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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 QuantConnect.Orders;
using QuantConnect.Interfaces;
using QuantConnect.Securities;
using System.Collections.Generic;
using QuantConnect.Data.Custom.AlphaStreams;
using QuantConnect.Algorithm.Framework.Alphas;
using QuantConnect.Algorithm.Framework.Execution;
using QuantConnect.Algorithm.Framework.Portfolio;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Example algorithm consuming an alpha streams portfolio state and trading based on it
/// </summary>
public class AlphaStreamsBasicTemplateAlgorithm : 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(2018, 04, 04);
SetEndDate(2018, 04, 06);
SetAlpha(new AlphaStreamAlphaModule());
SetExecution(new ImmediateExecutionModel());
Settings.MinimumOrderMarginPortfolioPercentage = 0.01m;
SetPortfolioConstruction(new EqualWeightingAlphaStreamsPortfolioConstructionModel());
SetSecurityInitializer(new BrokerageModelSecurityInitializer(BrokerageModel,
new FuncSecuritySeeder(GetLastKnownPrices)));
foreach (var alphaId in new [] { "623b06b231eb1cc1aa3643a46", "9fc8ef73792331b11dbd5429a" })
{
AddData<AlphaStreamsPortfolioState>(alphaId);
}
}
public override void OnOrderEvent(OrderEvent orderEvent)
{
Log($"OnOrderEvent: {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 };
/// <summary>
/// Data Points count of all timeslices of algorithm
/// </summary>
public virtual long DataPoints => 890;
/// <summary>
/// Data Points count of the algorithm history
/// </summary>
public virtual int AlgorithmHistoryDataPoints => 12;
/// <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 Trades", "2"},
{"Average Win", "0%"},
{"Average Loss", "-0.12%"},
{"Compounding Annual Return", "-14.722%"},
{"Drawdown", "0.200%"},
{"Expectancy", "-1"},
{"Net Profit", "-0.116%"},
{"Sharpe Ratio", "0"},
{"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", "2.474"},
{"Tracking Error", "0.339"},
{"Treynor Ratio", "0"},
{"Total Fees", "$0.00"},
{"Estimated Strategy Capacity", "$83000.00"},
{"Lowest Capacity Asset", "BTCUSD XJ"},
{"Portfolio Turnover", "2.31%"},
{"OrderListHash", "2b94bc50a74caebe06c075cdab1bc6da"}
};
}
}