", "0.434"},
|
||||
{"Annual Variance", "0.188"},
|
||||
{"Information Ratio", "1.016"},
|
||||
{"Tracking Error", "0.44"},
|
||||
{"Treynor Ratio", "-0.007"},
|
||||
{"Total Fees", "$20.35"},
|
||||
{"Estimated Strategy Capacity", "$13000000.00"},
|
||||
{"Estimated Strategy Capacity", "$19000000.00"},
|
||||
{"Lowest Capacity Asset", "ES VMKLFZIH2MTD"},
|
||||
{"Fitness Score", "0.125"},
|
||||
{"Fitness Score", "0.138"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-2.162"},
|
||||
{"Return Over Maximum Drawdown", "-8.144"},
|
||||
{"Portfolio Turnover", "3.184"},
|
||||
{"Sortino Ratio", "-1.727"},
|
||||
{"Return Over Maximum Drawdown", "-12.061"},
|
||||
{"Portfolio Turnover", "4.916"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
@@ -189,7 +189,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "7ff48adafe9676f341e64ac9388d3c2c"}
|
||||
{"OrderListHash", "7c841ca58a4385f42236838e5bf0c382"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -305,18 +305,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "0.400%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.329%"},
|
||||
{"Sharpe Ratio", "-7.887"},
|
||||
{"Probabilistic Sharpe Ratio", "1.216%"},
|
||||
{"Sharpe Ratio", "-11.083"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.001"},
|
||||
{"Alpha", "-0.003"},
|
||||
{"Beta", "0.097"},
|
||||
{"Annual Standard Deviation", "0.002"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "7.39"},
|
||||
{"Tracking Error", "0.015"},
|
||||
{"Treynor Ratio", "-0.131"},
|
||||
{"Information Ratio", "9.742"},
|
||||
{"Tracking Error", "0.021"},
|
||||
{"Treynor Ratio", "-0.26"},
|
||||
{"Total Fees", "$2.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "GOOCV VP83T1ZUHROL"},
|
||||
@@ -339,7 +339,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "f67306bc706a2cf66288f1cadf6148ed"}
|
||||
{"OrderListHash", "7f99e1a8ce4675a1e8bbe1ba45967ccd"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -45,7 +45,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
// Can specify the default exchange to execute an order on.
|
||||
// If not specified will default to the primary exchange
|
||||
Exchange = Exchange.BATS,
|
||||
Exchange = Exchange.NASDAQ,
|
||||
// Currently only support order for the day
|
||||
TimeInForce = TimeInForce.Day
|
||||
};
|
||||
@@ -59,7 +59,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
// will set 25% of our buying power with a market order that will be routed to exchange set in the default order properties (BATS)
|
||||
// will set 25% of our buying power with a market order that will be routed to exchange set in the default order properties (NASDAQ)
|
||||
SetHoldings("SPY", 0.25m);
|
||||
// will increase our SPY holdings to 50% of our buying power with a market order that will be routed to ARCA
|
||||
SetHoldings("SPY", 0.50m, orderProperties: new AtreyuOrderProperties { Exchange = Exchange.ARCA });
|
||||
@@ -86,10 +86,10 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "93.340%"},
|
||||
{"Compounding Annual Return", "93.443%"},
|
||||
{"Drawdown", "1.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.846%"},
|
||||
{"Net Profit", "0.847%"},
|
||||
{"Sharpe Ratio", "6.515"},
|
||||
{"Probabilistic Sharpe Ratio", "67.535%"},
|
||||
{"Loss Rate", "0%"},
|
||||
@@ -102,14 +102,14 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Information Ratio", "6.515"},
|
||||
{"Tracking Error", "0.11"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$1.20"},
|
||||
{"Total Fees", "$0.52"},
|
||||
{"Estimated Strategy Capacity", "$8600000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.124"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "78.222"},
|
||||
{"Return Over Maximum Drawdown", "78.376"},
|
||||
{"Portfolio Turnover", "0.124"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
@@ -124,7 +124,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "01a751a837beafd90015b2fd82edf994"}
|
||||
{"OrderListHash", "cb542eaaeab5eac3bcae5d915ded30da"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,161 +0,0 @@
|
||||
/*
|
||||
* 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 QuantConnect.Data;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Securities;
|
||||
using QuantConnect.Securities.Future;
|
||||
using Futures = QuantConnect.Securities.Futures;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Basic Continuous Futures Template Algorithm
|
||||
/// </summary>
|
||||
public class BasicTemplateContinuousFutureAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Future _continuousContract;
|
||||
private Security _currentContract;
|
||||
private SimpleMovingAverage _fast;
|
||||
private SimpleMovingAverage _slow;
|
||||
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 7, 1);
|
||||
SetEndDate(2014, 1, 1);
|
||||
|
||||
_continuousContract = AddFuture(Futures.Indices.SP500EMini,
|
||||
dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
|
||||
dataMappingMode: DataMappingMode.LastTradingDay,
|
||||
contractDepthOffset: 0
|
||||
);
|
||||
|
||||
_fast = SMA(_continuousContract.Symbol, 3, Resolution.Daily);
|
||||
_slow = SMA(_continuousContract.Symbol, 10, 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 data)
|
||||
{
|
||||
foreach (var changedEvent in data.SymbolChangedEvents.Values)
|
||||
{
|
||||
Log($"{Time} - SymbolChanged event: {changedEvent}");
|
||||
}
|
||||
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
if(_fast > _slow)
|
||||
{
|
||||
_currentContract = Securities[_continuousContract.Mapped];
|
||||
Buy(_currentContract.Symbol, 1);
|
||||
}
|
||||
}
|
||||
else if(_fast < _slow)
|
||||
{
|
||||
Liquidate();
|
||||
}
|
||||
|
||||
if (_currentContract != null && _currentContract.Symbol != _continuousContract.Mapped)
|
||||
{
|
||||
Log($"{Time} - rolling position from {_currentContract.Symbol} to {_continuousContract.Mapped}");
|
||||
|
||||
var currentPositionSize = _currentContract.Holdings.Quantity;
|
||||
Liquidate(_currentContract.Symbol);
|
||||
Buy(_continuousContract.Mapped, currentPositionSize);
|
||||
_currentContract = Securities[_continuousContract.Mapped];
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
Debug($"{orderEvent}");
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
Debug($"{Time}-{changes}");
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <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 Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0.00%"},
|
||||
{"Compounding Annual Return", "-0.007%"},
|
||||
{"Drawdown", "0.000%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.004%"},
|
||||
{"Sharpe Ratio", "-0.369"},
|
||||
{"Probabilistic Sharpe Ratio", "10.640%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-2.751"},
|
||||
{"Tracking Error", "0.082"},
|
||||
{"Treynor Ratio", "-0.616"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "ES VMKLFZIH2MTD"},
|
||||
{"Fitness Score", "0.007"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-0.738"},
|
||||
{"Portfolio Turnover", "0.01"},
|
||||
{"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", "bd7fbe57802dfedb36c85609b7234016"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -75,18 +75,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.200%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "3.464%"},
|
||||
{"Sharpe Ratio", "19.148"},
|
||||
{"Probabilistic Sharpe Ratio", "97.754%"},
|
||||
{"Sharpe Ratio", "9.933"},
|
||||
{"Probabilistic Sharpe Ratio", "82.470%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.005"},
|
||||
{"Beta", "0.998"},
|
||||
{"Annual Standard Deviation", "0.138"},
|
||||
{"Annual Variance", "0.019"},
|
||||
{"Information Ratio", "-34.028"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "2.651"},
|
||||
{"Alpha", "1.957"},
|
||||
{"Beta", "-0.125"},
|
||||
{"Annual Standard Deviation", "0.164"},
|
||||
{"Annual Variance", "0.027"},
|
||||
{"Information Ratio", "-4.577"},
|
||||
{"Tracking Error", "0.225"},
|
||||
{"Treynor Ratio", "-13.006"},
|
||||
{"Total Fees", "$3.45"},
|
||||
{"Estimated Strategy Capacity", "$970000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -134,18 +134,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "13.500%"},
|
||||
{"Expectancy", "-0.818"},
|
||||
{"Net Profit", "-13.517%"},
|
||||
{"Sharpe Ratio", "-98.781"},
|
||||
{"Sharpe Ratio", "-2.678"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "89%"},
|
||||
{"Win Rate", "11%"},
|
||||
{"Profit-Loss Ratio", "0.69"},
|
||||
{"Alpha", "-1.676"},
|
||||
{"Beta", "0.042"},
|
||||
{"Annual Standard Deviation", "0.01"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-73.981"},
|
||||
{"Tracking Error", "0.233"},
|
||||
{"Treynor Ratio", "-23.975"},
|
||||
{"Alpha", "4.469"},
|
||||
{"Beta", "-0.961"},
|
||||
{"Annual Standard Deviation", "0.373"},
|
||||
{"Annual Variance", "0.139"},
|
||||
{"Information Ratio", "-13.191"},
|
||||
{"Tracking Error", "0.507"},
|
||||
{"Treynor Ratio", "1.04"},
|
||||
{"Total Fees", "$15207.00"},
|
||||
{"Estimated Strategy Capacity", "$8000.00"},
|
||||
{"Lowest Capacity Asset", "GC VOFJUCDY9XNH"},
|
||||
|
||||
@@ -1,152 +0,0 @@
|
||||
/*
|
||||
* 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.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// This example demonstrates how to add futures with daily resolution.
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="benchmarks" />
|
||||
/// <meta name="tag" content="futures" />
|
||||
public class BasicTemplateFuturesDailyAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _contractSymbol;
|
||||
protected virtual Resolution Resolution => Resolution.Daily;
|
||||
|
||||
// S&P 500 EMini futures
|
||||
private const string RootSP500 = Futures.Indices.SP500EMini;
|
||||
|
||||
// Gold futures
|
||||
private const string RootGold = Futures.Metals.Gold;
|
||||
|
||||
/// <summary>
|
||||
/// Initialize your algorithm and add desired assets.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 08);
|
||||
SetEndDate(2013, 10, 10);
|
||||
SetCash(1000000);
|
||||
|
||||
var futureSP500 = AddFuture(RootSP500, Resolution);
|
||||
var futureGold = AddFuture(RootGold, Resolution);
|
||||
|
||||
// set our expiry filter for this futures chain
|
||||
// SetFilter method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yield the same filtering criteria
|
||||
futureSP500.SetFilter(TimeSpan.Zero, TimeSpan.FromDays(182));
|
||||
futureGold.SetFilter(0, 182);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event
|
||||
/// </summary>
|
||||
/// <param name="slice">The current slice of data keyed by symbol string</param>
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
foreach(var chain in slice.FutureChains)
|
||||
{
|
||||
// find the front contract expiring no earlier than in 90 days
|
||||
var contract = (
|
||||
from futuresContract in chain.Value.OrderBy(x => x.Expiry)
|
||||
where futuresContract.Expiry > Time.Date.AddDays(90)
|
||||
select futuresContract
|
||||
).FirstOrDefault();
|
||||
|
||||
// if found, trade it
|
||||
if (contract != null)
|
||||
{
|
||||
_contractSymbol = contract.Symbol;
|
||||
MarketOrder(_contractSymbol, 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
Liquidate();
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 virtual bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public virtual Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "6"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.10%"},
|
||||
{"Compounding Annual Return", "-23.119%"},
|
||||
{"Drawdown", "0.300%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.276%"},
|
||||
{"Sharpe Ratio", "-13.736"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.526"},
|
||||
{"Beta", "0.057"},
|
||||
{"Annual Standard Deviation", "0.015"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-31.088"},
|
||||
{"Tracking Error", "0.189"},
|
||||
{"Treynor Ratio", "-3.51"},
|
||||
{"Total Fees", "$11.10"},
|
||||
{"Estimated Strategy Capacity", "$200000000.00"},
|
||||
{"Lowest Capacity Asset", "GC VOFJUCDY9XNH"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-17.118"},
|
||||
{"Return Over Maximum Drawdown", "-83.844"},
|
||||
{"Portfolio Turnover", "0.16"},
|
||||
{"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", "512f55519e5221c7e82e1d9f5ddd1b9f"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -142,18 +142,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "5.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-3.312%"},
|
||||
{"Sharpe Ratio", "-6.305"},
|
||||
{"Probabilistic Sharpe Ratio", "9.342%"},
|
||||
{"Sharpe Ratio", "-7.795"},
|
||||
{"Probabilistic Sharpe Ratio", "0.164%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-1.465"},
|
||||
{"Beta", "0.312"},
|
||||
{"Annual Standard Deviation", "0.134"},
|
||||
{"Annual Variance", "0.018"},
|
||||
{"Information Ratio", "-14.77"},
|
||||
{"Tracking Error", "0.192"},
|
||||
{"Treynor Ratio", "-2.718"},
|
||||
{"Alpha", "-1.362"},
|
||||
{"Beta", "0.257"},
|
||||
{"Annual Standard Deviation", "0.109"},
|
||||
{"Annual Variance", "0.012"},
|
||||
{"Information Ratio", "-14.947"},
|
||||
{"Tracking Error", "0.19"},
|
||||
{"Treynor Ratio", "-3.309"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$52000000.00"},
|
||||
{"Lowest Capacity Asset", "GC VL5E74HP3EE5"},
|
||||
|
||||
@@ -1,96 +0,0 @@
|
||||
/*
|
||||
* 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.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// This regressions tests the BasicTemplateFuturesDailyAlgorithm with hour data
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="benchmarks" />
|
||||
/// <meta name="tag" content="futures" />
|
||||
public class BasicTemplateFuturesHourlyAlgorithm : BasicTemplateFuturesDailyAlgorithm
|
||||
{
|
||||
private Symbol _contractSymbol;
|
||||
protected override Resolution Resolution => 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 override bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public override Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "140"},
|
||||
{"Average Win", "0.01%"},
|
||||
{"Average Loss", "-0.02%"},
|
||||
{"Compounding Annual Return", "-38.171%"},
|
||||
{"Drawdown", "0.400%"},
|
||||
{"Expectancy", "-0.369"},
|
||||
{"Net Profit", "-0.394%"},
|
||||
{"Sharpe Ratio", "-24.82"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "66%"},
|
||||
{"Win Rate", "34%"},
|
||||
{"Profit-Loss Ratio", "0.84"},
|
||||
{"Alpha", "0.42"},
|
||||
{"Beta", "-0.041"},
|
||||
{"Annual Standard Deviation", "0.01"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-65.112"},
|
||||
{"Tracking Error", "0.253"},
|
||||
{"Treynor Ratio", "6.024"},
|
||||
{"Total Fees", "$259.00"},
|
||||
{"Estimated Strategy Capacity", "$130000.00"},
|
||||
{"Lowest Capacity Asset", "GC VOFJUCDY9XNH"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-43.422"},
|
||||
{"Return Over Maximum Drawdown", "-100.459"},
|
||||
{"Portfolio Turnover", "4.716"},
|
||||
{"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", "320067074c8dd771f69602ab07001f1e"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,124 +0,0 @@
|
||||
/*
|
||||
* 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>
|
||||
/// Basic template algorithm simply initializes the date range and cash. This is a skeleton
|
||||
/// framework you can use for designing an algorithm.
