Files
quantconnect--lean/Algorithm.CSharp/ScheduledEventsOrderRegressionAlgorithm.cs
T
Alexandre Catarino 2b0fd2e607 Updates SPY Market Data (#5493)
* Fixes Double to Decimal Cast in GetAnnualPerformance

`GetAnnualPerformance` raises an exception if the `AnnualPerformance` calculation returns a double that cannot be cast to decimal (smaller than `decimal.MinValue` or bigger than `decimal.MaxValue`).
See `ProbabilisticSharpeRatio` where the same solution was applied.

* Updates SPY Market Data

SPY is a key asset since it is the default benchmark, and any change can lead to different `Alpha` and `Beta`

* Updates Unit Tests to Reflect Data Update

* Updates Regression Tests to Reflect Data Update I

Most of the regression tests change because of updated data (market and factors) of SPY (default benchmark) while the total trade remain the same.

* Updates Regression Tests to Reflect Data Update II

The following regression tests were changed to adapt to adjusted prices and keep the total trades:
- `BacktestingBrokerageRegressionAlgorithm`
- `LimitIfTouchedRegressionAlgorithm`
- `PortfolioRebalanceOnCustomFuncRegressionAlgorithm`
- `SetAccountCurrencySecurityMarginModelRegressionAlgorithm`
- `StopLossOnOrderEventRegressionAlgorithm`
- `TimeInForceAlgorithm`

The following regression tests have more trades since adjusted prices allowed more 1-2 shares trades that were rounded down to zero before:
- `FreePortfolioValueRegressionAlgorithm` 2 -> 3
- `PortfolioRebalanceOnDateRulesRegressionAlgorithm` 291 -> 298
- `TrailingStopRiskFrameworkAlgorithm` 5 -> 7

Especial cases:
- `AutoRegressiveIntegratedMovingAverageRegressionAlgorithm` 65 -> 52
 - ARIMA model sensibility
- `BlackLittermanPortfolioOptimizationFrameworkAlgorithm` 18 -> 19
 - BLM model sensibility
- `ExtendedMarketHoursHistoryRegressionAlgorithm` 20 -> 18
 - Less minute bars before market opens

* Addresses Peer-Review

Fix `BacktestingBrokerageRegressionAlgorithm` to use `CalculateOrderQuantity` and round down `quantity` to an even number to pass a value assertion and update the expected value from 50 to 52.
The quantity calculated by `CalculateOrderQuantity` has changed from 50 to 53 because of factor file update.
2021-04-19 13:31:01 -03:00

185 lines
7.5 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>
/// Regression algorithm which reproduces GH issue 4131, we assert order events are executed in order
/// event outside market ours
/// </summary>
public class ScheduledEventsOrderRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private int _scheduledEventCount;
private Symbol _spy;
private DateTime _lastTime = DateTime.MinValue;
/// <summary>
/// Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.
/// </summary>
public override void Initialize()
{
SetStartDate(2013, 10, 07);
SetEndDate(2013, 10, 11);
_spy = AddEquity("SPY").Symbol;
var test = 0;
var dateRule = DateRules.EveryDay(_spy);
var aEventCount = 0;
var bEventCount = 0;
var cEventCount = 0;
// we add each twice and assert the order in which they are added is also respected for events at the same time
for (var i = 0; i < 2; i++)
{
var id = i;
Schedule.On(dateRule, TimeRules.At(9, 25), (name, time) =>
{
// for id 0 event count should always be 0, for id 1 should be 1
if (aEventCount != id)
{
throw new Exception($"Scheduled event triggered out of order: {Time} expected id {id} but was {aEventCount}");
}
aEventCount++;
// goes from 0 to 1
aEventCount %= 2;
AssertScheduledEventTime();
Debug($"{Time} :: Test: {test}"); test++;
});
Schedule.On(dateRule, TimeRules.BeforeMarketClose(_spy, 5), (name, time) =>
{
// for id 0 event count should always be 0, for id 1 should be 1
if (bEventCount != id)
{
throw new Exception($"Scheduled event triggered out of order: {Time} expected id {id} but was {bEventCount}");
}
bEventCount++;
// goes from 0 to 1
bEventCount %= 2;
AssertScheduledEventTime();
Debug($"{Time} :: Test: {test}"); test++;
});
Schedule.On(dateRule, TimeRules.At(16, 5), (name, time) =>
{
// for id 0 event count should always be 0, for id 1 should be 1
if (cEventCount != id)
{
throw new Exception($"Scheduled event triggered out of order: {Time} expected id {id} but was {cEventCount}");
}
cEventCount++;
// goes from 0 to 1
cEventCount %= 2;
AssertScheduledEventTime();
Debug($"{Time} :: Test: {test}"); test = 0;
});
}
}
private void AssertScheduledEventTime()
{
if (_lastTime > Time)
{
throw new Exception($"Scheduled event time shouldn't go backwards, last time {_lastTime}, current {Time}");
}
_lastTime = Time;
_scheduledEventCount++;
}
public override void OnEndOfAlgorithm()
{
if (_scheduledEventCount != 28)
{
throw new Exception($"OnEndOfAlgorithm expected scheduled events but was {_scheduledEventCount}");
}
}
/// <summary>
/// OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
/// </summary>
/// <param name="data">Slice object keyed by symbol containing the stock data</param>
public override void OnData(Slice data)
{
if (!Portfolio.Invested)
{
SetHoldings(_spy, 1);
}
}
/// <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 };
/// <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", "1"},
{"Average Win", "0%"},
{"Average Loss", "0%"},
{"Compounding Annual Return", "271.453%"},
{"Drawdown", "2.200%"},
{"Expectancy", "0"},
{"Net Profit", "1.692%"},
{"Sharpe Ratio", "8.888"},
{"Probabilistic Sharpe Ratio", "67.609%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.005"},
{"Beta", "0.996"},
{"Annual Standard Deviation", "0.222"},
{"Annual Variance", "0.049"},
{"Information Ratio", "-14.565"},
{"Tracking Error", "0.001"},
{"Treynor Ratio", "1.978"},
{"Total Fees", "$3.44"},
{"Estimated Strategy Capacity", "$48000000.00"},
{"Fitness Score", "0.248"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "79228162514264337593543950335"},
{"Return Over Maximum Drawdown", "93.728"},
{"Portfolio Turnover", "0.248"},
{"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", "9e4bfd2eb0b81ee5bc1b197a87ccedbe"}
};
}
}