HAN's BLOG

Download financial news of yahoo with python

27 Jun 2021

Financial news

It is so important statemennts for study stock.

I’m simply check only headline and summary.

It is expected to study natural language processing (NLP) to future :)

Class code


# class_Strategy.py
# import yahoo_fin import news as ynews

class NLPStrategy:
    def __init__(self, url, etfname, Offline = False):
        self.url = url
        self.etfname = etfname
        self.Offline = Offline

    def get_news_title(self, ticker = 'AAPL'):
        if self.Offline == True:
            url_news = self.url+'/data_origin/FS_'+self.etfname+'_title.json'
            with open (url_news, 'r') as f:
                title = json.load(f)
        else:
            title = []
            news = ynews.get_yf_rss(ticker)
            for k,v in enumerate(news):
                title.append(v['title'])

        return title

    def get_news_summary(self, ticker = 'AAPL'):
        if self.Offline == True:
            url_news = self.url+'/data_origin/FS_'+self.etfname+'_summary.json'
            with open (url_news, 'r') as f:
                title = json.load(f)

        else:
            summary = []
            news = ynews.get_yf_rss(ticker)
            for k,v in enumerate(news):
                summary.append(v['summary'])

            return summary

yahoo_fin API is very useful for a lot of sotck’s information. If you are interesting this api please check and use page

I choose data list divided online and offline.

  • Online test can download only one ticker and then gather using loop in main code.
  • Offline test can download index stock after pre-download below code for choice.

Choice Ticker

import json
import pandas as pd
import yahoo_fin.stock_info as yfs
from yahoo_fin import news as ynews

from tqdm import tqdm

def main(stock_list):

    etf_list = yfs.tickers_dow()
    if stock_list == 'dow':
        etf_list = yfs.tickers_dow()
        filename = 'dow'
    elif stock_list == 'sp500':
        filename = 'sp500'
        etf_list = yfs.tickers_sp500()
    elif stock_list == 'nasdaq':
        filename = 'nasdaq'
        etf_list = yfs.tickers_nasdaq()
    elif stock_list == 'other':
        filename = 'other'
        etf_list = yfs.tickers_other()
    elif stock_list == 'selected':
        filename = 'selected'
        url = '/Users/hanseopark/Work/stock/data_ForTrading/selected_ticker.json'
        temp_pd = pd.read_json(url)
        temp_pd = temp_pd['Ticker']
        etf_list = temp_pd.values.tolist()

    print(etf_list)

    error_symbols = []

    total_news_title = {}
    total_news_summary = {}

    for ticker in tqdm(etf_list):
        list_title = []
        list_summary = []
        try:
            list_news= ynews.get_yf_rss(ticker)
            for k,v in enumerate(list_news):
                list_title.append(v['title'])
                list_summary.append(v['summary'])

        except:
            error_symbols.append(ticker)
        total_news_title[ticker] = list_title
        total_news_summary[ticker] = list_title

    #print(total_news_title)
    #print(total_news_summary)
    print(error_symbols)

    url_title = '/Users/hanseopark/Work/stock/data_origin/FS_{0}_title.json'.format(filename)
    url_summary = '/Users/hanseopark/Work/stock/data_origin/FS_{0}_summary.json'.format(filename)
    with open (url_title, 'w') as f:
        json.dump(total_news_title, f)

    with open (url_summary, 'w') as f:
        json.dump(total_news_summary, f)

    ## temp reading
#    with open (url, 'r') as f:
#        temp = json.load(f)
#    print(temp)

if __name__ == '__main__':
    s= input("Choice of stock's list (dow, sp500, nasdaq, other, selected): ")

    print("News' title and summary of stcok in {0} at yahoo finance".format(s))
    main(stock_list=s)

This code only needs for offilne test.

Conclusion

Let us know how to download fincancial news.

I have been steuding to make model for stock using many startegy curruntly.

If you want to use this code, I’m very sorry that you should change code and make directory for data a little bit.

I should appreciate and refer for many blog on google. Thanks a lot.

If you satisfied this post you should check Github and please Star :)

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