为什么实盘没有交易信号

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    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e, p in context.portfolio.positions.items() if p.amount>0}\n for instrument in equities:\n sid = equities[instrument].sid # 交易标的\n # 今天和上次交易的时间相隔hold_days就全部卖出\n dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.options['hold_days']].values[0])\n if pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):\n context.order_target_percent(sid, 0)\n cash_for_buy += positions[instrument]\n #--------------------------------END:持有固定天数卖出--------------------------- \n \n\n # 3. 生成买入订单:按StockRanker预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n counto = 0\n str_list = [ ]\n \n for i in range(len(buy_cash_weights)):\n while (' '.join(ranker_prediction.instrument[counto:counto+1])) in positions.keys():\n counto += 1\n str_list.append(list(ranker_prediction.instrument[counto:counto+1]))\n \n counto += 1\n \n buy_instruments = [ ]\n buy_instruments.extend([x[0] for x in str_list])\n \n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n #print(context.symbol(instrument), cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"def bigquant_run(context):\n #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度\n # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值),其中context.start_date和context.end_date是回测指定的起始时间和终止时间\n start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d') \n df=DataSource('bar1d_index_CN_STOCK_A').read(start_date=start_date,end_date=context.end_date,fields=['close'])\n benckmark_data=df[df.instrument=='000001.HIX']\n #计算上证指数5日涨幅\n 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    In [5]:
    # 本代码由可视化策略环境自动生成 2020年10月30日 09:55
    # 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。
    
    
    # Python 代码入口函数,input_1/2/3 对应三个输入端,data_1/2/3 对应三个输出端
    def m17_run_bigquant_run(input_1):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read_df()
        df = pd.DataFrame(df)
        df = df.groupby(['date','score'], as_index = True, sort = False).apply(lambda x: x.sort_values('ranker', ascending = False))
        df = df.reset_index(drop=True)
        data_1 = DataSource.write_df(df)
       
        return Outputs(data_1=data_1)
    
    # 后处理函数,可选。输入是主函数的输出,可以在这里对数据做处理,或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m17_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
    
        # 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))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 5
        # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]
        context.stock_weights = [0.2]*5
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.04
        context.options['hold_days'] = 5
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        today = data.current_dt.strftime('%Y-%m-%d')
        stock_hold_now = [equity.symbol for equity in context.portfolio.positions ]
        ##大盘风控模块,读取风控数据    
        #benckmark_risk=context.benckmark_risk[today]
        #context.symbol
        ##当risk为1时,市场有风险,全部平仓,不再执行其它操作
        #if benckmark_risk > 0:
        #    for instrument in stock_hold_now:
        #        context.order_target(symbol(instrument), 0)
        #    print(today,'大盘风控止损触发,全仓卖出')
        #    return
    
        
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
        
        if len(ranker_prediction.instrument) < 3:
            return
        
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.portfolio.positions.items()}
       
        #----------------------------START:持有固定交易日天数卖出---------------------------
        today = data.current_dt.strftime('%Y-%m-%d')
        # 不是建仓期(在前hold_days属于建仓期)
        if not is_staging:
            equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
            for instrument in equities:
                sid = equities[instrument].sid  # 交易标的
                # 今天和上次交易的时间相隔hold_days就全部卖出
                dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.options['hold_days']].values[0])
                if  pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):
                    context.order_target_percent(sid, 0)
                    cash_for_buy += positions[instrument]
        #--------------------------------END:持有固定天数卖出--------------------------- 
              
    
        # 3. 生成买入订单:按StockRanker预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        counto = 0
        str_list = [ ]
        
        for i in range(len(buy_cash_weights)):
            while (' '.join(ranker_prediction.instrument[counto:counto+1])) in positions.keys():
                counto += 1
            str_list.append(list(ranker_prediction.instrument[counto:counto+1]))
            
            counto += 1
        
        buy_instruments = [ ]
        buy_instruments.extend([x[0] for x in str_list])
        
