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8 Commits
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1ee6db8b60 |
4
.vscode/launch.json
vendored
4
.vscode/launch.json
vendored
@@ -8,8 +8,8 @@
|
||||
marketplace.
|
||||
|
||||
Attach to Python:
|
||||
Will attempt to attach to LEAN running locally using DebugPy. Requires that the process is
|
||||
actively running and config is set: "debugging": true, "debugging-method": "DebugPy",
|
||||
Will attempt to attach to LEAN running locally using PTVSD. Requires that the process is
|
||||
actively running and config is set: "debugging": true, "debugging-method": "PTVSD",
|
||||
Requires Python extension from the marketplace. Currently only works with algorithms in
|
||||
Algorithm.Python directory. This is because we map that directory to our build directory
|
||||
that contains the py file at runtime. If using another location change "localRoot" value
|
||||
|
||||
2
.vscode/readme.md
vendored
2
.vscode/readme.md
vendored
@@ -95,7 +95,7 @@ Python algorithms require a little extra work in order to be able to debug them.
|
||||
First in order to debug a Python algorithm in VS Code we must make the following change to our configuration (Launcher\config.json) under the comment debugging configuration:
|
||||
|
||||
"debugging": true,
|
||||
"debugging-method": "DebugPy,
|
||||
"debugging-method": "PTVSD",
|
||||
|
||||
In setting this we are telling Lean to expect a debugger connection using ‘Python Tools for Visual Studio Debugger’. Once this is set Lean will stop upon initialization and await a connection to the debugger via port 5678.
|
||||
|
||||
|
||||
@@ -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"},
|
||||
|
||||
@@ -1,134 +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 #5748 where in some cases an option underlying symbol was not being
|
||||
/// removed from all universes it was hold
|
||||
/// </summary>
|
||||
public class AddAndRemoveOptionContractRegressionAlgorithm : 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 = 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 (slice.HasData)
|
||||
{
|
||||
if (!_hasRemoved)
|
||||
{
|
||||
RemoveOptionContract(_contract);
|
||||
RemoveSecurity(_contract.Underlying);
|
||||
_hasRemoved = true;
|
||||
}
|
||||
else
|
||||
{
|
||||
throw new Exception("Expect a single call to OnData where we removed the option and underlying");
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (!_hasRemoved)
|
||||
{
|
||||
throw new Exception("Expect a single call to OnData where we removed the option and underlying");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,131 +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", "486118a60d78f74811fe8d927c2c6b43"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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", "$0"},
|
||||
{"Mean Population Direction", "0%"},
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "7fbcd12db40304d50b3a34d7878eb3cf"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -40,8 +40,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
private int _expectedContractIndex;
|
||||
private readonly List<Symbol> _expectedContracts = new List<Symbol>
|
||||
{
|
||||
SymbolRepresentation.ParseOptionTickerOSI("GOOG 151224P00747500"),
|
||||
SymbolRepresentation.ParseOptionTickerOSI("GOOG 151224P00750000"),
|
||||
SymbolRepresentation.ParseOptionTickerOSI("GOOG 151224P00747500"),
|
||||
SymbolRepresentation.ParseOptionTickerOSI("GOOG 151224P00752500")
|
||||
};
|
||||
|
||||
@@ -109,11 +109,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
var googOptionChain = AddOption(UnderlyingTicker);
|
||||
googOptionChain.SetFilter(u =>
|
||||
{
|
||||
// we added the universe at 10, the universe selection data should not be from before
|
||||
if (u.Underlying.EndTime.Hour < 10)
|
||||
{
|
||||
throw new Exception($"Unexpected underlying data point {u.Underlying.EndTime} {u.Underlying}");
|
||||
}
|
||||
// find first put above market price
|
||||
return u.IncludeWeeklys()
|
||||
.Strikes(+1, +1)
|
||||
@@ -236,7 +231,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$6.00"},
|
||||
{"Estimated Strategy Capacity", "$2000.00"},
|
||||
{"Lowest Capacity Asset", "GOOCV 305RBR0BSWIX2|GOOCV VP83T1ZUHROL"},
|
||||
{"Lowest Capacity Asset", "GOOCV 305RBQ2BZBZT2|GOOCV VP83T1ZUHROL"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
@@ -256,7 +251,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "550a99c482106defd8ba15f48183768e"}
|
||||
{"OrderListHash", "1e7b3e90918777b9dbf46353a96f3329"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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 +13,13 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
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 +30,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// </summary>
|
||||
public class AlphaStreamsBasicTemplateAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Dictionary<Symbol, HashSet<Symbol>> _symbolsPerAlpha;
|
||||
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
@@ -37,22 +40,60 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetStartDate(2018, 04, 04);
|
||||
SetEndDate(2018, 04, 06);
|
||||
|
||||
SetAlpha(new AlphaStreamAlphaModule());
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
Settings.MinimumOrderMarginPortfolioPercentage = 0.01m;
|
||||
_symbolsPerAlpha = new Dictionary<Symbol, HashSet<Symbol>>();
|
||||
SetPortfolioConstruction(new EqualWeightingAlphaStreamsPortfolioConstructionModel());
|
||||
SetSecurityInitializer(new BrokerageModelSecurityInitializer(BrokerageModel,
|
||||
new FuncSecuritySeeder(GetLastKnownPrices)));
|
||||
|
||||
foreach (var alphaId in new [] { "623b06b231eb1cc1aa3643a46", "9fc8ef73792331b11dbd5429a" })
|
||||
{
|
||||
AddData<AlphaStreamsPortfolioState>(alphaId);
|
||||
var alpha = AddData<AlphaStreamsPortfolioState>(alphaId);
|
||||
_symbolsPerAlpha[alpha.Symbol] = new HashSet<Symbol>();
|
||||
}
|
||||
}
|
||||
|
||||
/// <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)
|
||||
{
|
||||
var alphaId = portfolioState.Symbol;
|
||||
var currentSymbols = _symbolsPerAlpha[alphaId];
|
||||
|
||||
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);
|
||||
}
|
||||
}
|
||||
_symbolsPerAlpha[alphaId] = newSymbols;
|
||||
|
||||
foreach (var symbol in currentSymbols.Where(symbol => !UsedBySomeAlpha(symbol)))
|
||||
{
|
||||
RemoveSecurity(symbol);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
Log($"OnOrderEvent: {orderEvent}");
|
||||
Debug($"OnOrderEvent: {orderEvent}");
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
Debug($"OnSecuritiesChanged: {changes}");
|
||||
}
|
||||
|
||||
private bool UsedBySomeAlpha(Symbol asset)
|
||||
{
|
||||
return _symbolsPerAlpha.Any(pair => pair.Value.Contains(asset));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
@@ -68,15 +109,15 @@ namespace QuantConnect.Algorithm.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 Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "-0.12%"},
|
||||
{"Compounding Annual Return", "-14.722%"},
|
||||
{"Compounding Annual Return", "-14.756%"},
|
||||
{"Drawdown", "0.200%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.116%"},
|
||||
{"Net Profit", "-0.117%"},
|
||||
{"Sharpe Ratio", "0"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "100%"},
|
||||
@@ -96,7 +137,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "-138.588"},
|
||||
{"Return Over Maximum Drawdown", "-138.559"},
|
||||
{"Portfolio Turnover", "0.034"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
@@ -111,7 +152,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "2b94bc50a74caebe06c075cdab1bc6da"}
|
||||
{"OrderListHash", "d3b6e0db0929e96d23c1cffd394858f1"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,93 +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>
|
||||
/// Example algorithm with existing holdings consuming an alpha streams portfolio state and trading based on it
|
||||
/// </summary>
|
||||
public class AlphaStreamsDifferentAccountCurrencyBasicTemplateAlgorithm : AlphaStreamsWithHoldingsBasicTemplateAlgorithm
|
||||
{
|
||||
/// <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()
|
||||
{
|
||||
SetAccountCurrency("EUR");
|
||||
base.Initialize();
|
||||
}
|
||||
|
||||
/// <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 override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0.01%"},
|
||||
{"Average Loss", "0.00%"},
|
||||
{"Compounding Annual Return", "-78.502%"},
|
||||
{"Drawdown", "3.100%"},
|
||||
{"Expectancy", "7.797"},
|
||||
{"Net Profit", "-1.134%"},
|
||||
{"Sharpe Ratio", "-2.456"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "16.59"},
|
||||
{"Alpha", "0.006"},
|
||||
{"Beta", "1.011"},
|
||||
{"Annual Standard Deviation", "0.343"},
|
||||
{"Annual Variance", "0.117"},
|
||||
{"Information Ratio", "-0.859"},
|
||||
{"Tracking Error", "0.004"},
|
||||
{"Treynor Ratio", "-0.832"},
|
||||
{"Total Fees", "$2.89"},
|
||||
{"Estimated Strategy Capacity", "$8900000000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.506"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
|
||||
{"Portfolio Turnover", "0.506"},
|
||||
{"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", "a9dd0a0ab6070455479d1b9caaa4e69c"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,130 +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 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;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Example algorithm consuming an alpha streams portfolio state and trading based on it
|
||||
/// </summary>
|
||||
public class AlphaStreamsUniverseSelectionTemplateAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
/// <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(2018, 04, 04);
|
||||
SetEndDate(2018, 04, 06);
|
||||
|
||||
SetAlpha(new AlphaStreamAlphaModule());
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
Settings.MinimumOrderMarginPortfolioPercentage = 0.01m;
|
||||
SetPortfolioConstruction(new EqualWeightingAlphaStreamsPortfolioConstructionModel());
|
||||
|
||||
SetUniverseSelection(new ScheduledUniverseSelectionModel(
|
||||
DateRules.EveryDay(),
|
||||
TimeRules.Midnight,
|
||||
SelectAlphas,
|
||||
new UniverseSettings(UniverseSettings)
|
||||
{
|
||||
SubscriptionDataTypes = new List<Tuple<Type, TickType>>
|
||||
{new(typeof(AlphaStreamsPortfolioState), TickType.Trade)},
|
||||
FillForward = false,
|
||||
}
|
||||
));
|
||||
}
|
||||
|
||||
private IEnumerable<Symbol> SelectAlphas(DateTime dateTime)
|
||||
{
|
||||
Log($"SelectAlphas() {Time}");
|
||||
foreach (var alphaId in new[] {"623b06b231eb1cc1aa3643a46", "9fc8ef73792331b11dbd5429a"})
|
||||
{
|
||||
var alphaSymbol = new Symbol(SecurityIdentifier.GenerateBase(typeof(AlphaStreamsPortfolioState), alphaId, Market.USA),
|
||||
alphaId);
|
||||
|
||||
yield return alphaSymbol;
|
||||
}
|
||||
}
|
||||
|
||||
/// <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>
|
||||
{
|
||||
{"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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -1,144 +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 QuantConnect.Orders;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Data.Custom.AlphaStreams;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Example algorithm with existing holdings consuming an alpha streams portfolio state and trading based on it
|
||||
/// </summary>
|
||||
public class AlphaStreamsWithHoldingsBasicTemplateAlgorithm : AlphaStreamsBasicTemplateAlgorithm
