quantconnect--lean/Algorithm.CSharp/LimitFillRegressionAlgorithm.cs

/*
 * 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;
using QuantConnect.Orders;

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.ContainsKey("SPY"))
            {
                if (Time.Second == 0 && Time.Minute == 0)
                {
                    var goLong = Time < StartDate.AddDays(2);
                    var negative = goLong ? 1 : -1;
                    LimitOrder("SPY", negative*10, data["SPY"].Price);
                }
            }
        }

        public override void OnOrderEvent(OrderEvent orderEvent)
        {
            Debug($"{orderEvent}");
        }

        /// <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>
        /// Data Points count of all timeslices of algorithm
        /// </summary>
        public long DataPoints => 234043;

        /// <summary>
        /// Data Points count of the algorithm history
        /// </summary>
        public int AlgorithmHistoryDataPoints => 0;

        /// <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", "-0.02%"},
            {"Compounding Annual Return", "-6.417%"},
            {"Drawdown", "0.300%"},
            {"Expectancy", "-0.241"},
            {"Net Profit", "-0.085%"},
            {"Sharpe Ratio", "-1.246"},
            {"Probabilistic Sharpe Ratio", "40.325%"},
            {"Loss Rate", "50%"},
            {"Win Rate", "50%"},
            {"Profit-Loss Ratio", "0.52"},
            {"Alpha", "-0.217"},
            {"Beta", "0.096"},
            {"Annual Standard Deviation", "0.022"},
            {"Annual Variance", "0"},
            {"Information Ratio", "-9.984"},
            {"Tracking Error", "0.201"},
            {"Treynor Ratio", "-0.287"},
            {"Total Fees", "$34.00"},
            {"Estimated Strategy Capacity", "$180000000.00"},
            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
            {"Fitness Score", "0.005"},
            {"Kelly Criterion Estimate", "0"},
            {"Kelly Criterion Probability Value", "0"},
            {"Sortino Ratio", "-4.103"},
            {"Return Over Maximum Drawdown", "-25.584"},
            {"Portfolio Turnover", "0.097"},
            {"Total Insights Generated", "0"},
            {"Total Insights Closed", "0"},
            {"Total Insights Analysis Completed", "0"},
            {"Long Insight Count", "0"},
            {"Short Insight Count", "0"},
            {"Long/Short Ratio", "100%"},
            {"Estimated Monthly Alpha Value", "$0"},
            {"Total Accumulated Estimated Alpha Value", "$0"},
            {"Mean Population Estimated Insight Value", "$0"},
            {"Mean Population Direction", "0%"},
            {"Mean Population Magnitude", "0%"},
            {"Rolling Averaged Population Direction", "0%"},
            {"Rolling Averaged Population Magnitude", "0%"},
            {"OrderListHash", "a44bfc3854508a12ccffd5bb8fcd044b"}
        };
    }
}