Overall Statistics |
Total Trades 2 Average Win 0% Average Loss 0% Compounding Annual Return 92.363% Drawdown 43.400% Expectancy 0 Net Profit 39.980% Sharpe Ratio 1.449 Probabilistic Sharpe Ratio 50.989% Loss Rate 0% Win Rate 0% Profit-Loss Ratio 0 Alpha -0.092 Beta 4.529 Annual Standard Deviation 0.635 Annual Variance 0.403 Information Ratio 1.295 Tracking Error 0.538 Treynor Ratio 0.203 Total Fees $2.00 Estimated Strategy Capacity $2000.00 Lowest Capacity Asset SPY YYFADQB3UR1I|SPY R735QTJ8XC9X Portfolio Turnover 0.70% |
from AlgorithmImports import * import numpy as np from math import log, sqrt, exp from scipy.stats import norm from QuantConnect.DataSource import * class NOKLeapsBreakout(QCAlgorithm): def Initialize(self): self.Balance = 10000 self.SetStartDate(2023, 1, 1) self.SetEndDate(2024, 3, 20) self.SetCash(self.Balance) equity = self.AddEquity("SPY", Resolution.Minute) equity.SetDataNormalizationMode(DataNormalizationMode.Raw) self.equity = equity.Symbol self.SetBenchmark(self.equity) self.SetSecurityInitializer(lambda x: x.SetMarketPrice(self.GetLastKnownPrice(x))) option = self.AddOption("SPY", Resolution.Minute) self.option_symbol = option.Symbol self.Minimum_Expiry = 648 self.Maximum_Expiry = 470 self.Profit_Taking = 1.5 self.number_options = 2 self.Min_Option_Price = 0 option.SetFilter(0, 5, timedelta(self.Minimum_Expiry), timedelta(99999)) self.yieldCurveTwo = self.AddData(USTreasuryYieldCurveRate, "USTYCR").Symbol history = self.History(USTreasuryYieldCurveRate, self.yieldCurveTwo, 60, Resolution.Daily) self.SetWarmUp(timedelta(10)) self.sigma = 0.165 self.r = None self.LastTime = None self.Schedule.On(self.DateRules.EveryDay("SPY"), self.TimeRules.BeforeMarketClose("SPY", 0), self.Plotting) self.mkt = [] def OnData(self,data): if self.IsWarmingUp: return if data.ContainsKey(self.yieldCurveTwo): rates = data[self.yieldCurveTwo] self.r = ((rates.TwoYear) / 100) self.Log(f"| {self.Time} Rates found - {self.r}") option_invested = [x.Key for x in self.Portfolio if x.Value.Invested and x.Value.Type==SecurityType.Option] if option_invested: for i in range(len(option_invested)): if (self.Time + timedelta(self.Maximum_Expiry) > option_invested[i].ID.Date): self.Liquidate(option_invested[i], "Too close to expiration") self.Log (f"| {self.Time} [+]--- Liquidate @ {str(self.Portfolio[option_invested[i]].Symbol.Value)} || Stock @ {str(self.Portfolio[self.equity].Price)}|| Profit/Loss @ {str(self.Portfolio[option_invested[i]].LastTradeProfit)}") self.Log (f"| {self.Time} [-]--- REASON: || <{(self.Maximum_Expiry)} DTE | {(option_invested[i].ID.Date - self.Time).days} DTE") if not self.LastTime == self.Time.day: if len(option_invested)> ((self.number_options)-1): self.Log("Currently Holding 2 Contracts - MAXIMUM") return else: self.Log("only holding one contract") for i in data.OptionChains: chains = i.Value self.BuyCall(chains) if not option_invested: self.Log("No Options invested") for i in data.OptionChains: chains = i.Value self.BuyCall(chains) # Buy options - Call def BuyCall(self,chains): self.S = self.Securities[self.equity].Close expiry = sorted(chains,key = lambda x: x.Expiry, reverse=True)[0].Expiry calls = [i for i in chains if