Juan José D'Ambrosio
105 lines
4.2 KiB
C#
105 lines
4.2 KiB
C#
/*
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* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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using System;
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using System.Collections.Generic;
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using QuantConnect.Data;
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using QuantConnect.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// This regression algorithm is a test case for validation of conversion rates during warm up.
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/// </summary>
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public class WarmupConversionRatesRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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/// <summary>
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/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
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/// </summary>
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public override void Initialize()
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{
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SetStartDate(2018, 4, 5);
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SetEndDate(2018, 4, 5);
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SetCash(10000);
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SetWarmUp(TimeSpan.FromDays(1));
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AddCrypto("BTCEUR");
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AddCrypto("LTCUSD");
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}
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/// <summary>
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/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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/// </summary>
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/// <param name="data">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice data)
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{
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if (Portfolio.CashBook["EUR"].ConversionRate == 0
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|| Portfolio.CashBook["BTC"].ConversionRate == 0
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|| Portfolio.CashBook["LTC"].ConversionRate == 0)
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{
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Log($"BTCEUR current price: {Securities["BTCEUR"].Price}");
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Log($"LTCUSD current price: {Securities["LTCUSD"].Price}");
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Log($"EUR conversion rate: {Portfolio.CashBook["EUR"].ConversionRate}");
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Log($"BTC conversion rate: {Portfolio.CashBook["BTC"].ConversionRate}");
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Log($"LTC conversion rate: {Portfolio.CashBook["LTC"].ConversionRate}");
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throw new Exception("Conversion rate is 0");
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}
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if (IsWarmingUp) return;
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if (!Portfolio.Invested)
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{
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SetHoldings("LTCUSD", 1);
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Debug("Purchased Stock");
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}
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}
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/// <summary>
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/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
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/// </summary>
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public bool CanRunLocally { get; } = true;
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/// <summary>
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/// This is used by the regression test system to indicate which languages this algorithm is written in.
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/// </summary>
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public Language[] Languages { get; } = { Language.CSharp };
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/// <summary>
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/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
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/// </summary>
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public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Trades", "1"},
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{"Average Win", "0%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "-95.719%"},
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{"Drawdown", "4.200%"},
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{"Expectancy", "0"},
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{"Net Profit", "-0.859%"},
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{"Sharpe Ratio", "-10.507"},
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{"Loss Rate", "0%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0.074"},
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{"Beta", "-1.33"},
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{"Annual Standard Deviation", "0.103"},
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{"Annual Variance", "0.011"},
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{"Information Ratio", "-10.815"},
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{"Tracking Error", "0.18"},
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{"Treynor Ratio", "0.814"},
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{"Total Fees", "$0.00"}
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};
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}
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} |