wehub-resource-sync
325 lines
13 KiB
C#
325 lines
13 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.Data.Market;
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using QuantConnect.Interfaces;
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using QuantConnect.Orders;
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using QuantConnect.Orders.Fees;
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using QuantConnect.Orders.Fills;
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using QuantConnect.Orders.Slippage;
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using QuantConnect.Securities;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Demonstration of using custom fee, slippage, fill, and buying power models for modelling transactions in backtesting.
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/// QuantConnect allows you to model all orders as deeply and accurately as you need.
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/// </summary>
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/// <meta name="tag" content="trading and orders" />
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/// <meta name="tag" content="transaction fees and slippage" />
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/// <meta name="tag" content="custom buying power models" />
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/// <meta name="tag" content="custom transaction models" />
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/// <meta name="tag" content="custom slippage models" />
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/// <meta name="tag" content="custom fee models" />
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public class CustomModelsAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private Security _security;
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private Symbol _spy;
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public override void Initialize()
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{
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SetStartDate(2013, 10, 01);
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SetEndDate(2013, 10, 31);
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_security = AddEquity("SPY", Resolution.Hour);
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_spy = _security.Symbol;
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// set our models
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_security.SetFeeModel(new CustomFeeModel(this));
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_security.SetFillModel(new CustomFillModel(this));
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_security.SetSlippageModel(new CustomSlippageModel(this));
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_security.SetBuyingPowerModel(new CustomBuyingPowerModel(this));
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}
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public override void OnData(Slice slice)
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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 && _security.Holdings.Quantity <= 0)
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{
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var quantity = CalculateOrderQuantity(_spy, .5m);
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Log($"MarketOrder: {quantity}");
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MarketOrder(_spy, quantity, asynchronous: true); // async needed for partial fill market orders
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}
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else if (Time.Day > 20 && _security.Holdings.Quantity >= 0)
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{
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var quantity = CalculateOrderQuantity(_spy, -.5m);
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Log($"MarketOrder: {quantity}");
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MarketOrder(_spy, quantity, asynchronous: true); // async needed for partial fill market orders
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}
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}
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public class CustomFillModel : ImmediateFillModel
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{
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private readonly QCAlgorithm _algorithm;
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private readonly Random _random = new Random(387510346); // seed it for reproducibility
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private readonly Dictionary<long, decimal> _absoluteRemainingByOrderId = new Dictionary<long, decimal>();
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public CustomFillModel(QCAlgorithm algorithm)
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{
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_algorithm = algorithm;
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}
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public override OrderEvent MarketFill(Security asset, MarketOrder order)
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{
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// this model randomly fills market orders
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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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_absoluteRemainingByOrderId.Add(order.Id, order.AbsoluteQuantity);
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}
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var fill = base.MarketFill(asset, order);
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var absoluteFillQuantity = (int) (Math.Min(absoluteRemaining, _random.Next(0, 2*(int)order.AbsoluteQuantity)));
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fill.FillQuantity = Math.Sign(order.Quantity) * absoluteFillQuantity;
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if (absoluteRemaining == absoluteFillQuantity)
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{
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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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absoluteRemaining = absoluteRemaining - absoluteFillQuantity;
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_absoluteRemainingByOrderId[order.Id] = absoluteRemaining;
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fill.Status = OrderStatus.PartiallyFilled;
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}
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_algorithm.Log($"CustomFillModel: {fill}");
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return fill;
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}
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}
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public class CustomFeeModel : FeeModel
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{
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private readonly QCAlgorithm _algorithm;
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public CustomFeeModel(QCAlgorithm algorithm)
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{
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_algorithm = algorithm;
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}
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public override OrderFee GetOrderFee(OrderFeeParameters parameters)
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{
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// custom fee math
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var fee = Math.Max(
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1m,
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parameters.Security.Price*parameters.Order.AbsoluteQuantity*0.00001m);
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_algorithm.Log($"CustomFeeModel: {fee}");
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return new OrderFee(new CashAmount(fee, "USD"));
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}
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}
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public class CustomSlippageModel : ISlippageModel
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{
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private readonly QCAlgorithm _algorithm;
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public CustomSlippageModel(QCAlgorithm algorithm)
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{
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_algorithm = algorithm;
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}
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public decimal GetSlippageApproximation(Security asset, Order order)
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{
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// custom slippage math
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var slippage = asset.Price*0.0001m*(decimal) Math.Log10(2*(double) order.AbsoluteQuantity);
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_algorithm.Log($"CustomSlippageModel: {slippage}");
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return slippage;
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}
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}
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public class CustomBuyingPowerModel : BuyingPowerModel
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{
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private readonly QCAlgorithm _algorithm;
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public CustomBuyingPowerModel(QCAlgorithm algorithm)
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{
