#coding=utf-8
from __future__ import division

import pandas as pd
import db
import utils
import volume
import ma
import macd
import kdj
import kline
import log
import multiprocessing as mp
import stock

import public
import config

#选股结果列表
result_list = []

def reset_resultlist():
	global result_list
	result_list = []

def xuangu(cycle, techniquedata, funcstr, funcdesc, opttype=1):
	public.cls_progressbar()
	reset_resultlist()

	d = db.DB.getInstance()
	basics = d.get_basics()
	pool = mp.Pool(config.XUGU_CPU_COUNT)
	progress = 0
	totalCount = len(basics.index)
	try:
		for code in basics.index:
			progress += 1
			pool.apply_async(xuangu_handle, (cycle, techniquedata, code, progress, totalCount, funcstr, opttype), callback=xuangu_result)
		pool.close()
		pool.join()
	except Exception, e:
		print(e)
	#xuangu_handle(cycle, techniquedata, '300602', progress, totalCount, funcstr, opttype)
	xuangu_output(cycle, techniquedata, funcstr, funcdesc, opttype)

def xuangu_handle(cycle, techniquedata, code, progress, totalCount, funcstr, opttype):
	d = db.DB.getInstance()
	s = stock.Stock.getInstance()
	ret = progress/totalCount*100
	if opttype == 1:
		tbname = code+'_'+cycle.lower()+config.TABLENAME_STR_RECENT+config.DATA_SOURCE
		df = d.get_df(tbname)
	else:
		start = '2017-01-01'
		df = s.get_stock_data(code, ty=cycle, start=start)
		df = s.set_integrated_data(df)

	if df is None or df.shape[0] == 0 or (not utils.xuangu_filter(code, df)):
		return 0, ret, code, techniquedata

	func = getattr(eval(techniquedata), funcstr)
	if func is None:
		return 0, ret, code, techniquedata
	retcode = func(df, code)
	return retcode, ret, code, techniquedata

def xuangu_result(ret):
	global result_list
	retcode = ret[0]
	progress = ret[1]
	code = ret[2]
	techniquedata = ret[3]
	if ret[0] != 0:
		result_list.append(retcode)
	print(techniquedata+'_xuangu_progress===============:%s, %d%%'%(code, progress))
	public.setprogressbar(progress)

def xuangu_output(cycle, techniquedata, funcstr, funcdesc, opttype):
	if len(result_list) == 0:
		return
	ret = {}
	ret[funcstr] = pd.Series(result_list)
	df = pd.DataFrame(ret)
	if opttype == 1:
		df.to_csv(u'./output/选股/'+techniquedata+'/'+funcdesc+cycle.lower()+'.csv', encoding='gbk', index=False)
	else:
		df.to_csv(u'./output/选股/'+techniquedata+u'/尾盘'+funcdesc+cycle.lower()+'.csv', encoding='gbk', index=False)
	'''
	ret = {}
	ret = {u'代码':[], u'名字':[], u'主力流入':[], u'主力流出':[], u'主力净流入':[],
		   u'主力净流入/资金流入流出总和':[], u'散户流入':[],
		   u'散户流出':[], u'散户净流入':[], u'散户净流入/资金流入流出总和':[],
		   u'资金流入流出总和':[]}
	s = stock.Stock.getInstance()
	for code in result_list:
		info = s.get_realtime_capital(code)
		if info is None or len(info) == 0:
			continue
		ret[u'代码'].append(info[0])
		ret[u'名字'].append(info[12])
		ret[u'主力流入'].append(info[1])
		ret[u'主力流出'].append(info[2])
		ret[u'主力净流入'].append(info[3])
		ret[u'主力净流入/资金流入流出总和'].append(info[4])
		ret[u'散户流入'].append(info[5])
		ret[u'散户流出'].append(info[6])
		ret[u'散户净流入'].append(info[7])
		ret[u'散户净流入/资金流入流出总和'].append(info[8])
		ret[u'资金流入流出总和'].append(info[9])

	df = pd.DataFrame(ret).T


	#todaylist = []
	#fivedaylist = []
	#buycountlist = []
	#sellcountlist = []
	#for code in result_list:
		#todaymoney, fivedaymoney, buy_count, sell_count = s.get_capital_info(code)
		#todaylist.append(todaymoney)
		#fivedaylist.append(fivedaymoney)
		#buycountlist.append(buy_count)
		#sellcountlist.append(sell_count)

	#df[u'今天资金'] = pd.Series(todaylist)
	#df[u'5日资金'] = pd.Series(fivedaylist)
	#df[u'今天买盘次数'] = pd.Series(buycountlist)
	#df[u'今天卖盘次数'] = pd.Series(sellcountlist)
	
	if opttype == 1:
		df.to_csv(u'./output/选股/'+techniquedata+'/'+funcdesc+cycle.lower()+'.csv', encoding='gbk')
	else:
		df.to_csv(u'./output/选股/'+techniquedata+u'/尾盘'+funcdesc+cycle.lower()+'.csv', encoding='gbk')
	'''

def testxuangu():
	s = stock.Stock.getInstance()
	d = db.DB.getInstance()

	basics = d.get_basics()
	pool = mp.Pool(mp.cpu_count())
	progress = 0
	totalCount = len(basics.index)
	try:
		for code in basics.index:
			progress += 1
			df = s.get_stock_data(code, 'D')
			if df is not None and df.shape[0] > 0:
				df = df.sort_values('date', ascending=False)
				df = s.set_integrated_data(df, type='R')
			df = df[:60]

			if df is None or df.shape[0] == 0 or utils.isnew(df):
				continue
			pool.apply_async(xuangu_handle, (df, 'macd', code, progress, totalCount, 'macd.macd_zeroup_first_gc'), callback=xuangu_result)
		pool.close()
		pool.join()
	except Exception, e:
		print(e)