|
||||
{
|
||||
private decimal _expectedSpyQuantity;
|
||||
/// <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(2018, 04, 04);
|
||||
SetEndDate(2018, 04, 06);
|
||||
SetCash(100000);
|
||||
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
UniverseSettings.Resolution = Resolution.Hour;
|
||||
Settings.MinimumOrderMarginPortfolioPercentage = 0.001m;
|
||||
SetPortfolioConstruction(new EqualWeightingAlphaStreamsPortfolioConstructionModel());
|
||||
|
||||
// AAPL should be liquidated since it's not hold by the alpha
|
||||
// This is handled by the PCM
|
||||
var aapl = AddEquity("AAPL", Resolution.Hour);
|
||||
aapl.Holdings.SetHoldings(40, 10);
|
||||
|
||||
// SPY will be bought following the alpha streams portfolio
|
||||
// This is handled by the PCM + Execution Model
|
||||
var spy = AddEquity("SPY", Resolution.Hour);
|
||||
spy.Holdings.SetHoldings(246, -10);
|
||||
|
||||
AddData<AlphaStreamsPortfolioState>("94d820a93fff127fa46c15231d");
|
||||
}
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
if (_expectedSpyQuantity == 0 && orderEvent.Symbol == "SPY" && orderEvent.Status == OrderStatus.Filled)
|
||||
{
|
||||
var security = Securities["SPY"];
|
||||
var priceInAccountCurrency = Portfolio.CashBook.ConvertToAccountCurrency(security.AskPrice, security.QuoteCurrency.Symbol);
|
||||
_expectedSpyQuantity = (Portfolio.TotalPortfolioValue - Settings.FreePortfolioValue) / priceInAccountCurrency;
|
||||
_expectedSpyQuantity = _expectedSpyQuantity.DiscretelyRoundBy(1, MidpointRounding.ToZero);
|
||||
}
|
||||
|
||||
base.OnOrderEvent(orderEvent);
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (Securities["AAPL"].HoldStock)
|
||||
{
|
||||
throw new Exception("We should no longer hold AAPL since the alpha does not");
|
||||
}
|
||||
|
||||
// we allow some padding for small price differences
|
||||
if (Math.Abs(Securities["SPY"].Holdings.Quantity - _expectedSpyQuantity) > _expectedSpyQuantity * 0.03m)
|
||||
{
|
||||
throw new Exception($"Unexpected SPY holdings. Expected {_expectedSpyQuantity} was {Securities["SPY"].Holdings.Quantity}");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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 override Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
|
||||
{
|
||||
{"Total Trades", "2"},
|
||||
{"Average Win", "0.01%"},
|
||||
{"Average Loss", "0.00%"},
|
||||
{"Compounding Annual Return", "-87.617%"},
|
||||
{"Drawdown", "3.100%"},
|
||||
{"Expectancy", "8.518"},
|
||||
{"Net Profit", "-1.515%"},
|
||||
{"Sharpe Ratio", "-2.45"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Loss Rate", "50%"},
|
||||
{"Win Rate", "50%"},
|
||||
{"Profit-Loss Ratio", "18.04"},
|
||||
{"Alpha", "0.008"},
|
||||
{"Beta", "1.015"},
|
||||
{"Annual Standard Deviation", "0.344"},
|
||||
{"Annual Variance", "0.118"},
|
||||
{"Information Ratio", "-0.856"},
|
||||
{"Tracking Error", "0.005"},
|
||||
{"Treynor Ratio", "-0.83"},
|
||||
{"Total Fees", "$3.09"},
|
||||
{"Estimated Strategy Capacity", "$8900000000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.511"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "6113.173"},
|
||||
{"Portfolio Turnover", "0.511"},
|
||||
{"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", "788eb2c74715a78476ba0db3b2654eb6"}
|
||||
};
|
||||
}
|
||||
}
|
||||
162
Algorithm.CSharp/Alphas/MortgageRateVolatilityAlpha.cs
Normal file
162
Algorithm.CSharp/Alphas/MortgageRateVolatilityAlpha.cs
Normal file
@@ -0,0 +1,162 @@
|
||||
/*
|
||||
* 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.Indicators;
|
||||
using QuantConnect.Orders.Fees;
|
||||
using QuantConnect.Data.Custom;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Algorithm.Framework.Alphas;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
using QuantConnect.Algorithm.Framework.Risk;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.Alphas
|
||||
{
|
||||
///<summary>
|
||||
/// This Alpha Model uses Wells Fargo 30-year Fixed Rate Mortgage data from Quandl to
|
||||
/// generate Insights about the movement of Real Estate ETFs. Mortgage rates can provide information
|
||||
/// regarding the general price trend of real estate, and ETFs provide good continuous-time instruments
|
||||
/// to measure the impact against. Volatility in mortgage rates tends to put downward pressure on real
|
||||
/// estate prices, whereas stable mortgage rates, regardless of true rate, lead to stable or higher real
|
||||
/// estate prices. This Alpha model seeks to take advantage of this correlation by emitting insights
|
||||
/// based on volatility and rate deviation from its historic mean.
|
||||
///
|
||||
/// This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
|
||||
/// sourced so the community and client funds can see an example of an alpha.
|
||||
///</summary>
|
||||
public class MortgageRateVolatilityAlgorithm : QCAlgorithm
|
||||
{
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2017, 1, 1); //Set Start Date
|
||||
SetCash(100000); //Set Strategy Cash
|
||||
|
||||
UniverseSettings.Resolution = Resolution.Daily;
|
||||
SetSecurityInitializer(security => security.FeeModel = new ConstantFeeModel(0));
|
||||
|
||||
// Basket of 6 liquid real estate ETFs
|
||||
Func<string, Symbol> toSymbol = x => QuantConnect.Symbol.Create(x, SecurityType.Equity, Market.USA);
|
||||
var realEstateETFs = new[] { "VNQ", "REET", "TAO", "FREL", "SRET", "HIPS" }.Select(toSymbol).ToArray();
|
||||
SetUniverseSelection(new ManualUniverseSelectionModel(realEstateETFs));
|
||||
|
||||
SetAlpha(new MortgageRateVolatilityAlphaModel(this));
|
||||
|
||||
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
|
||||
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
|
||||
SetRiskManagement(new NullRiskManagementModel());
|
||||
|
||||
}
|
||||
|
||||
private class MortgageRateVolatilityAlphaModel : AlphaModel
|
||||
{
|
||||
private readonly int _indicatorPeriod;
|
||||
private readonly Resolution _resolution;
|
||||
private readonly TimeSpan _insightDuration;
|
||||
private readonly int _deviations;
|
||||
private readonly double _insightMagnitude;
|
||||
private readonly Symbol _mortgageRate;
|
||||
private readonly SimpleMovingAverage _mortgageRateSma;
|
||||
private readonly StandardDeviation _mortgageRateStd;
|
||||
|
||||
public MortgageRateVolatilityAlphaModel(
|
||||
QCAlgorithm algorithm,
|
||||
int indicatorPeriod = 15,
|
||||
double insightMagnitude = 0.0005,
|
||||
int deviations = 2,
|
||||
Resolution resolution = Resolution.Daily
|
||||
)
|
||||
{
|
||||
// Add Quandl data for a Well's Fargo 30-year Fixed Rate mortgage
|
||||
_mortgageRate = algorithm.AddData<QuandlMortgagePriceColumns>("WFC/PR_GOV_30YFIXEDVA_APR").Symbol;
|
||||
_indicatorPeriod = indicatorPeriod;
|
||||
_resolution = resolution;
|
||||
_insightDuration = resolution.ToTimeSpan().Multiply(indicatorPeriod);
|
||||
_insightMagnitude = insightMagnitude;
|
||||
_deviations = deviations;
|
||||
|
||||
// Add indicators for the mortgage rate -- Standard Deviation and Simple Moving Average
|
||||
_mortgageRateStd = algorithm.STD(_mortgageRate, _indicatorPeriod, resolution);
|
||||
_mortgageRateSma = algorithm.SMA(_mortgageRate, _indicatorPeriod, resolution);
|
||||
|
||||
// Use a history call to warm-up the indicators
|
||||
WarmUpIndicators(algorithm);
|
||||
}
|
||||
|
||||
public override IEnumerable<Insight> Update(QCAlgorithm algorithm, Slice data)
|
||||
{
|
||||
var insights = new List<Insight>();
|
||||
|
||||
// Return empty list if data slice doesn't contain monrtgage rate data
|
||||
if (!data.Keys.Contains(_mortgageRate))
|
||||
{
|
||||
return insights;
|
||||
}
|
||||
// Extract current mortgage rate, the current STD indicator value, and current SMA value
|
||||
var rate = data[_mortgageRate].Value;
|
||||
var deviation = _deviations * _mortgageRateStd;
|
||||
var sma = _mortgageRateSma;
|
||||
|
||||
// Loop through all Active Securities to emit insights
|
||||
foreach (var security in algorithm.ActiveSecurities.Keys)
|
||||
{
|
||||
// Mortgage rate Symbol will be in the collection, so skip it
|
||||
if (security == _mortgageRate)
|
||||
{
|
||||
return insights;
|
||||
}
|
||||
|
||||
// If volatility in mortgage rates is high, then we emit an Insight to sell
|
||||
if ((rate < sma - deviation) || (rate > sma + deviation))
|
||||
{
|
||||
insights.Add(Insight.Price(security, _insightDuration, InsightDirection.Down, _insightMagnitude));
|
||||
}
|
||||
|
||||
// If volatility in mortgage rates is low, then we emit an Insight to buy
|
||||
if ((rate < sma - (decimal)deviation/2) || (rate > sma + (decimal)deviation/2))
|
||||
{
|
||||
insights.Add(Insight.Price(security, _insightDuration, InsightDirection.Up, _insightMagnitude));
|
||||
}
|
||||
}
|
||||
|
||||
return insights;
|
||||
}
|
||||
|
||||
private void WarmUpIndicators(QCAlgorithm algorithm)
|
||||
{
|
||||
// Make a history call and update the indicators
|
||||
algorithm.History(new[] { _mortgageRate }, _indicatorPeriod, _resolution).PushThrough(bar =>
|
||||
{
|
||||
_mortgageRateSma.Update(bar.EndTime, bar.Value);
|
||||
_mortgageRateStd.Update(bar.EndTime, bar.Value);
|
||||
});
|
||||
}
|
||||
}
|
||||
public class QuandlMortgagePriceColumns : Quandl
|
||||
{
|
||||
public QuandlMortgagePriceColumns()
|
||||
|
||||
// Rename the Quandl object column to the data we want, which is the 'Value' column
|
||||
// of the CSV that our API call returns
|
||||
: base(valueColumnName: "Value")
|
||||
{
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
117
Algorithm.CSharp/AltData/BenzingaNewsAlgorithm.cs
Normal file
117
Algorithm.CSharp/AltData/BenzingaNewsAlgorithm.cs
Normal file
@@ -0,0 +1,117 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.Benzinga;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.AltData
|
||||
{
|
||||
/// <summary>
|
||||
/// Benzinga is a provider of news data. Their news is made in-house
|
||||
/// and covers stock related news such as corporate events.
|
||||
/// </summary>
|
||||
public class BenzingaNewsAlgorithm : QCAlgorithm
|
||||
{
|
||||
// Predefine a dictionary of words with scores to scan for in the description
|
||||
// of the Benzinga news article
|
||||
private readonly Dictionary<string, double> _words = new Dictionary<string, double>()
|
||||
{
|
||||
{"bad", -0.5}, {"good", 0.5},
|
||||
{"negative", -0.5}, {"great", 0.5},
|
||||
{"growth", 0.5}, {"fail", -0.5},
|
||||
{"failed", -0.5}, {"success", 0.5},
|
||||
{"nailed", 0.5}, {"beat", 0.5},
|
||||
{"missed", -0.5}
|
||||
};
|
||||
|
||||
// Trade only every 5 days
|
||||
private DateTime _lastTrade = DateTime.MinValue;
|
||||
|
||||
/// <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(2018, 6, 5);
|
||||
SetEndDate(2018, 8, 4);
|
||||
SetCash(100000);
|
||||
|
||||
var aapl = AddEquity("AAPL", Resolution.Hour).Symbol;
|
||||
var ibm = AddEquity("IBM", Resolution.Hour).Symbol;
|
||||
|
||||
AddData<BenzingaNews>(aapl);
|
||||
AddData<BenzingaNews>(ibm);
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if ((Time - _lastTrade) < TimeSpan.FromDays(5))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// Get rid of our holdings after 5 days, and start fresh
|
||||
Liquidate();
|
||||
|
||||
// Get all Benzinga data and loop over it
|
||||
foreach (var article in data.Get<BenzingaNews>().Values)
|
||||
{
|
||||
// Select the same Symbol we're getting a data point for
|
||||
// from the articles list so that we can get the sentiment of the article.
