QCAlgorithm's OptionChain() api refactor

Algorithm.CSharp/AddOptionContractFromUniverseRegressionAlgorithm.cs

@@ -117,7 +117,7 @@ public override void OnSecuritiesChanged(SecurityChanges changes)
                                                       && optionContract.ID.OptionStyle == OptionStyle.American);
                 AddOptionContract(option);
 
-                foreach (var symbol in new[] { option.Symbol, option.Underlying.Symbol })
+                foreach (var symbol in new[] { option.Symbol, option.UnderlyingSymbol })
                 {
                     var config = SubscriptionManager.SubscriptionDataConfigService.GetSubscriptionDataConfigs(symbol).ToList();
 

Algorithm.CSharp/OptionChainsMultipleFullDataRegressionAlgorithm.cs

@@ -0,0 +1,142 @@
+/*
+ * 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;
+
+namespace QuantConnect.Algorithm.CSharp
+{
+    /// <summary>
+    /// Regression algorithm illustrating the usage of the <see cref="QCAlgorithm.OptionChains(IEnumerable{Symbol})"/> method
+    /// to get multiple option chains, which contains additional data besides the symbols, including prices, implied volatility and greeks.
+    /// It also shows how this data can be used to filter the contracts based on certain criteria.
+    /// </summary>
+    public class OptionChainsMultipleFullDataRegressionAlgorithm : QCAlgorithm, IRegressionAlgorithmDefinition
+    {
+        private Symbol _googOptionContract;
+        private Symbol _spxOptionContract;
+
+        public override void Initialize()
+        {
+            SetStartDate(2015, 12, 24);
+            SetEndDate(2015, 12, 24);
+            SetCash(100000);
+
+            var goog = AddEquity("GOOG").Symbol;
+            var spx = AddIndex("SPX").Symbol;
+
+            var chains = OptionChains(new[] { goog, spx });
+
+            _googOptionContract = GetContract(chains, goog, TimeSpan.FromDays(10));
+            _spxOptionContract = GetContract(chains, spx, TimeSpan.FromDays(60));
+
+            AddOptionContract(_googOptionContract);
+            AddIndexOptionContract(_spxOptionContract);
+        }
+
+        private Symbol GetContract(OptionChains chains, Symbol underlying, TimeSpan expirySpan)
+        {
+            return chains
+                .Where(kvp => kvp.Key.Underlying == underlying)
+                .Select(kvp => kvp.Value)
+                .Single()
+                // Get contracts expiring within a given span, with an implied volatility greater than 0.5 and a delta less than 0.5
+                .Where(contractData => contractData.ID.Date - Time <= expirySpan &&
+                    contractData.ImpliedVolatility > 0.5m &&
+                    contractData.Greeks.Delta < 0.5m)
+                // Get the contract with the latest expiration date
+                .OrderByDescending(x => x.ID.Date)
+                .First();
+        }
+
+        public override void OnData(Slice slice)
+        {
+            // Do some trading with the selected contract for sample purposes
+            if (!Portfolio.Invested)
+            {
+                MarketOrder(_googOptionContract, 1);
+            }
+            else
+            {
+                Liquidate();
+            }
+        }
+
+        /// <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 virtual List<Language> Languages { get; } = new() { Language.CSharp, Language.Python };
+
+        /// <summary>
+        /// Data Points count of all timeslices of algorithm
+        /// </summary>
+        public long DataPoints => 1059;
+
+        /// <summary>
+        /// Data Points count of the algorithm history
+        /// </summary>
+        public int AlgorithmHistoryDataPoints => 2;
+
+        /// <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", "210"},
+            {"Average Win", "0%"},
+            {"Average Loss", "0%"},
+            {"Compounding Annual Return", "0%"},
+            {"Drawdown", "0%"},
+            {"Expectancy", "0"},
+            {"Start Equity", "100000"},
+            {"End Equity", "96041"},
+            {"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", "$209.00"},
+            {"Estimated Strategy Capacity", "$0"},
+            {"Lowest Capacity Asset", "GOOCV W6U7PD1F2WYU|GOOCV VP83T1ZUHROL"},
+            {"Portfolio Turnover", "85.46%"},
+            {"OrderListHash", "a7ab1a9e64fe9ba76ea33a40a78a4e3b"}
+        };
+    }
+}

