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BasicTemplateIndiaIndexAlgorithm.cs
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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 QuantConnect.Data;
using System.Collections.Generic;
using QuantConnect.Indicators;
using QuantConnect.Interfaces;
using QuantConnect.Orders;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// This example demonstrates how to add index asset types.
/// </summary>
/// <meta name="tag" content="using data" />
/// <meta name="tag" content="benchmarks" />
/// <meta name="tag" content="indexes" />
public class BasicTemplateIndiaIndexAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
protected Symbol Nifty { get; set; }
protected Symbol NiftyETF { get; set; }
private ExponentialMovingAverage _emaSlow;
private ExponentialMovingAverage _emaFast;
/// <summary>
/// Initialize your algorithm and add desired assets.
/// </summary>
public override void Initialize()
{
SetAccountCurrency("INR"); //Set Account Currency
SetStartDate(2019, 1, 1); //Set End Date
SetEndDate(2019, 1, 5); //Set End Date
SetCash(1000000); //Set Strategy Cash
// Use indicator for signal; but it cannot be traded
Nifty = AddIndex("NIFTY50", Resolution.Minute, Market.India).Symbol;
//Trade Index based ETF
NiftyETF = AddEquity("JUNIORBEES", Resolution.Minute, Market.India).Symbol;
//Set Order Properties as per the requirements for order placement
DefaultOrderProperties = new IndiaOrderProperties(exchange: Exchange.NSE);
_emaSlow = EMA(Nifty, 80);
_emaFast = EMA(Nifty, 200);
}
/// <summary>
/// Index EMA Cross trading underlying.
/// </summary>
public override void OnData(Slice slice)
{
if (!slice.Bars.ContainsKey(Nifty) || !slice.Bars.ContainsKey(NiftyETF))
{
return;
}
// Warm up indicators
if (!_emaSlow.IsReady)
{
return;
}
if (_emaFast > _emaSlow)
{
if (!Portfolio.Invested)
{
var marketTicket = MarketOrder(NiftyETF, 1);
}
}
else
{
Liquidate();
}
}
public override void OnEndOfAlgorithm()
{
if (Portfolio[Nifty].TotalSaleVolume > 0)
{
throw new RegressionTestException("Index is not tradable.");
}
}
/// <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 virtual bool CanRunLocally { get; } = true;
/// <summary>
/// This is used by the regression test system to indicate which languages this algorithm is written in.
/// </summary>
public virtual List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public long DataPoints => 2882;
/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public int AlgorithmHistoryDataPoints => 0;
/// <summary>
/// Final status of the algorithm
/// </summary>
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
/// <summary>
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
/// </summary>
public virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
{
{"Total Orders", "6"},
{"Average Win", "0%"},
{"Average Loss", "0.00%"},
{"Compounding Annual Return", "-0.386%"},
{"Drawdown", "0.000%"},
{"Expectancy", "-1"},
{"Start Equity", "1000000"},
{"End Equity", "999961.17"},
{"Net Profit", "-0.004%"},
{"Sharpe Ratio", "-328.371"},
{"Sortino Ratio", "-328.371"},
{"Probabilistic Sharpe Ratio", "0%"},
{"Loss Rate", "100%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0"},
{"Beta", "0"},
{"Annual Standard Deviation", "0"},
{"Annual Variance", "0"},
{"Information Ratio", "-23.595"},
{"Tracking Error", "0"},
{"Treynor Ratio", "0"},
{"Total Fees", "₹36.00"},
{"Estimated Strategy Capacity", "₹84000.00"},
{"Lowest Capacity Asset", "JUNIORBEES UL"},
{"Portfolio Turnover", "0.04%"},
{"Drawdown Recovery", "0"},
{"OrderListHash", "8790bec8175539e6d92e01608ac57733"}
};
}
}