import datetime
import talib
import numpy as np
import math
import pandas

def init(context):
    context.stocknum = 30
    context.numberlist = []

def before_trading(context):
    pass

def handle_bar(context,bar_dict):
    pass

def after_trading(context):
    context.pool = []
    for s in all_instruments(type='CS').order_book_id:
        price = history_bars(s,251,'1d','close')
        if len(price)==0 or math.isnan(price[0]):
            pass
        else:
            nowprice = price[-1]
#            logger.info(str(price))
            ma250 = talib.MA(price, 250)[-1]
            ma200 = talib.MA(price, 200)[-1]
            ma120 = talib.MA(price, 120)[-1]
            # ma60 = talib.MA(price, 60)[-1]
            # ma30 = talib.MA(price, 30)[-1]
            ma20 = talib.MA(price, 20)[-1]
            turnover = history_bars(s,5,'1d','total_turnover')[-1]
            
            macd, signal, hist = talib.MACD(price, fastperiod=12, slowperiod=26, signalperiod=9)
            
            
            if math.isnan(ma250):
                pass
            else:
                if (ma250<nowprice) and (ma200<nowprice) and ma120<nowprice and (ma20<nowprice) and turnover>400000000 and macd[-1]>0  and hist[-1]>0:
                    context.pool.append(s)
                else:
                    pass
            
    logger.info('共有'+str(len(context.poo

用米筐的框架跑的，主要考虑短线量价因素，选出来的股票池比较大，据说效果还是比较好的。