为什么 模拟交易 出错呢,谢谢

策略分享
标签: #<Tag:0x00007fcf608d0ab8>

(ZjFy) #1

https://i.bigquant.com/user/18875112642/lab/share/0-%E4%BA%A4%E6%98%93%E4%B8%AD%EF%BC%88%E6%97%A0%E9%80%89%E8%82%A1%EF%BC%89%2Fk%E7%BA%BF%20-%20%E9%87%91%E5%8F%89%E6%AD%BB%E5%8F%89.ipynb?_t=1528874806244


(达达) #2

您好~您的问题很典型,由于平台功能的迭代更新一些老的回测模板不能实现模拟交易功能,希望您在平台尽量使用新的可视化模板。不能进行模拟交易的核心原因是很多代码类老模板中的策略把enddate写死了,而模拟交易的实现原理就是每天刷新enddate,因此老模版无法更新新的日期。这里给您将策略改造成了可视化的策略,可视化模板中的enddate是可以通过context.enddate驱动实现动态更新的,建议您使用可视化策略开发器。这个策略包含了一些技巧:1.在特征抽取中直接用表达式生成技术指标;2.将技术指标放在准备函数中计算通过context全局变量传递给handle模块可以不用每天计算指标而加快速度;3.有的股票停牌当日没有数据因此在循环前获取当日的有效股票列表

克隆策略

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    In [30]:
    # 本代码由可视化策略环境自动生成 2018年6月14日 08:28
    # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
    
    
    m1 = M.input_features.v1(
        features="""# #号开始的表示注释
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    mean(close_0,5)
    mean(close_0,50)"""
    )
    
    m2 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2018-01-02'),
        end_date=T.live_run_param('trading_date', '2018-06-11'),
        market='CN_STOCK_A',
        instrument_list="""600036.SHA
    000651.SZA
    600887.SHA
    000423.SZA
    002415.SZA
    000848.SZA
    002507.SZA
    600600.SHA
    601888.SHA
    000538.SZA
    600048.SHA""",
        max_count=0
    )
    
    m3 = M.general_feature_extractor.v6(
        instruments=m2.data,
        features=m1.data,
        start_date='',
        end_date='',
        before_start_days=200
    )
    
    m4 = M.derived_feature_extractor.v2(
        input_data=m3.data,
        features=m1.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m6 = M.dropnan.v1(
        input_data=m4.data
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m5_handle_data_bigquant_run(context, data):
        today=data.current_dt.strftime('%Y-%m-%d')
        #从context中读取已经计算好的均线
        short_mavg_all=context.short_mavg
        long_mavg_all=context.long_mavg
        #有的个股当日停牌,没有均线值,选出当日有均线值的股票作为股票池
        instruments=list(short_mavg_all[short_mavg_all.date==today].instrument)
        #获取当日的均线值
        short_mavg_today=short_mavg_all[short_mavg_all.date==today]
        long_mavg_today=long_mavg_all[long_mavg_all.date==today]
        # 每天买入的股票量buy_num
        buy_num = 0
    
        # 先遍历股票池卖出   
        for k in instruments:
            sid = context.symbol(k) # 字符型股票代码转化成BigQuant回测引擎所需的股票代码
            price = data.current(sid, 'price') # 最新价格
            
            short_mavg = short_mavg_today[short_mavg_today.instrument==k]['mean(close_0,5)'].values[0] # 短期均线值
            long_mavg =  long_mavg_today[long_mavg_today.instrument==k]['mean(close_0,50)'].values[0] # 长期均线值
            
            cur_position = context.portfolio.positions[sid].amount # 持仓       
            
            # 卖出逻辑 - 死亡交叉, 全部卖出
            if short_mavg < long_mavg and cur_position > 0 and data.can_trade(sid):  
                context.order_target_percent(sid, 0)
                
