Ist es möglich, in Python monatliche historische Aktienkurse zu erhalten?

Question

Ich weiß, mit Pandas, wie Sie normalerweise tägliche Aktienkurse erhalten. Aber ich frage mich, ob es möglich ist, monatliche oder wöchentliche Notierungen zu erhalten, gibt es vielleicht einen Parameter, den ich durchgehen kann, um monatliche Kurse zu erhalten?

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']) 

Answer 1

versuchen Sie dies:

In [175]: from pandas_datareader.data import DataReader 

In [176]: ibm = DataReader('IBM', 'yahoo', '2001-01-01', '2012-01-01') 

UPDATE: Show Durchschnitt Adj Close nur (Monat 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 Durchschnitt Adj Schließen nur (Monatsende)

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 

Monatsmittelwerte (alle Spalten):

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] 

Wochendurchschnitt (alle Spalten):

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] 

Answer 2

es von Quandl Get:

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") 

Answer 3

Monatsschlusskurse von Yahoo! Finanzen ...

import pandas_datareader.data as web 

data = web.get_data_yahoo('IBM','01/01/2015',interval='m') 

wo Sie das Intervall Eingabe nach Bedarf ersetzen ('d', 'w', 'm' usw.).