from FinancialData import financial_data iex = financial_data() print(iex.get_trade_bars(['AAPL'], '1m')[:10]) print(iex.get_news(['AAPL', 'MSFT'])[['symbol','time','headline']][:10])

# list(iex.all_ticker()) : list off all valid securities' symbols # This step could take a while, considering that this API provides 8750 security's related news df = iex.get_news(list(iex.all_ticker())) df.to_excel("assets/news.xlsx")

##### IOdy.py : ###### import pandas as pd from datetime import datetime, timedelta from pandas_datareader import data import numpy as np news = pd.read_excel('assets/news.xlsx', index_col=0) #remove news that are published earlier than 30 days from today absolute_thresh = pd.Timestamp(datetime.today().date()-timedelta(days=37)) news = pd.read_excel('assets/news.xlsx', index_col=0) news = news.loc[news['time']>=absolute_thresh] news[['symbol', 'time', 'headline', 'summary']].head(2)

# This function uses Yahoo Finance to market data def get_market(security, start_date, end_date): end_date_str = end_date.strftime("%Y-%m-%d") start_date_str = start_date.strftime("%Y-%m-%d") asset_ = data.DataReader(security, start=start_date_str, end=end_date_str, data_source='yahoo') return asset_ get_market('AAPL', intervals_[0][0], intervals_[0][1])