DataAPI.MktBonddGet(secID=u"",ticker=u"122560",tradeDate=u"",beginDate=u"20180301",endDate=u"20180315",field=u"",pandas="1")

 tickerBond  closePriceBond  bondPremRatio secShortNameBond tickerEqu  \

secID                                                                         

110066     110066          199.94        -1.2442             盛屯转债    600711   

110067     110067          119.53        25.9204             华安转债    600909   

113021     113021          105.81        45.0989             中信转债    601998   

113024     113024          101.94        36.6668             核建转债    601611   

113025     113025          129.16         0.0409             明泰转债    601677

        ROE tickerEqu

0  2.642931    600711

1  4.425438    600909

2  6.259092    601998

3  4.432315    601611

4  6.454054    601677

先 df1 = df1.reset_index()，合并之后再把 secID 那一列设为 index 。

def initialize(context):

    # 初始化此策略

    # 设置我们要操作的股票池, 这里我们只操作一支股票

    g.security = '600570.SS'

    set_universe(g.security)

    #每天9:23分运行集合竞价处理函数

    run_daily(context, aggregate_auction_func, time='9:23')  



def aggregate_auction_func(context):

    stock = g.security

    #最新价

    snapshot = get_snapshot(stock)

    price = snapshot[stock]['last_px']

    #涨停价

    up_limit = snapshot[stock]['up_px']

    #如果最新价不小于涨停价，买入

    if float(price) >= float(up_limit):

        order(g.security, 100, limit_price=up_limit)

    

def handle_data(context, data):

    pass

def initialize(context):

    # 初始化此策略

    # 设置我们要操作的股票池, 这里我们只操作一支股票

    g.security = '600570.SS'

    set_universe(g.security)

    #每3秒运行一次主函数

    run_interval(context, func, seconds=3)

      

#盘前准备历史数据

def before_trading_start(context, data):

    history = get_history(10, '1d', 'close', g.security, fq='pre', include=False)

    g.close_array = history['close'].values

    

#当五日均线高于十日均线时买入，当五日均线低于十日均线时卖出

def func(context):

    

    stock = g.security

    

    #获取最新价

    snapshot = get_snapshot(stock)

    price = snapshot[stock]['last_px']

    

    # 得到五日均线价格

    days = 5

    ma5 = get_MA_day(stock, days, g.close_array[-4:], price)   

    # 得到十日均线价格

    days = 10

    ma10 = get_MA_day(stock, days, g.close_array[-9:], price)



    # 得到当前资金余额

    cash = context.portfolio.cash

    

    # 如果当前有余额，并且五日均线大于十日均线

    if ma5 > ma10:

        # 用所有 cash 买入股票

        order_value(stock, cash)

        # 记录这次买入

        log.info("Buying %s" % (stock))

        

    # 如果五日均线小于十日均线，并且目前有头寸

    elif ma5 < ma10 and get_position(stock).amount > 0:

        # 全部卖出

        order_target(stock, 0)

        # 记录这次卖出

        log.info("Selling %s" % (stock))    



#计算实时均线函数

def get_MA_day(stock,days,close_array,current_price):

    close_sum = close_array[-(days-1):].sum()

    MA = (current_price + close_sum)/days

    return MA



def handle_data(context, data):

    pass

def initialize(context):

    # 初始化此策略

    # 设置我们要操作的股票池, 这里我们只操作一支股票

    g.security = '600570.SS'

    set_universe(g.security)

    pass

    

#当五日均线高于十日均线时买入，当五日均线低于十日均线时卖出

def handle_data(context, data):

    security = g.security



    #得到十日历史价格

    df = get_history(10, '1d', 'close', security, fq=None, include=False)

    

    # 得到五日均线价格

    ma5 = round(df['close'][-5:].mean(), 3)

    

    # 得到十日均线价格

    ma10 = round(df['close'][-10:].mean(), 3)



    # 取得昨天收盘价

    price = data[security]['close']



    # 得到当前资金余额

    cash = context.portfolio.cash

    

    # 如果当前有余额，并且五日均线大于十日均线

    if ma5 > ma10:

        # 用所有 cash 买入股票

        order_value(security, cash)

        # 记录这次买入

        log.info("Buying %s" % (security))

        

    # 如果五日均线小于十日均线，并且目前有头寸

    elif ma5 < ma10 and get_position(security).amount > 0:

        # 全部卖出

        order_target(security, 0)

        # 记录这次卖出

        log.info("Selling %s" % (security))

def f_expma(N,m,EXPMA1,price):

    a = m/(N+1)

    EXPMA2 = a * price + (1 - a)*EXPMA1

    return EXPMA2 #2为后一天值



#定义macd函数，输入平滑系数参数、前一日值，输出当日值

def macd(N1,N2,N3,m,EXPMA12_1,EXPMA26_1,DEA1,price):

    EXPMA12_2 = f_expma(N1,m,EXPMA12_1,price)

    EXPMA26_2 = f_expma(N2,m,EXPMA26_1,price)

    DIF2 = EXPMA12_2 - EXPMA26_2

    a = m/(N3+1)

