Martin-Molinero
bbbab6d9a8
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* Refactor alpha statistics - Refactor alpha statistics, cleaning up and simplifying no longer required calculations and scoring - Adding new InsightEvaluator abstraction, adding C# & PY regression algorithms * Optimization backtest result json converter update * Address reviews - Remove IAlphaHandler, move insight storage responsability to IResultHandler and centralizing insight collection on the QCAlgorithm.Insights to be reused by the framework models - Fix portfolio turnover single day backtests and duplicate time sampling handling. Updating regression algorithms * Add InsightCollection tests and minor fixes * Adding more & improved tests
110 lines
4.4 KiB
C#
110 lines
4.4 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 QuantConnect.Orders;
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using QuantConnect.Interfaces;
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using QuantConnect.Securities;
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using System.Collections.Generic;
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using QuantConnect.Data.Custom.AlphaStreams;
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using QuantConnect.Algorithm.Framework.Alphas;
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using QuantConnect.Algorithm.Framework.Execution;
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using QuantConnect.Algorithm.Framework.Portfolio;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Example algorithm consuming an alpha streams portfolio state and trading based on it
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/// </summary>
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public class AlphaStreamsBasicTemplateAlgorithm : 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, 04, 04);
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SetEndDate(2018, 04, 06);
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SetAlpha(new AlphaStreamAlphaModule());
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SetExecution(new ImmediateExecutionModel());
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Settings.MinimumOrderMarginPortfolioPercentage = 0.01m;
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SetPortfolioConstruction(new EqualWeightingAlphaStreamsPortfolioConstructionModel());
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SetSecurityInitializer(new BrokerageModelSecurityInitializer(BrokerageModel,
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new FuncSecuritySeeder(GetLastKnownPrices)));
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foreach (var alphaId in new [] { "623b06b231eb1cc1aa3643a46", "9fc8ef73792331b11dbd5429a" })
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{
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AddData<AlphaStreamsPortfolioState>(alphaId);
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}
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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Log($"OnOrderEvent: {orderEvent}");
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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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/// Data Points count of all timeslices of algorithm
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/// </summary>
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public virtual long DataPoints => 890;
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/// <summary>
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/// Data Points count of the algorithm history
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/// </summary>
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public virtual int AlgorithmHistoryDataPoints => 12;
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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 virtual Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>
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{
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{"Total Trades", "2"},
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{"Average Win", "0%"},
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{"Average Loss", "-0.12%"},
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{"Compounding Annual Return", "-14.722%"},
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{"Drawdown", "0.200%"},
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{"Expectancy", "-1"},
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{"Net Profit", "-0.116%"},
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{"Sharpe Ratio", "0"},
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{"Probabilistic Sharpe Ratio", "0%"},
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{"Loss Rate", "100%"},
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{"Win Rate", "0%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "0"},
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{"Beta", "0"},
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{"Annual Standard Deviation", "0"},
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{"Annual Variance", "0"},
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{"Information Ratio", "2.474"},
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{"Tracking Error", "0.339"},
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{"Treynor Ratio", "0"},
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{"Total Fees", "$0.00"},
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{"Estimated Strategy Capacity", "$83000.00"},
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{"Lowest Capacity Asset", "BTCUSD XJ"},
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{"Portfolio Turnover", "2.31%"},
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{"OrderListHash", "2b94bc50a74caebe06c075cdab1bc6da"}
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};
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}
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}
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