Different results in DMI indicators (ex: adx_X_ema) between 0.5.X and 0.6.X.

[truchindedios]

If we compute the same indicators, with same code/data we obtain different results between branchs 0.5.X and 0.6.X.
In this screen capture you have same code executing with 2 clone conda env , only changing the stockstats version.
Which of them is correct?

df_prices_l[['tic','volume','high','low','open','close']].tail()
Field tic volume ... open close
date ...
2023-11-02 945384 24946.0 ... 35.31 35.90
2023-11-02 945388 35788.8 ... 15.62 15.73
2023-11-02 992816 77334.8 ... 175.52 177.57
2023-11-02 993249 8135.1 ... 102.05 105.08
2023-11-02 998171 3628.0 ... 290.25 294.53

stock = Sdf.retype(df_prices_l.copy())
stock['adx_5_ema']

VERSION 0.5.4
stock[['tic','close','adx_5_ema']]
Field tic close adx_5_ema
date
2023-01-03 13967E 66.25 NaN
2023-01-03 28236L 134.78 100.000000
2023-01-03 51683M 207.39 100.000000
2023-01-03 542868 47.94 81.839948
2023-01-03 696738 346.22 71.437125
... ... ... ...
2023-11-02 945384 35.90 16.147743
2023-11-02 945388 15.73 15.958998
2023-11-02 992816 177.57 15.557359
2023-11-02 993249 105.08 13.905666
2023-11-02 998171 294.53 14.194133

VERSION 0.6.0
stock[['tic','close','adx_5_ema']]
Field tic close adx_5_ema
date
2023-01-03 13967E 66.25 NaN
2023-01-03 28236L 134.78 100.000000
2023-01-03 51683M 207.39 100.000000
2023-01-03 542868 47.94 80.787141
2023-01-03 696738 346.22 70.113103
... ... ... ...
2023-11-02 945384 35.90 7.794167
2023-11-02 945388 15.73 7.855445
2023-11-02 992816 177.57 7.757818
2023-11-02 993249 105.08 6.994558
2023-11-02 998171 294.53 7.368469

Thanks for you code!! It's really good!!