kdj columns and rsv_9 all NaN

[think-nice-things]

Hi,

there are cases where the kdj columns are all NaN. This seems to be related to rsv_9 beeing NaN.

Here is sample data (the first 20 lines of ASML stock quote) which shows the problem:

date        adj_close     close      high       low      open     volume
2010-01-01   29.28827  23.99998  23.99998  23.99998  23.99998        0.0
2010-01-04   29.60560  24.26000  24.31999  23.89002  23.90003  1563900.0
2010-01-05   29.66047  24.30497  24.62498  24.05996  24.11501  1550300.0
2010-01-06   29.97780  24.56500  24.56500  24.18000  24.20503  1133900.0
2010-01-07   29.42866  24.11501  24.45004  23.80001  24.45004  2648700.0
2010-01-08   28.44013  23.30497  24.08498  23.27502  23.99998  3064200.0
2010-01-11   27.37239  22.43002  23.45497  22.43002  23.31999  4640500.0
2010-01-12   27.82389  22.80001  22.92998  22.60004  22.65001  3098000.0
2010-01-13   28.28762  23.18000  23.39999  22.75003  22.80001  3732600.0
2010-01-14   28.26319  23.15998  23.59996  23.09499  23.46998  1851800.0
2010-01-15   27.89709  22.85999  23.43002  22.69498  23.28996  2738400.0
2010-01-18   27.93985  22.89503  22.98504  22.62499  22.90003  1132900.0
2010-01-19   27.67129  22.67496  22.91997  22.62499  22.80501  2392200.0
2010-01-20   28.53165  23.37997  23.87000  22.87501  22.98997  6490400.0
2010-01-21   29.00759  23.76998  24.06997  23.65001  23.74503  4068900.0
2010-01-22   28.53165  23.37997  23.82003  23.33500  23.53998  3842600.0
2010-01-25   27.78725  22.76998  23.18501  22.69998  22.70999  3091000.0
2010-01-26   28.34861  23.22998  23.37004  22.81002  22.99998  2716300.0
2010-01-27   28.12290  23.04502  23.13503  22.71500  23.08999  2130900.0
2010-01-28   27.86656  22.83497  23.73001  22.83497  23.44003  3445800.0

if rsv_9 is calculated with stockstats, the first value is NaN. This leads to all kdj columns beeing NaN as well. I guess this is due to a division by zero error in _get_rsv in line 251:

df[column_name] = ((df['close'] - low_min) /
                   (high_max - low_min).astype('float64') * 100)  

How should the code be modified so that this bug doesn't appear?
(or what's the correct value for rsv if high_max - low_min == 0?)

[jealous]

Hi,

I cannot reproduce it in my environment. Could you please try to upgrade to v0.1.1 and see if the problem persists? I am using the almost latest pandas and maybe pandas fixed it.

Here is the script I used to do the test.

import pandas.io.data as web
import datetime
start = datetime.datetime(2010, 1, 1)
end = datetime.datetime(2013, 1, 27)
f = web.DataReader("ASML ", 'yahoo', start, end)

from stockstats import StockDataFrame
df = StockDataFrame.retype(f)
df[['kdjk', 'kdjd', 'kdjj']]

Please let me know if the problem persists. It would be very helpful if you could also supply the script that reproduce the problem.

Thanks.

[think-nice-things]

Thanks for testing.
If you use ASML.AS (instead of ASML) you'll get the problem.
There are other stocks where it happens too.

[jealous]

I was able to reproduce the issue.
You are correct that the issue is caused by 0 division.
I am update the 'NaN' values in 'RSV' to 0 to fix this issue. At least we get rid of the 'NaN' values in KDJ for now.
But to tell the truth, I don't really know the correct way to calculate this, either.
The fix is in version 0.1.2.

[think-nice-things]

thanks - works great!