Files
quantconnect--lean/Algorithm.CSharp/FutureOptionCallITMGreeksExpiryRegressionAlgorithm.cs
T
Gerardo Salazar bb9cde1cee Adds StandardDeviationOfReturns configurability and improves greeks warmup for Futures/Index Options (#5495)
* Improves greeks configurability and defaults for all option asset types

  * Makes `StandardDeviationOfReturns` configurable by users, so that
    greeks can be loaded according to user expectations and the series
    of returns that they'd like to compute for `n` periods and timespan
    of `T`, as well as resolution of the data in live mode.

  * Changes resolution to max resolution available for the default
    volatility model created for the security. Usually this only applies
    to live mode, but if creating an instance of the
    `StandardDeviationOfReturns` volatility model and no `updateFrequency`
    is provided, the resolution's time span will be used as the default
    value. Backwards compatibility for equities is maintained.

  * Changes defaults for `StandardDeviationOfReturnsVolatilityModel`
    to warmup greeks faster for other derivative asset types

  * Improves comments on `StandardDeviationOfReturns` for clarity on how
    to use the volatility model for end users

* Fixes bug where TradeBar could not have proper Symbol set when getting
max resolution

  * Applies to QCAlgorithm.Universe and StandardDeviationOfReturnsVolatilityModel
  * Adds tests to check volatility model is updated at specified config intervals

* Address review: add shared method for (Relative)StandardDeviation
volatility models

  * Adjusts logic to determine bar type

* Address review: order by TickType when getting configs inside volatility models
2021-04-28 19:05:00 -03:00

215 lines
9.4 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.Collections.Generic;
using System.Linq;
using System.Reflection;
using QuantConnect.Data;
using QuantConnect.Interfaces;
using QuantConnect.Orders;
using QuantConnect.Securities;
using QuantConnect.Securities.Option;
namespace QuantConnect.Algorithm.CSharp
{
/// <summary>
/// This regression algorithm tests In The Money (ITM) future option expiry for calls.
/// We test to make sure that FOPs have greeks enabled, same as equity options.
/// </summary>
public class FutureOptionCallITMGreeksExpiryRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
{
private bool _invested;
private int _onDataCalls;
private Security _es19m20;
private Option _esOption;
private Symbol _expectedOptionContract;
public override void Initialize()
{
SetStartDate(2020, 1, 5);
SetEndDate(2020, 6, 30);
_es19m20 = AddFutureContract(
QuantConnect.Symbol.CreateFuture(
Futures.Indices.SP500EMini,
Market.CME,
new DateTime(2020, 6, 19)),
Resolution.Minute);
// We must set the volatility model on the underlying, since the defaults are
// too strict to calculate greeks with when we only have data for a single day
_es19m20.VolatilityModel = new StandardDeviationOfReturnsVolatilityModel(
60,
Resolution.Minute,
TimeSpan.FromMinutes(1));
// Select a future option expiring ITM, and adds it to the algorithm.
_esOption = AddFutureOptionContract(OptionChainProvider.GetOptionContractList(_es19m20.Symbol, new DateTime(2020, 1, 5))
.Where(x => x.ID.StrikePrice <= 3200m && x.ID.OptionRight == OptionRight.Call)
.OrderByDescending(x => x.ID.StrikePrice)
.Take(1)
.Single(), Resolution.Minute);
_esOption.PriceModel = OptionPriceModels.BjerksundStensland();
_expectedOptionContract = QuantConnect.Symbol.CreateOption(_es19m20.Symbol, Market.CME, OptionStyle.American, OptionRight.Call, 3200m, new DateTime(2020, 6, 19));
if (_esOption.Symbol != _expectedOptionContract)
{
throw new Exception($"Contract {_expectedOptionContract} was not found in the chain");
}
}
public override void OnData(Slice data)
{
// Let the algo warmup, but without using SetWarmup. Otherwise, we get
// no contracts in the option chain
if (_invested || _onDataCalls++ < 40)
{
return;
}
if (data.OptionChains.Count == 0)
{
return;
}
if (data.OptionChains.Values.All(o => o.Contracts.Values.Any(c => !data.ContainsKey(c.Symbol))))
{
return;
}
if (data.OptionChains.Values.First().Contracts.Count == 0)
{
throw new Exception($"No contracts found in the option {data.OptionChains.Keys.First()}");
}
var deltas = data.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Delta).ToList();
var gammas = data.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Gamma).ToList();
var lambda = data.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Lambda).ToList();
var rho = data.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Rho).ToList();
var theta = data.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Theta).ToList();
var vega = data.OptionChains.Values.OrderByDescending(y => y.Contracts.Values.Sum(x => x.Volume)).First().Contracts.Values.Select(x => x.Greeks.Vega).ToList();
// The commented out test cases all return zero.
// This is because of failure to evaluate the greeks in the option pricing model.
// For now, let's skip those.
if (deltas.Any(d => d == 0))
{
throw new AggregateException("Option contract Delta was equal to zero");
}
if (gammas.Any(g => g == 0))
{
throw new AggregateException("Option contract Gamma was equal to zero");
}
if (lambda.Any(l => l == 0))
{
throw new AggregateException("Option contract Lambda was equal to zero");
}
if (rho.Any(r => r == 0))
{
throw new AggregateException("Option contract Rho was equal to zero");
}
if (theta.Any(t => t == 0))
{
throw new AggregateException("Option contract Theta was equal to zero");
}
if (vega.Any(v => v == 0))
{
throw new AggregateException("Option contract Vega was equal to zero");
}
if (!_invested)
{
SetHoldings(data.OptionChains.Values.First().Contracts.Values.First().Symbol, 1);
_invested = true;
}
}
/// <summary>
/// Ran at the end of the algorithm to ensure the algorithm has no holdings
/// </summary>
/// <exception cref="Exception">The algorithm has holdings</exception>
public override void OnEndOfAlgorithm()
{
if (Portfolio.Invested)
{
throw new Exception($"Expected no holdings at end of algorithm, but are invested in: {string.Join(", ", Portfolio.Keys)}");
}
if (!_invested)
{
throw new Exception($"Never checked greeks, maybe we have no option data?");
}
}
/// <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", "3"},
{"Average Win", "27.44%"},
{"Average Loss", "-62.81%"},
{"Compounding Annual Return", "-78.438%"},
{"Drawdown", "52.600%"},
{"Expectancy", "-0.282"},
{"Net Profit", "-52.604%"},
{"Sharpe Ratio", "-0.862"},
{"Probabilistic Sharpe Ratio", "0.019%"},
{"Loss Rate", "50%"},
{"Win Rate", "50%"},
{"Profit-Loss Ratio", "0.44"},
{"Alpha", "-0.586"},
{"Beta", "0.031"},
{"Annual Standard Deviation", "0.679"},
{"Annual Variance", "0.461"},
{"Information Ratio", "-0.779"},
{"Tracking Error", "0.784"},
{"Treynor Ratio", "-18.756"},
{"Total Fees", "$66.60"},
{"Estimated Strategy Capacity", "$8300000.00"},
{"Fitness Score", "0.156"},
{"Kelly Criterion Estimate", "0"},
{"Kelly Criterion Probability Value", "0"},
{"Sortino Ratio", "-0.133"},
{"Return Over Maximum Drawdown", "-1.491"},
{"Portfolio Turnover", "0.408"},
{"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", "a7f76d1e2d6f27651465217c92deea80"}
};
}
}