| Overall Statistics |
|
Total Trades 94 Average Win 6.37% Average Loss -5.70% Compounding Annual Return -99.237% Drawdown 61.200% Expectancy -0.189 Net Profit -47.800% Sharpe Ratio -0.598 Probabilistic Sharpe Ratio 13.274% Loss Rate 62% Win Rate 38% Profit-Loss Ratio 1.12 Alpha 0 Beta 0 Annual Standard Deviation 1.524 Annual Variance 2.324 Information Ratio -0.598 Tracking Error 1.524 Treynor Ratio 0 Total Fees $94.00 Estimated Strategy Capacity $0 Lowest Capacity Asset AMZN Y3LLM9TN0Q3Q|AMZN R735QTJ8XC9X |
from AlgorithmImports import *
from datetime import datetime as date
class MACD(QCAlgorithm):
def Initialize(self):
self.SetStartDate(2022, 9, 1)
self.SetCash(500)
self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin)
self.LastTime = None
self.entryTicket = None
self.macd = dict()
self.symbols = []
symbols = ["AAPL","MSFT","GOOGL","AMZN"]
for ticker in symbols:
symbol = self.AddEquity(ticker, Resolution.Minute).Symbol
option = self.AddOption(symbol, Resolution.Minute)
self.symbols.append(option.Symbol)
option.SetFilter(-100, 100, timedelta(41), timedelta(60))
self.macd[symbol] = self.MACD(ticker, 12, 26, 9, MovingAverageType.Simple, Resolution.Minute)
self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.At(15, 45), self.Liquidate)
def OnData(self, data):
if not all([macd.IsReady for symbol, macd in self.macd.items()]):
return
if self.Portfolio.Invested:
return
for symbol, macd in self.macd.items():
if macd.Current.Value < -0.5:
for symbol in self.symbols:
for kvp in data.OptionChains:
if kvp.Key == symbol:
chains = kvp.Value
self.BuyCall(chains)
def BuyCall(self,chains):
expiry = sorted(chains,key = lambda x: x.Expiry, reverse=False)[0].Expiry
calls = [kvp for kvp in chains if
kvp.Expiry == expiry
and kvp.Right == OptionRight.Call
and kvp.AskPrice > 1]
call_contracts = sorted(sorted(calls,
key = lambda x: abs(x.Strike - x.UnderlyingLastPrice)),\
key = lambda x: x.AskPrice, reverse=False)
if len(call_contracts) == 0:
return
self.call = call_contracts[0]
quantity = math.ceil((0.1 * self.Portfolio.TotalPortfolioValue) / (self.call.AskPrice*100))
self.entry_ticket = self.LimitOrder(self.call.Symbol, quantity, round(self.call.AskPrice,2))