wehub-resource-sync
126 lines
4.9 KiB
C#
126 lines
4.9 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.Collections.Generic;
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using QuantConnect.Data;
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using QuantConnect.Interfaces;
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using QuantConnect.Orders;
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using QuantConnect.Orders.Slippage;
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namespace QuantConnect.Algorithm.CSharp
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{
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public class MarketImpactSlippageModelRegressionAlgorithm : 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(2013, 10, 07);
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SetEndDate(2013, 10, 13);
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SetCash(10000000);
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var spy = AddEquity("SPY", Resolution.Daily);
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var aapl = AddEquity("AAPL", Resolution.Daily);
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spy.SetSlippageModel(new MarketImpactSlippageModel(this));
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aapl.SetSlippageModel(new MarketImpactSlippageModel(this));
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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="slice">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice slice)
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{
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SetHoldings("SPY", 0.5d);
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SetHoldings("AAPL", -0.5d);
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}
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/// <summary>
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/// OnOrderEvent is called whenever an order is updated
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/// </summary>
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/// <param name="orderEvent">Order Event</param>
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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if (orderEvent.Status == OrderStatus.Filled)
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{
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Debug($"Price: {Securities[orderEvent.Symbol].Price}, filled price: {orderEvent.FillPrice}, quantity: {orderEvent.FillQuantity}");
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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 List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
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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 long DataPoints => 53;
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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 int AlgorithmHistoryDataPoints => 506;
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/// <summary>
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/// Final status of the algorithm
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/// </summary>
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public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
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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 Orders", "9"},
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{"Average Win", "0%"},
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{"Average Loss", "-0.04%"},
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{"Compounding Annual Return", "-93.847%"},
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{"Drawdown", "4.200%"},
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{"Expectancy", "-1"},
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{"Start Equity", "10000000"},
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{"End Equity", "9649801.20"},
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{"Net Profit", "-3.502%"},
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{"Sharpe Ratio", "-2.93"},
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{"Sortino Ratio", "-2.869"},
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{"Probabilistic Sharpe Ratio", "7.243%"},
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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", "-3.355"},
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{"Beta", "1.244"},
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{"Annual Standard Deviation", "0.306"},
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{"Annual Variance", "0.094"},
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{"Information Ratio", "-20.203"},
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{"Tracking Error", "0.142"},
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{"Treynor Ratio", "-0.722"},
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{"Total Fees", "$1859.00"},
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{"Estimated Strategy Capacity", "$470000000.00"},
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{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
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{"Portfolio Turnover", "21.04%"},
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{"Drawdown Recovery", "0"},
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{"OrderListHash", "fc0626f660981cb698f6a9a5d5d1389a"}
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};
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}
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}
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