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
                #print(context.symbol(instrument), cash)
    
    def m19_prepare_bigquant_run(context):
        #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度
        # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值),其中context.start_date和context.end_date是回测指定的起始时间和终止时间
        start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d') 
        df=DataSource('bar1d_index_CN_STOCK_A').read(start_date=start_date,end_date=context.end_date,fields=['close'])
        benckmark_data=df[df.instrument=='000001.HIX']
        #计算上证指数5日涨幅
        benckmark_data['ret5']=benckmark_data['close']/benckmark_data['close'].shift(1)-1
        #计算大盘风控条件,如果5日涨幅小于-4%则设置风险状态risk为1,否则为0
        benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.04,1,0)
        #修改日期格式为字符串(便于在handle中使用字符串日期索引来查看每日的风险状态)
        benckmark_data['date']=benckmark_data['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
        #设置日期为索引
        benckmark_data.set_index('date',inplace=True)
        #把风控序列输出给全局变量context.benckmark_risk
        context.benckmark_risk=benckmark_data['risk']
    
    
    m1 = M.instruments.v2(
        start_date='2016-01-01',
        end_date='2019-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -6)/shift(close, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""Alpha1 = rank_swing_volatility_60_0#相关性高
    #Alpha2 = rank_return_20#分位不区分
    Alpha3 = rank_avg_turn_5
    Alpha4 = rank_swing_volatility_120_0#相关性高
    Alpha6 = rank_swing_volatility_5_0
    
    Alpha7 = swing_volatility_30_0
    Alpha8 = swing_volatility_60_0
    
    Alpha9 = volatility_120_0
    Alpha39 = volatility_30_0
    
    #Alpha10 = avg_turn_5#分位不明显,可加入
    Alpha11 = avg_amount_5
    #Alpha12 = avg_amount_0/avg_amount_5#分位不区分
    #Alpha13 = avg_amount_5/avg_amount_20#分位不区分,IC低
    #Alpha14 = return_5#分位不区分
    #Alpha15 = return_10#分位不区分
    #Alpha16 = return_20#分位不区分
    
    Alpha17 = ta_rsi_14_0
    Alpha38 = ta_cci_28_0
    Alpha19 = ta_aroon_up_28_0
    Alpha40 = ta_mom_20_0
    
    Alpha20 = mean(turn_0*return_0, 15)
    Alpha36 = mean(mf_net_amount_l_0, 30)#正向
    
    Alpha24 = std(turn_0, 10)
    Alpha25 = std(return_0, 60)#相关性高
    Alpha26 = std(avg_amount_0, 60)#相关性高
    Alpha33 = std(avg_turn_5, 5)
    Alpha34 = std(mean(deal_number_0, 10),10)
    Alpha35 = std(mean(volume_0, 60), 60)
                
    Alpha29 = (-1 * correlation(high_0, rank(volume_0), 5))#正向
    Alpha30 = (-1 * rank(covariance(rank(close_0), rank(volume_0), 5)))#正向#相关性高
    Alpha31 = (-1 * rank(covariance(rank(high_0), rank(volume_0), 5)))#正向
    Alpha32 = ((-1 * rank(std(high_0, 10))) * correlation(high_0, volume_0, 10))#正向
    
    """
    )
    
    m12 = M.input_features.v1(
        features_ds=m3.data,
        features="""#每档排序指标,默认从大到小排序,若想从小到大排序,在前面加负号-
    ranker = close_0/mean(close_0, 5)
    
    #过滤条件
    #f15 = mean(close_1, 5)
    #f05 = mean(close_0, 5)
    #f110 = mean(close_1, 10)
    #f010 = mean(close_0, 10)
    
    
    """
    )
    
    m20 = M.input_features.v1(
        features_ds=m3.data,
        features="""
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    #f15 = mean(close_1, 5)
    #f05 = mean(close_0, 5)
    #f110 = mean(close_1, 10)
    #f010 = mean(close_0, 10)
    close_0"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m20.data,
        start_date='',
        end_date='',
        before_start_days=300
    )
    
    m24 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m20.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m10 = M.join.v3(
        data1=m2.data,
        data2=m24.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m6 = M.filtet_st_stock.v7(
        input_1=m10.data
    )
    
    m4 = M.filter.v3(
        input_data=m6.data_1,
        expr='close_0 > 0',
        output_left_data=False
    )
    
