#coding=utf-8
import public
import talib
import pandas as pd

class Kline:

	__instance = None
	def __init__(self):
		pass

	@classmethod
	def getInstance(cls):
		if(cls.__instance == None):
			cls.__instance = Kline()
		return cls.__instance


def shape(func_name, df):
	df = df[['date', 'open', 'high', 'low', 'close', 'code']]
	df = df.sort_values('date', ascending=True)
	open_p = df['open'].values
	high_p = df['high'].values
	low_p = df['low'].values
	close_p = df['close'].values
	if func_name == 'CDL2CROWS':
		ret = talib.CDL2CROWS(open_p, high_p, low_p, close_p)
		df['CDL2CROWS'] = pd.Series(ret)
	df = df.sort_values('date', ascending=False)
	return df

def CDL2CROWS(df):
	meettotalCount, meettotal = public.init_succ_var()
	df = shape(CDL2CROWS.func_name, df)
	dflen = df.shape[0]
	for i in xrange(dflen):
		if int(df.iloc[i]['CDL2CROWS']) == -100:
			meettotalCount = public.compute_succ_zf(df, i, meettotalCount, meettotal)
			df.to_csv(df.iloc[0]['code']+'.csv', encoding='gbk')
	return meettotalCount, meettotal


#(df.iloc[i]['high']-df.iloc[i]['open'])/(df.iloc[i]['open']-df.iloc[i]['close']) > 3.0) and \
def ZADF(df):
	meettotalCount, meettotal = public.init_succ_var()
	dflen = df.shape[0]
	for i in xrange(dflen):
		if i == 0:
			continue
		if i+2<dflen \
			and df.iloc[i]['MA_60'] > df.iloc[i+1]['MA_60'] and df.iloc[i+1]['MA_60'] > df.iloc[i+2]['MA_60'] \
			and df.iloc[i]['close'] > df.iloc[i]['MA_60']:
			if (df.iloc[i]['close'] < df.iloc[i]['open']*0.95 or (df.iloc[0]['close']==df.iloc[0]['open'] and df.iloc[0]['change']<-0.098)) \
				and df.iloc[i-1]['change'] > 0.095:
					meettotalCount = public.compute_succ_zf(df, i-1, meettotalCount, meettotal)
	return meettotalCount, meettotal

#============================选股==============================
def ZADF_XG(df, code):
	retcode = 0
	dflen = df.shape[0]
	if 2<dflen \
		and df.iloc[0]['MA_60'] > df.iloc[1]['MA_60'] and df.iloc[1]['MA_60'] > df.iloc[2]['MA_60'] \
		and df.iloc[0]['close'] > df.iloc[0]['MA_60']:
		if (df.iloc[0]['close'] < df.iloc[0]['open']*0.95) or (df.iloc[0]['close'] == df.iloc[0]['open'] and df.iloc[0]['change']<-0.098):
		#or ((df.iloc[0]['high']-df.iloc[0]['open'])/(df.iloc[0]['open']-df.iloc[0]['close']) > 2.0):
			retcode = code
	return retcode