/*
* 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 System.Linq;
using QuantConnect.Data;
using QuantConnect.Data.UniverseSelection;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
///
/// In this algortihm we show how you can easily use the universe selection feature to fetch symbols
/// to be traded using the BaseData custom data system in combination with the AddUniverse{T} method.
/// AddUniverse{T} requires a function that will return the symbols to be traded.
///
///
///
///
public class DropboxBaseDataUniverseSelectionAlgorithm : QCAlgorithm//, IRegressionAlgorithmDefinition
{
// Regression algorithm disabled due to dropbox file missing
// the changes from the previous universe selection
private SecurityChanges _changes = SecurityChanges.None;
///
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
///
///
///
///
public override void Initialize()
{
UniverseSettings.Resolution = Resolution.Daily;
SetStartDate(2013, 01, 01);
SetEndDate(2013, 12, 31);
AddUniverse("my-stock-data-source", stockDataSource =>
{
return stockDataSource.SelectMany(x => x.Symbols);
});
}
///
/// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event
///
///
/// TradeBars bars = slice.Bars;
/// Ticks ticks = slice.Ticks;
/// TradeBar spy = slice["SPY"];
/// List{Tick} aaplTicks = slice["AAPL"]
/// Quandl oil = slice["OIL"]
/// dynamic anySymbol = slice[symbol];
/// DataDictionary{Quandl} allQuandlData = slice.Get{Quand}
/// Quandl oil = slice.Get{Quandl}("OIL")
///
/// The current slice of data keyed by symbol string
public override void OnData(Slice slice)
{
if (slice.Bars.Count == 0) return;
if (_changes == SecurityChanges.None) return;
// start fresh
Liquidate();
var percentage = 1m / slice.Bars.Count;
foreach (var tradeBar in slice.Bars.Values)
{
SetHoldings(tradeBar.Symbol, percentage);
}
// reset changes
_changes = SecurityChanges.None;
}
///
/// Event fired each time the we add/remove securities from the data feed
///
///
public override void OnSecuritiesChanged(SecurityChanges changes)
{
// each time our securities change we'll be notified here
_changes = changes;
}
///
/// Our custom data type that defines where to get and how to read our backtest and live data.
///
class StockDataSource : BaseData
{
private const string LiveUrl = @"https://www.dropbox.com/s/2az14r5xbx4w5j6/daily-stock-picker-live.csv?dl=1";
private const string BacktestUrl = @"https://www.dropbox.com/s/rmiiktz0ntpff3a/daily-stock-picker-backtest.csv?dl=1";
///
/// The symbols to be selected
///
public List Symbols { get; set; }
///
/// Required default constructor
///
public StockDataSource()
{
// initialize our list to empty
Symbols = new List();
}
///
/// Return the URL string source of the file. This will be converted to a stream
///
/// Configuration object
/// Date of this source file
/// true if we're in live mode, false for backtesting mode
/// String URL of source file.
public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
{
var url = isLiveMode ? LiveUrl : BacktestUrl;
return new SubscriptionDataSource(url, SubscriptionTransportMedium.RemoteFile);
}
///
/// 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
/// each time it is called. The returned object is assumed to be time stamped in the config.ExchangeTimeZone.
///
/// Subscription data config setup object
/// Line of the source document
/// Date of the requested data
/// true if we're in live mode, false for backtesting mode
/// Instance of the T:BaseData object generated by this line of the CSV
public override BaseData Reader(SubscriptionDataConfig config, string line, DateTime date, bool isLiveMode)
{
try
{
// create a new StockDataSource and set the symbol using config.Symbol
var stocks = new StockDataSource {Symbol = config.Symbol};
// break our line into csv pieces
var csv = line.ToCsv();
if (isLiveMode)
{
// our live mode format does not have a date in the first column, so use date parameter
stocks.Time = date;
stocks.Symbols.AddRange(csv);
}
else
{
// our backtest mode format has the first column as date, parse it
stocks.Time = DateTime.ParseExact(csv[0], "yyyyMMdd", null);
// any following comma separated values are symbols, save them off
stocks.Symbols.AddRange(csv.Skip(1));
}
return stocks;
}
// return null if we encounter any errors
catch { return null; }
}
}
///
/// This is used by the regression test system to indicate if the open source Lean repository has the required data to run this algorithm.
///
public bool CanRunLocally { get; } = true;
///
/// This is used by the regression test system to indicate which languages this algorithm is written in.
///
public Language[] Languages { get; } = { Language.CSharp, Language.Python };
///
/// This is used by the regression test system to indicate what the expected statistics are from running the algorithm
///
public Dictionary ExpectedStatistics => new Dictionary
{
{"Total Trades", "90"},
{"Average Win", "0.78%"},
{"Average Loss", "-0.39%"},
{"Compounding Annual Return", "18.547%"},
{"Drawdown", "4.700%"},
{"Expectancy", "1.068"},
{"Net Profit", "18.547%"},
{"Sharpe Ratio", "1.993"},
{"Loss Rate", "30%"},
{"Win Rate", "70%"},
{"Profit-Loss Ratio", "1.96"},
{"Alpha", "0.071"},
{"Beta", "0.362"},
{"Annual Standard Deviation", "0.086"},
{"Annual Variance", "0.007"},
{"Information Ratio", "-1.021"},
{"Tracking Error", "0.103"},
{"Treynor Ratio", "0.471"},
{"Total Fees", "$251.12"}
};
}
}