import datetime
from datetime import date, timedelta
from numpy.lib.stride_tricks import as_strided
import pandas as pd
import numpy as np
from pathlib import Path
from os import path
import shutil
import os
import pdb
from digfilters import filters
filt = filters()
k = pd.read_pickle('projdir/dailydata.pkl')
k1 = k[k.Symbol == k.Symbol.unique()[0]]
k = k1
k['ret'] = k.Close - k.Open
k['dec'] = filt.highpass2(k.Close, 10) - filt.highpass2(k.Close, 5)
k['avg'] = k.Close.rolling(10).mean() - k.Close.rolling(5).mean()
k['retm'] = 0
for i in range(1, 6):
k['retm'] += k.ret.shift(-i)
def attr4(self,p):
global numvar
filt =filters()
print('in attr')#function for transformation of prices into required inputs
def h(l):
s = compute_Hc(l)
return s[0]
period=10
bandwidth=0.3
k = p.dropna()
hp = np.zeros(len(k))
bp = np.zeros(len(k))
cl = np.array(k.Close)
alpha2 =(np.cos(.25*bandwidth*2*np.pi/period)+np.sin(.25*bandwidth*2*np.pi/period)-1)/(np.cos(.25*bandwidth*2*np.pi)/period)
beta1 = np.cos(2*np.pi/period)
gamma1 = 1/np.cos(2*np.pi*bandwidth/period)
alpha1=gamma1-(gamma1**2-1)**0.5
for i in range(1,len(k)):
hp[i] = (1+alpha2/2)*(cl[i]-cl[i-1])+(1-alpha2)*hp[i-1]
peak=np.zeros(len(k))
for i in range(2,len(k)):
bp[i] = .5*(1-alpha1)*(hp[i]-hp[i-2])+beta1*(1+alpha1)*bp[i-1]-alpha1*bp[i-2]
peak[i]=0.991*peak[i-1]
alpha2 =(np.cos(1.5*bandwidth*2*np.pi/period)+np.sin(1.5*bandwidth*2*np.pi/period)-1)/(np.cos(1.5*bandwidth*2*np.pi/period))
signal=bp
peak=np.zeros(len(k))
trig = np.zeros(len(k))
for i in range(1,len(k)):
trig[i]=(1+alpha2/2)*(signal[i]-signal[i-1])+(1-alpha2)*trig[i-1]
k['bp']=filt.normalize(bp)
k['trig'] = filt.normalize(trig)
k['trig'] = filt.butterworth2(k.trig,3)
k['bp'] = filt.butterworth2(k.bp,15)
k['dec'] = filt.highpass2(k.Close,60)-filt.highpass2(k.Close,30)
k['dec'] = filt.normalize(k.dec)
k['dec'] = filt.butterworth2(k.dec,10)
k['Close1'] = list(cl)
k['mp'] = (k.High+k.Low)/2
print('mp done')
k['im'] = 1*(k.mp-(k.Low.rolling(window = 20).min()))/((k.High.rolling(window=20).max())-(k.Low.rolling(window = 20).min()))
print('mp done')
k['ft'] = 0.5*np.log((1+k.im)/(1-k.im))
k['delta'] = k.Close.shift(-1)-k.Close
k['du'] = ((k.delta/abs(k.delta)+1)/2)*k.delta#keeping only the positive k.delta
k['dd'] = ((abs((k.delta/abs(k.delta))-1)/2))*abs(k.delta)#keeping only the negative delta
k['rs'] = k.du.rolling(window=14).mean()/k.dd.rolling(window=14).mean()
k['rs5'] = k.du.rolling(window=5).mean()/k.dd.rolling(window=5).mean()
k['rsi'] = 100.0 - (100.0 / (1.0 + k.rs))
k['rsi5'] = 100.0 - (100.0 / (1.0 + k.rs5))
k['rsi'] = k.rsi/100
k['rsi5'] = k.rsi5/100
k['ft']= (k.ft-k.ft.min())/(k.ft.max()-k.ft.min())
k['h'] = k.Close.rolling(150).apply(h)
k['h'] = filt.butterworth2(k.h,10)
var_cols=['bp','trig','dec','h']
print(k[['bp','trig','dec']])
numvar = len(var_cols)
return k,var_cols