Algorithm.CSharp/OptionPriceModelForOptionStylesBaseRegressionAlgorithm.cs

@@ -86,7 +86,7 @@ public void CheckGreeks(OptionChain contracts)
 
             foreach (var contract in contracts)
             {
-                Greeks greeks = new Greeks();
+                Greeks greeks = null;
                 try
                 {
                     greeks = contract.Greeks;
@@ -110,7 +110,8 @@ public void CheckGreeks(OptionChain contracts)
                 // Greeks should be valid if they were successfuly accessed for supported option style
                 if (_optionStyleIsSupported)
                 {
-                    if (greeks.Delta == 0m && greeks.Gamma == 0m && greeks.Theta == 0m && greeks.Vega == 0m && greeks.Rho == 0m)
+                    if (greeks == null ||
+                        (greeks.Delta == 0m && greeks.Gamma == 0m && greeks.Theta == 0m && greeks.Vega == 0m && greeks.Rho == 0m))
                     {
                         throw new RegressionTestException($"Expected greeks to not be zero simultaneously for {contract.Symbol.Value}, an {_option.Style} style option, using {_option?.PriceModel.GetType().Name}, but they were");
                     }

Algorithm.CSharp/QuantConnect.Algorithm.CSharp.csproj

@@ -34,7 +34,7 @@
     <DebugType>portable</DebugType>
   </PropertyGroup>
   <ItemGroup>
-    <PackageReference Include="QuantConnect.pythonnet" Version="2.0.38" />
+    <PackageReference Include="QuantConnect.pythonnet" Version="2.0.39" />
     <PackageReference Include="Accord" Version="3.6.0" />
     <PackageReference Include="Accord.Fuzzy" Version="3.6.0" />
     <PackageReference Include="Accord.MachineLearning" Version="3.6.0" />

Algorithm.Framework/QuantConnect.Algorithm.Framework.csproj

@@ -30,7 +30,7 @@
     <PackageLicenseFile>LICENSE</PackageLicenseFile>
   </PropertyGroup>
   <ItemGroup>
-    <PackageReference Include="QuantConnect.pythonnet" Version="2.0.38" />
+    <PackageReference Include="QuantConnect.pythonnet" Version="2.0.39" />
     <PackageReference Include="Accord" Version="3.6.0" />
     <PackageReference Include="Accord.Math" Version="3.6.0" />
     <PackageReference Include="Accord.Statistics" Version="3.6.0" />

Algorithm.Python/OptionChainFullDataRegressionAlgorithm.py

@@ -12,6 +12,7 @@
 # limitations under the License.
 
 from AlgorithmImports import *
+from datetime import timedelta
 
 ### <summary>
 ### Regression algorithm illustrating the usage of the <see cref="QCAlgorithm.OptionChain(Symbol)"/> method
@@ -27,14 +28,16 @@ def initialize(self):
 
         goog = self.add_equity("GOOG").symbol
 
+        option_chain = self.option_chain(goog)
+
+        # Demonstration using data frame:
+        df = option_chain.data_frame
         # Get contracts expiring within 10 days, with an implied volatility greater than 0.5 and a delta less than 0.5
-        contracts = [
-            contract_data
-            for contract_data in self.option_chain(goog)
-            if contract_data.id.date - self.time <= timedelta(days=10) and contract_data.implied_volatility > 0.5 and contract_data.greeks.delta < 0.5
-        ]
-        # Get the contract with the latest expiration date
-        self._option_contract = sorted(contracts, key=lambda x: x.id.date, reverse=True)[0]
+        contracts = df.loc[(df.expiry <= self.time + timedelta(days=10)) & (df.impliedvolatility > 0.5) & (df.delta < 0.5)]
+
+        # Get the contract with the latest expiration date.
+        # Note: the result of df.loc[] is a series, and its name is a tuple with a single element (contract symbol)
+        self._option_contract = contracts.loc[contracts.expiry.idxmax()].name[0]
 
         self.add_option_contract(self._option_contract)
 

