Martin Molinero
93 lines
3.7 KiB
C#
93 lines
3.7 KiB
C#
/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using System.Collections.Generic;
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using QuantConnect.Data;
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using QuantConnect.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Demonstration of requesting daily resolution data for US Equities.
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/// This is a simple regression test algorithm using a skeleton algorithm and requesting daily data.
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/// </summary>
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/// <meta name="tag" content="using data" />
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public class BasicTemplateDailyAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2013, 10, 08); //Set Start Date
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SetEndDate(2013, 10, 17); //Set End Date
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SetCash(100000); //Set Strategy Cash
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// Find more symbols here: http://quantconnect.com/data
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AddEquity("SPY", Resolution.Daily);
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}
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/// <summary>
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/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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/// </summary>
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/// <param name="data">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice data)
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{
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if (!Portfolio.Invested)
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{
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SetHoldings(_spy, 1);
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Debug("Purchased Stock");
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}
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}
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/// <summary>
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/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
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/// </summary>
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public bool CanRunLocally { get; } = true;
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/// <summary>
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/// This is used by the regression test system to indicate which languages this algorithm is written in.
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/// </summary>
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public Language[] Languages { get; } = { Language.CSharp, Language.Python };
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Trades", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "246.000%"},
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{"Drawdown", "1.100%"},
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{"Expectancy", "0"},
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{"Net Profit", "3.459%"},
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{"Sharpe Ratio", "6.033"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0.012"},
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{"Beta", "0.991"},
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{"Annual Standard Deviation", "0.16"},
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{"Annual Variance", "0.026"},
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{"Information Ratio", "1.869"},
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{"Tracking Error", "0.002"},
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{"Treynor Ratio", "0.974"},
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{"Total Fees", "$3.26"}
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};
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}
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}
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