/*
* 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;
using QuantConnect.Orders;
namespace QuantConnect.Algorithm.CSharp
{
///
/// This regression test algorithm reproduces GH issue 3239, where the stopLoss order
/// place on was not being filled.
///
public class StopLossOnOrderEventRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _spy;
private bool _alreadyTraded;
public override void Initialize()
{
SetStartDate(2013, 10, 07);
SetEndDate(2013, 10, 11);
_spy = AddEquity("SPY").Symbol;
}
public override void OnOrderEvent(OrderEvent orderEvent)
{
Debug($"{orderEvent}");
var order = Transactions.GetOrderById(orderEvent.OrderId);
if (order.Tag == "Entry" && orderEvent.Status == OrderStatus.Filled)
{
Debug("Enter short at " + orderEvent.FillPrice + " set STOPLOSS at 151.0m");
StopMarketOrder(order.Symbol, -order.Quantity, 151.0m, "StopLoss");
}
}
public override void OnData(Slice data)
{
if (!Portfolio.Invested && !_alreadyTraded)
{
_alreadyTraded = true;
MarketOrder(_spy, -100, false, "Entry");
Debug("Purchased Stock");
}
}
///
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
///
public bool CanRunLocally { get; } = true;
///
/// This is used by the regression test system to indicate which languages this algorithm is written in.
///
public Language[] Languages { get; } = { Language.CSharp };
///
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
///
public Dictionary ExpectedStatistics => new Dictionary
{
{"Total Trades", "2"},
{"Average Win", "0.00%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "0.273%"},
{"Drawdown", "0.000%"},
{"Expectancy", "0"},
{"Net Profit", "0.003%"},
{"Sharpe Ratio", "0"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "0%"},
{"Win Rate", "100%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0"},
{"Beta", "0"},
{"Annual Standard Deviation", "0"},
{"Annual Variance", "0"},
{"Information Ratio", "-8.769"},
{"Tracking Error", "0.22"},
{"Treynor Ratio", "0"},
{"Total Fees", "$2.00"},
{"Fitness Score", "0.076"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "79228162514264337593543950335"},
{"Return Over Maximum Drawdown", "79228162514264337593543950335"},
{"Portfolio Turnover", "0.076"},
{"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", "-174329712"}
};
}
}