Algorithm.Python/OptionChainedUniverseSelectionModelRegressionAlgorithm.py

@@ -23,7 +23,7 @@ def initialize(self):
         self.set_start_date(2014, 6, 6)
         self.set_end_date(2014, 6, 6)
         self.set_cash(100000)
-        
+
         universe = self.add_universe("my-minute-universe-name", lambda time: [ "AAPL", "TWX" ])
         self.add_universe_selection(
             OptionChainedUniverseSelectionModel(
@@ -34,9 +34,9 @@ def initialize(self):
                                      .expiration(0, 180))
             )
         )
-        
+
     def on_data(self, slice):
-        if self.portfolio.invested or not (self.is_market_open("AAPL") and self.is_market_open("AAPL")): return
+        if self.portfolio.invested or not (self.is_market_open("AAPL") and self.is_market_open("TWX")): return
         values = list(map(lambda x: x.value, filter(lambda x: x.key == "?AAPL" or x.key == "?TWX",  slice.option_chains)))
         for chain in values:
             # we sort the contracts to find at the money (ATM) contract with farthest expiration

Algorithm.Python/OptionChainsMultipleFullDataRegressionAlgorithm.py

@@ -0,0 +1,62 @@
+# 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 *
+from datetime import timedelta
+
+### <summary>
+### Regression algorithm illustrating the usage of the <see cref="QCAlgorithm.OptionChains(IEnumerable{Symbol})"/> method
+### to get multiple option chains, which contains additional data besides the symbols, including prices, implied volatility and greeks.
+### It also shows how this data can be used to filter the contracts based on certain criteria.
+### </summary>
+class OptionChainsMultipleFullDataRegressionAlgorithm(QCAlgorithm):
+
+    def initialize(self):
+        self.set_start_date(2015, 12, 24)
+        self.set_end_date(2015, 12, 24)
+        self.set_cash(100000)
+
+        goog = self.add_equity("GOOG").symbol
+        spx = self.add_index("SPX").symbol
+
+        chains = self.option_chains([goog, spx])
+
+        self._goog_option_contract = self.get_contract(chains, goog, timedelta(days=10))
+        self._spx_option_contract = self.get_contract(chains, spx, timedelta(days=60))
+
+        self.add_option_contract(self._goog_option_contract)
+        self.add_index_option_contract(self._spx_option_contract)
+
+    def get_contract(self, chains: OptionChains, underlying: Symbol, expiry_span: timedelta) -> Symbol:
+        df = chains.data_frame
+
+        # Index by the requested underlying, by getting all data with canonicals which underlying is the requested underlying symbol:
+        canonicals = df.index.get_level_values('canonical')
+        condition = [canonical for canonical in canonicals if canonical.underlying == underlying]
+        df = df.loc[condition]
+
+        # Get contracts expiring in the next 10 days with an implied volatility greater than 0.5 and a delta less than 0.5
+        contracts = df.loc[(df.expiry <= self.time + expiry_span) & (df.impliedvolatility > 0.5) & (df.delta < 0.5)]
+
+        # Select the contract with the latest expiry date
+        contracts.sort_values(by='expiry', ascending=False, inplace=True)
+
+        # Get the symbol: the resulting series name is a tuple (canonical symbol, contract symbol)
+        return contracts.iloc[0].name[1]
+
+    def on_data(self, data):
+        # Do some trading with the selected contract for sample purposes
+        if not self.portfolio.invested:
+            self.market_order(self._goog_option_contract, 1)
+        else:
+            self.liquidate()

Algorithm.Python/OptionPriceModelForOptionStylesBaseRegressionAlgorithm.py

@@ -53,7 +53,7 @@ def check_greeks(self, contracts):
         self._tried_greeks_calculation = True
 
         for contract in contracts:
-            greeks = Greeks()
+            greeks = None
             try:
                 greeks = contract.greeks
 