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="using quantconnect" />
|
||||
/// <meta name="tag" content="trading and orders" />
|
||||
public class BasicTemplateHourlyAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
|
||||
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 07); //Set Start Date
|
||||
SetEndDate(2013, 10, 11); //Set End Date
|
||||
SetCash(100000); //Set Strategy Cash
|
||||
|
||||
// Find more symbols here: http://quantconnect.com/data
|
||||
// Forex, CFD, Equities Resolutions: Tick, Second, Minute, Hour, Daily.
|
||||
// Futures Resolution: Tick, Second, Minute
|
||||
// Options Resolution: Minute Only.
|
||||
AddEquity("SPY", Resolution.Hour);
|
||||
|
||||
// There are other assets with similar methods. See "Selecting Options" etc for more details.
|
||||
// AddFuture, AddForex, AddCfd, AddOption
|
||||
}
|
||||
|
||||
/// <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 data)
|
||||
{
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
SetHoldings(_spy, 1);
|
||||
Debug("Purchased Stock");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <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 Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "227.693%"},
|
||||
{"Drawdown", "2.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.529%"},
|
||||
{"Sharpe Ratio", "8.889"},
|
||||
{"Probabilistic Sharpe Ratio", "67.609%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.005"},
|
||||
{"Beta", "0.996"},
|
||||
{"Annual Standard Deviation", "0.222"},
|
||||
{"Annual Variance", "0.049"},
|
||||
{"Information Ratio", "-14.564"},
|
||||
{"Tracking Error", "0.001"},
|
||||
{"Treynor Ratio", "1.978"},
|
||||
{"Total Fees", "$3.44"},
|
||||
{"Estimated Strategy Capacity", "$110000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.247"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "12.105"},
|
||||
{"Return Over Maximum Drawdown", "112.047"},
|
||||
{"Portfolio Turnover", "0.249"},
|
||||
{"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", "f409be3a7c63d9c1394c2e6c005a15ee"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -30,39 +30,36 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <meta name="tag" content="indexes" />
|
||||
public class BasicTemplateIndexAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
protected Symbol Spx;
|
||||
protected Symbol SpxOption;
|
||||
private Symbol _spx;
|
||||
private Symbol _spxOption;
|
||||
private ExponentialMovingAverage _emaSlow;
|
||||
private ExponentialMovingAverage _emaFast;
|
||||
|
||||
protected virtual Resolution Resolution => Resolution.Minute;
|
||||
protected virtual int StartDay => 4;
|
||||
|
||||
/// <summary>
|
||||
/// Initialize your algorithm and add desired assets.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2021, 1, StartDay);
|
||||
SetEndDate(2021, 1, 18);
|
||||
SetStartDate(2021, 1, 4);
|
||||
SetEndDate(2021, 1, 15);
|
||||
SetCash(1000000);
|
||||
|
||||
// Use indicator for signal; but it cannot be traded
|
||||
Spx = AddIndex("SPX", Resolution).Symbol;
|
||||
_spx = AddIndex("SPX", Resolution.Minute).Symbol;
|
||||
|
||||
// Trade on SPX ITM calls
|
||||
SpxOption = QuantConnect.Symbol.CreateOption(
|
||||
Spx,
|
||||
_spxOption = QuantConnect.Symbol.CreateOption(
|
||||
_spx,
|
||||
Market.USA,
|
||||
OptionStyle.European,
|
||||
OptionRight.Call,
|
||||
3200m,
|
||||
new DateTime(2021, 1, 15));
|
||||
|
||||
AddIndexOptionContract(SpxOption, Resolution);
|
||||
AddIndexOptionContract(_spxOption, Resolution.Minute);
|
||||
|
||||
_emaSlow = EMA(Spx, 80);
|
||||
_emaFast = EMA(Spx, 200);
|
||||
_emaSlow = EMA(_spx, 80);
|
||||
_emaFast = EMA(_spx, 200);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
@@ -70,7 +67,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (!slice.Bars.ContainsKey(Spx) || !slice.Bars.ContainsKey(SpxOption))
|
||||
if (!slice.Bars.ContainsKey(_spx) || !slice.Bars.ContainsKey(_spxOption))
|
||||
{
|
||||
return;
|
||||
}
|
||||
@@ -83,7 +80,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
if (_emaFast > _emaSlow)
|
||||
{
|
||||
SetHoldings(SpxOption, 1);
|
||||
SetHoldings(_spxOption, 1);
|
||||
}
|
||||
else
|
||||
{
|
||||
@@ -93,7 +90,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (Portfolio[Spx].TotalSaleVolume > 0)
|
||||
if (Portfolio[_spx].TotalSaleVolume > 0)
|
||||
{
|
||||
throw new Exception("Index is not tradable.");
|
||||
}
|
||||
@@ -102,46 +99,46 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <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 virtual bool CanRunLocally { get; } = true;
|
||||
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 virtual Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "4"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-53.10%"},
|
||||
{"Compounding Annual Return", "-92.544%"},
|
||||
{"Compounding Annual Return", "-96.172%"},
|
||||
{"Drawdown", "10.100%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-9.915%"},
|
||||
{"Sharpe Ratio", "-3.845"},
|
||||
{"Probabilistic Sharpe Ratio", "0.053%"},
|
||||
{"Sharpe Ratio", "-4.217"},
|
||||
{"Probabilistic Sharpe Ratio", "0.052%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.558"},
|
||||
{"Beta", "0.313"},
|
||||
{"Annual Standard Deviation", "0.112"},
|
||||
{"Annual Variance", "0.013"},
|
||||
{"Information Ratio", "-6.652"},
|
||||
{"Tracking Error", "0.125"},
|
||||
{"Treynor Ratio", "-1.379"},
|
||||
{"Alpha", "-0.908"},
|
||||
{"Beta", "0.468"},
|
||||
{"Annual Standard Deviation", "0.139"},
|
||||
{"Annual Variance", "0.019"},
|
||||
{"Information Ratio", "-9.003"},
|
||||
{"Tracking Error", "0.142"},
|
||||
{"Treynor Ratio", "-1.251"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$13000000.00"},
|
||||
{"Estimated Strategy Capacity", "$14000000.00"},
|
||||
{"Lowest Capacity Asset", "SPX XL80P3GHDZXQ|SPX 31"},
|
||||
{"Fitness Score", "0.039"},
|
||||
{"Fitness Score", "0.044"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-1.763"},
|
||||
{"Return Over Maximum Drawdown", "-9.371"},
|
||||
{"Portfolio Turnover", "0.278"},
|
||||
{"Sortino Ratio", "-1.96"},
|
||||
{"Return Over Maximum Drawdown", "-10.171"},
|
||||
{"Portfolio Turnover", "0.34"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
@@ -155,7 +152,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "0668385036aba3e95127607dfc2f1a59"}
|
||||
{"OrderListHash", "52521ab779446daf4d38a7c9bbbdd893"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,108 +0,0 @@
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Data;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression for running an Index algorithm with Daily data
|
||||
/// </summary>
|
||||
public class BasicTemplateIndexDailyAlgorithm : BasicTemplateIndexAlgorithm
|
||||
{
|
||||
protected override Resolution Resolution => Resolution.Daily;
|
||||
protected override int StartDay => 1;
|
||||
|
||||
// two complete weeks starting from the 5th plus the 18th bar
|
||||
protected virtual int ExpectedBarCount => 2 * 5 + 1;
|
||||
protected int BarCounter = 0;
|
||||
|
||||
/// <summary>
|
||||
/// Purchase a contract when we are not invested, liquidate otherwise
|
||||
/// </summary>
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
// SPX Index is not tradable, but we can trade an option
|
||||
MarketOrder(SpxOption, 1);
|
||||
}
|
||||
else
|
||||
{
|
||||
Liquidate();
|
||||
}
|
||||
|
||||
// Count how many slices we receive with SPX data in it to assert later
|
||||
if (slice.ContainsKey(Spx))
|
||||
{
|
||||
BarCounter++;
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (BarCounter != ExpectedBarCount)
|
||||
{
|
||||
throw new ArgumentException($"Bar Count {BarCounter} is not expected count of {ExpectedBarCount}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 override bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public override Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "9"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-39.42%"},
|
||||
{"Compounding Annual Return", "394.321%"},
|
||||
{"Drawdown", "0.200%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "8.219%"},
|
||||
{"Sharpe Ratio", "6.812"},
|
||||
{"Probabilistic Sharpe Ratio", "91.380%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "2.236"},
|
||||
{"Beta", "-1.003"},
|
||||
{"Annual Standard Deviation", "0.317"},
|
||||
{"Annual Variance", "0.101"},
|
||||
{"Information Ratio", "5.805"},
|
||||
{"Tracking Error", "0.359"},
|
||||
{"Treynor Ratio", "-2.153"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "SPX XL80P3GHDZXQ|SPX 31"},
|
||||
{"Fitness Score", "0.027"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "1776.081"},
|
||||
{"Portfolio Turnover", "0.027"},
|
||||
{"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", "474e8e0e28ee84c869f8c69ec3efe371"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,72 +0,0 @@
|
||||
using System.Collections.Generic;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression for running an Index algorithm with Hourly data
|
||||
/// </summary>
|
||||
public class BasicTemplateIndexHourlyAlgorithm : BasicTemplateIndexDailyAlgorithm
|
||||
{
|
||||
protected override Resolution Resolution => Resolution.Hour;
|
||||
protected override int ExpectedBarCount => base.ExpectedBarCount * 8;
|
||||
|
||||
/// <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 override bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public override Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "70"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.23%"},
|
||||
{"Compounding Annual Return", "-34.441%"},
|
||||
{"Drawdown", "2.000%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-2.028%"},
|
||||
{"Sharpe Ratio", "-11.139"},
|
||||
{"Probabilistic Sharpe Ratio", "0.000%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.269"},
|
||||
{"Beta", "0.086"},
|
||||
{"Annual Standard Deviation", "0.023"},
|
||||
{"Annual Variance", "0.001"},
|
||||
{"Information Ratio", "-3.624"},
|
||||
{"Tracking Error", "0.094"},
|
||||
{"Treynor Ratio", "-3.042"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$310000.00"},
|
||||
{"Lowest Capacity Asset", "SPX XL80P3GHDZXQ|SPX 31"},
|
||||
{"Fitness Score", "0.002"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-14.51"},
|
||||
{"Return Over Maximum Drawdown", "-17.213"},
|
||||
{"Portfolio Turnover", "0.299"},
|
||||
{"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", "3eb56c551f20e2ffa1c56c47c5ee6667"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -30,22 +30,20 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
private Symbol _spx;
|
||||
private ExponentialMovingAverage _emaSlow;
|
||||
private ExponentialMovingAverage _emaFast;
|
||||
protected virtual Resolution Resolution => Resolution.Minute;
|
||||
protected virtual int StartDay => 4;
|
||||
|
||||
/// <summary>
|
||||
/// Initialize your algorithm and add desired assets.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2021, 1, StartDay);
|
||||
SetStartDate(2021, 1, 4);
|
||||
SetEndDate(2021, 2, 1);
|
||||
SetCash(1000000);
|
||||
|
||||
// Use indicator for signal; but it cannot be traded.
|
||||
// We will instead trade on SPX options
|
||||
_spx = AddIndex("SPX", Resolution).Symbol;
|
||||
var spxOptions = AddIndexOption(_spx, Resolution);
|
||||
_spx = AddIndex("SPX", Resolution.Minute).Symbol;
|
||||
var spxOptions = AddIndexOption(_spx, Resolution.Minute);
|
||||
spxOptions.SetFilter(filterFunc => filterFunc.CallsOnly());
|
||||
|
||||
_emaSlow = EMA(_spx, 80);
|
||||
@@ -124,17 +122,17 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <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 virtual bool CanRunLocally { get; } = false;
|
||||
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 virtual Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "8220"},
|
||||
{"Average Win", "0.00%"},
|
||||
|
||||
@@ -1,117 +0,0 @@
|
||||
/*
|
||||
* 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;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression for running an IndexOptions algorithm with Daily data
|
||||
/// </summary>
|
||||
public class BasicTemplateIndexOptionsDailyAlgorithm : BasicTemplateIndexOptionsAlgorithm
|
||||
{
|
||||
protected override Resolution Resolution => Resolution.Daily;
|
||||
protected override int StartDay => 1;
|
||||
|
||||
/// <summary>
|
||||
/// Index EMA Cross trading index options of the index.
|
||||
/// </summary>
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
foreach (var chain in slice.OptionChains.Values)
|
||||
{
|
||||
// Select the contract with the lowest AskPrice
|
||||
var contract = chain.Contracts.OrderBy(x => x.Value.AskPrice).FirstOrDefault().Value;
|
||||
|
||||
if (contract == null)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
if (Portfolio.Invested)
|
||||
{
|
||||
Liquidate();
|
||||
}
|
||||
else
|
||||
{
|
||||
MarketOrder(contract.Symbol, 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 override bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public override Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "9"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.01%"},
|
||||
{"Compounding Annual Return", "-0.091%"},
|
||||
{"Drawdown", "0.000%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.008%"},
|
||||
{"Sharpe Ratio", "-4.033"},
|
||||
{"Probabilistic Sharpe Ratio", "0.013%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.001"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-0.447"},
|
||||
{"Tracking Error", "0.136"},
|
||||
{"Treynor Ratio", "-4.612"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "SPX XL80P59H5E6M|SPX 31"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-50718.291"},
|
||||
{"Return Over Maximum Drawdown", "-11.386"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"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", "5f5df233d68d9115a0d81785de54e71d"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,87 +0,0 @@
|
||||
/*
|
||||
* 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;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression for running an IndexOptions algorithm with Hourly data
|
||||
/// </summary>
|
||||
public class BasicTemplateIndexOptionsHourlyAlgorithm : BasicTemplateIndexOptionsDailyAlgorithm
|
||||
{
|
||||
protected override Resolution Resolution => 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 override bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public override Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "70"},
|
||||
{"Average Win", "0.00%"},
|
||||
{"Average Loss", "0.00%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0.000%"},
|
||||
{"Expectancy", "0.000"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "36.504%"},
|
||||
{"Loss Rate", "97%"},
|
||||
{"Win Rate", "3%"},
|
||||
{"Profit-Loss Ratio", "34.00"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "-0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-0.449"},
|
||||
{"Tracking Error", "0.138"},
|
||||
{"Treynor Ratio", "-0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "SPX XL80P59H5E6M|SPX 31"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "0"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"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", "f21910eb98ceaa39e02020de95354d86"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -40,14 +40,15 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
// Find more symbols here: http://quantconnect.com/data
|
||||
// Equities Resolutions: Tick, Second, Minute, Hour, Daily.
|
||||
AddEquity("UNIONBANK", Resolution.Second, Market.India);
|
||||
|
||||
|
||||
//Set Order Prperties as per the requirements for order placement
|
||||
DefaultOrderProperties = new IndiaOrderProperties(exchange: Exchange.NSE);
|
||||
DefaultOrderProperties = new ZerodhaOrderProperties(exchange: Exchange.NSE);
|
||||
//override default productType value set in config.json if needed - order specific productType value
|
||||
//DefaultOrderProperties = new IndiaOrderProperties(exchange: Exchange.NSE, IndiaOrderProperties.IndiaProductType.CNC);
|
||||
//DefaultOrderProperties = new ZerodhaOrderProperties(exchange: "nse",ZerodhaOrderProperties.KiteProductType.CNC);
|
||||
|
||||
// General Debug statement for acknowledgement
|
||||
Debug("Intialization Done");
|
||||
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
@@ -62,6 +63,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
if (orderEvent.Status.IsFill())
|
||||
|
||||
@@ -41,7 +41,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
private readonly Identity _brent = new Identity("Brent");
|
||||
private readonly Identity _wti = new Identity("WTI");
|
||||
|
||||
private CompositeIndicator _spread;
|
||||
private CompositeIndicator<IndicatorDataPoint> _spread;
|
||||
|
||||
private ExponentialMovingAverage _emaWti;
|
||||
|
||||
|
||||
@@ -1,171 +0,0 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*
|
||||
*/
|
||||
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Market;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// This example demonstrates how to add options for a given underlying equity security.
|
||||
/// It also shows how you can prefilter contracts easily based on strikes and expirations, and how you
|
||||
/// can inspect the option chain to pick a specific option contract to trade.
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="options" />
|
||||
/// <meta name="tag" content="filter selection" />
|
||||
public class BasicTemplateOptionsDailyAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private const string UnderlyingTicker = "GOOG";
|
||||
public Symbol OptionSymbol;
|
||||
private bool _optionExpired;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2015, 12, 23);
|
||||
SetEndDate(2016, 1, 20);
|
||||
SetCash(100000);
|
||||
|
||||
var equity = AddEquity(UnderlyingTicker, Resolution.Daily);
|
||||
var option = AddOption(UnderlyingTicker, Resolution.Daily);
|
||||
OptionSymbol = option.Symbol;
|
||||
|
||||
option.SetFilter(x => x.CallsOnly().Strikes(0, 1).Expiration(0, 30));
|
||||
|
||||
// use the underlying equity as the benchmark
|
||||
SetBenchmark(equity.Symbol);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event
|
||||
/// </summary>
|
||||
/// <param name="slice">The current slice of data keyed by symbol string</param>
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
OptionChain chain;
|
||||
if (slice.OptionChains.TryGetValue(OptionSymbol, out chain))
|
||||
{
|
||||
// Grab us the contract nearest expiry that is not today
|
||||
var contractsByExpiration = chain.Where(x => x.Expiry != Time.Date).OrderBy(x => x.Expiry);
|
||||
var contract = contractsByExpiration.FirstOrDefault();
|
||||
|
||||
if (contract != null)
|
||||
{
|
||||
// if found, trade it
|
||||
MarketOrder(contract.Symbol, 1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Order fill event handler. On an order fill update the resulting information is passed to this method.
