/*
* 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.Collections.Generic;
using QuantConnect.Data;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
///
/// Basic template algorithm simply initializes the date range and cash. This is a skeleton
/// framework you can use for designing an algorithm.
///
///
///
///
public class BasicTemplateAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
///
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
///
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
// Forex, CFD, Equities Resolutions: Tick, Second, Minute, Hour, Daily.
// Futures Resolution: Tick, Second, Minute
// Options Resolution: Minute Only.
AddEquity("SPY", Resolution.Minute);
// There are other assets with similar methods. See "Selecting Options" etc for more details.
// AddFuture, AddForex, AddCfd, AddOption
}
///
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
///
/// Slice object keyed by symbol containing the stock data
public override void OnData(Slice slice)
{
if (!Portfolio.Invested)
{
SetHoldings(_spy, 1);
Debug("Purchased Stock");
}
}
///
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
///
public bool CanRunLocally { get; } = true;
///
/// This is used by the regression test system to indicate which languages this algorithm is written in.
///
public List Languages { get; } = new() { Language.CSharp, Language.Python };
///
/// Data Points count of all timeslices of algorithm
///
public long DataPoints => 3943;
///
/// Data Points count of the algorithm history
///
public int AlgorithmHistoryDataPoints => 0;
///
/// Final status of the algorithm
///
public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
///
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
///
public Dictionary ExpectedStatistics => new Dictionary
{
{"Total Orders", "1"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "271.453%"},
{"Drawdown", "2.200%"},
{"Expectancy", "0"},
{"Start Equity", "100000"},
{"End Equity", "101691.92"},
{"Net Profit", "1.692%"},
{"Sharpe Ratio", "8.854"},
{"Sortino Ratio", "0"},
{"Probabilistic Sharpe Ratio", "67.609%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.005"},
{"Beta", "0.996"},
{"Annual Standard Deviation", "0.222"},
{"Annual Variance", "0.049"},
{"Information Ratio", "-14.565"},
{"Tracking Error", "0.001"},
{"Treynor Ratio", "1.97"},
{"Total Fees", "$3.44"},
{"Estimated Strategy Capacity", "$56000000.00"},
{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
{"Portfolio Turnover", "19.93%"},
{"Drawdown Recovery", "3"},
{"OrderListHash", "3da9fa60bf95b9ed148b95e02e0cfc9e"}
};
}
}