@@ -70,9 +70,10 @@ def check_greeks(self, contracts):
             # Delta can be {-1, 0, 1} if the price is too wild, rho can be 0 if risk free rate is 0
             # Vega can be 0 if the price is very off from theoretical price, Gamma = 0 if Delta belongs to {-1, 1}
             if (self._option_style_is_supported
-                and ((contract.right == OptionRight.CALL and (greeks.delta < 0.0 or greeks.delta > 1.0 or greeks.rho < 0.0))
-                    or (contract.right == OptionRight.PUT and (greeks.delta < -1.0 or greeks.delta > 0.0 or greeks.rho > 0.0))
-                    or greeks.theta == 0.0 or greeks.vega < 0.0 or greeks.gamma < 0.0)):
+                and (greeks is None
+                    or ((contract.right == OptionRight.CALL and (greeks.delta < 0.0 or greeks.delta > 1.0 or greeks.rho < 0.0))
+                        or (contract.right == OptionRight.PUT and (greeks.delta < -1.0 or greeks.delta > 0.0 or greeks.rho > 0.0))
+                        or greeks.theta == 0.0 or greeks.vega < 0.0 or greeks.gamma < 0.0))):
                 raise Exception(f'Expected greeks to have valid values. Greeks were: Delta: {greeks.delta}, Rho: {greeks.rho}, Theta: {greeks.theta}, Vega: {greeks.vega}, Gamma: {greeks.gamma}')
 
 

Algorithm.Python/QuantConnect.Algorithm.Python.csproj

@@ -39,7 +39,7 @@
     <Compile Include="..\Common\Properties\SharedAssemblyInfo.cs" Link="Properties\SharedAssemblyInfo.cs" />
   </ItemGroup>
   <ItemGroup>
-    <PackageReference Include="QuantConnect.pythonnet" Version="2.0.38" />
+    <PackageReference Include="QuantConnect.pythonnet" Version="2.0.39" />
   </ItemGroup>
   <ItemGroup>
     <Content Include="OptionUniverseFilterGreeksShortcutsRegressionAlgorithm.py" />

Algorithm/QCAlgorithm.Indicators.cs

@@ -498,7 +498,7 @@ public ChandeKrollStop CKS(Symbol symbol, int atrPeriod, decimal atrMult, int pe
             InitializeIndicator(indicator, resolution, selector, symbol);
             return indicator;
         }
-      
+
         /// <summary>
         /// Creates a new ChaikinMoneyFlow indicator.
         /// </summary>
@@ -4004,7 +4004,9 @@ void consumeLastPoint(IndicatorDataPoint newInputPoint)
             indicator.Updated -= callback;
 
             return new IndicatorHistory(indicatorsDataPointsByTime, indicatorsDataPointPerProperty,
-                new Lazy<PyObject>(() => PandasConverter.GetIndicatorDataFrame(indicatorsDataPointPerProperty.Select(x => new KeyValuePair<string, List<IndicatorDataPoint>>(x.Name, x.Values)))));
+                new Lazy<PyObject>(
+                    () => PandasConverter.GetIndicatorDataFrame(indicatorsDataPointPerProperty.Select(x => new KeyValuePair<string, List<IndicatorDataPoint>>(x.Name, x.Values))),
+                    isThreadSafe: false));
         }
 
         private Type GetDataTypeFromSelector(Func<IBaseData, decimal> selector)

Algorithm/QCAlgorithm.Python.cs

@@ -1638,6 +1638,21 @@ public void AddCommand(PyObject type)
             };
         }
 
+
+        /// <summary>
+        /// Get the option chains for the specified symbols at the current time (<see cref="Time"/>)
+        /// </summary>
+        /// <param name="symbols">
+        /// The symbols for which the option chain is asked for.
+        /// It can be either the canonical options or the underlying symbols.
+        /// </param>
+        /// <returns>The option chains</returns>
+        [DocumentationAttribute(AddingData)]
+        public OptionChains OptionChains(PyObject symbols)
+        {
+            return OptionChains(symbols.ConvertToSymbolEnumerable());
+        }
+
         /// <summary>
         /// Gets indicator base type
         /// </summary>
@@ -1752,8 +1767,9 @@ private PyObject TryCleanupCollectionDataFrame(Type dataType, PyObject history)
                 {
                     if (!dynamic.empty)
                     {
-                        using PyObject columns = dynamic.columns;
-                        if (columns.As<string[]>().Contains("data"))
+                        using var columns = new PySequence(dynamic.columns);
+                        using var dataKey = "data".ToPython();
+                        if (columns.Contains(dataKey))
                         {
                             history = dynamic["data"];
                         }