|
||||
/// </summary>
|
||||
/// <param name="orderEvent">Order event details containing details of the evemts</param>
|
||||
/// <remarks>This method can be called asynchronously and so should only be used by seasoned C# experts. Ensure you use proper locks on thread-unsafe objects</remarks>
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
Log(orderEvent.ToString());
|
||||
|
||||
// Check for our expected OTM option expiry
|
||||
if (orderEvent.Message == "OTM")
|
||||
{
|
||||
// Assert it is at midnight (5AM UTC)
|
||||
if (orderEvent.UtcTime != new DateTime(2016, 1, 16, 5, 0, 0))
|
||||
{
|
||||
throw new ArgumentException($"Expiry event was not at the correct time, {orderEvent.UtcTime}");
|
||||
}
|
||||
|
||||
_optionExpired = true;
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
// Assert we had our option expire and fill a liquidation order
|
||||
if (_optionExpired != true)
|
||||
{
|
||||
throw new ArgumentException("Algorithm did not process the option expiration like expected");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <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 Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-1.31%"},
|
||||
{"Compounding Annual Return", "-15.304%"},
|
||||
{"Drawdown", "1.300%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-1.311%"},
|
||||
{"Sharpe Ratio", "-3.31"},
|
||||
{"Probabilistic Sharpe Ratio", "0.035%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0.034"},
|
||||
{"Annual Variance", "0.001"},
|
||||
{"Information Ratio", "-3.31"},
|
||||
{"Tracking Error", "0.034"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$1.00"},
|
||||
{"Estimated Strategy Capacity", "$18000.00"},
|
||||
{"Lowest Capacity Asset", "GOOCV W78ZFMML01JA|GOOCV VP83T1ZUHROL"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-1.496"},
|
||||
{"Return Over Maximum Drawdown", "-11.673"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"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", "c6d089f1fb86379c74a7413a9c2f8553"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -41,7 +41,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2015, 12, 24);
|
||||
SetEndDate(2015, 12, 28);
|
||||
SetEndDate(2015, 12, 24);
|
||||
SetCash(100000);
|
||||
|
||||
var equity = AddEquity(UnderlyingTicker);
|
||||
@@ -104,14 +104,14 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.40%"},
|
||||
{"Compounding Annual Return", "-21.622%"},
|
||||
{"Drawdown", "0.300%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.311%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
@@ -124,12 +124,12 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Fees", "$1.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "GOOCV VP83T1ZUHROL"},
|
||||
{"Fitness Score", "0.188"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-73.268"},
|
||||
{"Portfolio Turnover", "0.376"},
|
||||
{"Sortino Ratio", "0"},
|
||||
{"Return Over Maximum Drawdown", "0"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
@@ -143,7 +143,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "452e7a36e0a95e33d3457a908add3ead"}
|
||||
{"OrderListHash", "92d8a50efe230524512404dab66b19dd"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -36,7 +36,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
UniverseSettings.Resolution = Resolution.Minute;
|
||||
|
||||
SetStartDate(2014, 06, 05);
|
||||
SetEndDate(2014, 06, 09);
|
||||
SetEndDate(2014, 06, 06);
|
||||
SetCash(100000);
|
||||
|
||||
// set framework models
|
||||
@@ -142,47 +142,47 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "4"},
|
||||
{"Average Win", "0.14%"},
|
||||
{"Average Loss", "-0.28%"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "385.400%"},
|
||||
{"Expectancy", "-0.249"},
|
||||
{"Net Profit", "-386.489%"},
|
||||
{"Sharpe Ratio", "-0.033"},
|
||||
{"Probabilistic Sharpe Ratio", "1.235%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "0.50"},
|
||||
{"Alpha", "-95.983"},
|
||||
{"Beta", "263.726"},
|
||||
{"Annual Standard Deviation", "30.617"},
|
||||
{"Annual Variance", "937.371"},
|
||||
{"Information Ratio", "-0.044"},
|
||||
{"Tracking Error", "30.604"},
|
||||
{"Treynor Ratio", "-0.004"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe 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", "$3.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.168"},
|
||||
{"Estimated Strategy Capacity", "$74000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL 2ZQGWTSSZ0WLI|AAPL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0.327"},
|
||||
{"Kelly Criterion Probability Value", "1"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "0"},
|
||||
{"Portfolio Turnover", "0.224"},
|
||||
{"Total Insights Generated", "28"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "26"},
|
||||
{"Total Insights Closed", "24"},
|
||||
{"Total Insights Analysis Completed", "24"},
|
||||
{"Long Insight Count", "28"},
|
||||
{"Long Insight Count", "26"},
|
||||
{"Short Insight Count", "0"},
|
||||
{"Long/Short Ratio", "100%"},
|
||||
{"Estimated Monthly Alpha Value", "$13.64796"},
|
||||
{"Estimated Monthly Alpha Value", "$31.01809"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$1.89555"},
|
||||
{"Mean Population Estimated Insight Value", "$0.07898125"},
|
||||
{"Mean Population Direction", "50%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "50.0482%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "87603bd45898dd9c456745fa51f989a5"}
|
||||
{"OrderListHash", "ce06ddfa4b2ffeb666a8910ac8836992"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,158 +0,0 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*
|
||||
*/
|
||||
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Market;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// This example demonstrates how to add options for a given underlying equity security.
|
||||
/// It also shows how you can prefilter contracts easily based on strikes and expirations, and how you
|
||||
/// can inspect the option chain to pick a specific option contract to trade.
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="options" />
|
||||
/// <meta name="tag" content="filter selection" />
|
||||
public class BasicTemplateOptionsHourlyAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private const string UnderlyingTicker = "AAPL";
|
||||
public Symbol OptionSymbol;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2014, 6, 6);
|
||||
SetEndDate(2014, 6, 9);
|
||||
SetCash(100000);
|
||||
|
||||
var equity = AddEquity(UnderlyingTicker, Resolution.Hour);
|
||||
var option = AddOption(UnderlyingTicker, Resolution.Hour);
|
||||
OptionSymbol = option.Symbol;
|
||||
|
||||
// set our strike/expiry filter for this option chain
|
||||
option.SetFilter(u => u.Strikes(-2, +2)
|
||||
// Expiration method accepts TimeSpan objects or integer for days.
|
||||
// The following statements yield the same filtering criteria
|
||||
.Expiration(0, 180));
|
||||
// .Expiration(TimeSpan.Zero, TimeSpan.FromDays(180)));
|
||||
|
||||
// use the underlying equity as the benchmark
|
||||
SetBenchmark(equity.Symbol);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event
|
||||
/// </summary>
|
||||
/// <param name="slice">The current slice of data keyed by symbol string</param>
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (!Portfolio.Invested && IsMarketOpen(OptionSymbol))
|
||||
{
|
||||
OptionChain chain;
|
||||
if (slice.OptionChains.TryGetValue(OptionSymbol, out chain))
|
||||
{
|
||||
// we find at the money (ATM) put contract with farthest expiration
|
||||
var atmContract = chain
|
||||
.OrderByDescending(x => x.Expiry)
|
||||
.ThenBy(x => Math.Abs(chain.Underlying.Price - x.Strike))
|
||||
.ThenByDescending(x => x.Right)
|
||||
.FirstOrDefault();
|
||||
|
||||
if (atmContract != null)
|
||||
{
|
||||
// if found, trade it
|
||||
MarketOrder(atmContract.Symbol, 1);
|
||||
MarketOnCloseOrder(atmContract.Symbol, -1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Order fill event handler. On an order fill update the resulting information is passed to this method.
|
||||
/// </summary>
|
||||
/// <param name="orderEvent">Order event details containing details of the evemts</param>
|
||||
/// <remarks>This method can be called asynchronously and so should only be used by seasoned C# experts. Ensure you use proper locks on thread-unsafe objects</remarks>
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
Log(orderEvent.ToString());
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <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 Trades", "4"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.07%"},
|
||||
{"Compounding Annual Return", "-12.496%"},
|
||||
{"Drawdown", "0.200%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.134%"},
|
||||
{"Sharpe Ratio", "-8.839"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.083"},
|
||||
{"Beta", "-0.054"},
|
||||
{"Annual Standard Deviation", "0.008"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-18.699"},
|
||||
{"Tracking Error", "0.155"},
|
||||
{"Treynor Ratio", "1.296"},
|
||||
{"Total Fees", "$4.00"},
|
||||
{"Estimated Strategy Capacity", "$1000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL 2ZTXYMUAHCIAU|AAPL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.04"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-118.28"},
|
||||
{"Portfolio Turnover", "0.081"},
|
||||
{"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", "81e8a822d43de2165c1d3f52964ec312"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -85,18 +85,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.100%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "0.667%"},
|
||||
{"Sharpe Ratio", "3.993"},
|
||||
{"Probabilistic Sharpe Ratio", "58.777%"},
|
||||
{"Sharpe Ratio", "3.507"},
|
||||
{"Probabilistic Sharpe Ratio", "59.181%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.598"},
|
||||
{"Beta", "0.569"},
|
||||
{"Annual Standard Deviation", "0.133"},
|
||||
{"Annual Variance", "0.018"},
|
||||
{"Information Ratio", "-13.973"},
|
||||
{"Tracking Error", "0.104"},
|
||||
{"Treynor Ratio", "0.932"},
|
||||
{"Alpha", "-0.384"},
|
||||
{"Beta", "0.564"},
|
||||
{"Annual Standard Deviation", "0.116"},
|
||||
{"Annual Variance", "0.013"},
|
||||
{"Information Ratio", "-10.791"},
|
||||
{"Tracking Error", "0.092"},
|
||||
{"Treynor Ratio", "0.718"},
|
||||
{"Total Fees", "$46.20"},
|
||||
{"Estimated Strategy Capacity", "$2300000.00"},
|
||||
{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
|
||||
|
||||
@@ -33,11 +33,11 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
SetAccountCurrency("EUR");
|
||||
|
||||
SetStartDate(2019, 2, 19);
|
||||
SetStartDate(2019, 2, 20);
|
||||
SetEndDate(2019, 2, 21);
|
||||
SetCash("EUR", 100000);
|
||||
|
||||
_symbol = AddCfd("DE30EUR").Symbol;
|
||||
_symbol = AddCfd("DE30EUR", Resolution.Minute, Market.Oanda).Symbol;
|
||||
|
||||
SetBenchmark(_symbol);
|
||||
}
|
||||
@@ -75,34 +75,34 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "279"},
|
||||
{"Total Trades", "251"},
|
||||
{"Average Win", "0.01%"},
|
||||
{"Average Loss", "-0.01%"},
|
||||
{"Compounding Annual Return", "-33.650%"},
|
||||
{"Compounding Annual Return", "-37.465%"},
|
||||
{"Drawdown", "0.300%"},
|
||||
{"Expectancy", "-0.345"},
|
||||
{"Net Profit", "-0.337%"},
|
||||
{"Sharpe Ratio", "-19.772"},
|
||||
{"Expectancy", "-0.285"},
|
||||
{"Net Profit", "-0.257%"},
|
||||
{"Sharpe Ratio", "-40.568"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "68%"},
|
||||
{"Win Rate", "32%"},
|
||||
{"Profit-Loss Ratio", "1.07"},
|
||||
{"Loss Rate", "66%"},
|
||||
{"Win Rate", "34%"},
|
||||
{"Profit-Loss Ratio", "1.08"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0.014"},
|
||||
{"Annual Standard Deviation", "0.006"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-19.772"},
|
||||
{"Tracking Error", "0.014"},
|
||||
{"Information Ratio", "-40.568"},
|
||||
{"Tracking Error", "0.006"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$670000.00"},
|
||||
{"Estimated Strategy Capacity", "$660000.00"},
|
||||
{"Lowest Capacity Asset", "DE30EUR 8I"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Fitness Score", "0.002"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-101.587"},
|
||||
{"Return Over Maximum Drawdown", "-110.633"},
|
||||
{"Portfolio Turnover", "9.513"},
|
||||
{"Sortino Ratio", "-23.868"},
|
||||
{"Return Over Maximum Drawdown", "-170.818"},
|
||||
{"Portfolio Turnover", "12.673"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
@@ -116,7 +116,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "64c098abe3c1e7206424b0c3825b0069"}
|
||||
{"OrderListHash", "6745cf313aa3ef780d052ca3ba933c6c"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -168,18 +168,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.400%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.163%"},
|
||||
{"Sharpe Ratio", "2.754"},
|
||||
{"Probabilistic Sharpe Ratio", "64.748%"},
|
||||
{"Sharpe Ratio", "2.876"},
|
||||
{"Probabilistic Sharpe Ratio", "64.984%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.277"},
|
||||
{"Beta", "0.436"},
|
||||
{"Annual Standard Deviation", "0.086"},
|
||||
{"Annual Variance", "0.007"},
|
||||
{"Information Ratio", "3.572"},
|
||||
{"Tracking Error", "0.092"},
|
||||
{"Treynor Ratio", "0.54"},
|
||||
{"Alpha", "0.237"},
|
||||
{"Beta", "-0.188"},
|
||||
{"Annual Standard Deviation", "0.089"},
|
||||
{"Annual Variance", "0.008"},
|
||||
{"Information Ratio", "2.409"},
|
||||
{"Tracking Error", "0.148"},
|
||||
{"Treynor Ratio", "-1.358"},
|
||||
{"Total Fees", "$2.00"},
|
||||
{"Estimated Strategy Capacity", "$49000000.00"},
|
||||
{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
|
||||
|
||||
@@ -41,7 +41,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
_aapl = QuantConnect.Symbol.Create("AAPL", SecurityType.Equity, Market.USA);
|
||||
UniverseSettings.Resolution = Resolution.Minute;
|
||||
|
||||
SetStartDate(2014, 06, 04);
|
||||
SetStartDate(2014, 06, 05);
|
||||
SetEndDate(2014, 06, 06);
|
||||
|
||||
var selectionUniverse = AddUniverse(enumerable => new[] { Time.Date <= new DateTime(2014, 6, 5) ? _twx : _aapl },
|
||||
@@ -144,12 +144,34 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Fitness Score", "0.12"},
|
||||
{"Total Trades", "13"},
|
||||
{"Average Win", "0.65%"},
|
||||
{"Average Loss", "-0.05%"},
|
||||
{"Compounding Annual Return", "79228162514264337593543950335%"},
|
||||
{"Drawdown", "0.500%"},
|
||||
{"Expectancy", "1.393"},
|
||||
{"Net Profit", "149.699%"},
|
||||
{"Sharpe Ratio", "4.743312616499238E+27"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "83%"},
|
||||
{"Win Rate", "17%"},
|
||||
{"Profit-Loss Ratio", "13.36"},
|
||||
{"Alpha", "7.922816251426434E+28"},
|
||||
{"Beta", "304.581"},
|
||||
{"Annual Standard Deviation", "16.703"},
|
||||
{"Annual Variance", "278.995"},
|
||||
{"Information Ratio", "4.75893717482582E+27"},
|
||||
{"Tracking Error", "16.648"},
|
||||
{"Treynor Ratio", "2.6012216611301735E+26"},
|
||||
{"Total Fees", "$13.20"},
|
||||
{"Estimated Strategy Capacity", "$3000000.00"},
|
||||
{"Lowest Capacity Asset", "AOL VRKS95ENLBYE|AOL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.18"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0.12"},
|
||||
{"Portfolio Turnover", "0.18"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
|
||||
@@ -123,18 +123,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.700%"},
|
||||
{"Expectancy", "0.850"},
|
||||
{"Net Profit", "0.637%"},
|
||||
{"Sharpe Ratio", "1.088"},
|
||||
{"Probabilistic Sharpe Ratio", "50.223%"},
|
||||
{"Sharpe Ratio", "1.131"},
|
||||
{"Probabilistic Sharpe Ratio", "50.538%"},
|
||||
{"Loss Rate", "40%"},
|
||||
{"Win Rate", "60%"},
|
||||
{"Profit-Loss Ratio", "2.08"},
|
||||
{"Alpha", "0.198"},
|
||||
{"Beta", "0.741"},
|
||||
{"Annual Standard Deviation", "0.118"},
|
||||
{"Annual Variance", "0.014"},
|
||||
{"Information Ratio", "2.294"},
|
||||
{"Tracking Error", "0.097"},
|
||||
{"Treynor Ratio", "0.173"},
|
||||
{"Alpha", "0.186"},
|
||||
{"Beta", "0.465"},
|
||||
{"Annual Standard Deviation", "0.123"},
|
||||
{"Annual Variance", "0.015"},
|
||||
{"Information Ratio", "1.908"},
|
||||
{"Tracking Error", "0.126"},
|
||||
{"Treynor Ratio", "0.299"},
|
||||
{"Total Fees", "$27.94"},
|
||||
{"Estimated Strategy Capacity", "$200000000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
