Michael Handschuh
8402b6f01e
It's important that we keep the factor files consistent with respect to the date that they were generated. This enables us to run the regression algorithms in the cloud and get the same results by using the factor files from the correct date.
203 lines
8.7 KiB
C#
203 lines
8.7 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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namespace QuantConnect.Algorithm.CSharp
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{
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/// <summary>
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/// In this algortihm we show how you can easily use the universe selection feature to fetch symbols
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/// to be traded using the BaseData custom data system in combination with the AddUniverse{T} method.
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/// AddUniverse{T} requires a function that will return the symbols to be traded.
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/// </summary>
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/// <meta name="tag" content="using data" />
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/// <meta name="tag" content="universes" />
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/// <meta name="tag" content="custom universes" />
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public class DropboxBaseDataUniverseSelectionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
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{
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// the changes from the previous universe selection
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private SecurityChanges _changes = SecurityChanges.None;
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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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/// <seealso cref="QCAlgorithm.SetStartDate(System.DateTime)"/>
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/// <seealso cref="QCAlgorithm.SetEndDate(System.DateTime)"/>
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/// <seealso cref="QCAlgorithm.SetCash(decimal)"/>
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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(2013, 01, 01);
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SetEndDate(2013, 12, 31);
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AddUniverse<StockDataSource>("my-stock-data-source", stockDataSource =>
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{
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return stockDataSource.SelectMany(x => x.Symbols);
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});
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}
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/// <summary>
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/// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event
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/// </summary>
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/// <code>
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/// TradeBars bars = slice.Bars;
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/// Ticks ticks = slice.Ticks;
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/// TradeBar spy = slice["SPY"];
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/// List{Tick} aaplTicks = slice["AAPL"]
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/// Quandl oil = slice["OIL"]
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/// dynamic anySymbol = slice[symbol];
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/// DataDictionary{Quandl} allQuandlData = slice.Get{Quand}
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/// Quandl oil = slice.Get{Quandl}("OIL")
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/// </code>
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/// <param name="slice">The current slice of data keyed by symbol string</param>
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public override void OnData(Slice slice)
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{
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if (slice.Bars.Count == 0) return;
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if (_changes == SecurityChanges.None) return;
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// start fresh
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Liquidate();
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var percentage = 1m / slice.Bars.Count;
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foreach (var tradeBar in slice.Bars.Values)
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{
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SetHoldings(tradeBar.Symbol, percentage);
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}
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// reset changes
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_changes = SecurityChanges.None;
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}
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/// <summary>
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/// Event fired each time the we add/remove securities from the data feed
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/// </summary>
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/// <param name="changes"></param>
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public override void OnSecuritiesChanged(SecurityChanges changes)
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{
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// each time our securities change we'll be notified here
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_changes = changes;
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}
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/// <summary>
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/// Our custom data type that defines where to get and how to read our backtest and live data.
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/// </summary>
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class StockDataSource : BaseData
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{
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private const string LiveUrl = @"https://www.dropbox.com/s/2az14r5xbx4w5j6/daily-stock-picker-live.csv?dl=1";
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private const string BacktestUrl = @"https://www.dropbox.com/s/rmiiktz0ntpff3a/daily-stock-picker-backtest.csv?dl=1";
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/// <summary>
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/// The symbols to be selected
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/// </summary>
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public List<string> Symbols { get; set; }
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/// <summary>
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/// Required default constructor
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/// </summary>
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public StockDataSource()
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{
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// initialize our list to empty
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Symbols = new List<string>();
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}
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/// <summary>
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/// Return the URL string source of the file. This will be converted to a stream
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/// </summary>
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/// <param name="config">Configuration object</param>
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/// <param name="date">Date of this source file</param>
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/// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
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/// <returns>String URL of source file.</returns>
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public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
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{
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var url = isLiveMode ? LiveUrl : BacktestUrl;
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return new SubscriptionDataSource(url, SubscriptionTransportMedium.RemoteFile);
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}
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/// <summary>
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/// Reader converts each line of the data source into BaseData objects. Each data type creates its own factory method, and returns a new instance of the object
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/// each time it is called. The returned object is assumed to be time stamped in the config.ExchangeTimeZone.
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/// </summary>
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/// <param name="config">Subscription data config setup object</param>
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/// <param name="line">Line of the source document</param>
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/// <param name="date">Date of the requested data</param>
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/// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
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/// <returns>Instance of the T:BaseData object generated by this line of the CSV</returns>
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public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
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{
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try
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{
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// create a new StockDataSource and set the symbol using config.Symbol
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var stocks = new StockDataSource {Symbol = config.Symbol};
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// break our line into csv pieces
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var csv = line.ToCsv();
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if (isLiveMode)
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{
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// our live mode format does not have a date in the first column, so use date parameter
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stocks.Time = date;
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stocks.Symbols.AddRange(csv);
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}
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else
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{
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// our backtest mode format has the first column as date, parse it
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stocks.Time = DateTime.ParseExact(csv[0], "yyyyMMdd", null);
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// any following comma separated values are symbols, save them off
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stocks.Symbols.AddRange(csv.Skip(1));
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}
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return stocks;
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}
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// return null if we encounter any errors
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catch { return null; }
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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 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", "90"},
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{"Average Win", "0.78%"},
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{"Average Loss", "-0.40%"},
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{"Compounding Annual Return", "18.516%"},
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{"Drawdown", "4.700%"},
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{"Expectancy", "1.061"},
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{"Net Profit", "18.516%"},
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{"Sharpe Ratio", "1.988"},
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{"Loss Rate", "30%"},
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{"Win Rate", "70%"},
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{"Profit-Loss Ratio", "1.95"},
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{"Alpha", "0.11"},
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{"Beta", "3.072"},
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{"Annual Standard Deviation", "0.086"},
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{"Annual Variance", "0.007"},
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{"Information Ratio", "1.759"},
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{"Tracking Error", "0.086"},
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{"Treynor Ratio", "0.055"},
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{"Total Fees", "$251.46"}
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};
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}
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}
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