这是我尝试在 python 中编写的 pinescript 代码。相同的优化等效 python 代码应该是什么
kama[1] 这里是以前的 kama 值，对于数组中的第一次计算，应该为这个 kama[1] 值做什么，因为它第一次不存在。
kama=nz(kama[1], close[1])+smooth*(close[1]-nz(kama[1], close[1]))
松稿信息：
nz
Replaces NaN values with zeros (or given value) in a series.
nz(x, y) → integer
nz(sma(close, 100))
RETURNS
Two args version: returns x if it's a valid (not NaN) number, otherwise y
One arg version: returns x if it's a valid (not NaN) number, otherwise 0
ARGUMENTS
x (series) Series of values to process.
y (float) Value that will be inserted instead of all NaN values in x series.
编辑 1 我尝试如下但不起作用的东西
stockdata['kama'] = stockdata['kama'](-1) if stockdata['kama'](-1) !=0 \
else stockdata['close'] + stockdata['smooth']*(stockdata['close'] - \
stockdata['kama'](-1) if stockdata['kama'](-1) !=0 else stockdata['close'])
编辑 2 我尝试的替代方案只是为了确保至少有一个部分正常工作，但这也失败了（nz(kama[1], close)）
stockdata['kama'] = np.where(stockdata['kama'][-1] != 0, stockdata['kama'][-1], stockdata['close'])
如果这条线现在完全罢工了
kama=nz(kama[1], close)+smooth*(close-nz(kama[1], close))
未转换为 python 的 pine-script 代码我的整个逻辑将被折腾。非常感谢您的任何工作解决方案。
编辑3： 系列的数据框输入
open high low close adjusted_close \
date
2002-07-01 5.2397 5.5409 5.2397 5.4127 0.0634
2002-07-02 5.5234 5.5370 5.4214 5.4438 0.0638
2002-07-03 5.5060 5.5458 5.3281 5.4661 0.0640
2002-07-04 5.5011 5.5720 5.4175 5.5283 0.0647
2002-07-05 5.5633 5.6566 5.4749 5.5905 0.0655
2002-07-08 5.5011 5.7187 5.5011 5.6255 0.0659
2002-07-09 5.5905 5.7586 5.5681 5.6167 0.0658
2002-07-10 5.4885 5.4885 5.1465 5.2222 0.0612
2002-07-11 4.9784 5.2135 4.9784 5.1863 0.0607
2002-07-12 5.5011 5.5011 5.2446 5.3194 0.0623
2002-07-15 5.3243 5.4797 5.1912 5.3330 0.0625
2002-07-16 5.1999 5.4389 5.1999 5.3155 0.0623
2002-07-17 4.7024 5.1377 4.6189 5.0445 0.0591
2002-07-18 4.8803 5.1465 4.8356 5.0804 0.0595
2002-07-19 5.0270 5.2038 5.0221 5.1513 0.0603
2002-07-22 5.0804 5.1465 4.9687 4.9735 0.0582
2002-07-23 4.8181 5.0843 4.8181 5.0619 0.0593
2002-07-24 5.0580 5.1290 4.9376 5.0619 0.0593
2002-07-25 5.0580 5.0580 4.7918 4.8492 0.0568
volume dividend_amount split_coefficient Om \
date
2002-07-01 21923 0.0 1.0 NaN
2002-07-02 61045 0.0 1.0 NaN
2002-07-03 34161 0.0 1.0 NaN
2002-07-04 27893 0.0 1.0 NaN
2002-07-05 58976 0.0 1.0 NaN
2002-07-08 48910 0.0 1.0 5.472433
2002-07-09 321846 0.0 1.0 5.530900
2002-07-10 138434 0.0 1.0 5.525083
2002-07-11 15027 0.0 1.0 5.437150
2002-07-12 24187 0.0 1.0 5.437150
2002-07-15 50330 0.0 1.0 5.397317
2002-07-16 24928 0.0 1.0 5.347117
2002-07-17 21357 0.0 1.0 5.199100
2002-07-18 27532 0.0 1.0 5.097733
2002-07-19 13380 0.0 1.0 5.105833
2002-07-22 21666 0.0 1.0 5.035717
2002-07-23 40161 0.0 1.0 4.951350
2002-07-24 34480 0.0 1.0 4.927700
2002-07-25 38185 0.0 1.0 4.986967
Hm Lm Cm vClose diff \
date
2002-07-01 NaN NaN NaN NaN 1669.8373
2002-07-02 NaN NaN NaN NaN 1669.8062
2002-07-03 NaN NaN NaN NaN 1669.7839
2002-07-04 NaN NaN NaN NaN 1669.7217
2002-07-05 NaN NaN NaN NaN 1669.6595
2002-07-08 5.595167 5.397117 5.511150 5.493967 1669.6245
2002-07-09 5.631450 5.451850 5.545150 5.539837 1669.6333
2002-07-10 5.623367 5.406033 5.508217 5.515675 1670.0278
2002-07-11 5.567983 5.347750 5.461583 5.453617 1670.0637
2002-07-12 5.556167 5.318933 5.426767 5.434754 1669.9306
2002-07-15 5.526683 5.271650 5.383850 5.394875 1669.9170
2002-07-16 5.480050 5.221450 5.332183 5.345200 1669.9345
2002-07-17 5.376567 5.063250 5.236817 5.218933 1670.2055
2002-07-18 5.319567 5.011433 5.213183 5.160479 1670.1696
2002-07-19 5.317950 5.018717 5.207350 5.162463 1670.0987
2002-07-22 5.258850 4.972733 5.149700 5.104250 1670.2765
2002-07-23 5.192950 4.910550 5.104517 5.039842 1670.1881
2002-07-24 5.141300 4.866833 5.062250 4.999521 1670.1881
2002-07-25 5.128017 4.895650 5.029700 5.010083 1670.4008
signal noise efratio smooth
date
2002-07-01 5.4127 1670.3373 0.003240 0.416113
2002-07-02 5.4438 1670.3062 0.003259 0.416113
2002-07-03 5.4661 1670.2839 0.003273 0.416114
2002-07-04 5.5283 1670.2217 0.003310 0.416115
2002-07-05 5.5905 1670.1595 0.003347 0.416116
2002-07-08 5.6255 1670.1245 0.003368 0.416116
2002-07-09 5.6167 1670.1333 0.003363 0.416116
2002-07-10 5.2222 1670.5278 0.003126 0.416110
2002-07-11 5.1863 1670.5637 0.003105 0.416109
2002-07-12 5.3194 1670.4306 0.003184 0.416111
2002-07-15 5.3330 1670.4170 0.003193 0.416111
2002-07-16 5.3155 1670.4345 0.003182 0.416111
2002-07-17 5.0445 1670.7055 0.003019 0.416107
2002-07-18 5.0804 1670.6696 0.003041 0.416107
2002-07-19 5.1513 1670.5987 0.003084 0.416109
2002-07-22 4.9735 1670.7765 0.002977 0.416106
2002-07-23 5.0619 1670.6881 0.003030 0.416107
2002-07-24 5.0619 1670.6881 0.003030 0.416107
2002-07-25 4.8492 1670.9008 0.002902 0.416104
kama=nz(kama[1], close)+smooth*(close-nz(kama[1], close)) 的预期是什么？
stockdata['kama'] =nz(stockdata[kama][-1],stockdata['close'] +stockdata['smooth']*(stockdata['close']-nz(stockdata['kama'][-1],stockdata['close'])
在这种情况下，对于第一次迭代，不会有任何以前的 kama 值，应该注意。所有输入都以上面的数据框格式给出。