i.Expiry == expiry and i.Right == OptionRight.Call] call_contracts = sorted(calls,key = lambda x: abs(x.Strike - x.UnderlyingLastPrice < 0)) if len(call_contracts) == 0: self.Debug("No Contracts Found") return self.Log("Contracts Found!") self.call = call_contracts[0] self.K = self.call.Strike self.T = ((self.call.Expiry - self.Time).days)/(365) option_invested = [x.Key for x in self.Portfolio if x.Value.Invested and x.Value.Type==SecurityType.Option] if (option_invested): for i in range(len(option_invested)): if (str(self.Portfolio[option_invested[i]].Symbol.Value) == str(self.call)): self.Log("Already bought the Contract") return if self.r is None: history = self.History(USTreasuryYieldCurveRate, self.yieldCurveTwo, 60, Resolution.Daily) twoyear = history['twoyear'][-1] self.r = twoyear / 100 self.Debug((f"| Previous Rates Used -- Succesful {self.Time} -- {self.r}")) self.option_price = self.BlackScholesCall(self.S, self.K, self.T, self.r, self.sigma) self.Log((f"| Option Price Calculated - Succesful {self.Time} [+]-- Contracts {str(self.call)} || Stock @ {str(self.call.UnderlyingLastPrice)} Option Price BS @ {(self.option_price)} rates: {self.r} Option Price Market @ {self.call.AskPrice} DTE @ {(self.call.Expiry - self.Time).days}")) if self.call.AskPrice > self.option_price: self.Log((f"| Option too Expensive! {self.Time})")) return if self.call.AskPrice == 0: return self.SetHoldings(self.call.Symbol, 0.7) quantity = self.Portfolio[self.call.Symbol].Quantity # <-- quantity is zero and the following orders will be invalid if quantity != 0: self.LimitOrder(self.call.Symbol, -quantity, (self.call.AskPrice * self.Profit_Taking)) self.Log ("\r+-------------") self.Log (f"| {self.Time} [+] Bought Contracts -- {str(self.call)}") self.Log(f"Order Quantity filled: {self.Portfolio[self.call.Symbol].Quantity}; Fill price: {self.Portfolio[self.call.Symbol].AveragePrice} Limit price: {self.call.AskPrice * self.Profit_Taking}") self.LastTime = self.Time.day self.Log ("\r+-------------") else: self.Log(f"| {self.Time} -- No Money") def OnOrderEvent(self, orderEvent): order = self.Transactions.GetOrderById(orderEvent.OrderId) # Cancel remaining order if limit order or stop loss order is executed if order.Status == OrderStatus.Filled: if order.Type == OrderType.Limit or OrderType.StopMarket: self.Transactions.CancelOpenOrders(order.Symbol) if order.Status == OrderStatus.Filled and order.Type == OrderType.Limit: self.Log(f"{self.Time} [+]--- SELL {str(order.Quantity)} || Price @ {str(order.LimitPrice)}|| Contract @ {str(order.Symbol)}") if order.Status == OrderStatus.Canceled: self.Log(f"| {self.Time} -- Order Cancelled") # Liquidate before options are exercised if order.Type == OrderType.OptionExercise: self.Liquidate() def BlackScholesCall(self, S, K, T, r, sigma): d1 = (log(S / K) + (r + sigma ** 2 / 2) * T) / (sigma * sqrt(T)) d2 = d1 - sigma * sqrt(T) return S * norm.cdf(d1) - K * exp(-r * T) * norm.cdf(d2) def Plotting(self): hist = self.History(self.equity, 2, Resolution.Daily)['close'].unstack(level= 0).dropna() self.mkt.append(hist[self.equity].iloc[-1]) spy_perf = self.mkt[-1] / self.mkt[0] * self.Balance self.Plot('Strategy Equity', 'SPY', spy_perf)