An algorithm that fetches premarket volume data

AI & Machine learning
1

Hello there:



As many of you… I am taking the course on machine learning: linear regression …  and I have an hypotesis…

I think that one of the input variables for a linear regression for stimating the prices of the next day… is the premarket volume… Now I want to fetch the premarket volume for a specific stock… from an initial dato to an end date… so I came up with the following code on ibreidgepy using  data from interactive brokers in order to do so:



""""""

import sys

import numpy as np

import pandas as pd

import datetime as dt

from datetime import datetime

from datetime import date

from datetime import timedelta

import pytz

import time

import os

from time import sleep



import warnings

warnings.filterwarnings("ignore")



def convertir_fecha(fecha):

    fecha_dt = pd.to_datetime(fecha, format='%y-%m-%d')

    ny_tz = pytz.timezone('America/New_York')

    fecha_dt_ny = ny_tz.localize(fecha_dt.replace(hour=9, minute=30))

    return fecha_dt_ny

 



def initialize(context):

    pass

    

def handle_data(context, data):

    # specify the ticker

    tickers = ['TMPO']

    



    for i in tickers:

        

        stock = superSymbol(secType='STK', symbol= 'TMPO', currency='USD', exchange='SMART')

           

        df1 = request_historical_data(stock, '1 day', '60 D',  datetime.now(pytz.timezone('America/New_York')) ,useRTH=1)

        del df1['index']

        df1.reset_index(inplace=True)

        df1.rename(columns={'index': 'index_column'}, inplace=True)

            

        df1['timestamp']= pd.to_datetime(df1['timestamp'])

        df1['timestamp']= df1['timestamp'].dt.date

        df1['timestamp'] = pd.to_datetime(df1['timestamp']) + pd.Timedelta(days=1)

        dates = df1['timestamp']

        dates = dates.apply(convertir_fecha)

        lenght = len(dates)

        pre_vol =

 

        

        for s in dates :

            data = request_historical_data(stock, '30 mins', '19800 S', s ,useRTH=0)

            premarket_vol = data['volume'].sum()

            pre_vol.append(premarket_vol)

        

        df1 = df1.assign(PreMKT_Volume = pre_vol)    

        

        print (df1)

        

    end()

""""""



This code actually fetches the premarket volume for a specific stock…  for the last 60 days… and stores it in an aditional column for the ohlcv data



What do you think about this code ???   

Is it good ??? is it bad ???..  How can I make it better…??



I would like to know your comments on order to  refine the code…  and correct it if it has anny errors… so please  tell me



Thanks

2

Hi Ghery,



The code appears to be functional for fetching premarket volume data. However, there are some potential areas for improvement:

  1. Data Error Handling: The code fetches premarket volume data using a loop that iterates over a list of dates. However, if there is missing or incomplete data for any of the dates, it may result in errors or incomplete results. It would be advisable to add error handling and data validation to handle such cases.
  2. Code Organization: The code currently includes all the logic in a single function handle_data(). It would be beneficial to consider organizing the code into separate functions or classes for better modularity and maintainability. For example, separate functions for fetching historical data, converting dates to the desired format, and calculating premarket volume could make the code more modular and easier to understand.
  3. Code Readability: It would be beneficial to include comments and documentation to explain the purpose and functionality of the code for better understanding.
  4. Testing and Debugging: It would be beneficial to thoroughly test the code with different inputs and scenarios to identify and fix any potential issues. Adding debug statements or logging can also help with troubleshooting and debugging in case of errors or unexpected behaviour.
Hope this helps!

Thanks,
Akshay
3

ok, Thanks a lot…