Adds support for custom option pricing models (#9270)

* Initial solution

* Made Greeks a non-abstract class

* Add regression algorithms

* Solve review comments

* Minor fix

* Minor fix

* Remove unnecessary imports

* Fix issues with regression algorithms

* Solve review comments

Author: JosueNina

Algorithm.CSharp/CustomOptionPriceModelRegressionAlgorithm.cs

@@ -0,0 +1,163 @@
+/*
+ * 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.Market;
+using QuantConnect.Interfaces;
+using QuantConnect.Securities.Option;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm to test the creation and usage of a custom option price model
+    /// </summary>
+    public class CustomOptionPriceModelRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _optionSymbol;
+        private CustomOptionPriceModel _optionPriceModel;
+
+        public override void Initialize()
+        {
+            SetStartDate(2015, 12, 24);
+            SetEndDate(2015, 12, 24);
+
+            var option = AddOption("GOOG");
+            _optionSymbol = option.Symbol;
+
+            option.SetFilter(u => u.StandardsOnly().Strikes(-2, +2).Expiration(0, 180));
+            _optionPriceModel = new CustomOptionPriceModel();
+            option.SetPriceModel(_optionPriceModel);
+        }
+
+        public override void OnData(Slice slice)
+        {
+            if (Portfolio.Invested)
+            {
+                return;
+            }
+
+            if (slice.OptionChains.TryGetValue(_optionSymbol, out var chain))
+            {
+                var underlyingPrice = chain.Underlying.Price;
+                var atmContract = chain
+                    .OrderByDescending(x => x.Expiry)
+                    .ThenBy(x => Math.Abs(chain.Underlying.Price - x.Strike))
+                    .ThenByDescending(x => x.Right)
+                    .FirstOrDefault();
+
+                if (atmContract != null && atmContract.TheoreticalPrice > 0)
+                {
+                    MarketOrder(atmContract.Symbol, 1);
+                }
+            }
+        }
+
+        public override void OnEndOfAlgorithm()
+        {
+            if (_optionPriceModel.EvaluationCount == 0)
+            {
+                throw new RegressionTestException("CustomOptionPriceModel.Evaluate() was never called");
+            }
+        }
+
+        private class CustomOptionPriceModel : IOptionPriceModel
+        {
+            public int EvaluationCount { get; private set; }
+            public OptionPriceModelResult Evaluate(OptionPriceModelParameters parameters)
+            {
+                EvaluationCount++;
+                var contract = parameters.Contract;
+                var underlying = contract.UnderlyingLastPrice;
+                var strike = contract.Strike;
+
+                decimal intrinsicValue;
+                if (contract.Right == OptionRight.Call)
+                {
+                    intrinsicValue = Math.Max(0, underlying - strike);
+                }
+                else
+                {
+                    intrinsicValue = Math.Max(0, strike - underlying);
+                }
+
+                var theoreticalPrice = intrinsicValue + 1.0m;
+                return new OptionPriceModelResult(theoreticalPrice, new Greeks(0.5m, 0.1m, 0.2m, -0.05m, 0.1m, 2.0m));
+            }
+        }
+
+        /// <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 List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 15023;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 0;
+
+        /// <summary>
+        /// Final status of the algorithm
+        /// </summary>
+        public AlgorithmStatus AlgorithmStatus => AlgorithmStatus.Completed;
+
+        /// <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 Orders", "1"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "99799"},
+            {"Net Profit", "0%"},
+            {"Sharpe Ratio", "0"},
+            {"Sortino Ratio", "0"},
+            {"Probabilistic Sharpe Ratio", "0%"},
+            {"Loss Rate", "0%"},
+            {"Win Rate", "0%"},
+            {"Profit-Loss Ratio", "0"},
+            {"Alpha", "0"},
+            {"Beta", "0"},
+            {"Annual Standard Deviation", "0"},
+            {"Annual Variance", "0"},
+            {"Information Ratio", "0"},
+            {"Tracking Error", "0"},
+            {"Treynor Ratio", "0"},
+            {"Total Fees", "$1.00"},
+            {"Estimated Strategy Capacity", "$2600000.00"},
+            {"Lowest Capacity Asset", "GOOCV 30AKMEIPOX2DI|GOOCV VP83T1ZUHROL"},
+            {"Portfolio Turnover", "5.49%"},
+            {"Drawdown Recovery", "0"},
+            {"OrderListHash", "1925127010d4a935c1efe4bce0375c15"}
+        };
+    }
+}

Algorithm.Python/CustomOptionPriceModelRegressionAlgorithm.py

@@ -0,0 +1,73 @@
+# 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.
+
+from AlgorithmImports import *
+
+### <summary>
+### Regression algorithm to test the creation and usage of a custom option price model
+### </summary>
+class CustomOptionPriceModelRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2015, 12, 24)
+        self.set_end_date(2015, 12, 24)
+        self.set_cash(100000)
+
+        option = self.add_option("GOOG")
+        self._option_symbol = option.symbol
+
+        option.set_filter(lambda u: u.standards_only().strikes(-2, +2).expiration(0, 180))
+        self._option_price_model = CustomOptionPriceModel()
+        option.set_price_model(self._option_price_model)
+
+    def on_data(self, slice):
+        if self.portfolio.invested:
+            return
+
+        chain = slice.option_chains.get(self._option_symbol)
+        if not chain:
+            return
+
+        contracts = sorted(sorted(sorted(chain, \
+            key = lambda x: abs(chain.underlying.price - x.strike)), \
+            key = lambda x: x.expiry, reverse=True), \
+            key = lambda x: x.right, reverse=True)
+
+        if len(contracts) == 0:
+            return
+        
+        if (contracts[0].theoretical_price > 0):
+            self.market_order(contracts[0].symbol, 1)
+    
+    def on_end_of_algorithm(self):
+        if self._option_price_model.evaluation_count == 0:
+            raise RegressionTestException("CustomOptionPriceModel.Evaluate() was never called")
+
+class CustomOptionPriceModel():
+    def __init__(self):
+        self.evaluation_count = 0
+
+    def evaluate(self, parameters):
+        self.evaluation_count += 1
+        contract = parameters.contract
+        underlying = contract.underlying_last_price
+        strike = contract.strike
+        
+        if contract.right == OptionRight.CALL:
+            intrinsic = max(0, underlying - strike)
+        else:
+            intrinsic = max(0, strike - underlying)
+        
+        theoretical_price = intrinsic + 1.0
+        
+        return OptionPriceModelResult(theoretical_price, Greeks(0.5, 0.1, 0.2, -0.05, 0.1, 2.0))