|
||||
// We use the underlying Symbol because the Symbols included in the `Symbols` property
|
||||
// are equity Symbols.
|
||||
var selectedSymbol = article.Symbols.SingleOrDefault(s => s == article.Symbol.Underlying);
|
||||
if (selectedSymbol == null)
|
||||
{
|
||||
throw new Exception($"Could not find current Symbol {article.Symbol.Underlying} even though it should exist");
|
||||
}
|
||||
|
||||
// The intersection of the article contents and the pre-defined words are the words that are included in both collections
|
||||
var intersection = article.Contents.ToLowerInvariant().Split(' ').Intersect(_words.Keys);
|
||||
// Get the words, then get the aggregate sentiment
|
||||
var sentimentSum = intersection.Select(x => _words[x]).Sum();
|
||||
|
||||
if (sentimentSum >= 0.5)
|
||||
{
|
||||
Log($"Longing {article.Symbol.Underlying} with sentiment score of {sentimentSum}");
|
||||
SetHoldings(article.Symbol.Underlying, sentimentSum / 5);
|
||||
|
||||
_lastTrade = Time;
|
||||
}
|
||||
if (sentimentSum <= -0.5)
|
||||
{
|
||||
Log($"Shorting {article.Symbol.Underlying} with sentiment score of {sentimentSum}");
|
||||
SetHoldings(article.Symbol.Underlying, sentimentSum / 5);
|
||||
|
||||
_lastTrade = Time;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
foreach (var r in changes.RemovedSecurities)
|
||||
{
|
||||
// If removed from the universe, liquidate and remove the custom data from the algorithm
|
||||
Liquidate(r.Symbol);
|
||||
RemoveSecurity(QuantConnect.Symbol.CreateBase(typeof(BenzingaNews), r.Symbol, Market.USA));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
66
Algorithm.CSharp/AltData/CachedAlternativeDataAlgorithm.cs
Normal file
66
Algorithm.CSharp/AltData/CachedAlternativeDataAlgorithm.cs
Normal file
@@ -0,0 +1,66 @@
|
||||
/*
|
||||
* 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.Data.Custom.CBOE;
|
||||
using QuantConnect.Data.Custom.Fred;
|
||||
using QuantConnect.Data.Custom.USEnergy;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.AltData
|
||||
{
|
||||
public class CachedAlternativeDataAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Symbol _cboeVix;
|
||||
private Symbol _usEnergy;
|
||||
private Symbol _fredPeakToTrough;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2003, 1, 1);
|
||||
SetEndDate(2019, 10, 11);
|
||||
SetCash(100000);
|
||||
|
||||
// QuantConnect caches a small subset of alternative data for easy consumption for the community.
|
||||
// You can use this in your algorithm as demonstrated below:
|
||||
|
||||
_cboeVix = AddData<CBOE>("VIX", Resolution.Daily).Symbol;
|
||||
// United States EIA data: https://eia.gov/
|
||||
_usEnergy = AddData<USEnergy>(USEnergy.Petroleum.UnitedStates.WeeklyGrossInputsIntoRefineries, Resolution.Daily).Symbol;
|
||||
// FRED data
|
||||
_fredPeakToTrough = AddData<Fred>(Fred.OECDRecessionIndicators.UnitedStatesFromPeakThroughTheTrough, Resolution.Daily).Symbol;
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (data.ContainsKey(_cboeVix))
|
||||
{
|
||||
var vix = data.Get<CBOE>(_cboeVix);
|
||||
Log($"VIX: {vix}");
|
||||
}
|
||||
|
||||
if (data.ContainsKey(_usEnergy))
|
||||
{
|
||||
var inputIntoRefineries = data.Get<USEnergy>(_usEnergy);
|
||||
Log($"U.S. Input Into Refineries: {Time}, {inputIntoRefineries.Value}");
|
||||
}
|
||||
|
||||
if (data.ContainsKey(_fredPeakToTrough))
|
||||
{
|
||||
var peakToTrough = data.Get<Fred>(_fredPeakToTrough);
|
||||
Log($"OECD based Recession Indicator for the United States from the Peak through the Trough: {peakToTrough}");
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,62 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.Quiver;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.AltData
|
||||
{
|
||||
/// <summary>
|
||||
/// Quiver Quantitative is a provider of alternative data.
|
||||
/// This algorithm shows how to consume the <see cref="QuiverWallStreetBets"/>
|
||||
/// </summary>
|
||||
public class QuiverWallStreetBetsDataAlgorithm : QCAlgorithm
|
||||
{
|
||||
/// <summary>
|
||||
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2019, 1, 1);
|
||||
SetEndDate(2020, 6, 1);
|
||||
SetCash(100000);
|
||||
|
||||
var aapl = AddEquity("AAPL", Resolution.Daily).Symbol;
|
||||
var quiverWSBSymbol = AddData<QuiverWallStreetBets>(aapl).Symbol;
|
||||
var history = History<QuiverWallStreetBets>(quiverWSBSymbol, 60, Resolution.Daily);
|
||||
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
var points = data.Get<QuiverWallStreetBets>();
|
||||
foreach (var point in points.Values)
|
||||
{
|
||||
// Go long in the stock if it was mentioned more than 5 times in the WallStreetBets daily discussion
|
||||
if (point.Mentions > 5)
|
||||
{
|
||||
SetHoldings(point.Symbol.Underlying, 1);
|
||||
}
|
||||
// Go short in the stock if it was mentioned less than 5 times in the WallStreetBets daily discussion
|
||||
if (point.Mentions < 5)
|
||||
{
|
||||
SetHoldings(point.Symbol.Underlying, -1);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
99
Algorithm.CSharp/AltData/SECReport8KAlgorithm.cs
Normal file
99
Algorithm.CSharp/AltData/SECReport8KAlgorithm.cs
Normal file
@@ -0,0 +1,99 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
public class SECReport8KAlgorithm : QCAlgorithm
|
||||
{
|
||||
/// <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(2019, 1, 1);
|
||||
SetEndDate(2019, 8, 21);
|
||||
SetCash(100000);
|
||||
|
||||
UniverseSettings.Resolution = Resolution.Minute;
|
||||
AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseSelector));
|
||||
|
||||
// Request underlying equity data.
|
||||
var ibm = AddEquity("IBM", Resolution.Minute).Symbol;
|
||||
// Add SEC report 10-Q data for the underlying IBM asset
|
||||
var earningsFiling = AddData<SECReport10Q>(ibm, Resolution.Daily).Symbol;
|
||||
// Request 120 days of history with the SECReport10Q IBM custom data Symbol.
|
||||
var history = History<SECReport10Q>(earningsFiling, 120, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public IEnumerable<Symbol> CoarseSelector(IEnumerable<CoarseFundamental> coarse)
|
||||
{
|
||||
// Add SEC data from the filtered coarse selection
|
||||
var symbols = coarse.Where(x => x.HasFundamentalData && x.DollarVolume > 50000000)
|
||||
.Select(x => x.Symbol)
|
||||
.Take(10);
|
||||
|
||||
foreach (var symbol in symbols)
|
||||
{
|
||||
AddData<SECReport8K>(symbol);
|
||||
}
|
||||
|
||||
return symbols;
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
// Store the symbols we want to long in a list
|
||||
// so that we can have an equal-weighted portfolio
|
||||
var longEquitySymbols = new List<Symbol>();
|
||||
|
||||
// Get all SEC data and loop over it
|
||||
foreach (var report in data.Get<SECReport8K>().Values)
|
||||
{
|
||||
// Get the length of all contents contained within the report
|
||||
var reportTextLength = report.Report.Documents.Select(x => x.Text.Length).Sum();
|
||||
|
||||
if (reportTextLength > 20000)
|
||||
{
|
||||
longEquitySymbols.Add(report.Symbol.Underlying);
|
||||
}
|
||||
}
|
||||
|
||||
foreach (var equitySymbol in longEquitySymbols)
|
||||
{
|
||||
SetHoldings(equitySymbol, 1m / longEquitySymbols.Count);
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
foreach (var r in changes.RemovedSecurities)
|
||||
{
|
||||
// If removed from the universe, liquidate and remove the custom data from the algorithm
|
||||
Liquidate(r.Symbol);
|
||||
RemoveSecurity(QuantConnect.Symbol.CreateBase(typeof(SECReport8K), r.Symbol, Market.USA));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
90
Algorithm.CSharp/AltData/SmartInsiderTransactionAlgorithm.cs
Normal file
90
Algorithm.CSharp/AltData/SmartInsiderTransactionAlgorithm.cs
Normal file
@@ -0,0 +1,90 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.SmartInsider;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
public class SmartInsiderTransactionAlgorithm : QCAlgorithm
|
||||
{
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2019, 3, 1);
|
||||
SetEndDate(2019, 7, 4);
|
||||
SetCash(1000000);
|
||||
|
||||
AddUniverseSelection(new CoarseFundamentalUniverseSelectionModel(CoarseUniverse));
|
||||
|
||||
// Request underlying equity data.
|
||||
var ibm = AddEquity("IBM", Resolution.Minute).Symbol;
|
||||
// Add Smart Insider stock buyback transaction data for the underlying IBM asset
|
||||
var si = AddData<SmartInsiderTransaction>(ibm).Symbol;
|
||||
// Request 60 days of history with the SmartInsiderTransaction IBM Custom Data Symbol.
|
||||
var history = History<SmartInsiderTransaction>(si, 60, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public IEnumerable<Symbol> CoarseUniverse(IEnumerable<CoarseFundamental> coarse)
|
||||
{
|
||||
var symbols = coarse.Where(x => x.HasFundamentalData && x.DollarVolume > 50000000)
|
||||
.Select(x => x.Symbol)
|
||||
.Take(10);
|
||||
|
||||
foreach (var symbol in symbols)
|
||||
{
|
||||
AddData<SmartInsiderTransaction>(symbol);
|
||||
}
|
||||
|
||||
return symbols;
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
// Get all SmartInsider data available
|
||||
var transactions = data.Get<SmartInsiderTransaction>();
|
||||
|
||||
foreach (var transaction in transactions.Values)
|
||||
{
|
||||
if (transaction.VolumePercentage == null || transaction.EventType == null)
|
||||
{
|
||||
continue;
|
||||
}
|
||||
|
||||
// Using the Smart Insider transaction information, buy when company does a stock buyback
|
||||
if (transaction.EventType == SmartInsiderEventType.Transaction && transaction.VolumePercentage > 5)
|
||||
{
|
||||
SetHoldings(transaction.Symbol.Underlying, (decimal)transaction.VolumePercentage / 100);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
foreach (var r in changes.RemovedSecurities)
|
||||
{
|
||||
// If removed from the universe, liquidate and remove the custom data from the algorithm
|
||||
Liquidate(r.Symbol);
|
||||
RemoveSecurity(QuantConnect.Symbol.CreateBase(typeof(SmartInsiderTransaction), r.Symbol, Market.USA));
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
85
Algorithm.CSharp/AltData/TiingoNewsAlgorithm.cs
Normal file
85
Algorithm.CSharp/AltData/TiingoNewsAlgorithm.cs
Normal file
@@ -0,0 +1,85 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.Tiingo;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Look for positive and negative words in the news article description
|
||||
/// and trade based on the sum of the sentiment
|
||||
/// </summary>
|
||||
public class TiingoNewsAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Symbol _tiingoSymbol;
|
||||
|
||||
// Predefine a dictionary of words with scores to scan for in the description
|
||||
// of the Tiingo news article
|
||||
private readonly Dictionary<string, double> _words = new Dictionary<string, double>()
|
||||
{
|
||||
{"bad", -0.5}, {"good", 0.5},
|
||||
{ "negative", -0.5}, {"great", 0.5},
|
||||
{"growth", 0.5}, {"fail", -0.5},
|
||||
{"failed", -0.5}, {"success", 0.5},
|
||||
{"nailed", 0.5}, {"beat", 0.5},
|
||||
{"missed", -0.5}
|
||||
};
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2019, 6, 10);
|
||||
SetEndDate(2019, 10, 3);
|
||||
SetCash(100000);
|
||||
|
||||
var aapl = AddEquity("AAPL", Resolution.Hour).Symbol;
|
||||
_tiingoSymbol = AddData<TiingoNews>(aapl).Symbol;
|
||||
|
||||
// Request underlying equity data
|
||||
var ibm = AddEquity("IBM", Resolution.Minute).Symbol;
|
||||
// Add news data for the underlying IBM asset
|
||||
var news = AddData<TiingoNews>(ibm).Symbol;
|
||||
// Request 60 days of history with the TiingoNews IBM Custom Data Symbol.
