I'm getting the error below when running the code below. I've been trying many things to try and format the ‘stop_loss’ and ‘take_profit’ variables, but I can't seem to fix it.

  1. #region imports
  2. from AlgorithmImports import *
  3. #endregion
  5. import numpy as np
  6. import pandas as pd
  8. class GapAndGoAlgorithm(QCAlgorithm):
  10.     def Initialize(self):
  11.         self.SetStartDate(2023, 1, 1)  # Set start date
  12.         self.SetEndDate(2023, 1, 31)  # Set end date
  13.         self.SetCash(100000)  # Set strategy cash
  15.         # Add SPY to the universe and request daily data for it
  16.         self.AddEquity("SPY", Resolution.Daily)
  17.         
  18.         # Use universe selection to select a universe of stocks that are gapping up
  19.         self.UniverseSettings.Resolution = Resolution.Daily
  20.         self.AddUniverse(self.CoarseSelectionFunction, self.FineSelectionFunction)
  21.         self.SelectionCount = 10
  22.         
  23.         self.SetBrokerageModel(BrokerageName.InteractiveBrokersBrokerage, AccountType.Margin)  # Set brokerage model
  24.         
  25.         self.Schedule.On(self.DateRules.EveryDay(), self.TimeRules.AfterMarketOpen("SPY", 30), self.GapAndGo)  # Schedule GapAndGo function to run after market open
  27.     def CoarseSelectionFunction(self, coarse):
  28.         # Select only stocks with a price above $5 and a daily dollar volume above $10,000,000
  29.         selected = [x.Symbol for x in coarse if x.Price > 5 and x.DollarVolume > 10000000]
  30.         return selected
  31.     
  32.     def FineSelectionFunction(self, fine):
  33.         # Select stocks that gapped up at least 1% and have positive earnings and revenue growth
  34.         fine = [x for x in fine if hasattr(x.EarningReports, 'BasicEPSGrowth') and x.EarningReports.BasicEPSGrowth.OneYear > 0 and x.FinancialStatements.RevenueGrowth.OneYear > 0]
  35.         fine = [x for x in fine if (x.Open - x.Close[-2])/x.Close[-2] > 0.01]
  36.         fine = sorted(fine, key=lambda x: (x.Volume, x.Price), reverse=True)[:self.SelectionCount]
  37.         return [x.Symbol for x in fine]
  39.     def GapAndGo(self):
  40.         # Loop over all securities in the portfolio
  41.         for security in self.Portfolio.Values:
  42.             # Check if the security is currently tradable
  43.             if not self.Securities[security.Symbol].IsTradable:
  44.                 continue
  46.             # Get daily historical data for the last 2 days
  47.             history = self.History([security.Symbol], 2, Resolution.Daily)
  49.             # Get opening and closing prices for the last 2 days
  50.             opens = history.loc[security.Symbol].open.values
  51.             closes = history.loc[security.Symbol].close.values
  53.             # Check if the open price today is greater than the close price of yesterday
  54.             if opens[-1] > closes[-2]:
  55.                 # Calculate the percentage change between the two days
  56.                 pct_change = (closes[-1] - closes[-2]) / closes[-2]
  58.                 # If the percentage change is greater than 1% (i.e., a gap), execute a trade
  59.                 if pct_change > 0.01:
  60.                     # Calculate the stop loss and take profit levels based on the current price
  61.                     current_price = self.Securities[security.Symbol].Price
  62.                     stop_loss = np.array([current_price * 0.98]).item()
  63.                     take_profit = np.array([current_price * 1.02]).item()
  65.                     # Place the market order with stop loss and take profit levels
  66.                     self.MarketOrder(security.Symbol, 100, False, None, stop_loss, take_profit)
+ Expand

Runtime Error: Trying to dynamically access a method that does not exist throws a TypeError exception. To prevent the exception, ensure each parameter type matches those required by the 'float'>) method. Please checkout the API documentation.
  at GapAndGo
    self.MarketOrder(security.Symbol in main.py: line 65

Author

Michael Kvalvik

February 2023