Files
quantconnect--lean/Algorithm.CSharp/InternalSubscriptionHistoryRequestAlgorithm.cs
T
Colton Sellers d2d99b1f10
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Algorithm Sampling and Statistics Fixes (#5936)
* Implement scheduled event sampling solution

* Use UTC time, only update daily portfolio value once a day

* For daily resolutions sample chart always

* Cleanup

* Drop resample daily all together

* Force final sample

* Regression updates

* FIx LiveResultHandler to update portfolio and benchmark values outside of sampling event

* Name the daily sampling event

* Address review pt 1

* Drop force and use reference wrapper

* Adjust tests

* Fix warning for Benchmark Timezone Misalignment and also add test

* Fix for daily resolution orders and test adjustments

* Also warn on universe settings with daily resolution

* Update missed regression

* Fix reference wrapper use

* Update regression after rebase

* Add values back in for Daylight Algo

* Have statistics builder skip day 1 performance

* Regression adjustments

* Test adjustments

* Update regression unit test

* Adjust some regressions starts to show performance values

* Add hourly algorithm for beta comparison

* Address missing Python regression changes

* Remove null comment
2021-10-05 19:31:25 -03:00

130 lines
5.4 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.Linq;
using QuantConnect.Data;
using QuantConnect.Interfaces;
using System.Collections.Generic;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// Regression algorithm asserting data returned by a history requests uses internal subscriptions correctly
/// </summary>
public class InternalSubscriptionHistoryRequestAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
/// <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);
AddEquity("AAPL", Resolution.Hour);
SetBenchmark("SPY");
}
/// <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("AAPL", 1);
var spy = QuantConnect.Symbol.Create("SPY", SecurityType.Equity, Market.USA);
var history = History(new[] { spy }, TimeSpan.FromDays(10));
if (!history.Any() || !history.All(slice => slice.Bars.All(pair => pair.Value.Period == TimeSpan.FromHours(1))))
{
throw new Exception("Unexpected history result for internal subscription");
}
// we add SPY using Daily > default benchmark using hourly
AddEquity("SPY", Resolution.Daily);
history = History(new[] { spy }, TimeSpan.FromDays(10));
if (!history.Any() || !history.All(slice => slice.Bars.All(pair => pair.Value.Period == TimeSpan.FromDays(1))))
{
throw new Exception("Unexpected history result for user subscription");
}
}
}
/// <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", "32.114%"},
{"Drawdown", "2.300%"},
{"Expectancy", "0"},
{"Net Profit", "0.382%"},
{"Sharpe Ratio", "5.488"},
{"Probabilistic Sharpe Ratio", "60.047%"},
{"Loss Rate", "0%"},
{"Win Rate", "0%"},
{"Profit-Loss Ratio", "0"},
{"Alpha", "-0.104"},
{"Beta", "0.548"},
{"Annual Standard Deviation", "0.179"},
{"Annual Variance", "0.032"},
{"Information Ratio", "-6.047"},
{"Tracking Error", "0.165"},
{"Treynor Ratio", "1.793"},
{"Total Fees", "$32.11"},
{"Estimated Strategy Capacity", "$66000000.00"},
{"Lowest Capacity Asset", "AAPL R735QTJ8XC9X"},
{"Fitness Score", "0.189"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "4.037"},
{"Return Over Maximum Drawdown", "15.534"},
{"Portfolio Turnover", "0.2"},
{"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", "b7b8e83e4456e143c2c4c11fa31a1cf2"}
};
}
}