|
||||
var history = History<TiingoNews>(news, 60, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
//Confirm that the data is in the collection
|
||||
if (!data.ContainsKey(_tiingoSymbol)) return;
|
||||
|
||||
// Gets the first piece of data from the Slice
|
||||
var article = data.Get<TiingoNews>(_tiingoSymbol);
|
||||
|
||||
// Article descriptions come in all caps. Lower and split by word
|
||||
var descriptionWords = article.Description.ToLowerInvariant().Split(' ');
|
||||
|
||||
// Take the intersection of predefined words and the words in the
|
||||
// description to get a list of matching words
|
||||
var intersection = _words.Keys.Intersect(descriptionWords);
|
||||
|
||||
// Get the sum of the article's sentiment, and go long or short
|
||||
// depending if it's a positive or negative description
|
||||
var sentiment = intersection.Select(x => _words[x]).Sum();
|
||||
|
||||
SetHoldings(article.Symbol.Underlying, sentiment);
|
||||
}
|
||||
}
|
||||
}
|
||||
80
Algorithm.CSharp/AltData/TradingEconomicsAlgorithm.cs
Normal file
80
Algorithm.CSharp/AltData/TradingEconomicsAlgorithm.cs
Normal file
@@ -0,0 +1,80 @@
|
||||
/*
|
||||
* 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.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.TradingEconomics;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Trades on interest rate announcements from data provided by Trading Economics
|
||||
/// </summary>
|
||||
public class TradingEconomicsAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Symbol _interestRate;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2013, 11, 1);
|
||||
SetEndDate(2019, 10, 3);
|
||||
SetCash(100000);
|
||||
|
||||
AddEquity("AGG", Resolution.Hour);
|
||||
AddEquity("SPY", Resolution.Hour);
|
||||
|
||||
_interestRate = AddData<TradingEconomicsCalendar>(TradingEconomics.Calendar.UnitedStates.InterestRate).Symbol;
|
||||
|
||||
// Request 365 days of interest rate history with the TradingEconomicsCalendar custom data Symbol.
|
||||
// We should expect no historical data because 2013-11-01 is before the absolute first point of data
|
||||
var history = History<TradingEconomicsCalendar>(_interestRate, 365, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request (should be zero)
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
// Make sure we have an interest rate calendar event
|
||||
if (!data.ContainsKey(_interestRate))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
var announcement = data.Get<TradingEconomicsCalendar>(_interestRate);
|
||||
|
||||
// Confirm it's a FED Rate Decision
|
||||
if (announcement.Event != TradingEconomics.Event.UnitedStates.FedInterestRateDecision)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// In the event of a rate increase, rebalance 50% to Bonds.
|
||||
var interestRateDecreased = announcement.Actual <= announcement.Previous;
|
||||
|
||||
if (interestRateDecreased)
|
||||
{
|
||||
SetHoldings("SPY", 1);
|
||||
SetHoldings("AGG", 0);
|
||||
}
|
||||
else
|
||||
{
|
||||
SetHoldings("SPY", 0.5);
|
||||
SetHoldings("AGG", 0.5);
|
||||
}
|
||||
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,87 @@
|
||||
/*
|
||||
* 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.Data.Custom.USTreasury;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
public class USTreasuryYieldCurveRateAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Symbol _yieldCurve;
|
||||
private Symbol _spy;
|
||||
private DateTime _lastInversion = DateTime.MinValue;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2000, 3, 1);
|
||||
SetEndDate(2019, 9, 15);
|
||||
SetCash(100000);
|
||||
|
||||
_spy = AddEquity("SPY", Resolution.Hour).Symbol;
|
||||
_yieldCurve = AddData<USTreasuryYieldCurveRate>("USTYCR", Resolution.Daily).Symbol;
|
||||
|
||||
// Request 60 days of history with the USTreasuryYieldCurveRate custom data Symbol.
|
||||
var history = History<USTreasuryYieldCurveRate>(_yieldCurve, 60, Resolution.Daily);
|
||||
|
||||
// Count the number of items we get from our history request
|
||||
Debug($"We got {history.Count()} items from our history request");
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
if (!data.ContainsKey(_yieldCurve))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// Preserve null values by getting the data with `slice.Get<T>`
|
||||
// Accessing the data using `data[_yieldCurve]` results in null
|
||||
// values becoming `default(decimal)` which is equal to 0
|
||||
var rates = data.Get<USTreasuryYieldCurveRate>().Values.First();
|
||||
|
||||
// Check for null before using the values
|
||||
if (!rates.TenYear.HasValue || !rates.TwoYear.HasValue)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// Only advance if a year has gone by
|
||||
if (Time - _lastInversion < TimeSpan.FromDays(365))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// if there is a yield curve inversion after not having one for a year, short SPY for two years
|
||||
if (!Portfolio.Invested && rates.TwoYear > rates.TenYear)
|
||||
{
|
||||
Debug($"{Time} - Yield curve inversion! Shorting the market for two years");
|
||||
SetHoldings(_spy, -0.5);
|
||||
|
||||
_lastInversion = Time;
|
||||
|
||||
return;
|
||||
}
|
||||
|
||||
// If two years have passed, liquidate our position in SPY
|
||||
if (Time - _lastInversion >= TimeSpan.FromDays(365 * 2))
|
||||
{
|
||||
Liquidate(_spy);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -1,130 +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 QuantConnect.Brokerages;
|
||||
using System.Collections.Generic;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Basic template algorithm for the Atreyu brokerage
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="using quantconnect" />
|
||||
/// <meta name="tag" content="trading and orders" />
|
||||
public class BasicTemplateAtreyuAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
/// <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);
|
||||
SetEndDate(2013, 10, 11);
|
||||
SetCash(100000);
|
||||
|
||||
SetBrokerageModel(BrokerageName.Atreyu);
|
||||
AddEquity("SPY", Resolution.Minute);
|
||||
|
||||
DefaultOrderProperties = new AtreyuOrderProperties
|
||||
{
|
||||
// Can specify the default exchange to execute an order on.
|
||||
// If not specified will default to the primary exchange
|
||||
Exchange = Exchange.BATS,
|
||||
// Currently only support order for the day
|
||||
TimeInForce = TimeInForce.Day
|
||||
};
|
||||
}
|
||||
|
||||
/// <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)
|
||||
{
|
||||
// will set 25% of our buying power with a market order that will be routed to exchange set in the default order properties (BATS)
|
||||
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 });
|
||||
|
||||
Debug("Purchased SPY!");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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%"},
|
||||
{"Compounding Annual Return", "93.340%"},
|
||||
{"Drawdown", "1.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.846%"},
|
||||
{"Sharpe Ratio", "6.515"},
|
||||
{"Probabilistic Sharpe Ratio", "67.535%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0.11"},
|
||||
{"Annual Variance", "0.012"},
|
||||
{"Information Ratio", "6.515"},
|
||||
{"Tracking Error", "0.11"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$1.20"},
|
||||
{"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"},
|
||||
{"Portfolio Turnover", "0.124"},
|
||||
{"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", "01a751a837beafd90015b2fd82edf994"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,53 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System;
|
||||
using QuantConnect.Algorithm.Framework.Alphas;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Basic template algorithm which showcases <see cref="ConstituentsUniverse"/> simple use case
|
||||
/// </summary>
|
||||
public class BasicTemplateConstituentUniverseAlgorithm : QCAlgorithm
|
||||
{
|
||||
/// <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);
|
||||
SetEndDate(2013, 10, 11);
|
||||
|
||||
// by default will use algorithms UniverseSettings
|
||||
AddUniverse(Universe.Constituent.Steel());
|
||||
|
||||
// we specify the UniverseSettings it should use
|
||||
AddUniverse(Universe.Constituent.AggressiveGrowth(
|
||||
new UniverseSettings(Resolution.Hour,
|
||||
2,
|
||||
false,
|
||||
false,
|
||||
UniverseSettings.MinimumTimeInUniverse)));
|
||||
|
||||
SetAlpha(new ConstantAlphaModel(InsightType.Price, InsightDirection.Up, TimeSpan.FromDays(1)));
|
||||
SetExecution(new ImmediateExecutionModel());
|
||||
SetPortfolioConstruction(new EqualWeightingPortfolioConstructionModel());
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -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", "$970000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
|
||||
@@ -82,7 +82,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate which languages this algorithm is written in.
|
||||
/// </summary>
|
||||
public virtual Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
|
||||
|
||||
@@ -18,7 +18,6 @@ using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
using QuantConnect.Securities.Future;
|
||||
@@ -65,9 +64,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
var benchmark = AddEquity("SPY");
|
||||
SetBenchmark(benchmark.Symbol);
|
||||
|
||||
var seeder = new FuncSecuritySeeder(GetLastKnownPrices);
|
||||
SetSecurityInitializer(security => seeder.SeedSecurity(security));
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
@@ -116,19 +112,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
var maintenanceIntraday = futureMarginModel.MaintenanceIntradayMarginRequirement;
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
foreach (var addedSecurity in changes.AddedSecurities)
|
||||
{
|
||||
if (addedSecurity.Symbol.SecurityType == SecurityType.Future
|
||||
&& !addedSecurity.Symbol.IsCanonical()
|
||||
&& !addedSecurity.HasData)
|
||||
{
|
||||
throw new Exception($"Future contracts did not work up as expected: {addedSecurity.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>
|
||||
@@ -151,18 +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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -13,10 +13,16 @@
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using QuantConnect.Data;
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Algorithm.Framework.Alphas;
|
||||
using QuantConnect.Algorithm.Framework.Execution;
|
||||
using QuantConnect.Algorithm.Framework.Portfolio;
|
||||
using QuantConnect.Algorithm.Framework.Risk;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Orders;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Data;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
@@ -27,29 +33,29 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="using quantconnect" />
|
||||
/// <meta name="tag" content="trading and orders" />
|
||||
public class BasicTemplateIndiaAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
public class BasicTemplateIndiaAlgorithm : QCAlgorithm
|
||||
{
|
||||
/// <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()
|
||||
{
|
||||
SetAccountCurrency("INR"); //Set Account Currency
|
||||
SetStartDate(2019, 1, 23); //Set Start Date
|
||||
SetEndDate(2019, 10, 31); //Set End Date
|
||||
SetCash(100000); //Set Strategy Cash
|
||||
SetStartDate(2003, 10, 07); //Set Start Date
|
||||
SetEndDate(2003, 10, 11); //Set End Date
|
||||
SetCash(100000); //Set Strategy Cash
|
||||
|
||||
// Find more symbols here: http://quantconnect.com/data
|
||||
// Equities Resolutions: Tick, Second, Minute, Hour, Daily.