|
||||
@@ -107,18 +107,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.003%"},
|
||||
{"Sharpe Ratio", "5.36"},
|
||||
{"Probabilistic Sharpe Ratio", "69.521%"},
|
||||
{"Sharpe Ratio", "5.024"},
|
||||
{"Probabilistic Sharpe Ratio", "68.421%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "1.003"},
|
||||
{"Alpha", "0.312"},
|
||||
{"Beta", "0.27"},
|
||||
{"Annual Standard Deviation", "0.087"},
|
||||
{"Annual Variance", "0.007"},
|
||||
{"Information Ratio", "6.477"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0.462"},
|
||||
{"Annual Variance", "0.008"},
|
||||
{"Information Ratio", "-0.242"},
|
||||
{"Tracking Error", "0.105"},
|
||||
{"Treynor Ratio", "1.616"},
|
||||
{"Total Fees", "$3.08"},
|
||||
{"Estimated Strategy Capacity", "$720000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -147,22 +147,22 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Compounding Annual Return", "-99.999%"},
|
||||
{"Drawdown", "16.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Net Profit", "-6.366%"},
|
||||
{"Sharpe Ratio", "1.194"},
|
||||
{"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"},
|
||||
{"Alpha", "5.56"},
|
||||
{"Beta", "-71.105"},
|
||||
{"Annual Standard Deviation", "0.434"},
|
||||
{"Annual Variance", "0.188"},
|
||||
{"Information Ratio", "1.016"},
|
||||
{"Tracking Error", "0.44"},
|
||||
{"Treynor Ratio", "-0.007"},
|
||||
{"Total Fees", "$20.35"},
|
||||
{"Estimated Strategy Capacity", "$19000000.00"},
|
||||
{"Lowest Capacity Asset", "ES VMKLFZIH2MTD"},
|
||||
|
||||
@@ -177,18 +177,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "0.900%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.540%"},
|
||||
{"Sharpe Ratio", "-3.349"},
|
||||
{"Probabilistic Sharpe Ratio", "25.715%"},
|
||||
{"Sharpe Ratio", "-3.168"},
|
||||
{"Probabilistic Sharpe Ratio", "23.963%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.724"},
|
||||
{"Beta", "0.22"},
|
||||
{"Annual Standard Deviation", "0.086"},
|
||||
{"Annual Variance", "0.007"},
|
||||
{"Information Ratio", "-12.125"},
|
||||
{"Tracking Error", "0.187"},
|
||||
{"Treynor Ratio", "-1.304"},
|
||||
{"Alpha", "-0.456"},
|
||||
{"Beta", "0.157"},
|
||||
{"Annual Standard Deviation", "0.075"},
|
||||
{"Annual Variance", "0.006"},
|
||||
{"Information Ratio", "-9.176"},
|
||||
{"Tracking Error", "0.178"},
|
||||
{"Treynor Ratio", "-1.514"},
|
||||
{"Total Fees", "$32.32"},
|
||||
{"Estimated Strategy Capacity", "$95000000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
|
||||
@@ -1,184 +0,0 @@
|
||||
/*
|
||||
* 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.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
using QuantConnect.Data.Market;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Securities.Future;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Continuous Back Month Raw Futures Regression algorithm. Asserting and showcasing the behavior of adding a continuous future
|
||||
/// </summary>
|
||||
public class ContinuousBackMonthRawFutureRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private List<SymbolChangedEvent> _mappings = new();
|
||||
private Future _continuousContract;
|
||||
private DateTime _lastDateLog;
|
||||
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 7, 1);
|
||||
SetEndDate(2014, 1, 1);
|
||||
|
||||
_continuousContract = AddFuture(Futures.Indices.SP500EMini,
|
||||
dataNormalizationMode: DataNormalizationMode.Raw,
|
||||
dataMappingMode: DataMappingMode.FirstDayMonth,
|
||||
contractDepthOffset: 1
|
||||
);
|
||||
}
|
||||
|
||||
/// <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 data)
|
||||
{
|
||||
if (data.Keys.Count != 1)
|
||||
{
|
||||
throw new Exception($"We are getting data for more than one symbols! {string.Join(",", data.Keys.Select(symbol => symbol))}");
|
||||
}
|
||||
|
||||
foreach (var changedEvent in data.SymbolChangedEvents.Values)
|
||||
{
|
||||
if (changedEvent.Symbol == _continuousContract.Symbol)
|
||||
{
|
||||
_mappings.Add(changedEvent);
|
||||
Log($"SymbolChanged event: {changedEvent}");
|
||||
|
||||
var currentExpiration = changedEvent.Symbol.Underlying.ID.Date;
|
||||
// +4 months cause we are actually using the back month, es is quarterly contract
|
||||
var frontMonthExpiration = FuturesExpiryFunctions.FuturesExpiryFunction(_continuousContract.Symbol)(Time.AddMonths(1 + 4));
|
||||
|
||||
if (currentExpiration != frontMonthExpiration.Date)
|
||||
{
|
||||
throw new Exception($"Unexpected current mapped contract expiration {currentExpiration}" +
|
||||
$" @ {Time} it should be AT front month expiration {frontMonthExpiration}");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (_lastDateLog.Month != Time.Month && _continuousContract.HasData)
|
||||
{
|
||||
_lastDateLog = Time;
|
||||
|
||||
Log($"{Time}- {Securities[_continuousContract.Symbol].GetLastData()}");
|
||||
if (Portfolio.Invested)
|
||||
{
|
||||
Liquidate();
|
||||
}
|
||||
else
|
||||
{
|
||||
// This works because we set this contract as tradable, even if it's a canonical security
|
||||
Buy(_continuousContract.Symbol, 1);
|
||||
}
|
||||
|
||||
if(Time.Month == 1 && Time.Year == 2013)
|
||||
{
|
||||
var response = History(new[] { _continuousContract.Symbol }, 60 * 24 * 90);
|
||||
if (!response.Any())
|
||||
{
|
||||
throw new Exception("Unexpected empty history response");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
if (orderEvent.Status == OrderStatus.Filled)
|
||||
{
|
||||
Log($"{orderEvent}");
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
var expectedMappingCounts = 2;
|
||||
if (_mappings.Count != expectedMappingCounts)
|
||||
{
|
||||
throw new Exception($"Unexpected symbol changed events: {_mappings.Count}, was expecting {expectedMappingCounts}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <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 Trades", "2"},
|
||||
{"Average Win", "1.16%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "2.311%"},
|
||||
{"Drawdown", "1.600%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.159%"},
|
||||
{"Sharpe Ratio", "0.753"},
|
||||
{"Probabilistic Sharpe Ratio", "39.483%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.006"},
|
||||
{"Beta", "0.099"},
|
||||
{"Annual Standard Deviation", "0.022"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-2.732"},
|
||||
{"Tracking Error", "0.076"},
|
||||
{"Treynor Ratio", "0.165"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$3900000.00"},
|
||||
{"Lowest Capacity Asset", "ES 1S1"},
|
||||
{"Fitness Score", "0.007"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "0.563"},
|
||||
{"Return Over Maximum Drawdown", "1.87"},
|
||||
{"Portfolio Turnover", "0.01"},
|
||||
{"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", "8aa2ed1319e8bb5beb403476a5aebfef"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,197 +0,0 @@
|
||||
/*
|
||||
* 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.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
using QuantConnect.Data.Market;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Securities.Future;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Continuous Futures Back Month #1 Regression algorithm. Asserting and showcasing the behavior of adding a continuous future
|
||||
/// </summary>
|
||||
public class ContinuousFutureBackMonthRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private List<SymbolChangedEvent> _mappings = new();
|
||||
private Future _continuousContract;
|
||||
private DateTime _lastDateLog;
|
||||
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 7, 1);
|
||||
SetEndDate(2014, 1, 1);
|
||||
|
||||
try
|
||||
{
|
||||
AddFuture(Futures.Indices.SP500EMini,
|
||||
dataNormalizationMode: DataNormalizationMode.BackwardsPanamaCanal,
|
||||
dataMappingMode: DataMappingMode.OpenInterest,
|
||||
contractDepthOffset: 5
|
||||
);
|
||||
throw new Exception("Expected out of rage exception. We don't support that many back months");
|
||||
}
|
||||
catch (ArgumentOutOfRangeException)
|
||||
{
|
||||
// expected
|
||||
}
|
||||
|
||||
_continuousContract = AddFuture(Futures.Indices.SP500EMini,
|
||||
dataNormalizationMode: DataNormalizationMode.BackwardsPanamaCanal,
|
||||
dataMappingMode: DataMappingMode.OpenInterest,
|
||||
contractDepthOffset: 1
|
||||
);
|
||||
}
|
||||
|
||||
/// <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 data)
|
||||
{
|
||||
if (data.Keys.Count != 1)
|
||||
{
|
||||
throw new Exception($"We are getting data for more than one symbols! {string.Join(",", data.Keys.Select(symbol => symbol))}");
|
||||
}
|
||||
|
||||
foreach (var changedEvent in data.SymbolChangedEvents.Values)
|
||||
{
|
||||
if (changedEvent.Symbol == _continuousContract.Symbol)
|
||||
{
|
||||
_mappings.Add(changedEvent);
|
||||
Log($"SymbolChanged event: {changedEvent}");
|
||||
|
||||
var backMonthExpiration = changedEvent.Symbol.Underlying.ID.Date;
|
||||
var frontMonthExpiration = FuturesExpiryFunctions.FuturesExpiryFunction(_continuousContract.Symbol)(Time.AddMonths(1));
|
||||
|
||||
if (backMonthExpiration <= frontMonthExpiration.Date)
|
||||
{
|
||||
throw new Exception($"Unexpected current mapped contract expiration {backMonthExpiration}" +
|
||||
$" @ {Time} it should be AFTER front month expiration {frontMonthExpiration}");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (_lastDateLog.Month != Time.Month && _continuousContract.HasData)
|
||||
{
|
||||
_lastDateLog = Time;
|
||||
|
||||
Log($"{Time}- {Securities[_continuousContract.Symbol].GetLastData()}");
|
||||
if (Portfolio.Invested)
|
||||
{
|
||||
Liquidate();
|
||||
}
|
||||
else
|
||||
{
|
||||
// This works because we set this contract as tradable, even if it's a canonical security
|
||||
Buy(_continuousContract.Symbol, 1);
|
||||
}
|
||||
|
||||
if(Time.Month == 1 && Time.Year == 2013)
|
||||
{
|
||||
var response = History(new[] { _continuousContract.Symbol }, 60 * 24 * 90);
|
||||
if (!response.Any())
|
||||
{
|
||||
throw new Exception("Unexpected empty history response");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
if (orderEvent.Status == OrderStatus.Filled)
|
||||
{
|
||||
Log($"{orderEvent}");
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
var expectedMappingCounts = 2;
|
||||
if (_mappings.Count != expectedMappingCounts)
|
||||
{
|
||||
throw new Exception($"Unexpected symbol changed events: {_mappings.Count}, was expecting {expectedMappingCounts}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <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 Trades", "3"},
|
||||
{"Average Win", "1.16%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "2.229%"},
|
||||
{"Drawdown", "1.600%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.118%"},
|
||||
{"Sharpe Ratio", "0.726"},
|
||||
{"Probabilistic Sharpe Ratio", "38.511%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.007"},
|
||||
{"Beta", "0.099"},
|
||||
{"Annual Standard Deviation", "0.022"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-2.74"},
|
||||
{"Tracking Error", "0.076"},
|
||||
{"Treynor Ratio", "0.159"},
|
||||
{"Total Fees", "$5.55"},
|
||||
{"Estimated Strategy Capacity", "$290000.00"},
|
||||
{"Lowest Capacity Asset", "ES 1S1"},
|
||||
{"Fitness Score", "0.009"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "0.498"},
|
||||
{"Return Over Maximum Drawdown", "1.803"},
|
||||
{"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", "e669103cc598f59d85f5e8d5f0b8df30"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,163 +0,0 @@
|
||||
/*
|
||||
* 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.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Securities.Future;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Continuous Futures History Regression algorithm. Asserting and showcasing the behavior of adding a continuous future
|
||||
/// </summary>
|
||||
public class ContinuousFutureHistoryRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Future _continuousContract;
|
||||
private bool _warmedUp;
|
||||
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 10);
|
||||
SetEndDate(2013, 10, 11);
|
||||
|
||||
_continuousContract = AddFuture(Futures.Indices.SP500EMini,
|
||||
dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
|
||||
dataMappingMode: DataMappingMode.OpenInterest,
|
||||
contractDepthOffset: 1
|
||||
);
|
||||
SetWarmup(10);
|
||||
}
|
||||
|
||||
/// <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 data)
|
||||
{
|
||||
if (IsWarmingUp)
|
||||
{
|
||||
// warm up data
|
||||
_warmedUp = true;
|
||||
|
||||
if (!_continuousContract.HasData)
|
||||
{
|
||||
throw new Exception($"ContinuousContract did not get any data during warmup!");
|
||||
}
|
||||
|
||||
var backMonthExpiration = data.Keys.Single().Underlying.ID.Date;
|
||||
var frontMonthExpiration = FuturesExpiryFunctions.FuturesExpiryFunction(_continuousContract.Symbol)(Time.AddMonths(1));
|
||||
if (backMonthExpiration <= frontMonthExpiration.Date)
|
||||
{
|
||||
throw new Exception($"Unexpected current mapped contract expiration {backMonthExpiration}" +
|
||||
$" @ {Time} it should be AFTER front month expiration {frontMonthExpiration}");
|
||||
}
|
||||
}
|
||||
if (data.Keys.Count != 1)
|
||||
{
|
||||
throw new Exception($"We are getting data for more than one symbols! {string.Join(",", data.Keys.Select(symbol => symbol))}");
|
||||
}
|
||||
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
Buy(_continuousContract.Symbol, 1);
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (!_warmedUp)
|
||||
{
|
||||
throw new Exception("Algorithm didn't warm up!");
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
Debug($"{Time}-{changes}");
|
||||
if (changes.AddedSecurities.Any(security => security.Symbol != _continuousContract.Symbol)
|
||||
|| changes.RemovedSecurities.Any(security => security.Symbol != _continuousContract.Symbol))
|
||||
{
|
||||
throw new Exception($"We got an unexpected security changes {changes}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp };
|
||||
|
||||
/// <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 Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe 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", "$1.85"},
|
||||
{"Estimated Strategy Capacity", "$42000000.00"},
|
||||
{"Lowest Capacity Asset", "ES 1S1"},
|
||||
{"Fitness Score", "0.76"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0.76"},
|
||||
{"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", "4de9344671d542e30066338e2bf9d400"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,212 +0,0 @@
|
||||
/*
|
||||
* 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.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
using QuantConnect.Data.Market;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Securities.Future;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Continuous Futures Regression algorithm. Asserting and showcasing the behavior of adding a continuous future
|
||||
/// </summary>
|
||||
public class ContinuousFutureRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private List<SymbolChangedEvent> _mappings = new();
|
||||
private Symbol _currentMappedSymbol;
|
||||
private Future _continuousContract;
|
||||
private DateTime _lastMonth;
|
||||
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 7, 1);
|
||||
SetEndDate(2014, 1, 1);
|
||||
|
||||
_continuousContract = AddFuture(Futures.Indices.SP500EMini,
|
||||
dataNormalizationMode: DataNormalizationMode.BackwardsRatio,
|
||||
dataMappingMode: DataMappingMode.LastTradingDay,
|
||||
contractDepthOffset: 0
|
||||
);
|
||||
}
|
||||
|
||||
/// <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 data)
|
||||
{
|
||||
// we subtract a minute cause we can get data on the market close, from the previous minute
|
||||
if (!_continuousContract.Exchange.DateTimeIsOpen(Time.AddMinutes(-1)))
|
||||
{
|
||||
if (data.Bars.Count > 0 || data.QuoteBars.Count > 0)
|
||||
{
|
||||
throw new Exception($"We are getting data during closed market!");
|
||||
}
|
||||
}
|
||||
|
||||
var currentlyMappedSecurity = Securities[_continuousContract.Mapped];
|
||||
|
||||
if (data.Keys.Count != 1)
|
||||
{
|
||||
throw new Exception($"We are getting data for more than one symbols! {string.Join(",", data.Keys.Select(symbol => symbol))}");
|
||||
}
|
||||
|
||||
foreach (var changedEvent in data.SymbolChangedEvents.Values)
|
||||