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_algorithm = algorithm;
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}
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public override HasSufficientBuyingPowerForOrderResult HasSufficientBuyingPowerForOrder(
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HasSufficientBuyingPowerForOrderParameters parameters)
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{
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// custom behavior: this model will assume that there is always enough buying power
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var hasSufficientBuyingPowerForOrderResult = new HasSufficientBuyingPowerForOrderResult(true);
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_algorithm.Log($"CustomBuyingPowerModel: {hasSufficientBuyingPowerForOrderResult.IsSufficient}");
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return hasSufficientBuyingPowerForOrderResult;
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}
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}
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/// <summary>
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/// The simple fill model shows how to implement a simpler version of
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/// the most popular order fills: Market, Stop Market and Limit
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/// </summary>
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public class SimpleCustomFillModel : FillModel
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{
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private static OrderEvent CreateOrderEvent(Security asset, Order order)
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{
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var utcTime = asset.LocalTime.ConvertToUtc(asset.Exchange.TimeZone);
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return new OrderEvent(order, utcTime, OrderFee.Zero);
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}
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private static OrderEvent SetOrderEventToFilled(OrderEvent fill, decimal fillPrice, decimal fillQuantity)
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{
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fill.Status = OrderStatus.Filled;
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fill.FillQuantity = fillQuantity;
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fill.FillPrice = fillPrice;
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return fill;
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}
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private static TradeBar GetTradeBar(Security asset, OrderDirection orderDirection)
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{
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var tradeBar = asset.Cache.GetData<TradeBar>();
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if (tradeBar != null) return tradeBar;
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// Tick-resolution data doesn't have TradeBar, use the asset price
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var price = asset.Price;
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return new TradeBar(asset.LocalTime, asset.Symbol, price, price, price, price, 0);
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}
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public override OrderEvent MarketFill(Security asset, MarketOrder order)
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{
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var fill = CreateOrderEvent(asset, order);
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if (order.Status == OrderStatus.Canceled) return fill;
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return SetOrderEventToFilled(fill,
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order.Direction == OrderDirection.Buy
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? asset.Cache.AskPrice
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: asset.Cache.BidPrice,
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order.Quantity);
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}
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public override OrderEvent StopMarketFill(Security asset, StopMarketOrder order)
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{
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var fill = CreateOrderEvent(asset, order);
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if (order.Status == OrderStatus.Canceled) return fill;
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var stopPrice = order.StopPrice;
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var tradeBar = GetTradeBar(asset, order.Direction);
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return order.Direction switch
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{
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OrderDirection.Buy => tradeBar.Low < stopPrice
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? SetOrderEventToFilled(fill, stopPrice, order.Quantity)
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: fill,
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OrderDirection.Sell => tradeBar.High > stopPrice
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? SetOrderEventToFilled(fill, stopPrice, order.Quantity)
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: fill,
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_ => fill
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};
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}
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public override OrderEvent LimitFill(Security asset, LimitOrder order)
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{
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var fill = CreateOrderEvent(asset, order);
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if (order.Status == OrderStatus.Canceled) return fill;
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var limitPrice = order.LimitPrice;
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var tradeBar = GetTradeBar(asset, order.Direction);
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return order.Direction switch
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{
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OrderDirection.Buy => tradeBar.High > limitPrice
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? SetOrderEventToFilled(fill, limitPrice, order.Quantity)
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: fill,
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OrderDirection.Sell => tradeBar.Low < limitPrice
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? SetOrderEventToFilled(fill, limitPrice, order.Quantity)
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: fill,
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_ => fill
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};
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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 => 330;
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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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/// 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", "63"},
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{"Average Win", "0.10%"},
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{"Average Loss", "-0.06%"},
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{"Compounding Annual Return", "-7.101%"},
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{"Drawdown", "2.400%"},
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{"Expectancy", "-0.181"},
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{"Start Equity", "100000"},
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{"End Equity", "99383.07"},
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{"Net Profit", "-0.617%"},
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{"Sharpe Ratio", "-1.441"},
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{"Sortino Ratio", "-1.977"},
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{"Probabilistic Sharpe Ratio", "20.329%"},
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{"Loss Rate", "69%"},
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{"Win Rate", "31%"},
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{"Profit-Loss Ratio", "1.64"},
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{"Alpha", "-0.101"},
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{"Beta", "0.121"},
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{"Annual Standard Deviation", "0.04"},
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{"Annual Variance", "0.002"},
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{"Information Ratio", "-4.109"},
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{"Tracking Error", "0.102"},
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{"Treynor Ratio", "-0.475"},
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{"Total Fees", "$62.23"},
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{"Estimated Strategy Capacity", "$52000000.00"},
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{"Lowest Capacity Asset", "SPY R735QTJ8XC9X"},
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{"Portfolio Turnover", "197.93%"},
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{"Drawdown Recovery", "2"},
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{"OrderListHash", "fe01fe4923e8856fe3376ece636b4e23"}
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};
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}
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}
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