|
||||
AddEquity("YESBANK", Resolution.Minute, Market.India);
|
||||
|
||||
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: "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>
|
||||
@@ -60,10 +66,11 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
var marketTicket = MarketOrder("YESBANK", 1);
|
||||
var marketTicket = MarketOrder("UNIONBANK", 1);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
public override void OnOrderEvent(OrderEvent orderEvent)
|
||||
{
|
||||
if (orderEvent.Status.IsFill())
|
||||
@@ -75,60 +82,60 @@ 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;
|
||||
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 Language[] Languages { get; } = { Language.CSharp, Language.Python };
|
||||
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"},
|
||||
{"Total Trades", "3"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "-0.010%"},
|
||||
{"Drawdown", "0.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-0.008%"},
|
||||
{"Sharpe Ratio", "-1.183"},
|
||||
{"Probabilistic Sharpe Ratio", "0.001%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Average Loss", "-1.01%"},
|
||||
{"Compounding Annual Return", "261.134%"},
|
||||
{"Drawdown", "2.200%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "1.655%"},
|
||||
{"Sharpe Ratio", "8.505"},
|
||||
{"Probabilistic Sharpe Ratio", "66.840%"},
|
||||
{"Loss Rate", "100%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-1.183"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$6.00"},
|
||||
{"Estimated Strategy Capacity", "$61000000000.00"},
|
||||
{"Lowest Capacity Asset", "YESBANK UL"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-0.247"},
|
||||
{"Return Over Maximum Drawdown", "-1.104"},
|
||||
{"Portfolio Turnover", "0"},
|
||||
{"Total Insights Generated", "0"},
|
||||
{"Total Insights Closed", "0"},
|
||||
{"Total Insights Analysis Completed", "0"},
|
||||
{"Long Insight Count", "0"},
|
||||
{"Alpha", "-0.091"},
|
||||
{"Beta", "1.006"},
|
||||
{"Annual Standard Deviation", "0.224"},
|
||||
{"Annual Variance", "0.05"},
|
||||
{"Information Ratio", "-33.445"},
|
||||
{"Tracking Error", "0.002"},
|
||||
{"Treynor Ratio", "1.893"},
|
||||
{"Total Fees", "$10.32"},
|
||||
{"Estimated Strategy Capacity", "$27000000.00"},
|
||||
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.747"},
|
||||
{"Kelly Criterion Estimate", "38.796"},
|
||||
{"Kelly Criterion Probability Value", "0.228"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "85.095"},
|
||||
{"Portfolio Turnover", "0.747"},
|
||||
{"Total Insights Generated", "100"},
|
||||
{"Total Insights Closed", "99"},
|
||||
{"Total Insights Analysis Completed", "99"},
|
||||
{"Long Insight Count", "100"},
|
||||
{"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", "6cc69218edd7bd461678b9ee0c575db5"}
|
||||
{"Estimated Monthly Alpha Value", "$135639.1761"},
|
||||
{"Total Accumulated Estimated Alpha Value", "$21852.9784"},
|
||||
{"Mean Population Estimated Insight Value", "$220.7372"},
|
||||
{"Mean Population Direction", "53.5354%"},
|
||||
{"Mean Population Magnitude", "53.5354%"},
|
||||
{"Rolling Averaged Population Direction", "58.2788%"},
|
||||
{"Rolling Averaged Population Magnitude", "58.2788%"},
|
||||
{"OrderListHash", "ad2216297c759d8e5aef48ff065f8919"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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 QuantConnect.Data;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Orders;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// This example demonstrates how to add index asset types.
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="benchmarks" />
|
||||
/// <meta name="tag" content="indexes" />
|
||||
public class BasicTemplateIndiaIndexAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
protected Symbol Nifty;
|
||||
protected Symbol NiftyETF;
|
||||
private ExponentialMovingAverage _emaSlow;
|
||||
private ExponentialMovingAverage _emaFast;
|
||||
|
||||
/// <summary>
|
||||
/// Initialize your algorithm and add desired assets.
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
SetAccountCurrency("INR"); //Set Account Currency
|
||||
SetStartDate(2019, 1, 1); //Set End Date
|
||||
SetEndDate(2019, 1, 5); //Set End Date
|
||||
SetCash(1000000); //Set Strategy Cash
|
||||
|
||||
// Use indicator for signal; but it cannot be traded
|
||||
Nifty = AddIndex("NIFTY50", Resolution.Minute, Market.India).Symbol;
|
||||
|
||||
//Trade Index based ETF
|
||||
NiftyETF = AddEquity("JUNIORBEES", Resolution.Minute, Market.India).Symbol;
|
||||
|
||||
//Set Order Prperties as per the requirements for order placement
|
||||
DefaultOrderProperties = new IndiaOrderProperties(exchange: Exchange.NSE);
|
||||
|
||||
_emaSlow = EMA(Nifty, 80);
|
||||
_emaFast = EMA(Nifty, 200);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Index EMA Cross trading underlying.
|
||||
/// </summary>
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
if (!slice.Bars.ContainsKey(Nifty) || !slice.Bars.ContainsKey(NiftyETF))
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
// Warm up indicators
|
||||
if (!_emaSlow.IsReady)
|
||||
{
|
||||
return;
|
||||
}
|
||||
|
||||
if (_emaFast > _emaSlow)
|
||||
{
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
var marketTicket = MarketOrder(NiftyETF, 1);
|
||||
}
|
||||
}
|
||||
else
|
||||
{
|
||||
Liquidate();
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnEndOfAlgorithm()
|
||||
{
|
||||
if (Portfolio[Nifty].TotalSaleVolume > 0)
|
||||
{
|
||||
throw new Exception("Index is not tradable.");
|
||||
}
|
||||
}
|
||||
|
||||
/// <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", "0.00%"},
|
||||
{"Compounding Annual Return", "-0.395%"},
|
||||
{"Drawdown", "0.000%"},
|
||||
{"Expectancy", "-1"},
|
||||
{"Net Profit", "-0.004%"},
|
||||
{"Sharpe Ratio", "-23.595"},
|
||||
{"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", "-23.595"},
|
||||
{"Tracking Error", "0"},
|
||||
{"Treynor Ratio", "0"},
|
||||
{"Total Fees", "$36.00"},
|
||||
{"Estimated Strategy Capacity", "$74000.00"},
|
||||
{"Lowest Capacity Asset", "JUNIORBEES UL"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-29.6"},
|
||||
{"Return Over Maximum Drawdown", "-123.624"},
|
||||
{"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", "4637f26543287548b28a3c296db055d3"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -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"}
|
||||
};
|
||||
}
|
||||
}
|
||||
63
Algorithm.CSharp/Benchmarks/SECReportBenchmarkAlgorithm.cs
Normal file
63
Algorithm.CSharp/Benchmarks/SECReportBenchmarkAlgorithm.cs
Normal file
@@ -0,0 +1,63 @@
|
||||
/*
|
||||
* 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.Data.Custom.SEC;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.Benchmarks
|
||||
{
|
||||
public class SECReportBenchmarkAlgorithm : QCAlgorithm
|
||||
{
|
||||
private List<Security> _securities;
|
||||
/// <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(2018, 1, 1);
|
||||
SetEndDate(2019, 1, 1);
|
||||
|
||||
var tickers = new List<string> {"AAPL", "AMZN", "MSFT", "IBM", "FB", "QQQ",
|
||||
"IWM", "BAC", "BNO", "AIG", "UW", "WM" };
|
||||
_securities = new List<Security>();
|
||||
|
||||
foreach (var ticker in tickers)
|
||||
{
|
||||
var equity = AddEquity(ticker);
|
||||
_securities.Add(equity);
|
||||
|
||||
AddData<SECReport8K>(equity.Symbol, Resolution.Daily);
|
||||
AddData<SECReport10K>(equity.Symbol, Resolution.Daily);
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
foreach (var security in _securities)
|
||||
{
|
||||
SECReport8K report8K = security.Data.Get<SECReport8K>();
|
||||
SECReport10K report10K = security.Data.Get<SECReport10K>();
|
||||
|
||||
if (!security.HoldStock && report8K != null && report10K != null)
|
||||
{
|
||||
SetHoldings(security.Symbol, 1d / _securities.Count);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,81 @@
|
||||
/*
|
||||
* 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.Data.Custom.SmartInsider;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp.Benchmarks
|
||||
{
|
||||
public class SmartInsiderEventBenchmarkAlgorithm : QCAlgorithm
|
||||
{
|
||||
private List<Security> _securities;
|
||||
private List<Symbol> _customSymbols;
|
||||
private int _historySymbolCount;
|
||||
|
||||
/// <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(2010, 1, 1);
|
||||
SetEndDate(2019, 1, 1);
|
||||
|
||||
var tickers = new List<string> {"AAPL", "AMZN", "MSFT", "IBM", "FB", "QQQ",
|
||||
"IWM", "BAC", "BNO", "AIG", "UW", "WM" };
|
||||
_securities = new List<Security>();
|
||||
_customSymbols = new List<Symbol>();
|
||||
|
||||
foreach (var ticker in tickers)
|
||||
{
|
||||
var equity = AddEquity(ticker, Resolution.Hour);
|
||||
_securities.Add(equity);
|
||||
|
||||
_customSymbols.Add(
|
||||
AddData<SmartInsiderIntention>(equity.Symbol, Resolution.Daily).Symbol);
|
||||
_customSymbols.Add(
|
||||
AddData<SmartInsiderTransaction>(equity.Symbol, Resolution.Daily).Symbol);
|
||||
}
|
||||
|
||||
Schedule.On(DateRules.EveryDay(), TimeRules.At(16, 0), () =>
|
||||
{
|
||||
foreach (var slice in History(_customSymbols, TimeSpan.FromDays(5)))
|
||||
{
|
||||
_historySymbolCount += slice.Count;
|
||||
}
|
||||
|
||||
foreach (var security in _securities)
|
||||
{
|
||||
SmartInsiderIntention intention = security.Data.Get<SmartInsiderIntention>();
|
||||
SmartInsiderTransaction transaction = security.Data.Get<SmartInsiderTransaction>();
|
||||
|
||||
if (!security.HoldStock && intention != null && transaction != null)
|
||||
{
|
||||
SetHoldings(security.Symbol, 1d / _securities.Count);
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
var intentions = data.Get<SmartInsiderIntention>();
|
||||
var transactions = data.Get<SmartInsiderTransaction>();
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -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"},
|
||||
|
||||
@@ -17,7 +17,7 @@ using System;
|
||||
using System.Collections.Generic;
|
||||
using System.IO;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.IconicTypes;
|
||||
using QuantConnect.Data.Custom.CBOE;
|
||||
using QuantConnect.Indicators;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
@@ -26,14 +26,14 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <summary>
|
||||
/// Tests the consolidation of custom data with random data
|
||||
/// </summary>
|
||||
public class CustomDataUnlinkedTradeBarIconicTypeConsolidationRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
public class CBOECustomDataConsolidationRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _vix;
|
||||
private BollingerBands _bb;
|
||||
private bool _invested;
|
||||
|
||||
/// <summary>
|
||||
/// Initializes the algorithm with fake "VIX" data
|
||||
/// Initializes the algorithm with fake VIX data
|
||||
/// </summary>
|
||||
public override void Initialize()
|
||||
{
|
||||
@@ -66,7 +66,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <summary>
|
||||
/// Incrementally updating data
|
||||
/// </summary>
|
||||
private class IncrementallyGeneratedCustomData : UnlinkedDataTradeBar
|
||||
private class IncrementallyGeneratedCustomData : CBOE
|
||||
{
|
||||
private const decimal _start = 10.01m;
|
||||
private static decimal _step;
|
||||
@@ -94,7 +94,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <returns>Incremental BaseData instance</returns>
|
||||
public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
|
||||
{
|
||||
var unlinkedBar = new UnlinkedDataTradeBar();
|
||||
var vix = new CBOE();
|
||||
_step += 0.10m;
|
||||
var open = _start + _step;
|
||||
var close = _start + _step + 0.02m;
|
||||
@@ -111,8 +111,8 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
Symbol = new Symbol(
|
||||
SecurityIdentifier.GenerateBase(typeof(IncrementallyGeneratedCustomData), "VIX", Market.USA, false),
|
||||
"VIX"),
|
||||
Period = unlinkedBar.Period,
|
||||
DataType = unlinkedBar.DataType
|
||||
Period = vix.Period,
|
||||
DataType = vix.DataType
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -138,22 +138,22 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Total Trades", "1"},
|
||||
{"Average Win", "0%"},
|
||||
{"Average Loss", "0%"},
|
||||
{"Compounding Annual Return", "28.248%"},
|
||||
{"Compounding Annual Return", "0.029%"},
|
||||
{"Drawdown", "0%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.330%"},
|
||||
{"Sharpe Ratio", "326.006"},
|
||||
{"Probabilistic Sharpe Ratio", "0%"},
|
||||
{"Net Profit", "0.000%"},
|
||||
{"Sharpe Ratio", "28.4"},
|
||||
{"Probabilistic Sharpe Ratio", "88.597%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.228"},
|
||||
{"Beta", "0.002"},
|
||||
{"Annual Standard Deviation", "0.001"},
|
||||
{"Alpha", "0"},
|
||||
{"Beta", "0"},
|
||||
{"Annual Standard Deviation", "0"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-7.886"},
|
||||
{"Tracking Error", "0.222"},
|
||||
{"Treynor Ratio", "149.368"},
|
||||
{"Information Ratio", "-7.163"},
|
||||
{"Tracking Error", "0.195"},
|
||||
{"Treynor Ratio", "8.093"},
|
||||
{"Total Fees", "$0.00"},
|
||||
{"Estimated Strategy Capacity", "$0"},
|
||||
{"Lowest Capacity Asset", "VIX.IncrementallyGeneratedCustomData 2S"},
|
||||
@@ -176,7 +176,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "27b250ae26a92f52d39302b8c299726e"}
|
||||
{"OrderListHash", "918912ee4f64cd0290f3d58deca02713"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -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"},
|
||||
|
||||
119
Algorithm.CSharp/CoarseTiingoNewsUniverseSelectionAlgorithm.cs
Normal file
119
Algorithm.CSharp/CoarseTiingoNewsUniverseSelectionAlgorithm.cs
Normal file
@@ -0,0 +1,119 @@
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
using System;
|
||||
using QuantConnect.Interfaces;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.Tiingo;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Example algorithm of a custom universe selection using coarse data and adding TiingoNews
|
||||
/// If conditions are met will add the underlying and trade it
|
||||
/// </summary>
|
||||
public class CoarseTiingoNewsUniverseSelectionAlgorithm : QCAlgorithm
|
||||
{
|
||||
private const int NumberOfSymbols = 3;
|
||||
private List<Symbol> _symbols;
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2014, 03, 24);
|
||||
SetEndDate(2014, 04, 07);
|
||||
|
||||
UniverseSettings.FillForward = false;
|
||||
|
||||
AddUniverse(new CustomDataCoarseFundamentalUniverse(UniverseSettings, CoarseSelectionFunction));
|
||||
|
||||
_symbols = new List<Symbol>();
|
||||
}
|
||||
|
||||
// sort the data by daily dollar volume and take the top 'NumberOfSymbols'
|
||||
public IEnumerable<Symbol> CoarseSelectionFunction(IEnumerable<CoarseFundamental> coarse)
|
||||
{
|
||||
// sort descending by daily dollar volume
|
||||
var sortedByDollarVolume = coarse.OrderByDescending(x => x.DollarVolume);
|
||||
|
||||
// take the top entries from our sorted collection
|
||||
var top = sortedByDollarVolume.Take(NumberOfSymbols);
|
||||
|
||||
// we need to return only the symbol objects
|
||||
return top.Select(x => QuantConnect.Symbol.CreateBase(typeof(TiingoNews), x.Symbol, x.Symbol.ID.Market));
|
||||
}
|
||||
|
||||
public override void OnData(Slice data)
|
||||
{
|
||||
var articles = data.Get<TiingoNews>();
|
||||
|
||||
foreach (var kvp in articles)
|
||||
{
|
||||
var news = kvp.Value;
|
||||
if (news.Title.IndexOf("Stocks Drop", 0, StringComparison.CurrentCultureIgnoreCase) != -1)
|
||||
{
|
||||
if (!Securities.ContainsKey(kvp.Key.Underlying))
|
||||
{
|
||||
// add underlying we want to trade
|
||||
AddSecurity(kvp.Key.Underlying);
|
||||
_symbols.Add(kvp.Key.Underlying);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
foreach (var symbol in _symbols)
|
||||
{
|
||||
if (Securities[symbol].HasData)
|
||||
{
|
||||
SetHoldings(symbol, 1m / _symbols.Count);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
public override void OnSecuritiesChanged(SecurityChanges changes)
|
||||
{
|
||||
changes.FilterCustomSecurities = false;
|
||||
Log($"{Time} {changes}");
|
||||
}
|
||||
|
||||
private class CustomDataCoarseFundamentalUniverse : CoarseFundamentalUniverse
|
||||
{
|
||||
public CustomDataCoarseFundamentalUniverse(UniverseSettings universeSettings, Func<IEnumerable<CoarseFundamental>, IEnumerable<Symbol>> selector)
|
||||
: base(universeSettings, selector)
|
||||
{ }
|
||||
|
||||
public override IEnumerable<SubscriptionRequest> GetSubscriptionRequests(Security security, DateTime currentTimeUtc, DateTime maximumEndTimeUtc,
|
||||
ISubscriptionDataConfigService subscriptionService)
|
||||
{
|
||||
var config = subscriptionService.Add(
|
||||
typeof(TiingoNews),
|
||||
security.Symbol,
|
||||
UniverseSettings.Resolution,
|
||||
UniverseSettings.FillForward,
|
||||
UniverseSettings.ExtendedMarketHours,
|
||||
dataNormalizationMode: UniverseSettings.DataNormalizationMode);
|
||||
return new[]{new SubscriptionRequest(isUniverseSubscription: false,
|
||||
universe: this,
|
||||
security: security,
|
||||
configuration: config,
|
||||
startTimeUtc: currentTimeUtc,
|
||||
endTimeUtc: maximumEndTimeUtc)};
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -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"},
|
||||
|
||||
@@ -35,7 +35,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetCash(100000); // Set Strategy Cash
|
||||
|
||||
// Add QC500 Universe
|
||||
AddUniverse(Universe.QC500);
|
||||
AddUniverse(Universe.Index.QC500);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -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"},
|
||||
|
||||
@@ -117,7 +117,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "b7b8e83e4456e143c2c4c11fa31a1cf2"}
|
||||
{"OrderListHash", "9ce7252112d0ad7be0704297f7d48a88"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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"},
|
||||
@@ -138,7 +138,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "eba70a03119f2e8fe526d1092fbc36d0"}
|
||||
{"OrderListHash", "3df007afa8125770e8f1a49263af90a2"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -18,7 +18,7 @@ using System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.IconicTypes;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
@@ -30,7 +30,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="custom data" />
|
||||
/// <meta name="tag" content="regression test" />d
|
||||
public class CustomDataLinkedIconicTypeAddDataCoarseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
public class CustomDataAddDataCoarseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private List<Symbol> _customSymbols = new List<Symbol>();
|
||||
|
||||
@@ -61,7 +61,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
foreach (var symbol in symbols)
|
||||
{
|
||||
_customSymbols.Add(AddData<LinkedData>(symbol, Resolution.Daily).Symbol);
|
||||
_customSymbols.Add(AddData<SECReport8K>(symbol, Resolution.Daily).Symbol);
|
||||
}
|
||||
|
||||
return symbols;
|
||||
@@ -106,25 +106,25 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "2.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-1.658%"},
|
||||
{"Sharpe Ratio", "-4.707"},
|
||||
{"Probabilistic Sharpe Ratio", "5.401%"},
|
||||
{"Sharpe Ratio", "-4.841"},
|
||||
{"Probabilistic Sharpe Ratio", "5.714%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.211"},
|
||||
{"Beta", "0.503"},
|
||||
{"Annual Standard Deviation", "0.055"},
|
||||
{"Alpha", "-0.271"},
|
||||
{"Beta", "0.046"},
|
||||
{"Annual Standard Deviation", "0.057"},
|
||||
{"Annual Variance", "0.003"},
|
||||
{"Information Ratio", "-3.027"},
|
||||
{"Tracking Error", "0.054"},
|
||||
{"Treynor Ratio", "-0.514"},
|
||||
{"Information Ratio", "-1.563"},
|
||||
{"Tracking Error", "0.112"},
|
||||
{"Treynor Ratio", "-6.032"},
|
||||
{"Total Fees", "$14.45"},
|
||||
{"Estimated Strategy Capacity", "$370000000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.001"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-6.201"},
|
||||
{"Sortino Ratio", "-7.238"},
|
||||
{"Return Over Maximum Drawdown", "-16.848"},
|
||||
{"Portfolio Turnover", "0.038"},
|
||||
{"Total Insights Generated", "0"},
|
||||
@@ -18,7 +18,7 @@ using System;
|
||||
using System.Collections.Generic;
|
||||
using QuantConnect.Algorithm.Framework.Selection;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.IconicTypes;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.UniverseSelection;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
@@ -30,7 +30,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="custom data" />
|
||||
/// <meta name="tag" content="regression test" />
|
||||
public class CustomDataLinkedIconicTypeAddDataOnSecuritiesChangedRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
public class CustomDataAddDataOnSecuritiesChangedRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private List<Symbol> _customSymbols = new List<Symbol>();
|
||||
|
||||
@@ -85,7 +85,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
_customSymbols.Clear();
|
||||
iterated = true;
|
||||
}
|
||||
