Is it possible to get monthly historical stock prices in python?
12,614
Solution 1
Monthly closing prices from Yahoo! Finance...
import pandas_datareader.data as web
data = web.get_data_yahoo('IBM','01/01/2015',interval='m')
where you can replace the interval input as required ('d', 'w', 'm', etc).
Solution 2
try this:
In [175]: from pandas_datareader.data import DataReader
In [176]: ibm = DataReader('IBM', 'yahoo', '2001-01-01', '2012-01-01')
UPDATE: show average for Adj Close only (month start)
In [12]: ibm.groupby(pd.TimeGrouper(freq='MS'))['Adj Close'].mean()
Out[12]:
Date
2001-01-01 79.430605
2001-02-01 86.625519
2001-03-01 75.938913
2001-04-01 81.134375
2001-05-01 90.460754
2001-06-01 89.705042
2001-07-01 83.350254
2001-08-01 82.100543
2001-09-01 74.335789
2001-10-01 79.937451
...
2011-03-01 141.628553
2011-04-01 146.530774
2011-05-01 150.298053
2011-06-01 146.844772
2011-07-01 158.716834
2011-08-01 150.690990
2011-09-01 151.627555
2011-10-01 162.365699
2011-11-01 164.596963
2011-12-01 167.924676
Freq: MS, Name: Adj Close, dtype: float64
show average for Adj Close only (month end)
In [13]: ibm.groupby(pd.TimeGrouper(freq='M'))['Adj Close'].mean()
Out[13]:
Date
2001-01-31 79.430605
2001-02-28 86.625519
2001-03-31 75.938913
2001-04-30 81.134375
2001-05-31 90.460754
2001-06-30 89.705042
2001-07-31 83.350254
2001-08-31 82.100543
2001-09-30 74.335789
2001-10-31 79.937451
...
2011-03-31 141.628553
2011-04-30 146.530774
2011-05-31 150.298053
2011-06-30 146.844772
2011-07-31 158.716834
2011-08-31 150.690990
2011-09-30 151.627555
2011-10-31 162.365699
2011-11-30 164.596963
2011-12-31 167.924676
Freq: M, Name: Adj Close, dtype: float64
monthly averages (all columns):
In [179]: ibm.groupby(pd.TimeGrouper(freq='M')).mean()
Out[179]:
Open High Low Close Volume Adj Close
Date
2001-01-31 100.767857 103.553571 99.428333 101.870357 9474409 79.430605
2001-02-28 111.193160 113.304210 108.967368 110.998422 8233626 86.625519
2001-03-31 97.366364 99.423637 95.252272 97.281364 11570454 75.938913
2001-04-30 103.990500 106.112500 102.229501 103.936999 11310545 81.134375
2001-05-31 115.781363 117.104091 114.349091 115.776364 7243463 90.460754
2001-06-30 114.689524 116.199048 113.739523 114.777618 6806176 89.705042
2001-07-31 106.717143 108.028095 105.332857 106.646666 7667447 83.350254
2001-08-31 105.093912 106.196521 103.856522 104.939999 6234847 82.100543
2001-09-30 95.138667 96.740000 93.471334 94.987333 12620833 74.335789
2001-10-31 101.400870 103.140000 100.327827 102.145217 9754413 79.937451
2001-11-30 113.449047 114.875715 112.510952 113.938095 6435061 89.256046
2001-12-31 120.651001 122.076000 119.790500 121.087999 6669690 94.878736
2002-01-31 116.483334 117.509524 114.613334 115.994762 9217280 90.887920
2002-02-28 103.194210 104.389474 101.646316 102.961579 9069526 80.764672
2002-03-31 105.246500 106.764499 104.312999 105.478499 7563425 82.756873
... ... ... ... ... ... ...
