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/*
* 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;
using QuantConnect.Data.Market;
using QuantConnect.Orders;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Demonstration of using the Delisting event in your algorithm. Assets are delisted on their last day of trading, or when their contract expires.
/// This data is not included in the open source project.
/// </summary>
/// <meta name="tag" content="using data" />
/// <meta name="tag" content="data event handlers" />
/// <meta name="tag" content="delisting event" />
public class DelistingEventsAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private bool _receivedDelistedWarningEvent;
private bool _receivedDelistedEvent;
private int _dataCount;
/// <summary>
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
/// </summary>
public override void Initialize()
{
SetStartDate(2007, 05, 16); //Set Start Date
SetEndDate(2007, 05, 25); //Set End Date
SetCash(100000); //Set Strategy Cash
// Find more symbols here: http://quantconnect.com/data
AddSecurity(SecurityType.Equity, "AAA", Resolution.Daily);
AddSecurity(SecurityType.Equity, "SPY", Resolution.Daily);
}
/// <summary>
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
/// </summary>
/// <param name="data">Slice object keyed by symbol containing the stock data</param>
public override void OnData(Slice data)
{
_dataCount += data.Bars.Count;
if (Transactions.OrdersCount == 0)
{
SetHoldings("AAA", 1);
Debug("Purchased Stock");
}
foreach (var kvp in data.Bars)
{
var symbol = kvp.Key;
var tradeBar = kvp.Value;
Debug($"OnData(Slice): {Time}: {symbol}: {tradeBar.Close.ToStringInvariant("0.00")}");
}
// the slice can also contain delisting data: data.Delistings in a dictionary string->Delisting
var aaa = Securities["AAA"];
if (aaa.IsDelisted && aaa.IsTradable)
{
throw new Exception("Delisted security must NOT be tradable");
}
if (!aaa.IsDelisted && !aaa.IsTradable)
{
throw new Exception("Securities must be marked as tradable until they're delisted or removed from the universe");
}
}
public void OnData(Delistings data)
{
foreach (var kvp in data)
{
var symbol = kvp.Key;
var delisting = kvp.Value;
if (delisting.Type == DelistingType.Warning)
{
_receivedDelistedWarningEvent = true;
Debug($"OnData(Delistings): {Time}: {symbol} will be delisted at end of day today.");
// liquidate on delisting warning
SetHoldings(symbol, 0);
}
if (delisting.Type == DelistingType.Delisted)
{
_receivedDelistedEvent = true;
Debug($"OnData(Delistings): {Time}: {symbol} has been delisted.");
// fails because the security has already been delisted and is no longer tradable
SetHoldings(symbol, 1);
}
}
}
public override void OnOrderEvent(OrderEvent orderEvent)
{
Debug($"OnOrderEvent(OrderEvent): {Time}: {orderEvent}");
}
public override void OnEndOfAlgorithm()
{
if (!_receivedDelistedEvent)
{
throw new Exception("Did not receive expected delisted event");
}
if (!_receivedDelistedWarningEvent)
{
throw new Exception("Did not receive expected delisted warning event");
}
if (_dataCount != 13)
{
throw new Exception($"Unexpected data count {_dataCount}. Expected 13");
}
}
/// <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", "2"},
{"Average Win", "0%"},
{"Average Loss", "-5.58%"},
{"Compounding Annual Return", "-87.694%"},
{"Drawdown", "5.600%"},
{"Expectancy", "-1"},
{"Net Profit", "-5.578%"},
{"Sharpe Ratio", "-10.227"},
{"Loss Rate", "100%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-1.297"},
{"Beta", "-1.062"},
{"Annual Standard Deviation", "0.156"},
{"Annual Variance", "0.024"},
{"Information Ratio", "-8.704"},
{"Tracking Error", "0.215"},
{"Treynor Ratio", "1.499"},
{"Total Fees", "$36.70"}
};
}
}