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/*
* 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.Data;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Basic template algorithm simply initializes the date range and cash
/// </summary>
/// <meta name="tag" content="trading and orders" />
/// <meta name="tag" content="limit orders" />
/// <meta name="tag" content="placing orders" />
/// <meta name="tag" content="updating orders" />
/// <meta name="tag" content="regression test" />
public class LimitFillRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
/// <summary>
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
/// </summary>
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
AddSecurity(SecurityType.Equity, "SPY", Resolution.Second);
}
/// <summary>
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
/// </summary>
/// <param name="data">TradeBars IDictionary object with your stock data</param>
public override void OnData(Slice data)
{
if (data.Bars.ContainsKey("SPY"))
{
if (Time.TimeOfDay.Ticks%TimeSpan.FromHours(1).Ticks == 0)
{
var goLong = Time < StartDate + TimeSpan.FromTicks((EndDate - StartDate).Ticks/2);
var negative = goLong ? 1 : -1;
LimitOrder("SPY", negative*10, data["SPY"].Price);
}
}
}
/// <summary>
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
/// </summary>
public bool CanRunLocally { get; } = true;
/// <summary>
/// This is used by the regression test system to indicate which languages this algorithm is written in.
/// </summary>
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
/// <summary>
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
/// </summary>
public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
{
{"Total Trades", "34"},
{"Average Win", "0.01%"},
{"Average Loss", "-0.01%"},
{"Compounding Annual Return", "9.024%"},
{"Drawdown", "0.400%"},
{"Expectancy", "0.502"},
{"Net Profit", "0.111%"},
{"Sharpe Ratio", "1.922"},
{"Loss Rate", "25%"},
{"Win Rate", "75%"},
{"Profit-Loss Ratio", "1.00"},
{"Alpha", "-0.102"},
{"Beta", "14.351"},
{"Annual Standard Deviation", "0.029"},
{"Annual Variance", "0.001"},
{"Information Ratio", "1.547"},
{"Tracking Error", "0.029"},
{"Treynor Ratio", "0.004"},
{"Total Fees", "$34.00"}
};
}
}