2010-10-31 138.956188 140.259048 138.427142 139.631905 6537366 122.241844
2010-11-30 144.281429 145.164762 143.385241 144.439524 4956985 126.878319
2010-12-31 145.155909 145.959545 144.567273 145.251819 4245127 127.726929
2011-01-31 152.595000 153.950499 151.861000 153.181501 5941580 134.699880
2011-02-28 163.217895 164.089474 162.510002 163.339473 4687763 144.050847
2011-03-31 160.433912 161.745652 159.154349 160.425651 5639752 141.628553
2011-04-30 165.437501 166.587500 164.760500 165.978500 5038475 146.530774
2011-05-31 169.657144 170.679046 168.442858 169.632857 5276390 150.298053
2011-06-30 165.450455 166.559093 164.691819 165.593635 4792836 146.844772
2011-07-31 178.124998 179.866502 177.574998 178.981500 5679660 158.716834
2011-08-31 169.734350 171.690435 166.749567 169.360434 8480613 150.690990
2011-09-30 169.752858 172.034761 168.109999 170.245714 6566428 151.627555
2011-10-31 181.529525 183.597145 180.172379 182.302381 6883985 162.365699
2011-11-30 184.536668 185.950952 182.780477 184.244287 4619719 164.596963
2011-12-31 188.151428 189.373809 186.421905 187.789047 4925547 167.924676
[132 rows x 6 columns]
weekly averages (all columns):
In [180]: ibm.groupby(pd.TimeGrouper(freq='W')).mean()
Out[180]:
Open High Low Close Volume Adj Close
Date
2001-01-07 89.234375 94.234375 87.890625 91.656250 11060200 71.466436
2001-01-14 93.412500 95.062500 91.662500 93.412500 7470200 72.835824
2001-01-21 100.250000 103.921875 99.218750 102.250000 13851500 79.726621
2001-01-28 109.575000 111.537500 108.675000 110.600000 8056720 86.237303
2001-02-04 113.680000 115.465999 111.734000 113.582001 6538080 88.562436
2001-02-11 113.194002 115.815999 111.639999 113.884001 7269320 88.858876
2001-02-18 113.960002 116.731999 113.238000 115.106000 7225420 89.853021
2001-02-25 109.525002 111.375000 105.424999 107.977501 10722700 84.288436
2001-03-04 103.390001 106.052002 100.386000 103.228001 11982540 80.580924
2001-03-11 105.735999 106.920000 103.364002 104.844002 9226900 81.842391
2001-03-18 95.660001 97.502002 93.185997 94.899998 13863740 74.079992
2001-03-25 90.734000 92.484000 88.598000 90.518001 11382280 70.659356
2001-04-01 95.622000 97.748000 94.274000 96.106001 10467580 75.021411
2001-04-08 95.259999 97.360001 93.132001 94.642000 12312580 73.878595
2001-04-15 98.350000 99.520000 95.327502 97.170000 10218625 75.851980
... ... ... ... ... ... ...
2011-09-25 170.678003 173.695996 169.401996 171.766000 6358100 152.981582
2011-10-02 176.290002 178.850000 174.729999 176.762000 7373680 157.431216
2011-10-09 175.920001 179.200003 174.379999 177.792001 7623560 158.348576
2011-10-16 185.366000 187.732001 184.977997 187.017999 5244180 166.565614
2011-10-23 180.926001 182.052002 178.815997 180.351999 9359200 160.628611
2011-10-30 183.094003 184.742001 181.623996 183.582001 5743800 163.505379
2011-11-06 184.508002 186.067999 183.432004 184.716003 4583780 164.515366
2011-11-13 185.350000 186.690002 183.685999 185.508005 4180620 165.750791
2011-11-20 187.600003 189.101999 185.368002 186.738000 5104420 166.984809
2011-11-27 181.067497 181.997501 178.717499 179.449997 4089350 160.467733
2011-12-04 185.246002 187.182001 184.388000 186.052002 5168720 166.371376
2011-12-11 191.841998 194.141998 191.090002 192.794000 4828580 172.400204
2011-12-18 191.085999 191.537998 187.732001 188.619998 6037220 168.667729
2011-12-25 183.810001 184.634003 181.787997 183.678000 5433360 164.248496
2012-01-01 185.140003 185.989998 183.897499 184.750000 3029925 165.207100
[574 rows x 6 columns]
Solution 3
Get it from Quandl:
import pandas as pd
import quandl
quandl.ApiConfig.api_key = 'xxxxxxxxxxxx' # Optional
quandl.ApiConfig.api_version = '2015-04-09' # Optional
ibm = quandl.get("WIKI/IBM", start_date="2000-01-01", end_date="2012-01-01", collapse="monthly", returns="pandas")
Solution 4
Using Yahoo Finance, it is possible to get Stock Prices using "interval" option with instead of "m" as shown:
#Library
import yfinance as yf
from datetime import datetime
#Load Stock price
df = yf.download("IBM", start= datetime(2000,1,1), end = datetime(2012,1,1),interval='1mo')
df
The result is:
The other possible interval options are:
- 1m,
- 2m,
- 5m,
- 15m,
- 30m,
- 60m,
- 90m,
- 1h,
- 1d,
- 5d,
- 1wk,
- 1mo,
- 3mo.
Author by
Jeffrey Derose
Updated on June 28, 2022Comments
-
Jeffrey Derose almost 2 years
I know using pandas this is how you normally get daily stock price quotes. But I'm wondering if its possible to get monthly or weekly quotes, is there maybe a parameter I can pass through to get monthly quotes?
from pandas.io.data import DataReader from datetime import datetime ibm = DataReader('IBM', 'yahoo', datetime(2000,1,1), datetime(2012,1,1)) print(ibm['Adj Close'])