    m5 = M.dropnan.v2(
        input_data=m4.data
    )
    
    m16 = M.instruments.v2(
        start_date='2020-01-01',
        end_date='2020-10-29',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m18 = M.general_feature_extractor.v7(
        instruments=m16.data,
        features=m12.data,
        start_date='',
        end_date='',
        before_start_days=300
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m18.data,
        features=m12.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )
    
    m7 = M.filtet_st_stock.v7(
        input_1=m26.data
    )
    
    m22 = M.dropnan.v2(
        input_data=m7.data_1
    )
    
    m13 = M.select_columns.v3(
        input_ds=m22.data,
        columns='date,instrument,ranker',
        reverse_select=False
    )
    
    m9 = M.advanced_auto_labeler.v2(
        instruments=m16.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -6)/shift(close, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m8 = M.join.v3(
        data1=m9.data,
        data2=m26.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m27 = M.filtet_st_stock.v7(
        input_1=m8.data
    )
    
    m11 = M.dropnan.v2(
        input_data=m27.data_1
    )
    
    m23 = M.stock_ranker.v2(
        training_ds=m5.data,
        features=m3.data,
        test_ds=m11.data,
        predict_ds=m22.data,
        learning_algorithm='排序',
        number_of_leaves=30,
        minimum_docs_per_leaf=1000,
        number_of_trees=20,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        data_row_fraction=1,
        ndcg_discount_base=1,
        slim_data=True
    )
    
    m14 = M.join.v3(
        data1=m23.predictions,
        data2=m13.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m21 = M.sort.v4(
        input_ds=m14.data,
        sort_by='position',
        group_by='date',
        keep_columns='--',
        ascending=True
    )
    
    m17 = M.cached.v3(
        input_1=m21.sorted_data,
        run=m17_run_bigquant_run,
        post_run=m17_post_run_bigquant_run,
        input_ports='input_1',
        params='{}',
        output_ports=''
    )
    
    m19 = M.trade.v4(
        instruments=m16.data,
        options_data=m17.data_1,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='close',
        order_price_field_sell='close',
        capital_base=10000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark=''
    )
    
    列: ['date', 'instrument', 'ranker']
    /data: 991627
    
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3d452de68d6c4cca9e2ba48fdf25051a"}/bigcharts-data-end
    设置评估测试数据集,查看训练曲线
    [视频教程]StockRanker训练曲线
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-91fc70053e1346dc8accf4ea5bc4b8ba"}/bigcharts-data-end
    • 收益率38.1%
    • 年化收益率50.81%
    • 基准收益率16.51%
    • 阿尔法0.26
    • 贝塔0.77
    • 夏普比率1.78
    • 胜率0.51
    • 盈亏比1.49
    • 收益波动率22.92%
    • 信息比率0.09
    • 最大回撤10.67%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-1eec77e8394443f4b5613bc44ddcd83b"}/bigcharts-data-end
    In [6]:
    #m4.predictions.read_all_df().to_csv('3.csv')
    
    In [7]:
    #m21.sorted_data.read_all_df()
    
    In [8]:
    #m4.predictions.read_df().to_csv('1.csv')
    #dt.to_csv('3.csv')
    

    我这个策略回测没有任何问题,都按照正常逻辑买卖,但是模拟实盘中只有第一天有计划交易,从第二天开始都没有交易信号了,策略日志也没有报错,请问是什么问题?


    (tiantianz) #2

    请问可以讲一下你的策略逻辑吗


    (adhaha111) #3

    您好,您的预测集还需要绑定实盘参数:
    image
    并且,按照道理,验证集和预测集不能是同一数据集


    (zxc7573316672) #4

    您好,实盘绑定参数是什么意思,我的预测集开始和结束日期都设置了,验证集和预测集也不是同一数据集。


    (zxc7573316672) #5

    出问题是模拟实盘,没有进行实盘交易,是模拟实盘中有问题


    (adhaha111) #6

    如果您想提交模拟交易,需要绑定该参数,因为模拟交易需要每天更新数据来预测最新日的结果:AI量化策略开发第八步:模拟实盘