main.py

from datetime import datetime, date, timezone
import numpy as np
import pandas as pd
import sqlite3

def get_stock_from_db(ticker, interval, limit=-1):
	# Translate between local SQL db and expected formatting:
	pwd = os.getcwd()
	name = ticker.lower()
	con = sqlite3.connect(pwd + "/db/db_" + name + ".db")
	cur = con.cursor()

	ohlc_data = []
  
  # Create an SQL command to get data
	query = "SELECT * FROM " + interval.lower() + " ORDER BY date DESC"
	if limit > 0:
		query += " LIMIT " + str(limit)
	else:
		query += " LIMIT 4000"
  
  # Grab data in rows
	for row in cur.execute(query):
		ohlc_data.append(row)
	
	# Fix inverse sorting - ie making the earliest entry index 0
	ohlc_data.sort(key=lambda tup: tup[0])

	dt = {"date":[], "Open":[], "High":[], "Low":[], "Close":[], "sma":[]}
	
	if interval == "m1":
		for item in ohlc_data:
			dt["date"].append(datetime.utcfromtimestamp(int(item[0])).strftime('%H:%M:%S %d/%m/%y'))
			dt["Open"].append(float(item[1]))
			dt["High"].append(float(item[1]))
			dt["Low"].append(float(item[1]))
			dt["Close"].append(float(item[1]))
			dt["sma"].append(float(item[1]))
	else:
		for item in ohlc_data:
			dt["date"].append(datetime.utcfromtimestamp(int(item[0])).strftime('%H:%M:%S %d/%m/%y'))
			dt["Open"].append(float(item[1]))
			dt["High"].append(float(item[2]))
			dt["Low"].append(float(item[3]))
			dt["Close"].append(float(item[4]))
			dt["sma"].append(float(item[4]))
	return dt

your_stock_dataframe = get_stock_from_db("sym", "d1" 30)

print(your_stock_dataframe)