{
|
||||
if (changedEvent.Symbol == _continuousContract.Symbol)
|
||||
{
|
||||
_mappings.Add(changedEvent);
|
||||
Log($"{Time} - SymbolChanged event: {changedEvent}");
|
||||
|
||||
if (_currentMappedSymbol == _continuousContract.Mapped)
|
||||
{
|
||||
throw new Exception($"Continuous contract current symbol did not change! {_continuousContract.Mapped}");
|
||||
}
|
||||
|
||||
var currentExpiration = changedEvent.Symbol.Underlying.ID.Date;
|
||||
var frontMonthExpiration = FuturesExpiryFunctions.FuturesExpiryFunction(_continuousContract.Symbol)(Time.AddMonths(1));
|
||||
|
||||
if (currentExpiration != frontMonthExpiration.Date)
|
||||
{
|
||||
throw new Exception($"Unexpected current mapped contract expiration {currentExpiration}" +
|
||||
$" @ {Time} it should be AT front month expiration {frontMonthExpiration}");
|
||||
}
|
||||
}
|
||||
}
|
||||
if (_lastMonth.Month != Time.Month && currentlyMappedSecurity.HasData)
|
||||
{
|
||||
_lastMonth = Time;
|
||||
|
||||
Log($"{Time}- {currentlyMappedSecurity.GetLastData()}");
|
||||
if (Portfolio.Invested)
|
||||
{
|
||||
Liquidate();
|
||||
}
|
||||
else
|
||||
{
|
||||
// This works because we set this contract as tradable, even if it's a canonical security
|
||||
Buy(currentlyMappedSecurity.Symbol, 1);
|
||||
}
|
||||
|
||||
if(Time.Month == 1 && Time.Year == 2013)
|
||||
{
|
||||
var response = History(new[] { _continuousContract.Symbol }, 60 * 24 * 90);
|
||||
if (!response.Any())
|
||||
{
|
||||
throw new Exception("Unexpected empty history response");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
_currentMappedSymbol = _continuousContract.Mapped;
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
if (orderEvent.Status == OrderStatus.Filled)
|
||||
{
|
||||
Log($"{orderEvent}");
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
Debug($"{Time}-{changes}");
|
||||
if (changes.AddedSecurities.Any(security => security.Symbol != _continuousContract.Symbol)
|
||||
|| changes.RemovedSecurities.Any(security => security.Symbol != _continuousContract.Symbol))
|
||||
{
|
||||
throw new Exception($"We got an unexpected security changes {changes}");
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
var expectedMappingCounts = 2;
|
||||
if (_mappings.Count != expectedMappingCounts)
|
||||
{
|
||||
throw new Exception($"Unexpected symbol changed events: {_mappings.Count}, was expecting {expectedMappingCounts}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <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 Trades", "3"},
|
||||
{"Average Win", "1.21%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "2.392%"},
|
||||
{"Drawdown", "1.600%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.199%"},
|
||||
{"Sharpe Ratio", "0.775"},
|
||||
{"Probabilistic Sharpe Ratio", "40.287%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.006"},
|
||||
{"Beta", "0.099"},
|
||||
{"Annual Standard Deviation", "0.022"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-2.726"},
|
||||
{"Tracking Error", "0.076"},
|
||||
{"Treynor Ratio", "0.169"},
|
||||
{"Total Fees", "$5.55"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "ES VMKLFZIH2MTD"},
|
||||
{"Fitness Score", "0.01"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "0.516"},
|
||||
{"Return Over Maximum Drawdown", "1.935"},
|
||||
{"Portfolio Turnover", "0.016"},
|
||||
{"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", "8ad040c62ad255e4f9cd423364147e85"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -133,18 +133,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "53.000%"},
|
||||
{"Expectancy", "-0.053"},
|
||||
{"Net Profit", "-29.486%"},
|
||||
{"Sharpe Ratio", "-0.072"},
|
||||
{"Probabilistic Sharpe Ratio", "0.001%"},
|
||||
{"Sharpe Ratio", "-0.078"},
|
||||
{"Probabilistic Sharpe Ratio", "0.004%"},
|
||||
{"Loss Rate", "56%"},
|
||||
{"Win Rate", "44%"},
|
||||
{"Profit-Loss Ratio", "1.15"},
|
||||
{"Alpha", "-0.004"},
|
||||
{"Beta", "-0.095"},
|
||||
{"Annual Standard Deviation", "0.149"},
|
||||
{"Annual Variance", "0.022"},
|
||||
{"Information Ratio", "-0.34"},
|
||||
{"Tracking Error", "0.23"},
|
||||
{"Treynor Ratio", "0.113"},
|
||||
{"Alpha", "-0.013"},
|
||||
{"Beta", "0.007"},
|
||||
{"Annual Standard Deviation", "0.163"},
|
||||
{"Annual Variance", "0.027"},
|
||||
{"Information Ratio", "-0.393"},
|
||||
{"Tracking Error", "0.238"},
|
||||
{"Treynor Ratio", "-1.72"},
|
||||
{"Total Fees", "$796.82"},
|
||||
{"Estimated Strategy Capacity", "$1200000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -104,18 +104,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "21.600%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "38.619%"},
|
||||
{"Sharpe Ratio", "14.33"},
|
||||
{"Probabilistic Sharpe Ratio", "75.756%"},
|
||||
{"Sharpe Ratio", "33.779"},
|
||||
{"Probabilistic Sharpe Ratio", "77.029%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "10.389"},
|
||||
{"Beta", "8.754"},
|
||||
{"Annual Standard Deviation", "0.95"},
|
||||
{"Annual Variance", "0.903"},
|
||||
{"Information Ratio", "15.703"},
|
||||
{"Tracking Error", "0.844"},
|
||||
{"Treynor Ratio", "1.555"},
|
||||
{"Alpha", "32.812"},
|
||||
{"Beta", "8.756"},
|
||||
{"Annual Standard Deviation", "1.11"},
|
||||
{"Annual Variance", "1.231"},
|
||||
{"Information Ratio", "37.501"},
|
||||
{"Tracking Error", "0.985"},
|
||||
{"Treynor Ratio", "4.281"},
|
||||
{"Total Fees", "$30.00"},
|
||||
{"Estimated Strategy Capacity", "$22000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -123,13 +123,13 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "1.747"},
|
||||
{"Beta", "0.047"},
|
||||
{"Alpha", "1.736"},
|
||||
{"Beta", "0.142"},
|
||||
{"Annual Standard Deviation", "0.84"},
|
||||
{"Annual Variance", "0.706"},
|
||||
{"Information Ratio", "1.922"},
|
||||
{"Tracking Error", "0.848"},
|
||||
{"Treynor Ratio", "37.47"},
|
||||
{"Information Ratio", "1.925"},
|
||||
{"Tracking Error", "0.846"},
|
||||
{"Treynor Ratio", "12.334"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "BTC.Bitcoin 2S"},
|
||||
|
||||
@@ -93,13 +93,13 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "1.747"},
|
||||
{"Beta", "0.047"},
|
||||
{"Alpha", "1.736"},
|
||||
{"Beta", "0.142"},
|
||||
{"Annual Standard Deviation", "0.84"},
|
||||
{"Annual Variance", "0.706"},
|
||||
{"Information Ratio", "1.922"},
|
||||
{"Tracking Error", "0.848"},
|
||||
{"Treynor Ratio", "37.473"},
|
||||
{"Information Ratio", "1.925"},
|
||||
{"Tracking Error", "0.846"},
|
||||
{"Treynor Ratio", "12.333"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "BTC.Bitcoin 2S"},
|
||||
|
||||
@@ -128,18 +128,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "11.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-10.343%"},
|
||||
{"Sharpe Ratio", "-1.696"},
|
||||
{"Probabilistic Sharpe Ratio", "0.009%"},
|
||||
{"Sharpe Ratio", "-1.554"},
|
||||
{"Probabilistic Sharpe Ratio", "0.001%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.924"},
|
||||
{"Beta", "-5.612"},
|
||||
{"Annual Standard Deviation", "0.587"},
|
||||
{"Annual Variance", "0.345"},
|
||||
{"Information Ratio", "-1.517"},
|
||||
{"Tracking Error", "0.664"},
|
||||
{"Treynor Ratio", "0.177"},
|
||||
{"Alpha", "-0.91"},
|
||||
{"Beta", "-5.602"},
|
||||
{"Annual Standard Deviation", "0.643"},
|
||||
{"Annual Variance", "0.413"},
|
||||
{"Information Ratio", "-1.378"},
|
||||
{"Tracking Error", "0.736"},
|
||||
{"Treynor Ratio", "0.178"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "NWSA.CustomDataUsingMapping T3MO1488O0H0"},
|
||||
|
||||
@@ -199,18 +199,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "2.400%"},
|
||||
{"Expectancy", "-0.187"},
|
||||
{"Net Profit", "-0.629%"},
|
||||
{"Sharpe Ratio", "-1.281"},
|
||||
{"Probabilistic Sharpe Ratio", "21.874%"},
|
||||
{"Sharpe Ratio", "-1.475"},
|
||||
{"Probabilistic Sharpe Ratio", "23.597%"},
|
||||
{"Loss Rate", "70%"},
|
||||
{"Win Rate", "30%"},
|
||||
{"Profit-Loss Ratio", "1.73"},
|
||||
{"Alpha", "-0.096"},
|
||||
{"Beta", "0.122"},
|
||||
{"Annual Standard Deviation", "0.04"},
|
||||
{"Alpha", "-0.136"},
|
||||
{"Beta", "0.126"},
|
||||
{"Annual Standard Deviation", "0.047"},
|
||||
{"Annual Variance", "0.002"},
|
||||
{"Information Ratio", "-4.126"},
|
||||
{"Tracking Error", "0.102"},
|
||||
{"Treynor Ratio", "-0.417"},
|
||||
{"Information Ratio", "-5.094"},
|
||||
{"Tracking Error", "0.118"},
|
||||
{"Treynor Ratio", "-0.547"},
|
||||
{"Total Fees", "$62.25"},
|
||||
{"Estimated Strategy Capacity", "$52000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -90,18 +90,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.200%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.694%"},
|
||||
{"Sharpe Ratio", "8.671"},
|
||||
{"Probabilistic Sharpe Ratio", "67.159%"},
|
||||
{"Sharpe Ratio", "6.988"},
|
||||
{"Probabilistic Sharpe Ratio", "68.188%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.053"},
|
||||
{"Beta", "1.003"},
|
||||
{"Annual Standard Deviation", "0.223"},
|
||||
{"Annual Variance", "0.05"},
|
||||
{"Information Ratio", "-35.82"},
|
||||
{"Tracking Error", "0.001"},
|
||||
{"Treynor Ratio", "1.93"},
|
||||
{"Alpha", "1.172"},
|
||||
{"Beta", "0.14"},
|
||||
{"Annual Standard Deviation", "0.196"},
|
||||
{"Annual Variance", "0.038"},
|
||||
{"Information Ratio", "-0.118"},
|
||||
{"Tracking Error", "0.256"},
|
||||
{"Treynor Ratio", "9.783"},
|
||||
{"Total Fees", "$3.45"},
|
||||
{"Estimated Strategy Capacity", "$970000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -189,8 +189,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-2.094"},
|
||||
{"Tracking Error", "0.175"},
|
||||
{"Information Ratio", "-2.564"},
|
||||
{"Tracking Error", "0.214"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
|
||||
@@ -1,202 +0,0 @@
|
||||
/*
|
||||
* 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.Data.Market;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Data;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Regression test to check custom indicators warms up properly
|
||||
/// when one of them define WarmUpPeriod parameter and the other doesn't
|
||||
/// </summary>
|
||||
public class CustomWarmUpPeriodIndicatorAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private CustomSMA _customNotWarmUp;
|
||||
private CSMAWithWarmUp _customWarmUp;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 10, 7);
|
||||
SetEndDate(2013, 10, 11);
|
||||
AddEquity("SPY", Resolution.Second);
|
||||
|
||||
// Create two custom indicators, where one of them defines WarmUpPeriod parameter
|
||||
_customNotWarmUp = new CustomSMA("_customNotWarmUp", 60);
|
||||
_customWarmUp = new CSMAWithWarmUp("_customWarmUp", 60);
|
||||
|
||||
// Register the daily data of "SPY" to automatically update both indicators
|
||||
RegisterIndicator("SPY", _customWarmUp, Resolution.Minute);
|
||||
RegisterIndicator("SPY", _customNotWarmUp, Resolution.Minute);
|
||||
|
||||
// Warm up _customWarmUp indicator
|
||||
WarmUpIndicator("SPY", _customWarmUp, Resolution.Minute);
|
||||
|
||||
// Check _customWarmUp indicator has already been warmed up with the requested data
|
||||
if (!_customWarmUp.IsReady)
|
||||
{
|
||||
throw new Exception("_customWarmUp indicator was expected to be ready");
|
||||
}
|
||||
if (_customWarmUp.Samples != 60)
|
||||
{
|
||||
throw new Exception("_customWarmUp indicator was expected to have processed 60 datapoints already");
|
||||
}
|
||||
|
||||
// Try to warm up _customNotWarmUp indicator. It's expected from LEAN to skip the warm up process
|
||||
// because this indicator doesn't implement IIndicatorWarmUpPeriodProvider
|
||||
WarmUpIndicator("SPY", _customNotWarmUp, Resolution.Minute);
|
||||
|
||||
// Check _customNotWarmUp indicator is not ready, because the warm up process was skipped
|
||||
if (_customNotWarmUp.IsReady)
|
||||
{
|
||||
throw new Exception("_customNotWarmUp indicator wasn't expected to be warmed up");
|
||||
}
|
||||
}
|
||||
|
||||
public void OnData(TradeBars data)
|
||||
{
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
SetHoldings("SPY", 1);
|
||||
}
|
||||
|
||||
if (Time.Second == 0)
|
||||
{
|
||||
// Compute the difference between the indicators values
|
||||
var diff = Math.Abs(_customNotWarmUp.Current.Value - _customWarmUp.Current.Value);
|
||||
|
||||
// Check _customNotWarmUp indicator is ready when the number of samples is bigger than its period
|
||||
if (_customNotWarmUp.IsReady != (_customNotWarmUp.Samples >= 60))
|
||||
{
|
||||
throw new Exception("_customNotWarmUp indicator was expected to be ready when the number of samples were bigger that its WarmUpPeriod parameter");
|
||||
}
|
||||
|
||||
// Check their values are the same when both are ready
|
||||
if (diff > 1e-10m && _customNotWarmUp.IsReady && _customWarmUp.IsReady)
|
||||
{
|
||||
throw new Exception($"The values of the indicators are not the same. The difference is {diff}");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Custom implementation of SimpleMovingAverage.
|
||||
/// Represents the traditional simple moving average indicator (SMA) without WarmUpPeriod parameter defined
|
||||
/// </summary>
|
||||
private class CustomSMA : IndicatorBase<IBaseData>
|
||||
{
|
||||
private Queue<IBaseData> _queue;
|
||||
private int _period;
|
||||
public CustomSMA(string name, int period)
|
||||
: base(name)
|
||||
{
|
||||
_queue = new Queue<IBaseData>();
|
||||
_period = period;
|
||||
}
|
||||
|
||||
public override bool IsReady => _queue.Count == _period;
|
||||
|
||||
protected override decimal ComputeNextValue(IBaseData input)
|
||||
{
|
||||
_queue.Enqueue(input);
|
||||
if (_queue.Count > _period)
|
||||
{
|
||||
_queue.Dequeue();
|
||||
}
|
||||
var items = (_queue.ToArray());
|
||||
var sum = 0m;
|
||||
Array.ForEach(items, i => sum += i.Value);
|
||||
return sum / _queue.Count;
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Custom implementation of SimpleMovingAverage.
|
||||
/// Represents the traditional simple moving average indicator (SMA) with WarmUpPeriod defined
|
||||
/// </summary>
|
||||
private class CSMAWithWarmUp : CustomSMA, IIndicatorWarmUpPeriodProvider
|
||||
{
|
||||
public CSMAWithWarmUp(string name, int period)
|
||||
: base(name, period)
|
||||
{
|
||||
WarmUpPeriod = period;
|
||||
}
|
||||
public int WarmUpPeriod { get; private set; }
|
||||
}
|
||||
|
||||
/// <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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <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 Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "272.157%"},
|
||||
{"Drawdown", "2.200%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.694%"},
|
||||
{"Sharpe Ratio", "8.897"},
|
||||
{"Probabilistic Sharpe Ratio", "67.609%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.003"},
|
||||
{"Beta", "0.998"},
|
||||
{"Annual Standard Deviation", "0.222"},
|
||||
{"Annual Variance", "0.049"},
|
||||
{"Information Ratio", "-14.534"},
|
||||
{"Tracking Error", "0.001"},
|
||||
{"Treynor Ratio", "1.98"},
|
||||
{"Total Fees", "$3.45"},
|
||||
{"Estimated Strategy Capacity", "$310000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.246"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "9.761"},
|
||||
{"Return Over Maximum Drawdown", "107.509"},
|
||||
{"Portfolio Turnover", "0.249"},
|
||||
{"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", "e10039d74166b161f3ea2851a5e85843"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -114,8 +114,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-0.101"},
|
||||
{"Tracking Error", "0.185"},
|
||||
{"Information Ratio", "-0.098"},
|
||||
{"Tracking Error", "0.179"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
|
||||
@@ -114,8 +114,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-0.104"},
|
||||
{"Tracking Error", "0.192"},
|
||||
{"Information Ratio", "-0.111"},
|
||||
{"Tracking Error", "0.207"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
|
||||
@@ -94,32 +94,32 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "1.64%"},
|
||||
{"Average Win", "1.63%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "7.329%"},
|
||||
{"Compounding Annual Return", "7.292%"},
|
||||
{"Drawdown", "1.300%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.642%"},
|
||||
{"Sharpe Ratio", "2.36"},
|
||||
{"Probabilistic Sharpe Ratio", "94.555%"},
|
||||
{"Net Profit", "1.634%"},
|
||||