Common/Data/Market/Greeks.cs

@@ -20,7 +20,7 @@ namespace QuantConnect.Data.Market
     /// <summary>
     /// Defines the greeks
     /// </summary>
-    public abstract class Greeks
+    public class Greeks
     {
         /// <summary>
         /// Gets the delta.
@@ -29,7 +29,7 @@ public abstract class Greeks
         /// the underlying asset'sprice. (∂V/∂S)
         /// </para>
         /// </summary>
-        public abstract decimal Delta { get; }
+        public virtual decimal Delta { get; set; }
 
         /// <summary>
         /// Gets the gamma.
@@ -38,7 +38,7 @@ public abstract class Greeks
         /// the underlying asset'sprice. (∂²V/∂S²)
         /// </para>
         /// </summary>
-        public abstract decimal Gamma { get; }
+        public virtual decimal Gamma { get; set; }
 
         /// <summary>
         /// Gets the vega.
@@ -47,7 +47,7 @@ public abstract class Greeks
         /// the underlying's volatility. (∂V/∂σ)
         /// </para>
         /// </summary>
-        public abstract decimal Vega { get; }
+        public virtual decimal Vega { get; set; }
 
         /// <summary>
         /// Gets the theta.
@@ -56,7 +56,7 @@ public abstract class Greeks
         /// time. This is commonly known as the 'time decay.' (∂V/∂τ)
         /// </para>
         /// </summary>
-        public abstract decimal Theta { get; }
+        public virtual decimal Theta { get; set; }
 
         /// <summary>
         /// Gets the rho.
@@ -65,7 +65,7 @@ public abstract class Greeks
         /// the risk free interest rate. (∂V/∂r)
         /// </para>
         /// </summary>
-        public abstract decimal Rho { get; }
+        public virtual decimal Rho { get; set; }
 
         /// <summary>
         /// Gets the lambda.
@@ -76,7 +76,7 @@ public abstract class Greeks
         /// </para>
         /// </summary>
         [PandasIgnore]
-        public abstract decimal Lambda { get; }
+        public virtual decimal Lambda { get; set; }
 
         /// <summary>
         /// Gets the lambda.
@@ -91,7 +91,11 @@ public abstract class Greeks
         /// PEP8 API is used (lambda is a reserved keyword in Python).
         /// </remarks>
         [PandasIgnore]
-        public virtual decimal Lambda_ => Lambda;
+        public virtual decimal Lambda_
+        {
+            get { return Lambda; }
+            set { Lambda = value; }
+        }
 
         /// <summary>
         /// Gets the theta per day.
@@ -101,6 +105,30 @@ public abstract class Greeks
         /// </para>
         /// </summary>
         [PandasIgnore]
-        public virtual decimal ThetaPerDay => Theta / 365m;
+        public virtual decimal ThetaPerDay
+        {
+            get { return Theta / 365m; }
+            set { Theta = value * 365m; }
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Greeks"/> class.
+        /// </summary>
+        public Greeks()
+        {
+        }
+
+        /// <summary>
+        /// Initializes a new instance of the <see cref="Greeks"/> class with specified values.
+        /// </summary>
+        public Greeks(decimal delta, decimal gamma, decimal vega, decimal theta, decimal rho, decimal lambda)
+        {
+            Delta = delta;
+            Gamma = gamma;
+            Vega = vega;
+            Theta = theta;
+            Rho = rho;
+            Lambda = lambda;
+        }
     }
 }

Common/Python/OptionPriceModelPythonWrapper.cs

@@ -0,0 +1,49 @@
+/*
+ * 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 Python.Runtime;
+using QuantConnect.Python;
+using System;
+
+namespace QuantConnect.Securities.Option
+{
+    /// <summary>
+    /// Provides an implementation of <see cref="IOptionPriceModel"/> that wraps a <see cref="PyObject"/> object
+    /// </summary>
+    public class OptionPriceModelPythonWrapper : BasePythonWrapper<IOptionPriceModel>, IOptionPriceModel
+    {
+        /// <summary>
+        /// Creates a new instance
+        /// </summary>
+        /// <param name="model">The python model to wrap</param>
+        public OptionPriceModelPythonWrapper(PyObject model)
+            : base(model)
+        {
+        }
+
+        /// <summary>
+        /// Evaluates the specified option contract to compute a theoretical price, IV and greeks
+        /// </summary>
+        /// <param name="parameters">A <see cref="OptionPriceModelParameters"/> object
+        /// containing the security, slice and contract</param>
+        /// <returns>An instance of <see cref="OptionPriceModelResult"/> containing the theoretical
+        /// price of the specified option contract</returns>
+        public OptionPriceModelResult Evaluate(OptionPriceModelParameters parameters)
+        {
+            return InvokeMethod<OptionPriceModelResult>(nameof(Evaluate), parameters);
+        }
+    }
+}