Files
quantconnect--lean/Algorithm.CSharp/RegressionAlgorithm.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

133 lines
5.1 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 QuantConnect.Data;
using QuantConnect.Interfaces;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Algorithm used for regression tests purposes
/// </summary>
/// <meta name="tag" content="regression test" />
public class RegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
public override void Initialize()
{
SetStartDate(2013, 10, 07);
SetEndDate(2013, 10, 11);
SetCash(10000000);
// Find more symbols here: http://quantconnect.com/data
AddSecurity(SecurityType.Equity, "SPY", Resolution.Tick);
AddSecurity(SecurityType.Equity, "BAC", Resolution.Minute);
AddSecurity(SecurityType.Equity, "AIG", Resolution.Hour);
AddSecurity(SecurityType.Equity, "IBM", Resolution.Daily);
}
private DateTime lastTradeTradeBars;
private TimeSpan tradeEvery = TimeSpan.FromMinutes(1);
public override void OnData(Slice data)
{
if (Time - lastTradeTradeBars < tradeEvery) return;
lastTradeTradeBars = Time;
foreach (var kvp in data.Bars)
{
var symbol = kvp.Key;
var bar = kvp.Value;
if (bar.Time.RoundDown(bar.Period) != bar.Time)
{
// only trade on new data
continue;
}
var holdings = Portfolio[symbol];
if (!holdings.Invested)
{
MarketOrder(symbol, 10);
}
else
{
MarketOrder(symbol, -holdings.Quantity);
}
}
}
/// <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", "1590"},
{"Average Win", "0.00%"},
{"Average Loss", "0.00%"},
{"Compounding Annual Return", "-1.167%"},
{"Drawdown", "0.000%"},
{"Expectancy", "-0.974"},
{"Net Profit", "-0.016%"},
{"Sharpe Ratio", "-8.696"},
{"Probabilistic Sharpe Ratio", "0.002%"},
{"Loss Rate", "99%"},
{"Win Rate", "1%"},
{"Profit-Loss Ratio", "3.17"},
{"Alpha", "-0.006"},
{"Beta", "-0.001"},
{"Annual Standard Deviation", "0.001"},
{"Annual Variance", "0"},
{"Information Ratio", "-7.193"},
{"Tracking Error", "0.195"},
{"Treynor Ratio", "8.201"},
{"Total Fees", "$1590.00"},
{"Estimated Strategy Capacity", "$67000000.00"},
{"Lowest Capacity Asset", "AIG R735QTJ8XC9X"},
{"Fitness Score", "0"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-14.995"},
{"Return Over Maximum Drawdown", "-72.578"},
{"Portfolio Turnover", "0.018"},
{"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", "98388f6232375a64b6858a6e3e82c321"}
};
}
}