{"Sharpe Ratio", "2.495"},
|
||||
{"Probabilistic Sharpe Ratio", "92.298%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.006"},
|
||||
{"Beta", "0.158"},
|
||||
{"Annual Standard Deviation", "0.03"},
|
||||
{"Beta", "0.157"},
|
||||
{"Annual Standard Deviation", "0.033"},
|
||||
{"Annual Variance", "0.001"},
|
||||
{"Information Ratio", "-4.44"},
|
||||
{"Tracking Error", "0.075"},
|
||||
{"Treynor Ratio", "0.441"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Information Ratio", "-4.901"},
|
||||
{"Tracking Error", "0.081"},
|
||||
{"Treynor Ratio", "0.519"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$170000000.00"},
|
||||
{"Lowest Capacity Asset", "ES VMKLFZIH2MTD"},
|
||||
{"Fitness Score", "0.019"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "1.369"},
|
||||
{"Return Over Maximum Drawdown", "9.749"},
|
||||
{"Sortino Ratio", "1.362"},
|
||||
{"Return Over Maximum Drawdown", "9.699"},
|
||||
{"Portfolio Turnover", "0.023"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
@@ -134,7 +134,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "4c5e32aedcd5bb67642d1629628fe615"}
|
||||
{"OrderListHash", "00d6dc8775da38f7f79defad06de240a"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -132,18 +132,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "45.600%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-26.400%"},
|
||||
{"Sharpe Ratio", "-0.602"},
|
||||
{"Probabilistic Sharpe Ratio", "19.127%"},
|
||||
{"Sharpe Ratio", "-0.557"},
|
||||
{"Probabilistic Sharpe Ratio", "20.162%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.559"},
|
||||
{"Beta", "-0.807"},
|
||||
{"Annual Standard Deviation", "1.582"},
|
||||
{"Annual Variance", "2.502"},
|
||||
{"Information Ratio", "-0.905"},
|
||||
{"Tracking Error", "1.593"},
|
||||
{"Treynor Ratio", "1.181"},
|
||||
{"Alpha", "-0.564"},
|
||||
{"Beta", "-0.663"},
|
||||
{"Annual Standard Deviation", "1.752"},
|
||||
{"Annual Variance", "3.069"},
|
||||
{"Information Ratio", "-0.906"},
|
||||
{"Tracking Error", "1.763"},
|
||||
{"Treynor Ratio", "1.472"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$1000000.00"},
|
||||
{"Lowest Capacity Asset", "SPX 31KC0UJFONTBI|SPX 31"},
|
||||
@@ -166,7 +166,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "721fddfd1327f7adcc2883d1412708c9"}
|
||||
{"OrderListHash", "4ae4c1d8e4054c41908fd36e893699a6"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -140,34 +140,34 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-5.58%"},
|
||||
{"Compounding Annual Return", "-87.694%"},
|
||||
{"Drawdown", "5.600%"},
|
||||
{"Average Loss", "-3.23%"},
|
||||
{"Compounding Annual Return", "-79.990%"},
|
||||
{"Drawdown", "4.300%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-5.578%"},
|
||||
{"Sharpe Ratio", "-4.683"},
|
||||
{"Probabilistic Sharpe Ratio", "0.008%"},
|
||||
{"Net Profit", "-4.312%"},
|
||||
{"Sharpe Ratio", "-5.958"},
|
||||
{"Probabilistic Sharpe Ratio", "0.000%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.622"},
|
||||
{"Beta", "-0.877"},
|
||||
{"Annual Standard Deviation", "0.142"},
|
||||
{"Annual Variance", "0.02"},
|
||||
{"Information Ratio", "-3.844"},
|
||||
{"Tracking Error", "0.186"},
|
||||
{"Treynor Ratio", "0.759"},
|
||||
{"Total Fees", "$36.70"},
|
||||
{"Estimated Strategy Capacity", "$65000.00"},
|
||||
{"Alpha", "-0.685"},
|
||||
{"Beta", "-0.445"},
|
||||
{"Annual Standard Deviation", "0.119"},
|
||||
{"Annual Variance", "0.014"},
|
||||
{"Information Ratio", "-4.887"},
|
||||
{"Tracking Error", "0.155"},
|
||||
{"Treynor Ratio", "1.589"},
|
||||
{"Total Fees", "$55.05"},
|
||||
{"Estimated Strategy Capacity", "$43000.00"},
|
||||
{"Lowest Capacity Asset", "AAA SEVKGI6HF885"},
|
||||
{"Fitness Score", "0.003"},
|
||||
{"Fitness Score", "0.002"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-10.959"},
|
||||
{"Return Over Maximum Drawdown", "-15.72"},
|
||||
{"Portfolio Turnover", "0.224"},
|
||||
{"Sortino Ratio", "-15.687"},
|
||||
{"Return Over Maximum Drawdown", "-18.549"},
|
||||
{"Portfolio Turnover", "0.334"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
@@ -181,7 +181,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "2d66947eafcca81ba9a2cd3bb351eee2"}
|
||||
{"OrderListHash", "e357cfa77fd5e5b974c68d550fa66490"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -108,9 +108,9 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Average Loss", "-0.02%"},
|
||||
{"Compounding Annual Return", "-0.111%"},
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Expectancy", "-0.678"},
|
||||
{"Net Profit", "-0.111%"},
|
||||
{"Sharpe Ratio", "-0.967"},
|
||||
{"Expectancy", "-0.679"},
|
||||
{"Net Profit", "-0.112%"},
|
||||
{"Sharpe Ratio", "-1.052"},
|
||||
{"Probabilistic Sharpe Ratio", "0.000%"},
|
||||
{"Loss Rate", "80%"},
|
||||
{"Win Rate", "20%"},
|
||||
@@ -119,10 +119,10 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "-0.001"},
|
||||
{"Annual Standard Deviation", "0.001"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-1.075"},
|
||||
{"Tracking Error", "0.107"},
|
||||
{"Treynor Ratio", "1.353"},
|
||||
{"Total Fees", "$14.80"},
|
||||
{"Information Ratio", "-1.182"},
|
||||
{"Tracking Error", "0.117"},
|
||||
{"Treynor Ratio", "1.617"},
|
||||
{"Total Fees", "$37.00"},
|
||||
{"Estimated Strategy Capacity", "$860000000.00"},
|
||||
{"Lowest Capacity Asset", "DC V5E8P9SH0U0X"},
|
||||
{"Fitness Score", "0"},
|
||||
@@ -144,7 +144,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "d10e8665214344369e3e8f1c49dbdd67"}
|
||||
{"OrderListHash", "de309ab56d2fcd80ff03df2802d9feda"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -173,18 +173,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "12.400%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "153.224%"},
|
||||
{"Sharpe Ratio", "1.116"},
|
||||
{"Probabilistic Sharpe Ratio", "56.426%"},
|
||||
{"Sharpe Ratio", "1.233"},
|
||||
{"Probabilistic Sharpe Ratio", "65.906%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.054"},
|
||||
{"Beta", "0.507"},
|
||||
{"Annual Standard Deviation", "0.107"},
|
||||
{"Annual Variance", "0.011"},
|
||||
{"Information Ratio", "-0.082"},
|
||||
{"Tracking Error", "0.105"},
|
||||
{"Treynor Ratio", "0.235"},
|
||||
{"Alpha", "0.146"},
|
||||
{"Beta", "-0.016"},
|
||||
{"Annual Standard Deviation", "0.117"},
|
||||
{"Annual Variance", "0.014"},
|
||||
{"Information Ratio", "-0.052"},
|
||||
{"Tracking Error", "0.204"},
|
||||
{"Treynor Ratio", "-8.847"},
|
||||
{"Total Fees", "$49.43"},
|
||||
{"Estimated Strategy Capacity", "$740000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -195,18 +195,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "10.400%"},
|
||||
{"Expectancy", "0.068"},
|
||||
{"Net Profit", "14.802%"},
|
||||
{"Sharpe Ratio", "0.978"},
|
||||
{"Probabilistic Sharpe Ratio", "46.740%"},
|
||||
{"Sharpe Ratio", "1.077"},
|
||||
{"Probabilistic Sharpe Ratio", "50.578%"},
|
||||
{"Loss Rate", "46%"},
|
||||
{"Win Rate", "54%"},
|
||||
{"Profit-Loss Ratio", "0.97"},
|
||||
{"Alpha", "0.008"},
|
||||
{"Beta", "0.98"},
|
||||
{"Annual Standard Deviation", "0.109"},
|
||||
{"Annual Variance", "0.012"},
|
||||
{"Information Ratio", "0.158"},
|
||||
{"Tracking Error", "0.041"},
|
||||
{"Treynor Ratio", "0.109"},
|
||||
{"Alpha", "0.137"},
|
||||
{"Beta", "-0.069"},
|
||||
{"Annual Standard Deviation", "0.119"},
|
||||
{"Annual Variance", "0.014"},
|
||||
{"Information Ratio", "0.046"},
|
||||
{"Tracking Error", "0.169"},
|
||||
{"Treynor Ratio", "-1.869"},
|
||||
{"Total Fees", "$7495.19"},
|
||||
{"Estimated Strategy Capacity", "$320000.00"},
|
||||
{"Lowest Capacity Asset", "BNO UN3IMQ2JU1YD"},
|
||||
|
||||
@@ -168,18 +168,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "10.300%"},
|
||||
{"Expectancy", "0.081"},
|
||||
{"Net Profit", "16.153%"},
|
||||
{"Sharpe Ratio", "1.062"},
|
||||
{"Probabilistic Sharpe Ratio", "50.224%"},
|
||||
{"Sharpe Ratio", "1.17"},
|
||||
{"Probabilistic Sharpe Ratio", "54.048%"},
|
||||
{"Loss Rate", "45%"},
|
||||
{"Win Rate", "55%"},
|
||||
{"Profit-Loss Ratio", "0.97"},
|
||||
{"Alpha", "0.018"},
|
||||
{"Beta", "0.979"},
|
||||
{"Annual Standard Deviation", "0.109"},
|
||||
{"Annual Variance", "0.012"},
|
||||
{"Information Ratio", "0.38"},
|
||||
{"Tracking Error", "0.041"},
|
||||
{"Treynor Ratio", "0.118"},
|
||||
{"Alpha", "0.147"},
|
||||
{"Beta", "-0.068"},
|
||||
{"Annual Standard Deviation", "0.119"},
|
||||
{"Annual Variance", "0.014"},
|
||||
{"Information Ratio", "0.11"},
|
||||
{"Tracking Error", "0.169"},
|
||||
{"Treynor Ratio", "-2.057"},
|
||||
{"Total Fees", "$5869.25"},
|
||||
{"Estimated Strategy Capacity", "$320000.00"},
|
||||
{"Lowest Capacity Asset", "BNO UN3IMQ2JU1YD"},
|
||||
|
||||
@@ -106,8 +106,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-8.91"},
|
||||
{"Tracking Error", "0.223"},
|
||||
{"Information Ratio", "-7.163"},
|
||||
{"Tracking Error", "0.195"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
|
||||
@@ -107,18 +107,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.618%"},
|
||||
{"Sharpe Ratio", "8.815"},
|
||||
{"Probabilistic Sharpe Ratio", "99.065%"},
|
||||
{"Sharpe Ratio", "9.975"},
|
||||
{"Probabilistic Sharpe Ratio", "99.752%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.12"},
|
||||
{"Beta", "0.143"},
|
||||
{"Alpha", "0.23"},
|
||||
{"Beta", "-0.031"},
|
||||
{"Annual Standard Deviation", "0.022"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-3.746"},
|
||||
{"Tracking Error", "0.084"},
|
||||
{"Treynor Ratio", "1.349"},
|
||||
{"Information Ratio", "-2.899"},
|
||||
{"Tracking Error", "0.101"},
|
||||
{"Treynor Ratio", "-6.961"},
|
||||
{"Total Fees", "$1.00"},
|
||||
{"Estimated Strategy Capacity", "$1000000000.00"},
|
||||
{"Lowest Capacity Asset", "GOOG T1AZ164W5VTX"},
|
||||
|
||||
@@ -122,18 +122,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "2.900%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-1.740%"},
|
||||
{"Sharpe Ratio", "-2.985"},
|
||||
{"Probabilistic Sharpe Ratio", "24.619%"},
|
||||
{"Sharpe Ratio", "-2.982"},
|
||||
{"Probabilistic Sharpe Ratio", "22.315%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "1.316"},
|
||||
{"Beta", "-0.998"},
|
||||
{"Annual Standard Deviation", "0.222"},
|
||||
{"Annual Variance", "0.049"},
|
||||
{"Information Ratio", "-5.951"},
|
||||
{"Tracking Error", "0.445"},
|
||||
{"Treynor Ratio", "0.664"},
|
||||
{"Alpha", "-0.387"},
|
||||
{"Beta", "-0.138"},
|
||||
{"Annual Standard Deviation", "0.195"},
|
||||
{"Annual Variance", "0.038"},
|
||||
{"Information Ratio", "-6.727"},
|
||||
{"Tracking Error", "0.294"},
|
||||
{"Treynor Ratio", "4.201"},
|
||||
{"Total Fees", "$20.23"},
|
||||
{"Estimated Strategy Capacity", "$520000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -114,18 +114,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.200%"},
|
||||
{"Expectancy", "-0.021"},
|
||||
{"Net Profit", "-0.873%"},
|
||||
{"Sharpe Ratio", "-2.39"},
|
||||
{"Probabilistic Sharpe Ratio", "33.387%"},
|
||||
{"Sharpe Ratio", "-2.308"},
|
||||
{"Probabilistic Sharpe Ratio", "31.792%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "0.96"},
|
||||
{"Alpha", "-1.646"},
|
||||
{"Beta", "0.62"},
|
||||
{"Annual Standard Deviation", "0.175"},
|
||||
{"Annual Variance", "0.031"},
|
||||
{"Information Ratio", "-17.555"},
|
||||
{"Tracking Error", "0.137"},
|
||||
{"Treynor Ratio", "-0.674"},
|
||||
{"Alpha", "-0.675"},
|
||||
{"Beta", "0.232"},
|
||||
{"Annual Standard Deviation", "0.152"},
|
||||
{"Annual Variance", "0.023"},
|
||||
{"Information Ratio", "-8.38"},
|
||||
{"Tracking Error", "0.209"},
|
||||
{"Treynor Ratio", "-1.514"},
|
||||
{"Total Fees", "$17.19"},
|
||||
{"Estimated Strategy Capacity", "$640000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -139,18 +139,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "29.500%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-7.888%"},
|
||||
{"Sharpe Ratio", "-0.593"},
|
||||
{"Probabilistic Sharpe Ratio", "34.346%"},
|
||||
{"Sharpe Ratio", "-0.612"},
|
||||
{"Probabilistic Sharpe Ratio", "34.280%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-15.391"},
|
||||
{"Beta", "7.259"},
|
||||
{"Annual Standard Deviation", "1.679"},
|
||||
{"Annual Variance", "2.818"},
|
||||
{"Information Ratio", "-2.032"},
|
||||
{"Tracking Error", "1.466"},
|
||||
{"Treynor Ratio", "-0.137"},
|
||||
{"Alpha", "-13.93"},
|
||||
{"Beta", "6.525"},
|
||||
{"Annual Standard Deviation", "1.619"},
|
||||
{"Annual Variance", "2.62"},
|
||||
{"Information Ratio", "-2.091"},
|
||||
{"Tracking Error", "1.422"},
|
||||
{"Treynor Ratio", "-0.152"},
|
||||
{"Total Fees", "$40.70"},
|
||||
{"Estimated Strategy Capacity", "$4000000.00"},
|
||||
{"Lowest Capacity Asset", "GC VL5E74HP3EE5"},
|
||||
|
||||
@@ -171,18 +171,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "3.300%"},
|
||||
{"Expectancy", "-0.225"},
|
||||
{"Net Profit", "-2.705%"},
|
||||
{"Sharpe Ratio", "-5.022"},
|
||||
{"Probabilistic Sharpe Ratio", "1.586%"},
|
||||
{"Sharpe Ratio", "-4.958"},
|
||||
{"Probabilistic Sharpe Ratio", "1.087%"},
|
||||
{"Loss Rate", "65%"},
|
||||
{"Win Rate", "35%"},
|
||||
{"Profit-Loss Ratio", "1.20"},
|
||||
{"Alpha", "-1.879"},
|
||||
{"Beta", "0.571"},
|
||||
{"Annual Standard Deviation", "0.149"},
|
||||
{"Annual Variance", "0.022"},
|
||||
{"Information Ratio", "-22.181"},
|
||||
{"Tracking Error", "0.123"},
|
||||
{"Treynor Ratio", "-1.31"},
|
||||
{"Alpha", "-1.401"},
|
||||
{"Beta", "0.525"},
|
||||
{"Annual Standard Deviation", "0.135"},
|
||||
{"Annual Variance", "0.018"},
|
||||
{"Information Ratio", "-16.23"},
|
||||
{"Tracking Error", "0.127"},
|
||||
{"Treynor Ratio", "-1.27"},
|
||||
{"Total Fees", "$670.68"},
|
||||
{"Estimated Strategy Capacity", "$190000.00"},
|
||||
{"Lowest Capacity Asset", "IBM R735QTJ8XC9X"},
|
||||
|
||||
@@ -134,18 +134,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "2.500%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-1.040%"},
|
||||
{"Sharpe Ratio", "-9.302"},
|
||||
{"Sharpe Ratio", "-8.265"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.283"},
|
||||
{"Beta", "0.55"},
|
||||
{"Annual Standard Deviation", "0.075"},
|
||||
{"Annual Variance", "0.006"},
|
||||
{"Information Ratio", "0.914"},
|
||||
{"Tracking Error", "0.061"},
|
||||
{"Treynor Ratio", "-1.267"},
|
||||
{"Alpha", "-0.187"},
|
||||
{"Beta", "0.585"},
|
||||
{"Annual Standard Deviation", "0.065"},
|
||||
{"Annual Variance", "0.004"},
|
||||
{"Information Ratio", "1.345"},
|
||||
{"Tracking Error", "0.048"},
|
||||
{"Treynor Ratio", "-0.925"},
|
||||
{"Total Fees", "$21.45"},
|
||||
{"Estimated Strategy Capacity", "$830000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -109,18 +109,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.100%"},
|
||||
{"Sharpe Ratio", "8.342"},
|
||||
{"Probabilistic Sharpe Ratio", "83.750%"},
|
||||
{"Sharpe Ratio", "7.449"},
|
||||
{"Probabilistic Sharpe Ratio", "85.066%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Alpha", "0.006"},
|
||||
{"Beta", "0.033"},
|
||||
{"Annual Standard Deviation", "0.008"},
|
||||
{"Annual Standard Deviation", "0.007"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-8.908"},
|
||||
{"Tracking Error", "0.215"},
|
||||
{"Treynor Ratio", "1.992"},
|
||||
{"Information Ratio", "-7.131"},
|
||||
{"Tracking Error", "0.189"},
|
||||
{"Treynor Ratio", "1.576"},
|
||||
{"Total Fees", "$4.00"},
|
||||
{"Estimated Strategy Capacity", "$6000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -81,21 +81,21 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "16.086%"},
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.148%"},
|
||||
{"Sharpe Ratio", "9.758"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0.014"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "9.758"},
|
||||
{"Tracking Error", "0.014"},
|
||||
{"Information Ratio", "0"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$5.93"},
|
||||
{"Estimated Strategy Capacity", "$150000.00"},
|
||||
|
||||
@@ -92,18 +92,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "0.300%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.947%"},
|
||||
{"Sharpe Ratio", "21.391"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Sharpe Ratio", "14.546"},
|
||||
{"Probabilistic Sharpe Ratio", "90.065%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "4.505"},
|
||||
{"Beta", "0.567"},
|
||||