_customSymbols.Add(AddData<LinkedData>(added.Symbol, Resolution.Daily).Symbol);
|
||||
_customSymbols.Add(AddData<SECReport8K>(added.Symbol, Resolution.Daily).Symbol);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -111,25 +111,25 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "2.000%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "-1.658%"},
|
||||
{"Sharpe Ratio", "-4.707"},
|
||||
{"Probabilistic Sharpe Ratio", "5.401%"},
|
||||
{"Sharpe Ratio", "-4.841"},
|
||||
{"Probabilistic Sharpe Ratio", "5.714%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.211"},
|
||||
{"Beta", "0.503"},
|
||||
{"Annual Standard Deviation", "0.055"},
|
||||
{"Alpha", "-0.271"},
|
||||
{"Beta", "0.046"},
|
||||
{"Annual Standard Deviation", "0.057"},
|
||||
{"Annual Variance", "0.003"},
|
||||
{"Information Ratio", "-3.027"},
|
||||
{"Tracking Error", "0.054"},
|
||||
{"Treynor Ratio", "-0.514"},
|
||||
{"Information Ratio", "-1.563"},
|
||||
{"Tracking Error", "0.112"},
|
||||
{"Treynor Ratio", "-6.032"},
|
||||
{"Total Fees", "$14.45"},
|
||||
{"Estimated Strategy Capacity", "$370000000.00"},
|
||||
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
|
||||
{"Fitness Score", "0.001"},
|
||||
{"Fitness Score", "0"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "-6.201"},
|
||||
{"Sortino Ratio", "-7.238"},
|
||||
{"Return Over Maximum Drawdown", "-16.848"},
|
||||
{"Portfolio Turnover", "0.038"},
|
||||
{"Total Insights Generated", "0"},
|
||||
@@ -18,7 +18,8 @@ using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.IconicTypes;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.Custom.USTreasury;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
@@ -26,7 +27,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <summary>
|
||||
/// Regression algorithm ensures that added data matches expectations
|
||||
/// </summary>
|
||||
public class CustomDataIconicTypesAddDataRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
public class CustomDataAddDataRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Symbol _googlEquity;
|
||||
|
||||
@@ -37,17 +38,17 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetCash(100000);
|
||||
|
||||
var twxEquity = AddEquity("TWX", Resolution.Daily).Symbol;
|
||||
var customTwxSymbol = AddData<LinkedData>(twxEquity, Resolution.Daily).Symbol;
|
||||
var customTwxSymbol = AddData<SECReport8K>(twxEquity, Resolution.Daily).Symbol;
|
||||
|
||||
_googlEquity = AddEquity("GOOGL", Resolution.Daily).Symbol;
|
||||
var customGooglSymbol = AddData<LinkedData>("GOOGL", Resolution.Daily).Symbol;
|
||||
var customGooglSymbol = AddData<SECReport10K>("GOOGL", Resolution.Daily).Symbol;
|
||||
|
||||
var unlinkedDataSymbol = AddData<UnlinkedData>("GOOGL", Resolution.Daily).Symbol;
|
||||
var unlinkedDataSymbolUnderlyingEquity = QuantConnect.Symbol.Create("MSFT", SecurityType.Equity, Market.USA);
|
||||
var unlinkedDataSymbolUnderlying = AddData<UnlinkedData>(unlinkedDataSymbolUnderlyingEquity, Resolution.Daily).Symbol;
|
||||
var usTreasury = AddData<USTreasuryYieldCurveRate>("GOOGL", Resolution.Daily).Symbol;
|
||||
var usTreasuryUnderlyingEquity = QuantConnect.Symbol.Create("MSFT", SecurityType.Equity, Market.USA);
|
||||
var usTreasuryUnderlying = AddData<USTreasuryYieldCurveRate>(usTreasuryUnderlyingEquity, Resolution.Daily).Symbol;
|
||||
|
||||
var optionSymbol = AddOption("TWX", Resolution.Minute).Symbol;
|
||||
var customOptionSymbol = AddData<LinkedData>(optionSymbol, Resolution.Daily).Symbol;
|
||||
var customOptionSymbol = AddData<SECReport10K>(optionSymbol, Resolution.Daily).Symbol;
|
||||
|
||||
if (customTwxSymbol.Underlying != twxEquity)
|
||||
{
|
||||
@@ -57,17 +58,17 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
throw new Exception($"Underlying symbol for {customGooglSymbol} is not equal to GOOGL equity. Expected {_googlEquity} got {customGooglSymbol.Underlying}");
|
||||
}
|
||||
if (unlinkedDataSymbol.HasUnderlying)
|
||||
if (usTreasury.HasUnderlying)
|
||||
{
|
||||
throw new Exception($"Unlinked data type (no underlying) has underlying when it shouldn't. Found {unlinkedDataSymbol.Underlying}");
|
||||
throw new Exception($"US Treasury yield curve (no underlying) has underlying when it shouldn't. Found {usTreasury.Underlying}");
|
||||
}
|
||||
if (!unlinkedDataSymbolUnderlying.HasUnderlying)
|
||||
if (!usTreasuryUnderlying.HasUnderlying)
|
||||
{
|
||||
throw new Exception("Unlinked data type (with underlying) has no underlying Symbol even though we added with Symbol");
|
||||
throw new Exception("US Treasury yield curve (with underlying) has no underlying Symbol even though we added with Symbol");
|
||||
}
|
||||
if (unlinkedDataSymbolUnderlying.Underlying != unlinkedDataSymbolUnderlyingEquity)
|
||||
if (usTreasuryUnderlying.Underlying != usTreasuryUnderlyingEquity)
|
||||
{
|
||||
throw new Exception($"Unlinked data type underlying does not equal equity Symbol added. Expected {unlinkedDataSymbolUnderlyingEquity} got {unlinkedDataSymbolUnderlying.Underlying}");
|
||||
throw new Exception($"US Treasury yield curve underlying does not equal equity Symbol added. Expected {usTreasuryUnderlyingEquity} got {usTreasuryUnderlying.Underlying}");
|
||||
}
|
||||
if (customOptionSymbol.Underlying != optionSymbol)
|
||||
{
|
||||
@@ -76,13 +77,13 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
try
|
||||
{
|
||||
var customDataNoCache = AddData<LinkedData>("AAPL", Resolution.Daily);
|
||||
var customDataNoCache = AddData<SECReport10Q>("AAPL", Resolution.Daily);
|
||||
throw new Exception("AAPL was found in the SymbolCache, though it should be missing");
|
||||
}
|
||||
catch (InvalidOperationException)
|
||||
{
|
||||
// This is exactly what we wanted. AAPL shouldn't have been found in the SymbolCache, and because
|
||||
// LinkedData is mappable, we threw
|
||||
// SECReport10Q is a mappable type, we threw
|
||||
return;
|
||||
}
|
||||
}
|
||||
@@ -117,18 +118,18 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Drawdown", "0.700%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.383%"},
|
||||
{"Sharpe Ratio", "3.029"},
|
||||
{"Probabilistic Sharpe Ratio", "56.825%"},
|
||||
{"Sharpe Ratio", "2.719"},
|
||||
{"Probabilistic Sharpe Ratio", "57.021%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "-0.51"},
|
||||
{"Beta", "0.396"},
|
||||
{"Annual Standard Deviation", "0.091"},
|
||||
{"Annual Variance", "0.008"},
|
||||
{"Information Ratio", "-12.534"},
|
||||
{"Tracking Error", "0.136"},
|
||||
{"Treynor Ratio", "0.696"},
|
||||
{"Alpha", "0.086"},
|
||||
{"Beta", "0.092"},
|
||||
{"Annual Standard Deviation", "0.079"},
|
||||
{"Annual Variance", "0.006"},
|
||||
{"Information Ratio", "-6.123"},
|
||||
{"Tracking Error", "0.193"},
|
||||
{"Treynor Ratio", "2.331"},
|
||||
{"Total Fees", "$1.00"},
|
||||
{"Estimated Strategy Capacity", "$120000000.00"},
|
||||
{"Lowest Capacity Asset", "GOOG T1AZ164W5VTX"},
|
||||
@@ -1,4 +1,4 @@
|
||||
/*
|
||||
/*
|
||||
* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
||||
* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
||||
*
|
||||
@@ -14,17 +14,16 @@
|
||||
*/
|
||||
|
||||
using System;
|
||||
using System.IO;
|
||||
using System.Globalization;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Util;
|
||||
using QuantConnect.Data.Custom;
|
||||
using QuantConnect.Data.Market;
|
||||
using QuantConnect.Indicators;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// The algorithm creates new indicator value with the existing indicator method by Indicator Extensions
|
||||
/// Demonstration of using local custom datasource CustomData to request the IBM and SPY daily data
|
||||
/// Demonstration of using the external custom datasource Quandl to request the VIX and VXV daily data
|
||||
/// </summary>
|
||||
/// <meta name="tag" content="using data" />
|
||||
/// <meta name="tag" content="using quantconnect" />
|
||||
@@ -35,10 +34,10 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <meta name="tag" content="charting" />
|
||||
public class CustomDataIndicatorExtensionsAlgorithm : QCAlgorithm
|
||||
{
|
||||
private const string _ibm = "IBM";
|
||||
private const string _spy = "SPY";
|
||||
private SimpleMovingAverage _smaIBM;
|
||||
private SimpleMovingAverage _smaSPY;
|
||||
private const string _vix = "CBOE/VIX";
|
||||
private const string _vxv = "CBOE/VXV";
|
||||
private SimpleMovingAverage _smaVIX;
|
||||
private SimpleMovingAverage _smaVXV;
|
||||
private IndicatorBase<IndicatorDataPoint> _ratio;
|
||||
|
||||
/// <summary>
|
||||
@@ -51,82 +50,46 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
SetCash(25000);
|
||||
|
||||
// Define the symbol and "type" of our generic data
|
||||
AddData<CustomData>(_ibm, Resolution.Daily);
|
||||
AddData<CustomData>(_spy, Resolution.Daily);
|
||||
AddData<QuandlVix>(_vix, Resolution.Daily);
|
||||
AddData<Quandl>(_vxv, Resolution.Daily);
|
||||
// Set up default Indicators, these are just 'identities' of the closing price
|
||||
_smaIBM = SMA(_ibm, 1);
|
||||
_smaSPY = SMA(_spy, 1);
|
||||
// This will create a new indicator whose value is smaSPY / smaIBM
|
||||
_ratio = _smaSPY.Over(_smaIBM);
|
||||
_smaVIX = SMA(_vix, 1);
|
||||
_smaVXV = SMA(_vxv, 1);
|
||||
// This will create a new indicator whose value is smaVXV / smaVIX
|
||||
_ratio = _smaVXV.Over(_smaVIX);
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Custom data event handler:
|
||||
/// </summary>
|
||||
/// <param name="data">CustomData - dictionary Bars of custom data</param>
|
||||
public void OnData(CustomData data)
|
||||
/// <param name="data">Quandl - dictionary Bars of Quandl Data</param>
|
||||
public void OnData(Quandl data)
|
||||
{
|
||||
// Wait for all indicators to fully initialize
|
||||
if (_smaIBM.IsReady && _smaSPY.IsReady && _ratio.IsReady)
|
||||
if (_smaVIX.IsReady && _smaVXV.IsReady && _ratio.IsReady)
|
||||
{
|
||||
if (!Portfolio.Invested && _ratio > 1)
|
||||
{
|
||||
MarketOrder(_ibm, 100);
|
||||
MarketOrder(_vix, 100);
|
||||
}
|
||||
else if (_ratio < 1)
|
||||
{
|
||||
Liquidate();
|
||||
}
|
||||
// plot all indicators
|
||||
PlotIndicator("SMA", _smaIBM, _smaSPY);
|
||||
PlotIndicator("SMA", _smaVIX, _smaVXV);
|
||||
PlotIndicator("Ratio", _ratio);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// Custom data from local LEAN data
|
||||
/// In CBOE/VIX data, there is a "vix close" column instead of "close" which is the
|
||||
/// default column namein LEAN Quandl custom data implementation.
|
||||
/// This class assigns new column name to match the the external datasource setting.
