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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.Collections.Generic;
using QuantConnect.Algorithm.Framework.Alphas;
using QuantConnect.Data;
using QuantConnect.Brokerages;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// This algorithm showcases an <see cref="AccountType.Cash"/> emitting insights
/// and manually trading.
/// </summary>
public class EmitInsightCryptoCashAccountType : QCAlgorithm, IRegressionAlgorithmDefinition
{
private Symbol _symbol;
/// <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, 4, 4); // Set Start Date
SetEndDate(2018, 4, 4); // Set End Date
SetAccountCurrency("EUR");
SetCash("EUR", 10000);
_symbol = AddCrypto("BTCEUR").Symbol;
SetBrokerageModel(BrokerageName.GDAX, AccountType.Cash);
}
/// <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)
{
EmitInsights(Insight.Price(_symbol, Resolution.Daily, 1, InsightDirection.Up));
SetHoldings(_symbol, 0.5);
}
}
/// <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>
/// 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", "1"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "-100.000%"},
{"Drawdown", "5.500%"},
{"Expectancy", "0"},
{"Net Profit", "-3.802%"},
{"Sharpe Ratio", "-12.079"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "0"},
{"Beta", "0"},
{"Annual Standard Deviation", "0.397"},
{"Annual Variance", "0.158"},
{"Information Ratio", "0"},
{"Tracking Error", "0"},
{"Treynor Ratio", "0"},
{"Total Fees", "$14.92"},
{"Total Insights Generated", "1"},
{"Total Insights Closed", "1"},
{"Total Insights Analysis Completed", "1"},
{"Long Insight Count", "1"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "€-7.1039"},
{"Total Accumulated Estimated Alpha Value", "€-0.2762628"},
{"Mean Population Estimated Insight Value", "€-0.2762628"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"}
};
}
}