    DEA2 = a * DIF2 + (1 - a)*DEA1

    BAR2=2*(DIF2-DEA2)

    return EXPMA12_2,EXPMA26_2,DIF2,DEA2,BAR2



def initialize(context):

    global init_price

    init_price = None

    # 获取沪深300股票

    g.security = get_index_stocks('000300.SS')

    #g.security = ['600570.SS']

    # 设置我们要操作的股票池, 这里我们只操作一支股票

    set_universe(g.security)



def handle_data(context, data):

    # 获取历史数据，这里只获取了2天的数据，如果希望最终MACD指标结果更准确最好是获取

    # 从股票上市至今的所有历史数据，即增加获取的天数

    close_price = get_history(2, '1d', field='close', security_list=g.security)

    #如果是停牌不进行计算

    for security in g.security:

        if data[security].is_open >0:

            global init_price,EXPMA12_1,EXPMA26_1,EXPMA12_2,EXPMA26_2,DIF1,DIF2,DEA1,DEA2

            if init_price is None:

                init_price = close_price[security].mean()#nan和N-1个数，mean为N-1个数的均值

                EXPMA12_1 = init_price

                EXPMA26_1 = init_price

                DIF1 = init_price

                DEA1 = init_price

            # m用于计算平滑系数a=m/(N+1)

            m = 2.0

            #设定指数平滑基期数

            N1 = 12

            N2 = 26

            N3 = 9

            EXPMA12_2,EXPMA26_2,DIF2,DEA2,BAR2 = macd(N1,N2,N3,m,EXPMA12_1,EXPMA26_1,DEA1,close_price[security][-1])

            # 取得当前价格

            current_price = data[security].price

            # 取得当前的现金

            cash = context.portfolio.cash

            # DIF、DEA均为正，DIF向上突破DEA，买入信号参考

            if DIF2 > 0 and DEA2 > 0 and DIF1 < DEA1 and DIF2 > DEA2:

                # 计算可以买多少只股票

                number_of_shares = int(cash/current_price)

                # 购买量大于0时，下单

                if number_of_shares > 0:

                    # 以市单价买入股票，日回测时即是开盘价

                    order(security, +number_of_shares)

                    # 记录这次买入

                    log.info("Buying %s" % (security))

            # DIF、DEA均为负，DIF向下突破DEA，卖出信号参考

            elif DIF2 < 0 and DEA2 < 0 and DIF1 > DEA1 and DIF2 < DEA2 and get_position(security).amount > 0:

                # 卖出所有股票,使这只股票的最终持有量为0

                order_target(security, 0)

                # 记录这次卖出

                log.info("Selling %s" % (security))

            # 将今日的值赋给全局变量作为下一次前一日的值

            DEA1 = DEA2

            DIF1 = DIF2

            EXPMA12_1 = EXPMA12_2

            EXPMA26_1 = EXPMA26_2

    browser = await pyppeteer.launch(

        {'headless': False,

         'userDataDir': UserDataDir,

         'defaultViewport': {'width': 1800, 'height': 1000},

         # 'enable-automation':False,

         # 'ignoreDefaultArgs':['--enable-automation'],

         'ignoreDefaultArgs':True,

         }

    ERROR: Command errored out with exit status 1:

     command: 'C:\anaconda\python.exe' -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'C:\\Users\\xda\\AppData\

\Local\\Temp\\pip-install-ftotbzih\\peewee\\setup.py'"'"'; __file__='"'"'C:\\Users\\xda\\AppData\\Local\\Temp\\pip-insta

ll-ftotbzih\\peewee\\setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"

'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' egg_info --egg-base 'C:\Users\xda\AppData\Lo

cal\Temp\pip-pip-egg-info-8ou7yi3i'

         cwd: C:\Users\xda\AppData\Local\Temp\pip-install-ftotbzih\peewee\

    Complete output (15 lines):

    Traceback (most recent call last):

      File "<string>", line 1, in <module>

      File "C:\Users\xda\AppData\Local\Temp\pip-install-ftotbzih\peewee\setup.py", line 99, in <module>

        elif not _have_sqlite_extension_support():

      File "C:\Users\xda\AppData\Local\Temp\pip-install-ftotbzih\peewee\setup.py", line 76, in _have_sqlite_extension_su

pport

        compiler.compile([src_file], output_dir=tmp_dir),

      File "C:\anaconda\lib\distutils\_msvccompiler.py", line 327, in compile

        self.initialize()

      File "C:\anaconda\lib\distutils\_msvccompiler.py", line 224, in initialize

        vc_env = _get_vc_env(plat_spec)

      File "C:\anaconda\lib\site-packages\setuptools\msvc.py", line 314, in msvc14_get_vc_env

        return _msvc14_get_vc_env(plat_spec)

      File "C:\anaconda\lib\site-packages\setuptools\msvc.py", line 273, in _msvc14_get_vc_env

        out = subprocess.check_output(

    AttributeError: 'str' object has no attribute 'decode'

    ----------------------------------------

ERROR: Command errored out with exit status 1: python setup.py egg_info Check the logs for full command output.

        out = subprocess.check_output(

            'cmd /u /c "{}" {} && set'.format(vcvarsall, plat_spec),

            stderr=subprocess.STDOUT,

        )

        # ).decode('utf-16le', errors='replace')