/*

* QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.

* Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.

*

* Licensed under the Apache License, Version 2.0 (the "License");

* you may not use this file except in compliance with the License.

* You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0

*

* Unless required by applicable law or agreed to in writing, software

* distributed under the License is distributed on an "AS IS" BASIS,

* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

* See the License for the specific language governing permissions and

* limitations under the License.

*/

using System;

using System.Collections.Generic;

using QuantConnect.Data.Market;

using QuantConnect.Interfaces;

using QuantConnect.Orders;

using QuantConnect.Orders.Fees;

using QuantConnect.Orders.Fills;

using QuantConnect.Orders.Slippage;

using QuantConnect.Securities;

namespace QuantConnect.Algorithm.CSharp

{

/// <summary>

/// Demonstration of using custom fee, slippage and fill models for modelling transactions in backtesting.

/// QuantConnect allows you to model all orders as deeply and accurately as you need.

/// </summary>

/// <meta name="tag" content="trading and orders" />

/// <meta name="tag" content="transaction fees and slippage" />

/// <meta name="tag" content="custom transaction models" />

/// <meta name="tag" content="custom slippage models" />

/// <meta name="tag" content="custom fee models" />

public class CustomModelsAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition

{

private Security _security;

private Symbol _spy;

public override void Initialize()

{

SetStartDate(2013, 10, 01);

SetEndDate(2013, 10, 31);

_security = AddEquity("SPY", Resolution.Hour);

_spy = _security.Symbol;

// set our models

_security.SetFeeModel(new CustomFeeModel(this));

_security.SetFillModel(new CustomFillModel(this));

_security.SetSlippageModel(new CustomSlippageModel(this));

}

public void OnData(TradeBars data)

{

var openOrders = Transactions.GetOpenOrders(_spy);

if (openOrders.Count != 0) return;

if (Time.Day > 10 && _security.Holdings.Quantity <= 0)

{

var quantity = CalculateOrderQuantity(_spy, .5m);

Log("MarketOrder: " + quantity);

MarketOrder(_spy, quantity, asynchronous: true); // async needed for partial fill market orders

}

else if (Time.Day > 20 && _security.Holdings.Quantity >= 0)

{

var quantity = CalculateOrderQuantity(_spy, -.5m);

Log("MarketOrder: " + quantity);

MarketOrder(_spy, quantity, asynchronous: true); // async needed for partial fill market orders

}

}

public class CustomFillModel : ImmediateFillModel

{

private readonly QCAlgorithm _algorithm;

private readonly Random _random = new Random(387510346); // seed it for reproducibility

private readonly Dictionary<long, decimal> _absoluteRemainingByOrderId = new Dictionary<long, decimal>();

public CustomFillModel(QCAlgorithm algorithm)

{

_algorithm = algorithm;

}

public override OrderEvent MarketFill(Security asset, MarketOrder order)

{

// this model randomly fills market orders

decimal absoluteRemaining;

if (!_absoluteRemainingByOrderId.TryGetValue(order.Id, out absoluteRemaining))

{

absoluteRemaining = order.AbsoluteQuantity;

_absoluteRemainingByOrderId.Add(order.Id, order.AbsoluteQuantity);

}

var fill = base.MarketFill(asset, order);

var absoluteFillQuantity = (int) (Math.Min(absoluteRemaining, _random.Next(0, 2*(int)order.AbsoluteQuantity)));

fill.FillQuantity = Math.Sign(order.Quantity) * absoluteFillQuantity;

if (absoluteRemaining == absoluteFillQuantity)

{

fill.Status = OrderStatus.Filled;

_absoluteRemainingByOrderId.Remove(order.Id);

}

else

{

absoluteRemaining = absoluteRemaining - absoluteFillQuantity;

_absoluteRemainingByOrderId[order.Id] = absoluteRemaining;

fill.Status = OrderStatus.PartiallyFilled;

}

_algorithm.Log("CustomFillModel: " + fill);

return fill;

}

}

public class CustomFeeModel : FeeModel

{

private readonly QCAlgorithm _algorithm;

public CustomFeeModel(QCAlgorithm algorithm)

{

_algorithm = algorithm;

}

public override OrderFee GetOrderFee(OrderFeeParameters parameters)

{

// custom fee math

var fee = Math.Max(

1m,

parameters.Security.Price*parameters.Order.AbsoluteQuantity*0.00001m);

_algorithm.Log("CustomFeeModel: " + fee);

return new OrderFee(new CashAmount(fee, "USD"));

}

}

public class CustomSlippageModel : ISlippageModel

{

private readonly QCAlgorithm _algorithm;

public CustomSlippageModel(QCAlgorithm algorithm)

{

_algorithm = algorithm;

}

public decimal GetSlippageApproximation(Security asset, Order order)

{

// custom slippage math

var slippage = asset.Price*0.0001m*(decimal) Math.Log10(2*(double) order.AbsoluteQuantity);

_algorithm.Log("CustomSlippageModel: " + slippage);

return slippage;

}

}

/// <summary>

/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.

/// </summary>

public bool CanRunLocally { get; } = true;

/// <summary>

/// This is used by the regression test system to indicate which languages this algorithm is written in.

/// </summary>

public Language[] Languages { get; } = { Language.CSharp, Language.Python };

/// <summary>

/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm

/// </summary>

public Dictionary<string, string> ExpectedStatistics => new Dictionary<string, string>

{

{"Total Trades", "62"},

{"Average Win", "0.11%"},

{"Average Loss", "-0.06%"},

{"Compounding Annual Return", "-7.582%"},

{"Drawdown", "2.400%"},

{"Expectancy", "-0.193"},

{"Net Profit", "-0.660%"},

{"Sharpe Ratio", "-1.563"},

{"Loss Rate", "70%"},

{"Win Rate", "30%"},

{"Profit-Loss Ratio", "1.71"},

{"Alpha", "-0.174"},

{"Beta", "5.695"},

{"Annual Standard Deviation", "0.046"},

{"Annual Variance", "0.002"},

{"Information Ratio", "-1.959"},

{"Tracking Error", "0.046"},

{"Treynor Ratio", "-0.013"},

{"Total Fees", "$62.24"}

};

}

}