jwsmithers

def normalise_df(df,asset,norm_window_size=100,use_preloaded_scalers=False):
    """
    Normalise the dataframe.
    This normalises by column (i.e. feature). Some people call this scaling
    
    Args:
      df(pd.df): The dataframe to normalise
      asset (str): Stock name we're trying to predict (or first stock name)
      norm_window_size (int): The window size for each feature to normalise.
      use_preloaded_scalers (bool): False for training, True for inference

jwsmithers

Parameters	Description	Value
LSTM layers	-	2
LSTM Nodes	-	33
Dense Nodes	-	95
Dense Activation	-	selu
Normalisation window	-	1000
Batch size	Number of sequence in a batch	150
Sequence length	Number of previous points to take into account	22
News lag	Number of days prior to current day to take news into account	1

jwsmithers

from ib_insync import *
import asyncio
import nest_asyncio

async def update_from_IB(stock):
  """
  Asynchronous call example to update stocks from Interactive broker. 
  
  Args:
    stock(str): Stock, I.e. AAPL