#coding=utf-8
import pandas as pd
import time,datetime
import macd
import kdj
import rsi
import stock


class MacdKdjRsi:

	__instance = None

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

	def __init__(self):
		self.Macd = macd.Macd.getInstance()
		self.Kdj = kdj.Kdj.getInstance()
		self.Rsi = rsi.Rsi.getInstance()

	#修改了的函数，按照多个指标进行分析
	#按照MACD，KDJ等进行分析
	def get_TA_All(self):
		operate_array1=[]
		operate_array2=[]
		operate_array3=[]
		operate1 = 0
		operate2 = 0
		operate3 = 0
		count = 0

		#获取股票列表
		#code,代码 name,名称 industry,所属行业 area,地区 pe,
		#市盈率 outstanding,流通股本 totals,总股本(万) totalAssets,总资产(万)liquidAssets,流动资产
		# fixedAssets,固定资产 reserved,公积金 reservedPerShare,
		#每股公积金 eps,每股收益 bvps,每股净资 pb,市净率 timeToMarket,上市日期

		s = stock.Stock.getInstance()
		df_Code = s.get_Stock_List()

		delIndex = []
		for code in df_Code.index:
			count = count + 1

			if df_Code.loc[code]['name'][:2].lower() == "st" or df_Code.loc[code]['name'][1:3].lower() == "st":
				delIndex.append(code)
				continue
			#过滤大盘股
			if df_Code.loc[code]['outstanding']*10000 > 100000000:
				delIndex.append(code)
				continue
			#市盈率为负数或过大
			#if df_Code.loc[code]['pe'] < 0 or df_Code.loc[code]['pe'] > 500:
				#delIndex.append(code)
				#continue				
			#每股收益
			#if df_Code.loc[code]['bvps'] <= 0:
				#delIndex.append(code)
				#continue

			# index,0 - 6 date：日期 open：开盘价 high：最高价 close：收盘价 
			#low：最低价 volume：成交量 price_change：价格变动 p_change：涨跌幅
			# 7-12 ma5：5日均价 ma10：10日均价 ma20:20日均价 v_ma5:5日均量v_ma10:10日均量 v_ma20:20日均量
			operate1, operate2, operate3 = self.get_TA_One(code, start='2014-12-20')

			if operate1 <=0 or operate2 <=0 or operate3 <=0:
				delIndex.append(code)
				continue

			operate_array1.append(operate1)
			operate_array2.append(operate2)
			operate_array3.append(operate3)
			if count == 0:
				write_Blog(str(count))
			time.sleep(0.1)

		for code in delIndex:
			df_Code = df_Code.drop(code)

		df_Code['macd_score']=pd.Series(operate_array1,index=df_Code.index)
		df_Code['kdj_score']=pd.Series(operate_array2,index=df_Code.index)
		df_Code['rsi_score']=pd.Series(operate_array3,index=df_Code.index)
		return df_Code

	def get_TA_One(self, code, start):
		s = stock.Stock.getInstance()
		df = s.get_Hist_Data(code, start)

		if df is None:
			return (0, 0, 0)

		#停牌的股
		date = time.strftime('%Y-%m-%d',time.localtime(time.time()))
		if df.iloc[0]['date'] != date and df.iloc[0]['date'] != date:
			return (0, 0, 0)

		dflen = df.shape[0]
		operate1 = 0
		operate2 = 0
		operate3 = 0
		print(df)
		if dflen > 35:
			try:
				operate1, df = self.Macd.get_MACD(df)
				operate2, df = self.Kdj.get_KDJ(df)
				operate3, df = self.Rsi.get_RSI(df)
			except Exception, e:
				write_Blog(e)
		return operate1, operate2, operate3
		
#日志记录
def write_Blog(strinput):
	TODAY = datetime.date.today()
	CURRENTDAY=TODAY.strftime('%Y-%m-%d')
	TIME = time.strftime("%H:%M:%S")
	#写入本地文件
	fp = open('./output/'+'blog.txt','a') 
	fp.write('------------------------------\n'+CURRENTDAY +" "+TIME+" "+ str(strinput)+'  \n')
	fp.close()
	time.sleep(1)