/* * 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 { /// /// Basic template algorithm simply initializes the date range and cash. This is a skeleton /// framework you can use for designing an algorithm. /// /// /// /// public class BasicTemplateHourlyAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition { private Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA); /// /// 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 // 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 } /// /// 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 slice) { if (!Portfolio.Invested) { SetHoldings(_spy, 1); 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 List Languages { get; } = new() { Language.CSharp }; /// /// Data Points count of all timeslices of algorithm /// public long DataPoints => 78; /// /// Data Points count of the algorithm history /// public int AlgorithmHistoryDataPoints => 0; /// /// Final status of the algorithm /// public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed; /// /// 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 Orders", "1"}, {"Average Win", "0%"}, {"Average Loss", "0%"}, {"Compounding Annual Return", "227.693%"}, {"Drawdown", "2.000%"}, {"Expectancy", "0"}, {"Start Equity", "100000"}, {"End Equity", "101529.08"}, {"Net Profit", "1.529%"}, {"Sharpe Ratio", "8.855"}, {"Sortino Ratio", "0"}, {"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", "1.971"}, {"Total Fees", "$3.44"}, {"Estimated Strategy Capacity", "$110000000.00"}, {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"}, {"Portfolio Turnover", "19.96%"}, {"Drawdown Recovery", "2"}, {"OrderListHash", "60747dce5c2aed393b7dccc258d2c9b5"} }; } }