{"Annual Standard Deviation", "0.192"},
|
||||
{"Annual Variance", "0.037"},
|
||||
{"Information Ratio", "30.843"},
|
||||
{"Tracking Error", "0.156"},
|
||||
{"Treynor Ratio", "7.25"},
|
||||
{"Alpha", "2.13"},
|
||||
{"Beta", "-0.467"},
|
||||
{"Annual Standard Deviation", "0.165"},
|
||||
{"Annual Variance", "0.027"},
|
||||
{"Information Ratio", "7.676"},
|
||||
{"Tracking Error", "0.389"},
|
||||
{"Treynor Ratio", "-5.146"},
|
||||
{"Total Fees", "$22.30"},
|
||||
{"Estimated Strategy Capacity", "$250000000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
|
||||
@@ -149,8 +149,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "5.91"},
|
||||
{"Tracking Error", "0.13"},
|
||||
{"Information Ratio", "5.865"},
|
||||
{"Tracking Error", "0.106"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
|
||||
@@ -153,8 +153,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "5.91"},
|
||||
{"Tracking Error", "0.13"},
|
||||
{"Information Ratio", "5.865"},
|
||||
{"Tracking Error", "0.106"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
|
||||
@@ -101,21 +101,21 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "6"},
|
||||
{"Average Win", "6.02%"},
|
||||
{"Average Loss", "-2.40%"},
|
||||
{"Compounding Annual Return", "1497.266%"},
|
||||
{"Compounding Annual Return", "915.480%"},
|
||||
{"Drawdown", "5.500%"},
|
||||
{"Expectancy", "1.338"},
|
||||
{"Net Profit", "13.775%"},
|
||||
{"Sharpe Ratio", "3.309"},
|
||||
{"Probabilistic Sharpe Ratio", "61.758%"},
|
||||
{"Net Profit", "11.400%"},
|
||||
{"Sharpe Ratio", "9.507"},
|
||||
{"Probabilistic Sharpe Ratio", "76.768%"},
|
||||
{"Loss Rate", "33%"},
|
||||
{"Win Rate", "67%"},
|
||||
{"Profit-Loss Ratio", "2.51"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0.379"},
|
||||
{"Annual Variance", "0.144"},
|
||||
{"Information Ratio", "3.309"},
|
||||
{"Tracking Error", "0.379"},
|
||||
{"Annual Standard Deviation", "0.507"},
|
||||
{"Annual Variance", "0.257"},
|
||||
{"Information Ratio", "9.507"},
|
||||
{"Tracking Error", "0.507"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$2651.01"},
|
||||
{"Estimated Strategy Capacity", "$30000.00"},
|
||||
@@ -139,7 +139,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "604291218c630343a896bfa2f3104932"}
|
||||
{"OrderListHash", "2b3ac55337ce5619fc0388ccdac72c54"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -86,18 +86,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "55.000%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "125.420%"},
|
||||
{"Sharpe Ratio", "0.427"},
|
||||
{"Probabilistic Sharpe Ratio", "1.166%"},
|
||||
{"Sharpe Ratio", "0.469"},
|
||||
{"Probabilistic Sharpe Ratio", "2.603%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0"},
|
||||
{"Beta", "0.997"},
|
||||
{"Annual Standard Deviation", "0.164"},
|
||||
{"Annual Variance", "0.027"},
|
||||
{"Information Ratio", "-0.213"},
|
||||
{"Tracking Error", "0.001"},
|
||||
{"Treynor Ratio", "0.07"},
|
||||
{"Alpha", "0.092"},
|
||||
{"Beta", "-0.091"},
|
||||
{"Annual Standard Deviation", "0.179"},
|
||||
{"Annual Variance", "0.032"},
|
||||
{"Information Ratio", "-0.001"},
|
||||
{"Tracking Error", "0.265"},
|
||||
{"Treynor Ratio", "-0.927"},
|
||||
{"Total Fees", "$43.46"},
|
||||
{"Estimated Strategy Capacity", "$430000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -119,32 +119,32 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "6"},
|
||||
{"Average Win", "2.94%"},
|
||||
{"Average Win", "2.93%"},
|
||||
{"Average Loss", "-4.15%"},
|
||||
{"Compounding Annual Return", "-5.589%"},
|
||||
{"Drawdown", "5.600%"},
|
||||
{"Expectancy", "-0.145"},
|
||||
{"Net Profit", "-2.760%"},
|
||||
{"Sharpe Ratio", "-0.45"},
|
||||
{"Probabilistic Sharpe Ratio", "9.306%"},
|
||||
{"Compounding Annual Return", "-5.673%"},
|
||||
{"Drawdown", "5.700%"},
|
||||
{"Expectancy", "-0.148"},
|
||||
{"Net Profit", "-2.802%"},
|
||||
{"Sharpe Ratio", "-0.49"},
|
||||
{"Probabilistic Sharpe Ratio", "10.317%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "0.71"},
|
||||
{"Alpha", "-0.036"},
|
||||
{"Profit-Loss Ratio", "0.70"},
|
||||
{"Alpha", "-0.042"},
|
||||
{"Beta", "-0.012"},
|
||||
{"Annual Standard Deviation", "0.08"},
|
||||
{"Annual Variance", "0.006"},
|
||||
{"Information Ratio", "-0.149"},
|
||||
{"Tracking Error", "0.387"},
|
||||
{"Treynor Ratio", "2.943"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$280000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UPBIJ7O|ES XFH59UK0MYO1"},
|
||||
{"Annual Standard Deviation", "0.087"},
|
||||
{"Annual Variance", "0.007"},
|
||||
{"Information Ratio", "-0.162"},
|
||||
{"Tracking Error", "0.418"},
|
||||
{"Treynor Ratio", "3.493"},
|
||||
{"Total Fees", "$14.80"},
|
||||
{"Estimated Strategy Capacity", "$180000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0.017"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-0.096"},
|
||||
{"Return Over Maximum Drawdown", "-0.993"},
|
||||
{"Sortino Ratio", "-0.097"},
|
||||
{"Return Over Maximum Drawdown", "-1.002"},
|
||||
{"Portfolio Turnover", "0.043"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
@@ -159,7 +159,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "18f8a17034aa12be40581baecca96788"}
|
||||
{"OrderListHash", "fc9eb9b0a644e4890d5ec3d40367d0e1"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -126,7 +126,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
private void AssertFutureOptionOrderExercise(OrderEvent orderEvent, Security future, Security optionContract)
|
||||
{
|
||||
var expectedLiquidationTimeUtc = new DateTime(2020, 6, 20, 4, 0, 0);
|
||||
var expectedLiquidationTimeUtc = new DateTime(2020, 6, 19, 20, 0, 0);
|
||||
|
||||
if (orderEvent.Direction == OrderDirection.Sell && future.Holdings.Quantity != 0)
|
||||
{
|
||||
@@ -203,27 +203,27 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "1.26%"},
|
||||
{"Average Loss", "-7.41%"},
|
||||
{"Compounding Annual Return", "-12.424%"},
|
||||
{"Average Win", "1.22%"},
|
||||
{"Average Loss", "-7.42%"},
|
||||
{"Compounding Annual Return", "-12.482%"},
|
||||
{"Drawdown", "6.300%"},
|
||||
{"Expectancy", "-0.415"},
|
||||
{"Net Profit", "-6.252%"},
|
||||
{"Sharpe Ratio", "-1.226"},
|
||||
{"Probabilistic Sharpe Ratio", "0.002%"},
|
||||
{"Expectancy", "-0.417"},
|
||||
{"Net Profit", "-6.282%"},
|
||||
{"Sharpe Ratio", "-1.316"},
|
||||
{"Probabilistic Sharpe Ratio", "0.004%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "0.17"},
|
||||
{"Alpha", "-0.086"},
|
||||
{"Alpha", "-0.1"},
|
||||
{"Beta", "0.004"},
|
||||
{"Annual Standard Deviation", "0.07"},
|
||||
{"Annual Variance", "0.005"},
|
||||
{"Information Ratio", "-0.283"},
|
||||
{"Tracking Error", "0.379"},
|
||||
{"Treynor Ratio", "-23.811"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Estimated Strategy Capacity", "$270000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UPBIJ7O|ES XFH59UK0MYO1"},
|
||||
{"Annual Standard Deviation", "0.076"},
|
||||
{"Annual Variance", "0.006"},
|
||||
{"Information Ratio", "-0.305"},
|
||||
{"Tracking Error", "0.411"},
|
||||
{"Treynor Ratio", "-27.616"},
|
||||
{"Total Fees", "$7.40"},
|
||||
{"Estimated Strategy Capacity", "$180000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0.008"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
@@ -243,7 +243,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "b738fdaf1dae6849884df9e51eb6482b"}
|
||||
{"OrderListHash", "c59d790b89d76f1ad3bb7738b28567c9"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -168,32 +168,32 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "8.93%"},
|
||||
{"Average Loss", "-34.88%"},
|
||||
{"Compounding Annual Return", "-50.632%"},
|
||||
{"Drawdown", "29.100%"},
|
||||
{"Expectancy", "-0.372"},
|
||||
{"Net Profit", "-29.072%"},
|
||||
{"Sharpe Ratio", "-0.978"},
|
||||
{"Probabilistic Sharpe Ratio", "0.012%"},
|
||||
{"Average Win", "8.71%"},
|
||||
{"Average Loss", "-34.89%"},
|
||||
{"Compounding Annual Return", "-50.850%"},
|
||||
{"Drawdown", "29.200%"},
|
||||
{"Expectancy", "-0.375"},
|
||||
{"Net Profit", "-29.224%"},
|
||||
{"Sharpe Ratio", "-1.025"},
|
||||
{"Probabilistic Sharpe Ratio", "0.019%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "0.26"},
|
||||
{"Alpha", "-0.339"},
|
||||
{"Profit-Loss Ratio", "0.25"},
|
||||
{"Alpha", "-0.387"},
|
||||
{"Beta", "0.017"},
|
||||
{"Annual Standard Deviation", "0.347"},
|
||||
{"Annual Variance", "0.12"},
|
||||
{"Information Ratio", "-0.714"},
|
||||
{"Tracking Error", "0.505"},
|
||||
{"Treynor Ratio", "-19.672"},
|
||||
{"Total Fees", "$9.25"},
|
||||
{"Estimated Strategy Capacity", "$50000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UPBIJ7O|ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0.055"},
|
||||
{"Annual Standard Deviation", "0.377"},
|
||||
{"Annual Variance", "0.142"},
|
||||
{"Information Ratio", "-0.751"},
|
||||
{"Tracking Error", "0.548"},
|
||||
{"Treynor Ratio", "-22.299"},
|
||||
{"Total Fees", "$37.00"},
|
||||
{"Estimated Strategy Capacity", "$33000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0.056"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-0.155"},
|
||||
{"Return Over Maximum Drawdown", "-1.743"},
|
||||
{"Return Over Maximum Drawdown", "-1.741"},
|
||||
{"Portfolio Turnover", "0.152"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
@@ -208,7 +208,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "ed0cbd8487dd45519e5d0225e51ba29c"}
|
||||
{"OrderListHash", "ca0898608da51d972723b1065a3f0d47"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -179,24 +179,24 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-4.02%"},
|
||||
{"Compounding Annual Return", "-8.099%"},
|
||||
{"Average Loss", "-4.03%"},
|
||||
{"Compounding Annual Return", "-8.103%"},
|
||||
{"Drawdown", "4.000%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-4.027%"},
|
||||
{"Sharpe Ratio", "-1.175"},
|
||||
{"Probabilistic Sharpe Ratio", "0.009%"},
|
||||
{"Net Profit", "-4.029%"},
|
||||
{"Sharpe Ratio", "-1.266"},
|
||||
{"Probabilistic Sharpe Ratio", "0.015%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.056"},
|
||||
{"Alpha", "-0.065"},
|
||||
{"Beta", "0.002"},
|
||||
{"Annual Standard Deviation", "0.047"},
|
||||
{"Annual Variance", "0.002"},
|
||||
{"Information Ratio", "-0.206"},
|
||||
{"Tracking Error", "0.376"},
|
||||
{"Treynor Ratio", "-23.481"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Annual Standard Deviation", "0.051"},
|
||||
{"Annual Variance", "0.003"},
|
||||
{"Information Ratio", "-0.222"},
|
||||
{"Tracking Error", "0.408"},
|
||||
{"Treynor Ratio", "-27.32"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$200000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UPHGV9G|ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0"},
|
||||
@@ -218,7 +218,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "cf1c12b839e49456dc2793f0e63c7803"}
|
||||
{"OrderListHash", "5dc2591837f882d173d2d4852b3b0626"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,169 +0,0 @@
|
||||
/*
|
||||
* 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 System.Reflection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// This regression algorithm tests using FutureOptions daily resolution
|
||||
/// </summary>
|
||||
public class FutureOptionDailyRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
protected OrderTicket Ticket;
|
||||
protected Symbol DcOption;
|
||||
protected virtual Resolution Resolution => Resolution.Daily;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2012, 1, 3);
|
||||
SetEndDate(2012, 1, 4);
|
||||
|
||||
// Add our underlying future contract
|
||||
var dc = AddFutureContract(
|
||||
QuantConnect.Symbol.CreateFuture(
|
||||
Futures.Dairy.ClassIIIMilk,
|
||||
Market.CME,
|
||||
new DateTime(2012, 4, 1)),
|
||||
Resolution).Symbol;
|
||||
|
||||
// Attempt to fetch a specific future option contract
|
||||
DcOption = OptionChainProvider.GetOptionContractList(dc, Time)
|
||||
.Where(x => x.ID.StrikePrice == 17m && x.ID.OptionRight == OptionRight.Call)
|
||||
.Select(x => AddFutureOptionContract(x, Resolution).Symbol)
|
||||
.FirstOrDefault();
|
||||
|
||||
// Validate it is the expected contract
|
||||
var expectedContract = QuantConnect.Symbol.CreateOption(dc, Market.CME, OptionStyle.American,
|
||||
OptionRight.Call, 17m,
|
||||
new DateTime(2012, 4, 01));
|
||||
|
||||
if (DcOption != expectedContract)
|
||||
{
|
||||
throw new Exception($"Contract {DcOption} was not the expected contract {expectedContract}");
|
||||
}
|
||||
|
||||
ScheduleBuySell();
|
||||
}
|
||||
|
||||
protected virtual void ScheduleBuySell()
|
||||
{
|
||||
// Schedule a purchase of this contract tomorrow at 1AM
|
||||
Schedule.On(DateRules.Tomorrow, TimeRules.At(1,0,0), () =>
|
||||
{
|
||||
Ticket = MarketOrder(DcOption, 1);
|
||||
});
|
||||
|
||||
// Schedule liquidation tomorrow at 6PM
|
||||
Schedule.On(DateRules.Tomorrow, TimeRules.At(18,0,0), () =>
|
||||
{
|
||||
Liquidate();
|
||||
});
|
||||
}
|
||||
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
// Assert we are only getting data at 7PM (12AM UTC)
|
||||
if (slice.Time.Hour != 19)
|
||||
{
|
||||
throw new ArgumentException($"Expected data at 7PM each day; instead was {slice.Time}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Ran at the end of the algorithm to ensure the algorithm has no holdings
|
||||
/// </summary>
|
||||
/// <exception cref="Exception">The algorithm has holdings</exception>
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (Portfolio.Invested)
|
||||
{
|
||||
throw new Exception($"Expected no holdings at end of algorithm, but are invested in: {string.Join(", ", Portfolio.Keys)}");
|
||||
}
|
||||
|
||||
if (Ticket.Status != OrderStatus.Filled)
|
||||
{
|
||||
throw new Exception("Future option order failed to fill correctly");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 virtual bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public virtual Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.82%"},
|
||||
{"Compounding Annual Return", "-66.089%"},
|
||||
{"Drawdown", "0.800%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.824%"},
|
||||
{"Sharpe Ratio", "-6.993"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.466"},
|
||||
{"Beta", "-7.501"},
|
||||
{"Annual Standard Deviation", "0.092"},
|
||||
{"Annual Variance", "0.009"},
|
||||
{"Information Ratio", "-7.586"},
|
||||
{"Tracking Error", "0.105"},
|
||||
{"Treynor Ratio", "0.086"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "DC V5E8P9VAH3IC|DC V5E8P9SH0U0X"},
|
||||
{"Fitness Score", "0.006"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-80.233"},
|
||||
{"Portfolio Turnover", "0.013"},
|
||||
{"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", "f00013930ab4c104a6177485d8090b31"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,118 +0,0 @@
|
||||
/*
|
||||
* 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 System.Reflection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// This regression algorithm tests using FutureOptions hourly resolution
|
||||
/// </summary>
|
||||
public class FutureOptionHourlyRegressionAlgorithm : FutureOptionDailyRegressionAlgorithm
|
||||
{
|
||||
protected override Resolution Resolution => Resolution.Hour;
|
||||
|
||||
protected override void ScheduleBuySell()
|
||||
{
|
||||
// Schedule a purchase of this contract at Noon
|
||||
Schedule.On(DateRules.Today, TimeRules.Noon, () =>
|
||||
{
|
||||
Ticket = MarketOrder(DcOption, 1);
|
||||
});
|
||||
|
||||
// Schedule liquidation at 6PM
|
||||
Schedule.On(DateRules.Today, TimeRules.At(18,0,0), () =>
|
||||
{
|
||||
Liquidate();
|
||||
});
|
||||
}
|
||||
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
// Assert we are only getting data only hourly intervals
|
||||
if (slice.Time.Minute != 0)
|
||||
{
|
||||
throw new ArgumentException($"Expected data only on hourly intervals; instead was {slice.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 override bool CanRunLocally { get; } = true;
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public override Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
/// </summary>
|
||||
public override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "0%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0%"},
|
||||
{"Sharpe 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", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "DC V5E8P9VAH3IC|DC V5E8P9SH0U0X"},
