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。
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