Python pandas 考夫曼自适应移动平均值（KAMA）--- pandas 或Cython中的递归计算 [英] Python Pandas Kaufman Adaptive Moving Average (KAMA) --- Recursive Calculation in Pandas or Cython

问题描述

我正在尝试在Python熊猫中或使用Cython为考夫曼自适应移动平均线（KAMA）创建函数（我已经在R& Rcpp中完成了此操作）。在递归计算时遇到问题，filt1

I am trying to create a function for the Kaufman Adaptive Moving Average (KAMA), in Python Pandas or using Cython (I have already done this in R & Rcpp). Am having problems with the recursive calculation, filt1

filt1[i] =  ( filt1[i-1] + SC[i]*(price[i]-filt1[i-1]) )

我期待KAMA系列应该以
（i）NA开头，长度为n = 10
（ii）要启动KAMA，对于2010-01-19，价格的原始均值为1142.393，在这种情况下为收盘价
（iii）之后，递归公式filt1 [i]

I am expecting the KAMA Series should have (i) NA's to begin with, length of n=10 (ii) To start KAMA, for 2010-01-19 the raw mean of the price 1142.393, in this case the mean of the close's (iii) Thereafter KAMA values from the recursive formulae filt1[i]

So：

KAMA
2010-01-04       NA
2010-01-05       NA
2010-01-06       NA
2010-01-07       NA
2010-01-08       NA
2010-01-11       NA
2010-01-12       NA  
2010-01-13       NA
2010-01-14       NA
2010-01-15       NA
2010-01-19 1142.393
2010-01-20 1142.367
2010-01-21 1142.244
2010-01-22 1140.212
2010-01-25 1138.683
2010-01-26 1136.517

以

2013-12-24 1791.114
2013-12-26 1802.816
2013-12-27 1814.759
2013-12-30 1822.844
2013-12-31 1830.523

我已经开始了，

#%%
# Include this line for NEW WINDOW(S) for figures
%pylab qt4 

# start with getting some data to test on
import datetime
import tradingWithPython as twp # main toolkit functions
import tradingWithPython.lib.yahooFinance as yf # yahoo finance module   
import tradingWithPython.lib.backtest as backtest
from tradingWithPython.lib.extra import ProgressBar # import progress bar

#The python module (talib) that I will be using to calculate the technical 
#indicators is a wrapper around the open source TA-Lib. 
import talib
import numpy as np

import pandas.io.data as web  

import pandas.stats.moments

import pandas as pd
#pd.set_option('html', False) # how to display data - DEFAULT is True
#pd.set_option('display.height', int(1e7))
pd.set_option('display.max_rows', int(1e7))
#pd.set_option('display.max_columns', int(1e7))
pd.set_option('display.width', 3000)
#%%


#%%
def KAMA(x, n=10, pow1=2, pow2=30):
  ''' kama indicator '''    
  ''' accepts pandas dataframe of prices '''


  d['absDiffx'] = abs(x - x.shift(1) )  

  d['ER.num'] = ( x - x.shift(n) )
  d['ER.den'] = pandas.stats.moments.rolling_sum(d['absDiffx'],n)
  d['ER'] = d['ER.num'] / d['ER.den']

  d['SC'] = ( d['ER']*(2.0/(pow1+1)-2.0/(pow2+1.0))+2/(pow2+1.0) ) ** 2.0


  return d['SC']    
#%%



#%%
#Download data from yahoo finance
start = datetime.datetime(2010,1,1)
end = datetime.datetime(2013,12,31)
ticker = "^GSPC"
d=web.DataReader(ticker,'yahoo',start,end) 

d.info()
#<class 'pandas.core.frame.DataFrame'>
#DatetimeIndex: 1006 entries, 2010-01-04 00:00:00 to 2013-12-31 00:00:00
#Data columns (total 6 columns):
#Open         1006 non-null float64
#High         1006 non-null float64
#Low          1006 non-null float64
#Close        1006 non-null float64
#Volume       1006 non-null int64
#Adj Close    1006 non-null float64


type(d)
#pandas.core.frame.DataFrame


d.head()
d.tail()
#%%



#%%
#calculate KAMA
#---------------
kama = KAMA(d.Close, n=10, pow1=2, pow2=30)

type(kama)
#pandas.core.frame.DataFrame


kama.head(100)
kama.tail(10)
#%%



#%%
df = pd.DataFrame({'price':d.Close, 'KAMA':KAMA(d.Close, n=10, pow1=2, pow2=30) })
df.plot(subplots=True)
#%%

如何在熊猫内或使用Cython计算filt1 [i]，结果作为熊猫数据框？非常感谢。

How to do I calculate filt1[i] within Pandas or using Cython, with result as a pandas dataframe? Many Thanks.

推荐答案

感谢您的答复。

#%%
def KAMA(price, n=10, pow1=2, pow2=30):
    ''' kama indicator '''    
    ''' accepts pandas dataframe of prices '''

    absDiffx = abs(price - price.shift(1) )  

    ER_num = abs( price - price.shift(n) )
    ER_den = pandas.stats.moments.rolling_sum(absDiffx,n)
    ER = ER_num / ER_den

    sc = ( ER*(2.0/(pow1+1)-2.0/(pow2+1.0))+2/(pow2+1.0) ) ** 2.0


    answer = np.zeros(sc.size)
    N = len(answer)
    first_value = True

    for i in range(N):
        if sc[i] != sc[i]:
            answer[i] = np.nan
        else:
            if first_value:
                answer[i] = price[i]
                first_value = False
            else:
                answer[i] = answer[i-1] + sc[i] * (price[i] - answer[i-1])
    return answer
#%%


#%%
#calculate KAMA
#---------------
kama = KAMA(d.Close, n=10, pow1=2, pow2=30)
kama
#%%

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