|
||||
/// </summary>
|
||||
public class CustomData : BaseData
|
||||
public class QuandlVix : Quandl
|
||||
{
|
||||
public decimal Open;
|
||||
public decimal High;
|
||||
public decimal Low;
|
||||
public decimal Close;
|
||||
|
||||
public override DateTime EndTime
|
||||
{
|
||||
get { return Time + Period; }
|
||||
set { Time = value - Period; }
|
||||
}
|
||||
|
||||
public TimeSpan Period
|
||||
{
|
||||
get { return QuantConnect.Time.OneDay; }
|
||||
}
|
||||
|
||||
public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
|
||||
{
|
||||
var source = Path.Combine(Globals.DataFolder, "equity", "usa", config.Resolution.ToString().ToLower(), LeanData.GenerateZipFileName(config.Symbol, date, config.Resolution, config.TickType));
|
||||
return new SubscriptionDataSource(source, SubscriptionTransportMedium.LocalFile, FileFormat.Csv);
|
||||
}
|
||||
|
||||
public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
|
||||
{
|
||||
var csv = line.ToCsv(6);
|
||||
var _scaleFactor = 1 / 10000m;
|
||||
|
||||
var custom = new CustomData
|
||||
{
|
||||
Symbol = config.Symbol,
|
||||
Time = DateTime.ParseExact(csv[0], DateFormat.TwelveCharacter, CultureInfo.InvariantCulture),
|
||||
Open = csv[1].ToDecimal() * _scaleFactor,
|
||||
High = csv[2].ToDecimal() * _scaleFactor,
|
||||
Low = csv[3].ToDecimal() * _scaleFactor,
|
||||
Close = csv[4].ToDecimal() * _scaleFactor,
|
||||
Value = csv[4].ToDecimal() * _scaleFactor
|
||||
};
|
||||
return custom;
|
||||
}
|
||||
public QuandlVix() : base(valueColumnName: "vix close") { }
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -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"},
|
||||
|
||||
@@ -17,7 +17,13 @@
|
||||
using System;
|
||||
using System.Collections.Generic;
|
||||
using System.Linq;
|
||||
using QuantConnect.Data.Custom.IconicTypes;
|
||||
using QuantConnect.Data.Custom;
|
||||
using QuantConnect.Data.Custom.CBOE;
|
||||
using QuantConnect.Data.Custom.Fred;
|
||||
using QuantConnect.Data.Custom.SEC;
|
||||
using QuantConnect.Data.Custom.Tiingo;
|
||||
using QuantConnect.Data.Custom.USEnergy;
|
||||
using QuantConnect.Data.Custom.USTreasury;
|
||||
using QuantConnect.Interfaces;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
@@ -25,7 +31,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
/// <summary>
|
||||
/// This regression algorithm tests the performance related GH issue 3772
|
||||
/// </summary>
|
||||
public class CustomDataIconicTypesDefaultResolutionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
public class DefaultResolutionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
public override void Initialize()
|
||||
{
|
||||
@@ -35,9 +41,14 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
|
||||
var types = new[]
|
||||
{
|
||||
typeof(UnlinkedDataTradeBar),
|
||||
typeof(DailyUnlinkedData),
|
||||
typeof(DailyLinkedData)
|
||||
typeof(SECReport8K),
|
||||
typeof(SECReport10K),
|
||||
typeof(SECReport10Q),
|
||||
typeof(USTreasuryYieldCurveRate),
|
||||
typeof(USEnergy),
|
||||
typeof(CBOE),
|
||||
typeof(TiingoPrice),
|
||||
typeof(Fred)
|
||||
};
|
||||
|
||||
foreach (var type in types)
|
||||
@@ -62,13 +73,24 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
}
|
||||
}
|
||||
|
||||
var security = AddData<HourlyDefaultResolutionUnlinkedData>(spy);
|
||||
var security = AddData<USEnergyAPI>(spy);
|
||||
if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(security.Symbol)
|
||||
.Any(config => config.Resolution != Resolution.Hour))
|
||||
{
|
||||
throw new Exception("Was expecting resolution to be set to Hour");
|
||||
}
|
||||
|
||||
try
|
||||
{
|
||||
|
||||
AddOption("AAPL", Resolution.Daily);
|
||||
throw new Exception("Was expecting an ArgumentException to be thrown");
|
||||
}
|
||||
catch (ArgumentException)
|
||||
{
|
||||
// expected, options only support minute resolution
|
||||
}
|
||||
|
||||
var option = AddOption("AAPL");
|
||||
if (SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(option.Symbol)
|
||||
.Any(config => config.Resolution != Resolution.Minute))
|
||||
@@ -79,30 +101,6 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
Quit();
|
||||
}
|
||||
|
||||
private class DailyUnlinkedData : UnlinkedData
|
||||
{
|
||||
public override List<Resolution> SupportedResolutions()
|
||||
{
|
||||
return DailyResolution;
|
||||
}
|
||||
}
|
||||
|
||||
private class DailyLinkedData : LinkedData
|
||||
{
|
||||
public override List<Resolution> SupportedResolutions()
|
||||
{
|
||||
return DailyResolution;
|
||||
}
|
||||
}
|
||||
|
||||
private class HourlyDefaultResolutionUnlinkedData : UnlinkedData
|
||||
{
|
||||
public override Resolution DefaultResolution()
|
||||
{
|
||||
return Resolution.Hour;
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
@@ -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", "61f4d3c109fc4b6b9eb14d2e4eec4843"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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"},
|
||||
@@ -229,7 +229,7 @@ namespace QuantConnect.Algorithm.CSharp
|
||||
{"Mean Population Magnitude", "0%"},
|
||||
{"Rolling Averaged Population Direction", "0%"},
|
||||
{"Rolling Averaged Population Magnitude", "0%"},
|
||||
{"OrderListHash", "df66ec72bb4332b14bbe31ec9bea7ffc"}
|
||||
{"OrderListHash", "4e9dbe6c2640427a5f3e510b57c7155f"}
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
@@ -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"},
|
||||
|
||||
71
Algorithm.CSharp/DynamicSecurityDataAlgorithm.cs
Normal file
71
Algorithm.CSharp/DynamicSecurityDataAlgorithm.cs
Normal file
@@ -0,0 +1,71 @@
|
||||
/*
|
||||
* 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.Data.Custom.SEC;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Provides an example algorithm showcasing the <see cref="Security.Data"/> features
|
||||
/// </summary>
|
||||
public class DynamicSecurityDataAlgorithm : QCAlgorithm
|
||||
{
|
||||
private Security GOOGL;
|
||||
private const string Ticker = "GOOGL";
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2015, 10, 22);
|
||||
SetEndDate(2015, 10, 30);
|
||||
|
||||
GOOGL = AddEquity(Ticker, Resolution.Daily);
|
||||
|
||||
AddData<SECReport8K>(Ticker, Resolution.Daily);
|
||||
AddData<SECReport10K>(Ticker, Resolution.Daily);
|
||||
AddData<SECReport10Q>(Ticker, Resolution.Daily);
|
||||
}
|
||||
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
// The Security object's Data property provides convenient access
|
||||
// to the various types of data related to that security. You can
|
||||
// access not only the security's price data, but also any custom
|
||||
// data that is mapped to the security, such as our SEC reports.
|
||||
|
||||
// 1. Get the most recent data point of a particular type:
|
||||
// 1.a Using the C# generic method, Get<T>:
|
||||
SECReport8K googlSec8kReport = GOOGL.Data.Get<SECReport8K>();
|
||||
SECReport10K googlSec10kReport = GOOGL.Data.Get<SECReport10K>();
|
||||
Log($"{Time:o}: 8K: {googlSec8kReport}");
|
||||
Log($"{Time:o}: 10K: {googlSec10kReport}");
|
||||
|
||||
// 2. Get the list of data points of a particular type for the most recent time step:
|
||||
// 2.a Using the C# generic method, GetAll<T>:
|
||||
List<SECReport8K> googlSec8kReports = GOOGL.Data.GetAll<SECReport8K>();
|
||||
List<SECReport10K> googlSec10kReports = GOOGL.Data.GetAll<SECReport10K>();
|
||||
Log($"{Time:o}: List: 8K: {googlSec8kReports.Count}");
|
||||
Log($"{Time:o}: List: 10K: {googlSec10kReports.Count}");
|
||||
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
Buy(GOOGL.Symbol, 10);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1,147 +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.IO;
|
||||
using QuantConnect.Data;
|
||||
using QuantConnect.Data.Custom.IconicTypes;
|
||||
using QuantConnect.Data.Market;
|
||||
using QuantConnect.Interfaces;
|
||||
using QuantConnect.Securities;
|
||||
|
||||
namespace QuantConnect.Algorithm.CSharp
|
||||
{
|
||||
/// <summary>
|
||||
/// Provides an example algorithm showcasing the <see cref="Security.Data"/> features
|
||||
/// </summary>
|
||||
public class DynamicSecurityDataRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
|
||||
{
|
||||
private Security Equity;
|
||||
private const string Ticker = "GOOGL";
|
||||
|
||||
public override void Initialize()
|
||||
{
|
||||
SetStartDate(2015, 10, 22);
|
||||
SetEndDate(2015, 10, 30);
|
||||
|
||||
Equity = AddEquity(Ticker, Resolution.Daily);
|
||||
var customLinkedEquity = AddData<LinkedData>(Ticker, Resolution.Daily).Symbol;
|
||||
|
||||
// Adding linked data manually to cache for example purposes, since
|
||||
// LinkedData is a type used for testing and doesn't point to any real data.
|
||||
Equity.Cache.AddDataList(new List<LinkedData>
|
||||
{
|
||||
new LinkedData
|
||||
{
|
||||
Count = 100,
|
||||
|
||||
Symbol = customLinkedEquity,
|
||||
EndTime = StartDate,
|
||||
},
|
||||
new LinkedData
|
||||
{
|
||||
Count = 50,
|
||||
|
||||
Symbol = customLinkedEquity,
|
||||
EndTime = StartDate
|
||||
}
|
||||
}, typeof(LinkedData), false);
|
||||
}
|
||||
|
||||
public override void OnData(Slice slice)
|
||||
{
|
||||
// The Security object's Data property provides convenient access
|
||||
// to the various types of data related to that security. You can
|
||||
// access not only the security's price data, but also any custom
|
||||
// data that is mapped to the security, such as our SEC reports.
|
||||
|
||||
// 1. Get the most recent data point of a particular type:
|
||||
// 1.a Using the C# generic method, Get<T>:
|
||||
LinkedData customLinkedData = Equity.Data.Get<LinkedData>();
|
||||
Log($"{Time:o}: LinkedData: {customLinkedData}");
|
||||
|
||||
// 2. Get the list of data points of a particular type for the most recent time step:
|
||||
// 2.a Using the C# generic method, GetAll<T>:
|
||||
List<LinkedData> customLinkedDataList = Equity.Data.GetAll<LinkedData>();
|
||||
Log($"{Time:o}: List: LinkedData: {customLinkedDataList.Count}");
|
||||
|
||||
if (!Portfolio.Invested)
|
||||
{
|
||||
Buy(Equity.Symbol, 10);
|
||||
}
|
||||
}
|
||||
|
||||
/// <summary>
|
||||
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
|
||||
/// </summary>
|
||||
public bool CanRunLocally { get; } = 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", "28.411%"},
|
||||
{"Drawdown", "0.100%"},
|
||||
{"Expectancy", "0"},
|
||||
{"Net Profit", "0.618%"},
|
||||
{"Sharpe Ratio", "8.815"},
|
||||
{"Probabilistic Sharpe Ratio", "99.065%"},
|
||||
{"Loss Rate", "0%"},
|
||||
{"Win Rate", "0%"},
|
||||
{"Profit-Loss Ratio", "0"},
|
||||
{"Alpha", "0.12"},
|
||||
{"Beta", "0.143"},
|
||||
{"Annual Standard Deviation", "0.022"},
|
||||
{"Annual Variance", "0"},
|
||||
{"Information Ratio", "-3.746"},
|
||||
{"Tracking Error", "0.084"},
|
||||
{"Treynor Ratio", "1.349"},
|
||||
{"Total Fees", "$1.00"},
|
||||
{"Estimated Strategy Capacity", "$1000000000.00"},
|
||||
{"Lowest Capacity Asset", "GOOG T1AZ164W5VTX"},
|
||||
{"Fitness Score", "0.008"},
|
||||
{"Kelly Criterion Estimate", "0"},
|
||||
{"Kelly Criterion Probability Value", "0"},
|
||||
{"Sortino Ratio", "79228162514264337593543950335"},
|
||||
{"Return Over Maximum Drawdown", "383.48"},
|
||||
{"Portfolio Turnover", "0.008"},
|
||||
{"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", "668caaf6ff8f35e16f05228541e99720"}
|
||||
};
|
||||
}
|
||||
}
|
||||
@@ -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"},
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user