Martin Molinero
218 lines
9.1 KiB
C#
218 lines
9.1 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 System;
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using System.Collections.Generic;
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using System.Linq;
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using QuantConnect.Data;
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using QuantConnect.Data.UniverseSelection;
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using QuantConnect.Orders;
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using QuantConnect.Securities;
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using QuantConnect.Interfaces;
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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// Universe Selection regression algorithm simulates an edge case. In one week, Google listed two new symbols, delisted one of them and changed tickers.
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/// </summary>
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/// <meta name="tag" content="regression test" />
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public class UniverseSelectionRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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private HashSet<Symbol> _delistedSymbols = new HashSet<Symbol>();
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private SecurityChanges _changes;
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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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UniverseSettings.Resolution = Resolution.Daily;
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SetStartDate(2014, 03, 22); //Set Start Date
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SetEndDate(2014, 04, 07); //Set End Date
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SetCash(100000); //Set Strategy Cash
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// Find more symbols here: http://quantconnect.com/data
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// security that exists with no mappings
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AddSecurity(SecurityType.Equity, "SPY", Resolution.Daily);
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// security that doesn't exist until half way in backtest (comes in as GOOCV)
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AddSecurity(SecurityType.Equity, "GOOG", Resolution.Daily);
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AddUniverse(coarse =>
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{
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// select the various google symbols over the period
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return from c in coarse
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let sym = c.Symbol.Value
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where sym == "GOOG" || sym == "GOOCV" || sym == "GOOAV" || sym == "GOOGL"
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select c.Symbol;
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// Before March 28th 2014:
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// - Only GOOG T1AZ164W5VTX existed
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// On March 28th 2014
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// - GOOAV VP83T1ZUHROL and GOOCV VP83T1ZUHROL are listed
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// On April 02nd 2014
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// - GOOAV VP83T1ZUHROL is delisted
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// - GOOG T1AZ164W5VTX becomes GOOGL
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// - GOOCV VP83T1ZUHROL becomes GOOG
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});
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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="data">Slice object keyed by symbol containing the stock data</param>
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public override void OnData(Slice data)
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{
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// can access the current set of active securitie through UniverseManager.ActiveSecurities
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Log(Time + ": Active Securities: " + string.Join(", ", UniverseManager.ActiveSecurities.Keys));
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// verify we don't receive data for inactive securities
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var inactiveSymbols = data.Keys
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.Where(sym => !UniverseManager.ActiveSecurities.ContainsKey(sym))
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// on daily data we'll get the last data point and the delisting at the same time
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.Where(sym => !data.Delistings.ContainsKey(sym) || data.Delistings[sym].Type != DelistingType.Delisted)
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.ToList();
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if (inactiveSymbols.Any())
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{
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var symbols = string.Join(", ", inactiveSymbols);
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throw new Exception($"Received data for non-active security: {symbols}.");
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}
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if (Transactions.OrdersCount == 0)
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{
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MarketOrder("SPY", 100);
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}
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foreach (var kvp in data.Delistings)
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{
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_delistedSymbols.Add(kvp.Key);
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}
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if (_changes != null && _changes.AddedSecurities.All(x => data.Bars.ContainsKey(x.Symbol)))
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{
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foreach (var security in _changes.AddedSecurities)
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{
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Log(Time + ": Added Security: " + security.Symbol.ID);
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MarketOnOpenOrder(security.Symbol, 100);
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}
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foreach (var security in _changes.RemovedSecurities)
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{
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Log(Time + ": Removed Security: " + security.Symbol.ID);
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if (!_delistedSymbols.Contains(security.Symbol))
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{
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MarketOnOpenOrder(security.Symbol, -100);
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}
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}
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_changes = null;
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}
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}
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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_changes = changes;
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}
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public override void OnOrderEvent(OrderEvent orderEvent)
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{
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if (orderEvent.Status == OrderStatus.Submitted)
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{
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Log(Time + ": Submitted: " + Transactions.GetOrderById(orderEvent.OrderId));
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}
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if (orderEvent.Status.IsFill())
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{
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Log(Time + ": Filled: " + Transactions.GetOrderById(orderEvent.OrderId));
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}
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}
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public override void OnEndOfAlgorithm()
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{
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foreach (var security in Portfolio.Securities.Values.Where(x => x.Invested))
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{
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// At the end, we should hold 100 shares of:
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// - SPY (bought on March, 25th 2014),
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// - GOOG T1AZ164W5VTX (bought on March, 26th 2014),
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// - GOOCV VP83T1ZUHROL (bought on March, 28th 2014).
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AssertQuantity(security, 100);
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}
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}
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private void AssertQuantity(Security security, int expected)
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{
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var actual = security.Holdings.Quantity;
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if (actual != expected)
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{
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var symbol = security.Symbol;
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throw new Exception($"{symbol}({symbol.ID}) expected {expected.ToStringInvariant()}, but received {actual.ToStringInvariant()}.");
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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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/// 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", "5"},
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{"Average Win", "0.64%"},
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{"Average Loss", "0%"},
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{"Compounding Annual Return", "-74.197%"},
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{"Drawdown", "6.600%"},
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{"Expectancy", "0"},
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{"Net Profit", "-6.115%"},
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{"Sharpe Ratio", "-2.281"},
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{"Probabilistic Sharpe Ratio", "11.870%"},
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{"Loss Rate", "0%"},
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{"Win Rate", "100%"},
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{"Profit-Loss Ratio", "0"},
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{"Alpha", "-0.684"},
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{"Beta", "-0.113"},
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{"Annual Standard Deviation", "0.292"},
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{"Annual Variance", "0.085"},
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{"Information Ratio", "-1.606"},
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{"Tracking Error", "0.312"},
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{"Treynor Ratio", "5.866"},
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{"Total Fees", "$5.00"},
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{"Fitness Score", "0.017"},
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{"Kelly Criterion Estimate", "0"},
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{"Kelly Criterion Probability Value", "0"},
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{"Sortino Ratio", "-2.584"},
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{"Return Over Maximum Drawdown", "-11.287"},
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{"Portfolio Turnover", "0.177"},
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{"Total Insights Generated", "0"},
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{"Total Insights Closed", "0"},
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{"Total Insights Analysis Completed", "0"},
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{"Long Insight Count", "0"},
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{"Short Insight Count", "0"},
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{"Long/Short Ratio", "100%"},
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{"Estimated Monthly Alpha Value", "$0"},
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{"Total Accumulated Estimated Alpha Value", "$0"},
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{"Mean Population Estimated Insight Value", "$0"},
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{"Mean Population Direction", "0%"},
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{"Mean Population Magnitude", "0%"},
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{"Rolling Averaged Population Direction", "0%"},
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{"Rolling Averaged Population Magnitude", "0%"},
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{"OrderListHash", "-1386253041"}
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};
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}
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}
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