Files
quantconnect--lean/Algorithm.CSharp/DropboxBaseDataUniverseSelectionAlgorithm.cs
T
Alexandre Catarino 5361f87dd1
Regression Tests / build (push) Has been cancelled
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Updates Equity Market Data (#5576)
* Updates Equity Market Data

* Updates Unit Tests

* Updates Regression Tests

In this commit we include regression tests with small changes (slightly different CAGR, Alpha, etc, but same number of trades) due to the data update.

* Updates Regression Tests 2

The following regression tests were adapt because of verification of hard-coded market data values:
- `AdjustedVolumeRegressionAlgorithm`
- `HistoryWithSymbolChangesRegressionAlgorithm`
- `OptionRenameRegressionAlgorithm`
- `RawDataRegressionAlgorithm`
- `SwitchDataModeRegressionAlgorithm`

The following regression tests have more trades since adjusted prices allowed more 1-2 shares trades that were rounded down to zero before:
- `AddUniverseSelectionModelCoarseAlgorithm` 23 -> 35
- `MeanVarianceOptimizationFrameworkAlgorithm` 12 -> 14
- `PortfolioRebalanceOnDateRulesRegressionAlgorithm` 298 -> 324
- `PortfolioRebalanceOnInsightChangesRegressionAlgorithm` 83 -> 86
- `ScheduledUniverseSelectionModelRegressionAlgorithm` 86 -> 90
- `SectorExposureRiskFrameworkAlgorithm` 17 -> 22
- `SetHoldingsMultipleTargetsRegressionAlgorithm` 8 -> 9
- `StandardDeviationExecutionModelRegressionAlgorithm` 196 -> 199
- `UniverseUnchangedRegressionAlgorithm` 11 -> 17
- `VolumeWeightedAveragePriceExecutionModelRegressionAlgorithm` 237 -> 238

Especial cases:
- `BlackLittermanPortfolioOptimizationFrameworkAlgorithm` 18 -> 17
 - BLM model sensibility
- `OptionChainedAndUniverseSelectionRegressionAlgorithm`

The following regression tests have different Capacity because of different volume from lowest capacity asset, except:
- `OptionEquityCoveredCallRegressionAlgorithm` New lowest capacity asset is underlying
- `OptionEquityCoveredPutRegressionAlgorithm` New lowest capacity asset is underlying

* Revert File Update for SPWR and SPWRA

* Fix Regression Tests

Temporarily removes python regression test for `MeanVarianceOptimizationFrameworkAlgorithm` as the `MeanVarianceOptimizationPortfolioConstructionModel` for each version are yeilding different results. If we use C# version in `MeanVarianceOptimizationPortfolioConstructionModel.py`, the results match.

* Changes Optimization Method in MinimumVariancePortfolioOptimizer [Py]

Uses `trust-constr`  method.
See https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.minimize.html
2021-06-17 14:04:51 -03:00

