In [41]:

import matplotlib.pyplot as plt
import pandas as pd
import datetime

In [43]:

# download market data from Yahoo finance
# !pip install yfinance
import yfinance as yf

yfinance

https://www.learndatasci.com/tutorials/python-finance-part-yahoo-finance-api-pandas-matplotlib/

In [44]:

# Ticker Symbols
investments_list = ['BTC-USD']

# Specify the start and end dates
start = datetime.datetime(2010,1,1)
end = datetime.datetime(2021, 5, 16)

# Download the data from Yahoo Finance, make sure to reset index!
df = yf.download(investments_list, start = start, end = end)
df

[*********************100%***********************]  1 of 1 completed

Out[44]:

	Open	High	Low	Close	Adj Close	Volume
Date
2014-09-17 00:00:00+00:00	465.864014	468.174011	452.421997	457.334015	457.334015	21056800
2014-09-18 00:00:00+00:00	456.859985	456.859985	413.104004	424.440002	424.440002	34483200
2014-09-19 00:00:00+00:00	424.102997	427.834991	384.532013	394.795990	394.795990	37919700
2014-09-20 00:00:00+00:00	394.673004	423.295990	389.882996	408.903992	408.903992	36863600
2014-09-21 00:00:00+00:00	408.084991	412.425995	393.181000	398.821014	398.821014	26580100
...	...	...	...	...	...	...
2021-05-11 00:00:00+00:00	55847.242188	56872.542969	54608.652344	56704.574219	56704.574219	61308396325
2021-05-12 00:00:00+00:00	56714.531250	57939.363281	49150.535156	49150.535156	49150.535156	75215403907
2021-05-13 00:00:00+00:00	49735.433594	51330.843750	46980.019531	49716.191406	49716.191406	96721152926
2021-05-14 00:00:00+00:00	49682.980469	51438.117188	48868.578125	49880.535156	49880.535156	55737497453
2021-05-15 00:00:00+00:00	49855.496094	50639.664062	46664.140625	46760.187500	46760.187500	59161047474

2433 rows × 6 columns

cf) apple¶

https://www.analyticsvidhya.com/blog/2021/06/download-financial-dataset-using-yahoo-finance-in-python-a-complete-guide/

In [46]:

# !pip install yahoofinancials
from yahoofinancials import YahooFinancials

In [47]:

yahoo_financials = YahooFinancials('AAPL')
data = yahoo_financials.get_historical_price_data(start_date='2019-01-01', 
                                                  end_date='2019-12-31', 
                                                  time_interval='weekly')
aapl_df = pd.DataFrame(data['AAPL']['prices'])
aapl_df = aapl_df.drop('date', axis=1).set_index('formatted_date')
aapl_df.head()

Out[47]:

	high	low	open	close	volume	adjclose
formatted_date
2019-01-01	39.712502	35.500000	38.722500	36.982498	966947200	35.694611
2019-01-08	38.632500	37.130001	37.389999	37.500000	725470000	36.194096
2019-01-15	39.470001	37.512501	37.567501	39.205002	491411200	37.839706
2019-01-22	39.532501	37.924999	39.102501	39.075001	554774800	37.714245
2019-01-29	42.915001	38.527500	39.062500	42.812500	830400800	41.321583

In [48]:

# reset index 
df.reset_index(inplace = True)
df

Out[48]:

	Date	Open	High	Low	Close	Adj Close	Volume
0	2014-09-17 00:00:00+00:00	465.864014	468.174011	452.421997	457.334015	457.334015	21056800
1	2014-09-18 00:00:00+00:00	456.859985	456.859985	413.104004	424.440002	424.440002	34483200
2	2014-09-19 00:00:00+00:00	424.102997	427.834991	384.532013	394.795990	394.795990	37919700
3	2014-09-20 00:00:00+00:00	394.673004	423.295990	389.882996	408.903992	408.903992	36863600
4	2014-09-21 00:00:00+00:00	408.084991	412.425995	393.181000	398.821014	398.821014	26580100
...	...	...	...	...	...	...	...
2428	2021-05-11 00:00:00+00:00	55847.242188	56872.542969	54608.652344	56704.574219	56704.574219	61308396325
2429	2021-05-12 00:00:00+00:00	56714.531250	57939.363281	49150.535156	49150.535156	49150.535156	75215403907
2430	2021-05-13 00:00:00+00:00	49735.433594	51330.843750	46980.019531	49716.191406	49716.191406	96721152926
2431	2021-05-14 00:00:00+00:00	49682.980469	51438.117188	48868.578125	49880.535156	49880.535156	55737497453
2432	2021-05-15 00:00:00+00:00	49855.496094	50639.664062	46664.140625	46760.187500	46760.187500	59161047474

2433 rows × 7 columns

[matplotlib]S5_02_yahoo_finance

cf) apple¶