Overall Statistics |
Total Orders 3 Average Win 0% Average Loss -0.11% Compounding Annual Return 4.656% Drawdown 0.100% Expectancy -1 Start Equity 500000 End Equity 501040 Net Profit 0.208% Sharpe Ratio 6.165 Sortino Ratio 10.736 Probabilistic Sharpe Ratio 99.032% Loss Rate 100% Win Rate 0% Profit-Loss Ratio 0 Alpha 0.023 Beta -0.001 Annual Standard Deviation 0.004 Annual Variance 0 Information Ratio -14.303 Tracking Error 0.044 Treynor Ratio -41.661 Total Fees $2.00 Estimated Strategy Capacity $0 Lowest Capacity Asset GOOCV VP83T1ZUHROL Portfolio Turnover 0.97% |
#region imports using System; using System.Collections; using System.Collections.Generic; using System.Linq; using System.Globalization; using System.Drawing; using QuantConnect; using QuantConnect.Algorithm.Framework; using QuantConnect.Algorithm.Framework.Selection; using QuantConnect.Algorithm.Framework.Alphas; using QuantConnect.Algorithm.Framework.Portfolio; using QuantConnect.Algorithm.Framework.Execution; using QuantConnect.Algorithm.Framework.Risk; using QuantConnect.Parameters; using QuantConnect.Benchmarks; using QuantConnect.Brokerages; using QuantConnect.Util; using QuantConnect.Interfaces; using QuantConnect.Algorithm; using QuantConnect.Indicators; using QuantConnect.Data; using QuantConnect.Data.Consolidators; using QuantConnect.Data.Custom; using QuantConnect.DataSource; using QuantConnect.Data.Fundamental; using QuantConnect.Data.Market; using QuantConnect.Data.UniverseSelection; using QuantConnect.Notifications; using QuantConnect.Orders; using QuantConnect.Orders.Fees; using QuantConnect.Orders.Fills; using QuantConnect.Orders.Slippage; using QuantConnect.Scheduling; using QuantConnect.Securities; using QuantConnect.Securities.Equity; using QuantConnect.Securities.Future; using QuantConnect.Securities.Option; using QuantConnect.Securities.Forex; using QuantConnect.Securities.Crypto; using QuantConnect.Securities.Interfaces; using QuantConnect.Storage; using QuantConnect.Data.Custom.AlphaStreams; using QCAlgorithmFramework = QuantConnect.Algorithm.QCAlgorithm; using QCAlgorithmFrameworkBridge = QuantConnect.Algorithm.QCAlgorithm; #endregion namespace QuantConnect.Algorithm.CSharp { public class BearPutSpreadStrategy : QCAlgorithm { private Symbol _symbol; public override void Initialize() { SetStartDate(2017, 2, 1); SetEndDate(2017, 2, 19); SetCash(500000); var option = AddOption("GOOG", Resolution.Minute); _symbol = option.Symbol; option.SetFilter(universe => universe.IncludeWeeklys().CallCalendarSpread(0, 30, 60)); } public override void OnData(Slice slice) { if (Portfolio.Invested) return; // Get the OptionChain of the symbol var chain = slice.OptionChains.get(_symbol, null); if (chain == null || chain.Count() == 0) return; // get at-the-money strike var atmStrike = chain.OrderBy(x => Math.Abs(x.Strike - chain.Underlying.Price)).First().Strike; // filter the call options from the contracts which is ATM in the option chain. var calls = chain.Where(x => x.Strike == atmStrike && x.Right == OptionRight.Call); if (calls.Count() == 0) return; // sorted the optionchain by expiration date var expiries = calls.Select(x => x.Expiry).OrderBy(x => x); // select the farest expiry as far-leg expiry, and the nearest expiry as near-leg expiry var nearExpiry = expiries.First(); var farExpiry = expiries.Last(); var optionStrategy = OptionStrategies.ShortCallCalendarSpread(_symbol, atmStrike, nearExpiry, farExpiry); // We open a position with 1 unit of the option strategy Buy(optionStrategy, 1); } } }