Trying to calculate slope on SMA
df.date = pd.to_datetime(df.date)
df['date_ordinal'] = pd.to_datetime(df['date']).map(dt.toordinal)
slope, intercept, r_value, p_value, std_err = stats.linregress(df['date_ordinal'], df['SMA'])
df['slope'] = slope
Why slope is NaN? Dataframe:
date open high low close volume token SMA serial ate_ordinal slope
0 2021-07-05 59.15 60.75 58.75 59.85 219009 168456 NaN 26 737976 NaN
1 2021-07-06 59.90 63.90 59.90 61.40 345452 168456 NaN 25 737977 NaN
2 2021-07-07 61.65 62.90 60.70 61.75 120200 168456 NaN 24 737978 NaN
3 2021-07-08 61.00 63.80 61.00 61.85 173059 168456 NaN 23 737979 NaN
4 2021-07-09 62.20 62.60 61.00 61.30 80536 168456 NaN 22 737980 NaN
5 2021-07-12 61.30 65.50 61.30 64.25 433789 168456 NaN 21 737983 NaN
6 2021-07-13 65.05 66.75 65.00 65.80 343672 168456 NaN 20 737984 NaN
7 2021-07-14 66.70 66.70 63.25 64.60 186786 168456 NaN 19 737985 NaN
8 2021-07-15 64.95 66.70 64.00 64.55 267449 168456 NaN 18 737986 NaN
9 2021-07-16 65.00 69.45 63.60 65.15 824427 168456 NaN 17 737987 NaN
10 2021-07-19 65.55 70.00 65.55 67.60 506566 168456 63.463636 16 737990 NaN
11 2021-07-20 68.90 69.15 65.60 66.25 345355 168456 64.045455 15 737991 NaN
12 2021-07-22 67.50 67.90 66.05 66.65 101745 168456 64.522727 14 737993 NaN
13 2021-07-23 67.50 67.55 64.60 65.05 176110 168456 64.822727 13 737994 NaN
14 2021-07-26 65.40 65.80 63.35 63.95 114623 168456 65.013636 12 737997 NaN
15 2021-07-27 64.00 64.90 62.50 62.95 124095 168456 65.163636 11 737998 NaN
16 2021-07-28 63.80 63.80 60.20 62.85 110505 168456 65.036364 10 737999 NaN
17 2021-07-29 63.50 64.50 63.00 64.20 58880 168456 64.890909 9 738000 NaN
18 2021-07-30 64.00 68.65 62.70 66.50 505882 168456 65.063636 8 738001 NaN
19 2021-08-02 66.70 68.40 66.20 66.60 191472 168456 65.250000 7 738004 NaN
20 2021-08-03 67.50 69.90 65.55 67.45 581423 168456 65.459091 6 738005 NaN
21 2021-08-04 68.40 69.05 65.00 65.90 177188 168456 65.304545 5 738006 NaN
22 2021-08-05 66.50 66.50 63.50 63.75 112842 168456 65.077273 4 738007 NaN
23 2021-08-06 64.20 66.60 64.00 66.25 102939 168456 65.040909 3 738008 NaN
24 2021-08-09 67.40 67.40 63.25 63.90 88957 168456 64.936364 2 738011 NaN
25 2021-08-10 65.45 65.45 59.00 60.30 202877 168456 64.604545 1 738012 NaN
