Martin Molinero
111 lines
4.0 KiB
C#
111 lines
4.0 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.Market;
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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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/// Algorithm used for regression tests purposes
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/// </summary>
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/// <meta name="tag" content="regression test" />
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public class RegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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public override void Initialize()
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{
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SetStartDate(2013, 10, 07);
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SetEndDate(2013, 10, 11);
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SetCash(10000000);
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// Find more symbols here: http://quantconnect.com/data
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AddSecurity(SecurityType.Equity, "SPY", Resolution.Tick);
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AddSecurity(SecurityType.Equity, "BAC", Resolution.Minute);
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AddSecurity(SecurityType.Equity, "AIG", Resolution.Hour);
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AddSecurity(SecurityType.Equity, "IBM", Resolution.Daily);
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}
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private DateTime lastTradeTradeBars;
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private DateTime lastTradeTicks;
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private TimeSpan tradeEvery = TimeSpan.FromMinutes(1);
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public void OnData(TradeBars data)
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{
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if (Time - lastTradeTradeBars < tradeEvery) return;
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lastTradeTradeBars = Time;
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foreach (var kvp in data)
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{
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var symbol = kvp.Key;
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var bar = kvp.Value;
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if (bar.Time.RoundDown(bar.Period) != bar.Time)
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{
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// only trade on new data
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continue;
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}
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var holdings = Portfolio[symbol];
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if (!holdings.Invested)
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{
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MarketOrder(symbol, 10);
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}
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else
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{
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MarketOrder(symbol, -holdings.Quantity);
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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", "5433"},
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{"Average Win", "0.00%"},
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{"Average Loss", "0.00%"},
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{"Compounding Annual Return", "-3.894%"},
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{"Drawdown", "0.100%"},
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{"Expectancy", "-0.993"},
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{"Net Profit", "-0.054%"},
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{"Sharpe Ratio", "-30.322"},
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{"Loss Rate", "100%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "2.23"},
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{"Alpha", "-0.023"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0.001"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-0.604"},
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{"Tracking Error", "0.184"},
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{"Treynor Ratio", "-319.842"},
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{"Total Fees", "$5433.00"}
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};
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}
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}
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