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* Fix bug The algorithm `CustomPartialFillModelAlgorithm.cs` was not working as expected with short orders because in its Fill model, the variable `absoluteRemaining` instead of decrease was being increased since the `FillQuantity` in each call to this method was negative. Therefore when this amount was substracted to `absoluteRemaining`, instead of substract, it was added. Hence the method created more partial orders than expected, specifically: 580 short orders for SPY with Quantity -10. * Fix `CustomPartialFillModelAlgorithm.py` * Address required changes * Nit change * Address required changes
161 lines
6.1 KiB
C#
161 lines
6.1 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.Data;
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using QuantConnect.Orders;
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using QuantConnect.Orders.Fills;
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using QuantConnect.Securities;
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using QuantConnect.Interfaces;
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using System;
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using System.Collections.Generic;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Basic template algorithm that implements a fill model with partial fills
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/// </summary>
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/// <meta name="tag" content="transaction fees and slippage" />
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/// <meta name="tag" content="custom fill models" />
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public class CustomPartialFillModelAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Symbol _spy;
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private SecurityHolding _holdings;
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public override void Initialize()
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{
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SetStartDate(2019, 1, 1);
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SetEndDate(2019, 3, 1);
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var equity = AddEquity("SPY", Resolution.Hour);
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_spy = equity.Symbol;
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_holdings = equity.Holdings;
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// Set the fill model
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equity.SetFillModel(new CustomPartialFillModel(this));
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}
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public override void OnData(Slice data)
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{
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var openOrders = Transactions.GetOpenOrders(_spy);
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if (openOrders.Count != 0) return;
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if (Time.Day > 10 && _holdings.Quantity <= 0)
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{
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MarketOrder(_spy, 105, true);
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}
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else if (Time.Day > 20 && _holdings.Quantity >= 0)
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{
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MarketOrder(_spy, -100, true);
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}
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}
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/// <summary>
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/// Implements a custom fill model that inherit from FillModel. Override the MarketFill method to simulate partially fill orders
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/// </summary>
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internal class CustomPartialFillModel : FillModel
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{
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private readonly QCAlgorithm _algorithm;
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private readonly Dictionary<int, decimal> _absoluteRemainingByOrderId;
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public CustomPartialFillModel(QCAlgorithm algorithm)
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: base()
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{
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_algorithm = algorithm;
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_absoluteRemainingByOrderId = new Dictionary<int, decimal>();
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}
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public override OrderEvent MarketFill(Security asset, MarketOrder order)
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{
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decimal absoluteRemaining;
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if (!_absoluteRemainingByOrderId.TryGetValue(order.Id, out absoluteRemaining))
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{
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absoluteRemaining = order.AbsoluteQuantity;
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}
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// Create the object
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var fill = base.MarketFill(asset, order);
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// Set the fill amount
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fill.FillQuantity = Math.Sign(order.Quantity) * 10m;
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if (Math.Min(Math.Abs(fill.FillQuantity), absoluteRemaining) == absoluteRemaining)
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{
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fill.FillQuantity = Math.Sign(order.Quantity) * absoluteRemaining;
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fill.Status = OrderStatus.Filled;
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_absoluteRemainingByOrderId.Remove(order.Id);
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}
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else
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{
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fill.Status = OrderStatus.PartiallyFilled;
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_absoluteRemainingByOrderId[order.Id] = absoluteRemaining - Math.Abs(fill.FillQuantity);
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var price = fill.FillPrice;
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//_algorithm.Debug($"{_algorithm.Time} - Partial Fill - Remaining {_absoluteRemainingByOrderId[order.Id]} Price - {price}");
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}
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return fill;
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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, 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 => 582;
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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 => 0;
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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", "24"},
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{"Average Win", "0.02%"},
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{"Average Loss", "-0.01%"},
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{"Compounding Annual Return", "3.413%"},
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{"Drawdown", "0.600%"},
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{"Expectancy", "0.426"},
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{"Net Profit", "0.550%"},
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{"Sharpe Ratio", "1.604"},
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{"Probabilistic Sharpe Ratio", "61.217%"},
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{"Loss Rate", "44%"},
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{"Win Rate", "56%"},
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{"Profit-Loss Ratio", "1.52"},
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{"Alpha", "-0.009"},
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{"Beta", "0.05"},
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{"Annual Standard Deviation", "0.015"},
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{"Annual Variance", "0"},
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{"Information Ratio", "-5.465"},
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{"Tracking Error", "0.114"},
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{"Treynor Ratio", "0.476"},
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{"Total Fees", "$24.00"},
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{"Estimated Strategy Capacity", "$89000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "10.59%"},
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{"OrderListHash", "4b8c4127574cacaf8fd6e6d42c7f3f67"}
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};
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}
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}
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