232 lines
10 KiB
C#

/*
* 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
{
/// <summary>
/// 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.
/// </summary>
/// <meta name="tag" content="using data" />
/// <meta name="tag" content="universes" />
/// <meta name="tag" content="custom universes" />
public class DropboxBaseDataUniverseSelectionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
// the changes from the previous universe selection
private SecurityChanges _changes = SecurityChanges.None;
/// <summary>
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
/// </summary>
/// <seealso cref="QCAlgorithm.SetStartDate(System.DateTime)"/>
/// <seealso cref="QCAlgorithm.SetEndDate(System.DateTime)"/>
/// <seealso cref="QCAlgorithm.SetCash(decimal)"/>
public override void Initialize()
{
UniverseSettings.Resolution = Resolution.Daily;
SetStartDate(2017, 07, 04);
SetEndDate(2018, 07, 04);
AddUniverse<StockDataSource>("my-stock-data-source", stockDataSource =>
{
return stockDataSource.SelectMany(x => x.Symbols);
});
}
/// <summary>
/// Event - v3.0 DATA EVENT HANDLER: (Pattern) Basic template for user to override for receiving all subscription data in a single event
/// </summary>
/// <code>
/// 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")
/// </code>
/// <param name="slice">The current slice of data keyed by symbol string</param>
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;
}
/// <summary>
/// Event fired each time the we add/remove securities from the data feed
/// </summary>
/// <param name="changes"></param>
public override void OnSecuritiesChanged(SecurityChanges changes)
{
// each time our securities change we'll be notified here
_changes = changes;
}
/// <summary>
/// Our custom data type that defines where to get and how to read our backtest and live data.
/// </summary>
class StockDataSource : BaseData
{
private const string LiveUrl = @"https://www.dropbox.com/s/2l73mu97gcehmh7/daily-stock-picker-live.csv?dl=1";
private const string BacktestUrl = @"https://www.dropbox.com/s/ae1couew5ir3z9y/daily-stock-picker-backtest.csv?dl=1";
/// <summary>
/// The symbols to be selected
/// </summary>
public List<string> Symbols { get; set; }
/// <summary>
/// Required default constructor
/// </summary>
public StockDataSource()
{
// initialize our list to empty
Symbols = new List<string>();
}
/// <summary>
/// Return the URL string source of the file. This will be converted to a stream
/// </summary>
/// <param name="config">Configuration object</param>
/// <param name="date">Date of this source file</param>
/// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
/// <returns>String URL of source file.</returns>
public override SubscriptionDataSource GetSource(SubscriptionDataConfig config, DateTime date, bool isLiveMode)
{
var url = isLiveMode ? LiveUrl : BacktestUrl;
return new SubscriptionDataSource(url, SubscriptionTransportMedium.RemoteFile);
}
/// <summary>
/// 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.
/// </summary>
/// <param name="config">Subscription data config setup object</param>
/// <param name="line">Line of the source document</param>
/// <param name="date">Date of the requested data</param>
/// <param name="isLiveMode">true if we're in live mode, false for backtesting mode</param>
/// <returns>Instance of the T:BaseData object generated by this line of the CSV</returns>
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; }
}
}
/// <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", "6441"},
{"Average Win", "0.07%"},
{"Average Loss", "-0.07%"},
{"Compounding Annual Return", "14.722%"},
{"Drawdown", "10.700%"},
{"Expectancy", "0.067"},
{"Net Profit", "14.722%"},
{"Sharpe Ratio", "1.056"},
{"Probabilistic Sharpe Ratio", "49.750%"},
{"Loss Rate", "46%"},
{"Win Rate", "54%"},
{"Profit-Loss Ratio", "0.97"},
{"Alpha", "0.136"},
{"Beta", "-0.07"},
{"Annual Standard Deviation", "0.121"},
{"Annual Variance", "0.015"},
{"Information Ratio", "0.043"},
{"Tracking Error", "0.171"},
{"Treynor Ratio", "-1.836"},
{"Total Fees", "$7500.86"},
{"Estimated Strategy Capacity", "$320000.00"},
{"Lowest Capacity Asset", "BNO UN3IMQ2JU1YD"},
{"Fitness Score", "0.691"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "1.238"},
{"Return Over Maximum Drawdown", "1.378"},
{"Portfolio Turnover", "1.638"},
{"Total Insights Generated", "0"},
{"Total Insights Closed", "0"},
{"Total Insights Analysis Completed", "0"},
{"Long Insight Count", "0"},
{"Short Insight Count", "0"},
{"Long/Short Ratio", "100%"},
{"Estimated Monthly Alpha Value", "$0"},
{"Total Accumulated Estimated Alpha Value", "$0"},
{"Mean Population Estimated Insight Value", "$0"},
{"Mean Population Direction", "0%"},
{"Mean Population Magnitude", "0%"},
{"Rolling Averaged Population Direction", "0%"},
{"Rolling Averaged Population Magnitude", "0%"},
{"OrderListHash", "6047b278450c563805d0ea68c04c013a"}
};
}
}