stdb.py

"""
This module provides functionality for performing Automatic data updating tasks.
"""
import threading
import time
import pandas as pd
from stls import Stocks
from levels import eqt
from datetime import datetime
from pymongo import MongoClient
from datetime import datetime, timedelta
import pytz
import os

tz = pytz.timezone('Asia/Kolkata')
mongo_url = os.environ['MongoURL']

def UpdatedCollectionName():
  current_time = datetime.now(tz)
  collection_name = current_time.strftime('%Y-%m-%d')
  if current_time.time() >= datetime.strptime('15:30', '%H:%M').time():
      collection_name = (current_time + timedelta(days=1)).strftime('%Y-%m-%d')
      return collection_name
  else:
    return collection_name
  
import concurrent.futures
from stls import fetch_stock_data

def get_live_price(symbol):
    return fetch_stock_data(symbol.replace('.NS',''), interval=15, days=1).iloc[-1][['High', 'Close']].values

def status(row):
    if row['LTP'] > row['High'] or row['High_T'] > row['High']:
        return "Active"
    else:
        return "Pending"

def get_live_prices(df):
    print("it's live")
    symbols = df['Symbol'].tolist()
    with concurrent.futures.ThreadPoolExecutor() as executor:
        prices = list(executor.map(get_live_price, symbols))
        df[['High_T', 'LTP']] = prices
        df['Status'] = df.apply(status, axis=1)
    return df

class DataManager:
  """
  This is a DataManager class that demonstrates its functionality.
  """
  def __init__(self):
      self.stocks = None
      self.equity = None
      self.data_thread = threading.Thread(target=self.update_data)
      self.data_thread.daemon = True
      self.data_thread.start()

  def update_data(self):
    while True:
      client = MongoClient(mongo_url)
      db = client['mydatabase']
      collection_name = UpdatedCollectionName()
      if collection_name in db.list_collection_names():
        collection = db[collection_name]
        cursor = collection.find({})
        stocks = pd.DataFrame(list(cursor))
        stocks.drop('_id', axis=1, inplace=True)
        self.stocks = stocks           
      else: 
        stocks = Stocks()
        collection = db[collection_name]
        cursor = collection.find({})
        stocks = pd.DataFrame(list(cursor))
        stocks.drop('_id', axis=1, inplace=True) 
        self.stocks = stocks 
      time.sleep(120)
      
  def get_stocks_data(self):
    stocks = get_live_prices(self.stocks)
    self.results = stocks.to_dict(orient="records") 
    return self.results
  
  def get_equity_data(self, ticker, startdate, share_qty):
    self.equity = eqt(ticker, startdate, share_qty)
    return self.equity