Yahoo Finance¶
Getting historic data¶
The module is usually imported as follows:
In [1]: from tradingWithPython import yahooFinance as yf
Singe symbol¶
Then, to get raw yahoo finance data for a symbol use getSymbolData()
In [2]: df = yf.getSymbolData("SPY")
Got 4510 days of data
In [3]: df.head()
Out[3]:
open high low close adj_close volume
Date
1999-12-31 146.84 147.50 146.25 146.88 105.37 3172700
2000-01-03 148.25 148.25 143.88 145.44 104.34 8164300
2000-01-04 143.53 144.06 139.64 139.75 100.26 8089800
2000-01-05 139.94 141.53 137.25 140.00 100.44 12177900
2000-01-06 139.62 141.50 137.75 137.75 98.82 6227200
We can also normalize OHLC with the adj_close data column. After normalization, the close column will be equal to adj_close , so the latter is omitted from the result.
In [4]: df = yf.getSymbolData("SPY",adjust=True)
Got 4510 days of data
In [5]: df.head()
Out[5]:
close volume open high low
Date
1999-12-31 105.37 3172700 105.35 105.82 104.92
2000-01-03 104.34 8164300 106.36 106.36 103.22
2000-01-04 100.26 8089800 102.97 103.35 100.18
2000-01-05 100.44 12177900 100.39 101.54 98.46
2000-01-06 98.82 6227200 100.17 101.51 98.82
Multiple symbols¶
getHistoricData() will accept one ore more symbols and download them
while displaying a progress bar.
In [6]: symbols = ['XLE','USO','SPY']
In [7]: data = yf.getHistoricData(symbols)
Downloading data:
[ 0% ]
[**********************67%******* ] 2 of 3 complete
[*********************100%***********************] 3 of 3 complete
In [8]: print(data)
symbol SPY USO \
ohlcv open high low close adj_close volume open high
Date
1999-12-31 146.84 147.50 146.25 146.88 105.37 3172700 NaN NaN
2000-01-03 148.25 148.25 143.88 145.44 104.34 8164300 NaN NaN
2000-01-04 143.53 144.06 139.64 139.75 100.26 8089800 NaN NaN
... ... ... ... ... ... ... ... ...
2017-11-29 263.02 263.63 262.20 262.71 262.71 77512100 11.58 11.66
2017-11-30 263.76 266.05 263.67 265.01 265.01 122980900 11.55 11.58
2017-12-01 264.76 265.31 260.79 264.46 264.46 147858445 11.65 11.78
symbol XLE \
ohlcv low close adj_close volume open high low close
Date
1999-12-31 NaN NaN NaN NaN 27.00 27.25 26.81 27.09
2000-01-03 NaN NaN NaN NaN 27.31 27.31 26.38 26.56
2000-01-04 NaN NaN NaN NaN 26.31 26.31 25.88 26.06
... ... ... ... ... ... ... ... ...
2017-11-29 11.36 11.47 11.47 16093700.0 67.68 68.21 67.54 68.08
2017-11-30 11.37 11.47 11.47 17779500.0 68.24 69.23 68.24 69.10
2017-12-01 11.60 11.67 11.67 14139965.0 69.59 70.16 68.94 69.68
symbol
ohlcv adj_close volume
Date
1999-12-31 19.50 34800
2000-01-03 19.12 380300
2000-01-04 18.76 722200
... ... ...
2017-11-29 68.08 13599300
2017-11-30 69.10 21971900
2017-12-01 69.68 21109479
[4510 rows x 18 columns]
Getting current quotes¶
The getQuote() is used to get current quote
In [9]: quote = yf.getQuote(['SPY','XLE','QQQ'])
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-9-ba43f48120e7> in <module>()
----> 1 quote = yf.getQuote(['SPY','XLE','QQQ'])
AttributeError: module 'tradingWithPython.lib.yahooFinance' has no attribute 'getQuote'
In [10]: quote
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-10-0669fb119297> in <module>()
----> 1 quote
NameError: name 'quote' is not defined
Note
YahooFinance quotes may be delayed for more than 15 minutes
Functions¶
-
tradingWithPython.lib.yahooFinance.getSymbolData(symbol, sDate=(2000, 1, 1), adjust=False, verbose=True, dumpDest=None)[source]¶ get data from Yahoo finance and return pandas dataframe
Parameters: symbol : str
Yahoo finanance symbol
sDate : tuple , default (2000,1,1)
start date (y,m,d)
adjust : bool , default False
use adjusted close values to correct OHLC. adj_close will be ommited
verbose : bool , default True
print output
dumpDest : str, default None
dump raw data for debugging
Returns: DataFrame
-
tradingWithPython.lib.yahooFinance.getHistoricData(symbols, **options)[source]¶ get data from Yahoo finance and return pandas dataframe Will get OHLCV data frame if sinle symbol is provided. If many symbols are provided, it will return a wide panel
Parameters: symbols : str or list
Yahoo finanance symbol or a list of symbols
sDate : tuple (optional)
start date (y,m,d)
adjust : bool
T/[F] adjust data based on adj_close
Returns: DataFrame, multi-index