investpy | Financial Data Extraction from Investing.com with Python | Data Visualization library

import investpy as inv
import matplotlib.pyplot as plt
import pandas as pd

TICKER = "Nasdaq 100"
COUNTRY = "United States"
FROM_DATE = "01/03/2022"
TO_DATE = "07/03/2022"

historical_data = inv.indices.get_index_historical_data(index=TICK

import investpy

df = investpy.stocks.search_stocks(by='isin', value='US0126531013')
print(df)
#          country       name              full_name          isin currency symbol
# 0         mexico  Albemarle         Albemarle Corp  US01265

pip install investpy

import investpy

data = investpy.economic_calendar(
    from_date='12/09/2021',
    to_date  ='13/09/2021'
)
print(data.head())

       id        date   time            

import requests
from bs4 import BeautifulSoup
import pandas as pd
#import investpy
#from pandas import Timestamp
#import json
#from pandas.io.json import json_normalize
import time
import concurrent.futures
from functools import partial

d

import investpy
import sys
stocks_list = ['JFC','AAPL',....] # your stock lists
for stock in stocks_list:
   df = investpy.get_stock_historical_data(stock=stock,
                                        country='philippines',
              

df = investpy.get_index_historical_data(index="Nifty 50",
                                        country="India",
                                        from_date='01/01/2018',
                                        to_date='01/01/2019'

import investpy
data = investpy.bonds.get_bond_historical_data(bond='South Africa 2Y',
                                               from_date='01/01/2019',
                                               to_date='31/12/2019')
data[:10]

X=BTC_cleanData[-1:] # this has one more column compared to X_train and X_test
print(regressor.predict(X))

import pandas as pd
import numpy as np
import talib
import matplotlib.pyplot as plt
%matplotlib inline
impo

import investpy as env
import numpy as np
import pandas as pd

lt = ['ABEV3','CEAB3','ENBR3','FLRY3','IRBR3','ITSA4','JHSF3','STBP3']
prices = pd.DataFrame()
for i in lt:
    df = env.get_stock_historical_data(stock=i, from_date='01/01/201