Martin Molinero
99 lines
4.1 KiB
C#
99 lines
4.1 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;
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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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/// Basic template algorithm simply initializes the date range and cash
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/// </summary>
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/// <meta name="tag" content="trading and orders" />
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/// <meta name="tag" content="limit orders" />
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/// <meta name="tag" content="placing orders" />
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/// <meta name="tag" content="updating orders" />
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/// <meta name="tag" content="regression test" />
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public class LimitFillRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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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, 07); //Set Start Date
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SetEndDate(2013, 10, 11); //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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AddSecurity(SecurityType.Equity, "SPY", Resolution.Second);
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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">TradeBars IDictionary object with your stock data</param>
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public override void OnData(Slice data)
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{
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if (data.Bars.ContainsKey("SPY"))
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{
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if (Time.TimeOfDay.Ticks%TimeSpan.FromHours(1).Ticks == 0)
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{
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var goLong = Time < StartDate + TimeSpan.FromTicks((EndDate - StartDate).Ticks/2);
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var negative = goLong ? 1 : -1;
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LimitOrder("SPY", negative*10, data["SPY"].Price);
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}
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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", "34"},
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{"Average Win", "0.01%"},
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{"Average Loss", "-0.01%"},
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{"Compounding Annual Return", "8.398%"},
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{"Drawdown", "0.400%"},
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{"Expectancy", "0.502"},
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{"Net Profit", "0.111%"},
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{"Sharpe Ratio", "1.788"},
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{"Loss Rate", "25%"},
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{"Win Rate", "75%"},
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{"Profit-Loss Ratio", "1.00"},
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{"Alpha", "0.038"},
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{"Beta", "0.024"},
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{"Annual Standard Deviation", "0.026"},
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{"Annual Variance", "0.001"},
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{"Information Ratio", "-1.67"},
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{"Tracking Error", "0.186"},
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{"Treynor Ratio", "1.925"},
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{"Total Fees", "$34.00"}
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};
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}
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}
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