import talib

def initialize(account):

get_iwencai("PE小于40，PE大于35，ROE大于20%")
account.n=5
account.stk_costs=dict()

def macd_cal(stk, data):

# 获取智能选股股票列表
    values = data.attribute_history(stk, ['close'], 40, '1d', False, None)
    # 计算MACD值
    DIFF, DEA, MACD = talib.MACD(values['close'].values,
                fastperiod=12, slowperiod=26, signalperiod=9)
    # 将MACD值以局部变量形式返回
    return MACD

#强势行业阈值涉及强势行业的判断。首先我们定义一个行业为强势行业，只要该行业内的股票总值在过去1日内的均价大于其过去N日的均价，则我们认为该该行业在当前状况下处于强势状态：

def x(stock,data): for stock in list(account.positions): log.info(account.positions[stock].cost_basis)

 # 获取股票过去30日的收盘价数据
   close = data.attribute_history(stock, ['close'], 30, '1d')
 # 计算股票过去2日的收盘价平均値
 v = close.values[-2:].mean()
  #计算股票过去30日的收盘价平均値
  b = close.values.mean()
   if v>b:
    a=1
    else:
    a=0
     return a

--- 4. 盘中设置买卖条件，每个交易频率（日/分钟）调用一次-------------

def handle_data(account, data):

# 买入股票

for stock in account.iwencai_securities:
    a=x(stock,data)
    # 若股票数量到达限制，则跳出
    if len(account.positions) >= account.n:
        break
    if stock not in account.positions:
        # 若出现MACD金叉，则买入1/n仓位的股票
        MACD = macd_cal(stock, data)
        if MACD[-1] > 0 and MACD[-2] < 0 a==1:
            order_target_percent(stock, 1/account.n)


# 卖出股票
for stock in list(account.positions):
    # 获取股票收盘价数据
    MACD = macd_cal(stock, data)
    # 若出现MACD死叉（MACD为负值），则卖出股票
    if MACD[-1] < 0 and MACD[-2]>0  :
        order_target(stock, 0)