quantconnect--lean/Algorithm.CSharp/BasicTemplateIndiaAlgorithm.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 QuantConnect.Data;
using QuantConnect.Orders;
using System.Collections.Generic;

namespace QuantConnect.Algorithm.CSharp
{
    /// <summary>
    /// Basic template India algorithm simply initializes the date range and cash. This is a skeleton
    /// framework you can use for designing an algorithm.
    /// </summary>
    /// <meta name="tag" content="using data" />
    /// <meta name="tag" content="using quantconnect" />
    /// <meta name="tag" content="trading and orders" />
    public class BasicTemplateIndiaAlgorithm : QCAlgorithm
    {
        /// <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(2003, 10, 07);  //Set Start Date
            SetEndDate(2003, 10, 11);    //Set End Date
            SetCash(100000);             //Set Strategy Cash

            // Find more symbols here: http://quantconnect.com/data
            // Equities Resolutions: Tick, Second, Minute, Hour, Daily.
            AddEquity("UNIONBANK", Resolution.Second, Market.India);

            //Set Order Prperties as per the requirements for order placement
            DefaultOrderProperties = new IndiaOrderProperties(exchange: Exchange.NSE);
            //override default productType value set in config.json if needed - order specific productType value
            //DefaultOrderProperties = new IndiaOrderProperties(exchange: Exchange.NSE, IndiaOrderProperties.IndiaProductType.CNC);

            // General Debug statement for acknowledgement
            Debug("Intialization Done");
        }

        /// <summary>
        /// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
        /// </summary>
        /// <param name="data">Slice object keyed by symbol containing the stock data</param>
        public override void OnData(Slice data)
        {
            if (!Portfolio.Invested)
            {
                var marketTicket = MarketOrder("UNIONBANK", 1);
            }
        }

        public override void OnOrderEvent(OrderEvent orderEvent)
        {
            if (orderEvent.Status.IsFill())
            {
                Debug($"Purchased Complete: {orderEvent.Symbol}");
            }
        }

        /// <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; } = false;

        /// <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 };

        /// <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", "3"},
            {"Average Win", "0%"},
            {"Average Loss", "-1.01%"},
            {"Compounding Annual Return", "261.134%"},
            {"Drawdown", "2.200%"},
            {"Expectancy", "-1"},
            {"Net Profit", "1.655%"},
            {"Sharpe Ratio", "8.505"},
            {"Probabilistic Sharpe Ratio", "66.840%"},
            {"Loss Rate", "100%"},
            {"Win Rate", "0%"},
            {"Profit-Loss Ratio", "0"},
            {"Alpha", "-0.091"},
            {"Beta", "1.006"},
            {"Annual Standard Deviation", "0.224"},
            {"Annual Variance", "0.05"},
            {"Information Ratio", "-33.445"},
            {"Tracking Error", "0.002"},
            {"Treynor Ratio", "1.893"},
            {"Total Fees", "$10.32"},
            {"Estimated Strategy Capacity", "$27000000.00"},
            {"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
            {"Fitness Score", "0.747"},
            {"Kelly Criterion Estimate", "38.796"},
            {"Kelly Criterion Probability Value", "0.228"},
            {"Sortino Ratio", "79228162514264337593543950335"},
            {"Return Over Maximum Drawdown", "85.095"},
            {"Portfolio Turnover", "0.747"},
            {"Total Insights Generated", "100"},
            {"Total Insights Closed", "99"},
            {"Total Insights Analysis Completed", "99"},
            {"Long Insight Count", "100"},
            {"Short Insight Count", "0"},
            {"Long/Short Ratio", "100%"},
            {"Estimated Monthly Alpha Value", "$135639.1761"},
            {"Total Accumulated Estimated Alpha Value", "$21852.9784"},
            {"Mean Population Estimated Insight Value", "$220.7372"},
            {"Mean Population Direction", "53.5354%"},
            {"Mean Population Magnitude", "53.5354%"},
            {"Rolling Averaged Population Direction", "58.2788%"},
            {"Rolling Averaged Population Magnitude", "58.2788%"},
            {"OrderListHash", "ad2216297c759d8e5aef48ff065f8919"}
        };
    }
}