|
||||
{"Fitness Score", "0.01"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-101.911"},
|
||||
{"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", "665d06e4f758724b5b9576b14fd18743"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -37,7 +37,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2020, 1, 4);
|
||||
SetStartDate(2020, 1, 5);
|
||||
SetEndDate(2020, 1, 6);
|
||||
|
||||
var goldFutures = AddFuture("GC", Resolution.Minute, Market.COMEX);
|
||||
@@ -105,31 +105,31 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "4"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "-5.605%"},
|
||||
{"Compounding Annual Return", "-8.289%"},
|
||||
{"Drawdown", "3.500%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-0.047%"},
|
||||
{"Sharpe Ratio", "-10.898"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.135"},
|
||||
{"Beta", "0.124"},
|
||||
{"Annual Standard Deviation", "0.005"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-17.984"},
|
||||
{"Tracking Error", "0.038"},
|
||||
{"Treynor Ratio", "-0.467"},
|
||||
{"Information Ratio", "-14.395"},
|
||||
{"Tracking Error", "0.043"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$7.40"},
|
||||
{"Estimated Strategy Capacity", "$25000000.00"},
|
||||
{"Estimated Strategy Capacity", "$31000000.00"},
|
||||
{"Lowest Capacity Asset", "OG 31BFX0QKBVPGG|GC XE1Y0ZJ8NQ8T"},
|
||||
{"Fitness Score", "0.012"},
|
||||
{"Fitness Score", "0.019"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-126.806"},
|
||||
{"Portfolio Turnover", "0.025"},
|
||||
{"Return Over Maximum Drawdown", "-194.237"},
|
||||
{"Portfolio Turnover", "0.038"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
|
||||
@@ -127,7 +127,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
private void AssertFutureOptionOrderExercise(OrderEvent orderEvent, Security future, Security optionContract)
|
||||
{
|
||||
var expectedLiquidationTimeUtc = new DateTime(2020, 6, 20, 4, 0, 0);
|
||||
var expectedLiquidationTimeUtc = new DateTime(2020, 6, 19, 20, 0, 0);
|
||||
|
||||
if (orderEvent.Direction == OrderDirection.Buy && future.Holdings.Quantity != 0)
|
||||
{
|
||||
@@ -204,31 +204,31 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "4.18%"},
|
||||
{"Average Loss", "-8.26%"},
|
||||
{"Compounding Annual Return", "-8.884%"},
|
||||
{"Drawdown", "4.400%"},
|
||||
{"Expectancy", "-0.247"},
|
||||
{"Net Profit", "-4.427%"},
|
||||
{"Sharpe Ratio", "-1.283"},
|
||||
{"Probabilistic Sharpe Ratio", "0.001%"},
|
||||
{"Average Win", "4.15%"},
|
||||
{"Average Loss", "-8.27%"},
|
||||
{"Compounding Annual Return", "-8.944%"},
|
||||
{"Drawdown", "4.500%"},
|
||||
{"Expectancy", "-0.249"},
|
||||
{"Net Profit", "-4.457%"},
|
||||
{"Sharpe Ratio", "-1.381"},
|
||||
{"Probabilistic Sharpe Ratio", "0.002%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "0.51"},
|
||||
{"Alpha", "-0.061"},
|
||||
{"Beta", "0.002"},
|
||||
{"Annual Standard Deviation", "0.048"},
|
||||
{"Annual Variance", "0.002"},
|
||||
{"Information Ratio", "-0.221"},
|
||||
{"Tracking Error", "0.376"},
|
||||
{"Treynor Ratio", "-24.544"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Estimated Strategy Capacity", "$330000000.00"},
|
||||
{"Lowest Capacity Asset", "ES 31EL5FAOOQON8|ES XFH59UK0MYO1"},
|
||||
{"Profit-Loss Ratio", "0.50"},
|
||||
{"Alpha", "-0.072"},
|
||||
{"Beta", "0.003"},
|
||||
{"Annual Standard Deviation", "0.052"},
|
||||
{"Annual Variance", "0.003"},
|
||||
{"Information Ratio", "-0.239"},
|
||||
{"Tracking Error", "0.408"},
|
||||
{"Treynor Ratio", "-28.523"},
|
||||
{"Total Fees", "$7.40"},
|
||||
{"Estimated Strategy Capacity", "$220000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0.008"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-0.225"},
|
||||
{"Sortino Ratio", "-0.224"},
|
||||
{"Return Over Maximum Drawdown", "-2.009"},
|
||||
{"Portfolio Turnover", "0.023"},
|
||||
{"Total Insights Generated", "0"},
|
||||
@@ -244,7 +244,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "99f96f433bc76c31cb25bcd9117a6bf1"}
|
||||
{"OrderListHash", "d3fa88c3acadb9345ceac76a2dd3b520"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -178,24 +178,24 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-5.11%"},
|
||||
{"Compounding Annual Return", "-10.226%"},
|
||||
{"Average Loss", "-5.12%"},
|
||||
{"Compounding Annual Return", "-10.230%"},
|
||||
{"Drawdown", "5.100%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-5.114%"},
|
||||
{"Sharpe Ratio", "-1.164"},
|
||||
{"Probabilistic Sharpe Ratio", "0.009%"},
|
||||
{"Net Profit", "-5.116%"},
|
||||
{"Sharpe Ratio", "-1.253"},
|
||||
{"Probabilistic Sharpe Ratio", "0.015%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.07"},
|
||||
{"Alpha", "-0.082"},
|
||||
{"Beta", "0.003"},
|
||||
{"Annual Standard Deviation", "0.06"},
|
||||
{"Annual Standard Deviation", "0.065"},
|
||||
{"Annual Variance", "0.004"},
|
||||
{"Information Ratio", "-0.243"},
|
||||
{"Tracking Error", "0.378"},
|
||||
{"Treynor Ratio", "-23.284"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Information Ratio", "-0.262"},
|
||||
{"Tracking Error", "0.409"},
|
||||
{"Treynor Ratio", "-27.056"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$360000000.00"},
|
||||
{"Lowest Capacity Asset", "ES 31EL5FBZBMXES|ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0"},
|
||||
@@ -217,7 +217,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "ec799886c15ac6c4b8fb3d873d7e6b14"}
|
||||
{"OrderListHash", "a35054d03fd2caa0a96cbe12e427e928"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -189,31 +189,31 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "10.05%"},
|
||||
{"Average Loss", "-5.60%"},
|
||||
{"Compounding Annual Return", "8.148%"},
|
||||
{"Average Loss", "-5.63%"},
|
||||
{"Compounding Annual Return", "8.083%"},
|
||||
{"Drawdown", "0.500%"},
|
||||
{"Expectancy", "0.397"},
|
||||
{"Net Profit", "3.886%"},
|
||||
{"Sharpe Ratio", "1.088"},
|
||||
{"Probabilistic Sharpe Ratio", "53.421%"},
|
||||
{"Expectancy", "0.393"},
|
||||
{"Net Profit", "3.855%"},
|
||||
{"Sharpe Ratio", "1.182"},
|
||||
{"Probabilistic Sharpe Ratio", "57.797%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "1.79"},
|
||||
{"Alpha", "0.057"},
|
||||
{"Alpha", "0.067"},
|
||||
{"Beta", "-0.002"},
|
||||
{"Annual Standard Deviation", "0.053"},
|
||||
{"Annual Standard Deviation", "0.057"},
|
||||
{"Annual Variance", "0.003"},
|
||||
{"Information Ratio", "0.094"},
|
||||
{"Tracking Error", "0.379"},
|
||||
{"Treynor Ratio", "-23.276"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Estimated Strategy Capacity", "$300000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UP5K75W|ES XFH59UK0MYO1"},
|
||||
{"Information Ratio", "0.101"},
|
||||
{"Tracking Error", "0.41"},
|
||||
{"Treynor Ratio", "-27.331"},
|
||||
{"Total Fees", "$7.40"},
|
||||
{"Estimated Strategy Capacity", "$200000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0.02"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "17.34"},
|
||||
{"Return Over Maximum Drawdown", "17.201"},
|
||||
{"Portfolio Turnover", "0.02"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
@@ -228,7 +228,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "8a33f32b29bcc66d4dd779b36df9a010"}
|
||||
{"OrderListHash", "8e380e4d5c5e3e145ba388f7853829bb"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -173,30 +173,30 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "1.81%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "3.756%"},
|
||||
{"Compounding Annual Return", "3.752%"},
|
||||
{"Drawdown", "0.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.811%"},
|
||||
{"Sharpe Ratio", "1.183"},
|
||||
{"Probabilistic Sharpe Ratio", "60.811%"},
|
||||
{"Net Profit", "1.809%"},
|
||||
{"Sharpe Ratio", "1.283"},
|
||||
{"Probabilistic Sharpe Ratio", "65.521%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.026"},
|
||||
{"Alpha", "0.031"},
|
||||
{"Beta", "-0.001"},
|
||||
{"Annual Standard Deviation", "0.022"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "0.012"},
|
||||
{"Tracking Error", "0.375"},
|
||||
{"Treynor Ratio", "-24.052"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Annual Standard Deviation", "0.024"},
|
||||
{"Annual Variance", "0.001"},
|
||||
{"Information Ratio", "0.013"},
|
||||
{"Tracking Error", "0.406"},
|
||||
{"Treynor Ratio", "-28.184"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$78000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UPNF7B8|ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "95.594"},
|
||||
{"Return Over Maximum Drawdown", "95.495"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
@@ -211,7 +211,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "8cb012d36057103bf26a897fe5fa54d6"}
|
||||
{"OrderListHash", "35baadd70ec72c735eadbf55d702fe04"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -185,32 +185,32 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "10.19%"},
|
||||
{"Average Loss", "-8.02%"},
|
||||
{"Compounding Annual Return", "2.790%"},
|
||||
{"Average Win", "10.18%"},
|
||||
{"Average Loss", "-8.05%"},
|
||||
{"Compounding Annual Return", "2.726%"},
|
||||
{"Drawdown", "0.500%"},
|
||||
{"Expectancy", "0.135"},
|
||||
{"Net Profit", "1.348%"},
|
||||
{"Sharpe Ratio", "0.862"},
|
||||
{"Probabilistic Sharpe Ratio", "42.945%"},
|
||||
{"Expectancy", "0.133"},
|
||||
{"Net Profit", "1.318%"},
|
||||
{"Sharpe Ratio", "0.927"},
|
||||
{"Probabilistic Sharpe Ratio", "46.325%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "1.27"},
|
||||
{"Alpha", "0.02"},
|
||||
{"Alpha", "0.022"},
|
||||
{"Beta", "-0.001"},
|
||||
{"Annual Standard Deviation", "0.023"},
|
||||
{"Annual Standard Deviation", "0.024"},
|
||||
{"Annual Variance", "0.001"},
|
||||
{"Information Ratio", "-0.006"},
|
||||
{"Tracking Error", "0.375"},
|
||||
{"Treynor Ratio", "-20.61"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Estimated Strategy Capacity", "$200000000.00"},
|
||||
{"Lowest Capacity Asset", "ES 31EL5FAOUP0P0|ES XFH59UK0MYO1"},
|
||||
{"Information Ratio", "-0.008"},
|
||||
{"Tracking Error", "0.406"},
|
||||
{"Treynor Ratio", "-24.058"},
|
||||
{"Total Fees", "$7.40"},
|
||||
{"Estimated Strategy Capacity", "$130000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0.021"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "5.859"},
|
||||
{"Return Over Maximum Drawdown", "5.725"},
|
||||
{"Portfolio Turnover", "0.022"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
@@ -225,7 +225,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "eb37251ad1e32dd348af8a69e1888053"}
|
||||
{"OrderListHash", "519cc7c39703b6e6913dbe98468da872"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -170,32 +170,32 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "3.29%"},
|
||||
{"Average Win", "3.28%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "6.869%"},
|
||||
{"Compounding Annual Return", "6.865%"},
|
||||
{"Drawdown", "0.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "3.286%"},
|
||||
{"Sharpe Ratio", "1.205"},
|
||||
{"Probabilistic Sharpe Ratio", "61.483%"},
|
||||
{"Net Profit", "3.284%"},
|
||||
{"Sharpe Ratio", "1.309"},
|
||||
{"Probabilistic Sharpe Ratio", "66.205%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "100%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.048"},
|
||||
{"Alpha", "0.056"},
|
||||
{"Beta", "-0.002"},
|
||||
{"Annual Standard Deviation", "0.04"},
|
||||
{"Annual Standard Deviation", "0.043"},
|
||||
{"Annual Variance", "0.002"},
|
||||
{"Information Ratio", "0.07"},
|
||||
{"Tracking Error", "0.377"},
|
||||
{"Treynor Ratio", "-24.401"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Information Ratio", "0.076"},
|
||||
{"Tracking Error", "0.408"},
|
||||
{"Treynor Ratio", "-28.646"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Estimated Strategy Capacity", "$80000000.00"},
|
||||
{"Lowest Capacity Asset", "ES 31EL5FAJQ6SBO|ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "150.849"},
|
||||
{"Return Over Maximum Drawdown", "150.763"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
@@ -210,7 +210,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "76ed4eaa5f6ed50aa6134aecfbbe9e29"}
|
||||
{"OrderListHash", "5571280f1efb15dd3899896fb72385ff"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -89,18 +89,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "5.400%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-3.474%"},
|
||||
{"Sharpe Ratio", "-68.954"},
|
||||
{"Sharpe Ratio", "-4.693"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-1.896"},
|
||||
{"Beta", "0.058"},
|
||||
{"Annual Standard Deviation", "0.014"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-75.062"},
|
||||
{"Tracking Error", "0.229"},
|
||||
{"Treynor Ratio", "-16.736"},
|
||||
{"Alpha", "1.575"},
|
||||
{"Beta", "-0.437"},
|
||||
{"Annual Standard Deviation", "0.194"},
|
||||
{"Annual Variance", "0.038"},
|
||||
{"Information Ratio", "-19.629"},
|
||||
{"Tracking Error", "0.336"},
|
||||
{"Treynor Ratio", "2.082"},
|
||||
{"Total Fees", "$1.85"},
|
||||
{"Estimated Strategy Capacity", "$93000000.00"},
|
||||
{"Lowest Capacity Asset", "GC VOFJUCDY9XNH"},
|
||||
|
||||
@@ -38,7 +38,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
private readonly DateTime _expectedExpiryWarningTime = new DateTime(2020, 6, 19);
|
||||
private readonly DateTime _expectedExpiryDelistingTime = new DateTime(2020, 6, 20);
|
||||
private readonly DateTime _expectedLiquidationTime = new DateTime(2020, 6, 20);
|
||||
private readonly DateTime _expectedLiquidationTime = new DateTime(2020, 6, 19, 16, 0, 0);
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
@@ -183,23 +183,23 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "10.15%"},
|
||||
{"Average Loss", "-11.34%"},
|
||||
{"Compounding Annual Return", "-2.573%"},
|
||||
{"Compounding Annual Return", "-2.578%"},
|
||||
{"Drawdown", "2.300%"},
|
||||
{"Expectancy", "-0.052"},
|
||||
{"Net Profit", "-2.341%"},
|
||||
{"Sharpe Ratio", "-0.867"},
|
||||
{"Probabilistic Sharpe Ratio", "0.001%"},
|
||||
{"Expectancy", "-0.053"},
|
||||
{"Net Profit", "-2.345%"},
|
||||
{"Sharpe Ratio", "-0.969"},
|
||||
{"Probabilistic Sharpe Ratio", "0.004%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "0.90"},
|
||||
{"Alpha", "-0.014"},
|
||||
{"Profit-Loss Ratio", "0.89"},
|
||||
{"Alpha", "-0.018"},
|
||||
{"Beta", "0.001"},
|
||||
{"Annual Standard Deviation", "0.016"},
|
||||
{"Annual Standard Deviation", "0.018"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-0.603"},
|
||||
{"Tracking Error", "0.291"},
|
||||
{"Treynor Ratio", "-13.292"},
|
||||
{"Total Fees", "$3.70"},
|
||||
{"Information Ratio", "-0.696"},
|
||||
{"Tracking Error", "0.33"},
|
||||
{"Treynor Ratio", "-16.321"},
|
||||
{"Total Fees", "$7.40"},
|
||||
{"Estimated Strategy Capacity", "$45000000.00"},
|
||||
{"Lowest Capacity Asset", "ES XFH59UK0MYO1"},
|
||||
{"Fitness Score", "0.005"},
|
||||
@@ -221,7 +221,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "67d8ad460ff796937ee252c3e4340e62"}
|
||||
{"OrderListHash", "0128b145984582f5eba7e95881d9b62d"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -99,8 +99,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-3.102"},
|
||||
{"Tracking Error", "0.091"},
|
||||
{"Information Ratio", "-3.432"},
|
||||
{"Tracking Error", "0.098"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
|
||||
@@ -304,18 +304,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "1.200%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "1.694%"},
|
||||
{"Sharpe Ratio", "57.51"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Sharpe Ratio", "8.671"},
|
||||
{"Probabilistic Sharpe Ratio", "67.159%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.041"},
|
||||
{"Beta", "0.998"},
|
||||
{"Annual Standard Deviation", "0.177"},
|
||||
{"Annual Variance", "0.031"},
|
||||
{"Information Ratio", "-150.576"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "10.228"},
|
||||
{"Alpha", "1.614"},
|
||||
{"Beta", "0.062"},
|
||||
{"Annual Standard Deviation", "0.223"},
|
||||
{"Annual Variance", "0.05"},
|
||||
{"Information Ratio", "-11.911"},
|
||||
{"Tracking Error", "0.271"},
|
||||
{"Treynor Ratio", "31.034"},
|
||||
{"Total Fees", "$3.45"},
|
||||
{"Estimated Strategy Capacity", "$970000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user