/* * QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals. * Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation. * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. */ using System; using System.Collections.Generic; using QuantConnect.Algorithm.Framework.Alphas; using QuantConnect.Data; using QuantConnect.Interfaces; namespace QuantConnect.Algorithm.CSharp { /// /// Regression test showcasing backwards incompatible behavior /// for framework bridge algorithms /// public class InvalidEmitInsightsAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private Symbol _symbol; /// /// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized. /// public override void Initialize() { SetStartDate(2013, 10, 07); //Set Start Date SetEndDate(2013, 10, 11); //Set End Date SetCash(100000); //Set Strategy Cash _symbol = AddEquity("SPY").Symbol; } /// /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here. /// /// Slice object keyed by symbol containing the stock data public override void OnData(Slice data) { SetHoldings(_symbol, 1); try { EmitInsights(Insight.Price(_symbol, Resolution.Daily, 1, InsightDirection.Up)); throw new Exception("Expected an exception"); } catch (InvalidOperationException exception) { if (!exception.Message.Contains("EmitInsights should be called before placing an order")) { throw; } } } /// /// 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", "1"}, {"Average Win", "0%"}, {"Average Loss", "0%"}, {"Compounding Annual Return", "263.153%"}, {"Drawdown", "2.200%"}, {"Expectancy", "0"}, {"Net Profit", "1.663%"}, {"Sharpe Ratio", "4.41"}, {"Loss Rate", "0%"}, {"Win Rate", "0%"}, {"Profit-Loss Ratio", "0"}, {"Alpha", "0.007"}, {"Beta", "76.118"}, {"Annual Standard Deviation", "0.192"}, {"Annual Variance", "0.037"}, {"Information Ratio", "4.354"}, {"Tracking Error", "0.192"}, {"Treynor Ratio", "0.011"}, {"Total Fees", "$3.26"}, {"Total Insights Generated", "1"}, {"Total Insights Closed", "0"}, {"Total Insights Analysis Completed", "0"}, {"Long Insight Count", "1"}, {"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%"} }; } }