        # 再遍历股票池买入        
        for k in instruments:        
            cash = context.portfolio.cash # 现金    
            # 买入逻辑 - 黄金交叉, 空仓买入
            if short_mavg > long_mavg and cur_position == 0 and data.can_trade(sid) and buy_num<1: 
                context.order(sid, np.floor(cash/price/100) * 100)
                buy_num=buy_num+1
    # 回测引擎:准备数据,只执行一次
    def m5_prepare_bigquant_run(context):
        # 加载预测数据
        df = context.options['data'].read_df().reset_index()
        df['date'] = df['date'].apply(lambda x : x.strftime('%Y-%m-%d'))
        context.instruments=df.instrument.unique()
        context.short_mavg=df[['date','instrument','mean(close_0,5)']]
        context.long_mavg=df[['date','instrument','mean(close_0,50)']]
        ss=context.short_mavg
    # 回测引擎:初始化函数,只执行一次
    def m5_initialize_bigquant_run(context):
    
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    
    m5 = M.trade.v3(
        instruments=m2.data,
        options_data=m6.data,
        start_date='',
        end_date='',
        handle_data=m5_handle_data_bigquant_run,
        prepare=m5_prepare_bigquant_run,
        initialize=m5_initialize_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        benchmark='000300.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-06-14 08:22:23.958303] INFO: bigquant: input_features.v1 开始运行..
    [2018-06-14 08:22:23.961303] INFO: bigquant: 命中缓存
    [2018-06-14 08:22:23.962314] INFO: bigquant: input_features.v1 运行完成[0.004039s].
    [2018-06-14 08:22:23.967521] INFO: bigquant: instruments.v2 开始运行..
    [2018-06-14 08:22:23.969956] INFO: bigquant: 命中缓存
    [2018-06-14 08:22:23.971228] INFO: bigquant: instruments.v2 运行完成[0.003698s].
    [2018-06-14 08:22:23.979187] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-06-14 08:22:23.981645] INFO: bigquant: 命中缓存
    [2018-06-14 08:22:23.982943] INFO: bigquant: general_feature_extractor.v6 运行完成[0.003758s].
    [2018-06-14 08:22:23.989603] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-06-14 08:22:23.991857] INFO: bigquant: 命中缓存
    [2018-06-14 08:22:23.992861] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.003261s].
    [2018-06-14 08:22:23.999729] INFO: bigquant: dropnan.v1 开始运行..
    [2018-06-14 08:22:24.002361] INFO: bigquant: 命中缓存
    [2018-06-14 08:22:24.003562] INFO: bigquant: dropnan.v1 运行完成[0.003842s].
    [2018-06-14 08:22:24.023381] INFO: bigquant: backtest.v7 开始运行..
    [2018-06-14 08:22:24.161448] INFO: algo: set price type:backward_adjusted
    [2018-06-14 08:22:26.918126] INFO: Performance: Simulated 106 trading days out of 106.
    [2018-06-14 08:22:26.919317] INFO: Performance: first open: 2018-01-02 01:30:00+00:00
    [2018-06-14 08:22:26.920188] INFO: Performance: last close: 2018-06-11 07:00:00+00:00
    
    • 收益率33.24%
    • 年化收益率97.82%
    • 基准收益率-6.22%
    • 阿尔法1.1
    • 贝塔0.92
    • 夏普比率2.42
    • 胜率1.0
    • 盈亏比--
    • 收益波动率38.53%
    • 信息比率3.22
    • 最大回撤13.1%
    [2018-06-14 08:22:27.529136] INFO: bigquant: backtest.v7 运行完成[3.505723s].
    

    (ZjFy) #3

    谢谢哈,已经好了


    回测没有问题,但点击开始交易后没有任何结果
    (siyishenqing) #4

    我跟你一样的问题,请问你是怎么解决的啊


    (达达) #5

    就是这段中的代码,您的start_date和end_date可能是个具体的日期,不会在模拟实盘更新的,需要改成T.live_run_param(‘trading_date’,‘2018-01-02’)这种写法,建议您用可视化模板,可以克隆上述案例
    image


    (siyishenqing) #6

    好的,我改好了,还不知道行不行,谢啦