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策略简介CNN- 卷积神经网络 - --资金流大单DDX----大单流入zha

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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)\n is_staging = context.trading_day_index < context.hold_days # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.hold_days\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按StockRanker预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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    In [3]:
    # 本代码由可视化策略环境自动生成 2021年12月24日 18:52
    # 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 m4_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle())
        
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        
        data_1 = DataSource.write_pickle(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 m4_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m8_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df =  input_1.read_pickle()
        feature_len = len(input_2.read_pickle())
        
        
        df['x'] = df['x'].reshape(df['x'].shape[0], int(feature_len), int(df['x'].shape[1]/feature_len))
        
        data_1 = DataSource.write_pickle(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 m8_post_run_bigquant_run(outputs):
        return outputs
    
    # 用户的自定义层需要写到字典中,比如
    # {
    #   "MyLayer": MyLayer
    # }
    m5_custom_objects_bigquant_run = {
        
    }
    
    # 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 m24_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        pred_label = input_1.read_pickle()
        df = input_2.read_df()
        df = pd.DataFrame({'pred_label':pred_label[:,0], 'instrument':df.instrument, 'date':df.date})
        df.sort_values(['date','pred_label'],inplace=True, ascending=[True,False])
        return Outputs(data_1=DataSource.write_df(df), data_2=None, data_3=None)
    
    # 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 m24_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m2_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 = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.2
        context.hold_days = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m2_handle_data_bigquant_run(context, data):
        # 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')]
    
        # 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.hold_days # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.hold_days
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.perf_tracker.position_tracker.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按StockRanker预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            # print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 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
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        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)
    
    # 回测引擎:准备数据,只执行一次
    def m2_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m2_before_trading_start_bigquant_run(context, data):
        pass
    
    
    m1 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2010-01-01'),
        end_date=T.live_run_param('trading_date', '2019-10-01'),
        market='CN_STOCK_A',
        instrument_list=' ',
        max_count=0
    )
    
    m21 = M.use_datasource.v1(
        instruments=m1.data,
        datasource_id='stock_status_CN_STOCK_A',
        start_date='',
        end_date=''
    )
    
    m22 = M.filter.v3(
        input_data=m21.data,
        expr='price_limit_status==3',
        output_left_data=False
    )
    
    m23 = M.select_columns.v3(
        input_ds=m22.data,
        columns='date,instrument',
        reverse_select=False
    )
    
    m20 = M.use_datasource.v1(
        instruments=m1.data,
        datasource_id='bar1d_CN_STOCK_A',
        start_date='',
        end_date=''
    )
    
    m29 = M.join.v3(
        data1=m20.data,
        data2=m23.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m31 = M.auto_labeler_on_datasource.v1(
        input_data=m29.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/develop/datasource/deprecated/history_data.html
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.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, -5) / shift(open, -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)
    # 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)
    -1*((shift(close,-3) / shift(open, -1))-1)
    #where(shift(volume, -2)>shift(volume, -1), 1, 0)>0
    #where(shift(close, -2)>shift(close, -1), 1, 0)>0
    #where(shift(close, -3)>shift(close, -2), 1, 0)>0
    #
    # 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))
    
    all_wbins(label, 5)
    # 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))|(shift(high, -1) == shift(open, -1))&(shift(open, -1) != shift(close, -1))|(shift(low, -1) == shift(close, -1))&(shift(open, -1) != shift(close, -1)), NaN, label)
    
    #where(label>0.5, NaN, label)
    #where(label<-0.5, NaN, label)
    """,
        drop_na_label=True,
        cast_label_int=False,
        date_col='date',
        instrument_col='instrument'
    )
    
    m3 = M.input_features.v1(
        features="""volume_0/volume_1
    volume_0/volume_1
    rank_avg_mf_net_amount_5
    correlation(volume_0/volume_1, return_0, 5)
    ta_mfi_14_0
    daily_return_1
    return_10
    return_20
    covariance(avg_amount_5, return_5, 10)
    avg_amount_5
    avg_amount_20
    """
    )
    
    m25 = M.input_features.v1(
        features_ds=m3.data,
        features="""open_1
    close_1
    close_0
    high_1
    open_0
    low_0
    
    price_limit_status_0
    volume_0
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m25.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m25.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m31.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m39 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m37 = M.features_short.v1(
        input_1=m3.data
    )
    
    m26 = M.dl_convert_to_bin.v2(
        input_data=m39.data,
        features=m37.data_1,
        window_size=5,
        feature_clip=5,
        flatten=True,
        window_along_col='instrument'
    )
    
    m4 = M.cached.v3(
        input_1=m26.data,
        input_2=m37.data_1,
        run=m4_run_bigquant_run,
        post_run=m4_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m43 = M.input_features.v1(
        features_ds=m3.data,
        features="""
    open_1
    close_1
    close_0
    high_1
    open_0
    low_0
    
    price_limit_status_0
    volume_0
    """
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2020-09-02'),
        end_date=T.live_run_param('trading_date', '2021-12-23'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m43.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m43.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=True,
        remove_extra_columns=False
    )
    
    m13 = M.use_datasource.v1(
        instruments=m9.data,
        datasource_id='stock_status_CN_STOCK_A',
        start_date='',
        end_date=''
    )
    
    m14 = M.filter.v3(
        input_data=m13.data,
        expr='price_limit_status==3',
        output_left_data=False
    )
    
    m35 = M.select_columns.v3(
        input_ds=m14.data,
        columns='date,instrument',
        reverse_select=False
    )
    
    m36 = M.join.v3(
        data1=m18.data,
        data2=m35.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m45 = M.filtet_st_stock.v7(
        input_1=m36.data
    )
    
    m27 = M.dl_convert_to_bin.v2(
        input_data=m45.data_1,
        features=m37.data_1,
        window_size=5,
        feature_clip=2,
        flatten=True,
        window_along_col='instrument'
    )
    
    m8 = M.cached.v3(
        input_1=m27.data,
        input_2=m37.data_1,
        run=m8_run_bigquant_run,
        post_run=m8_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = M.dl_layer_input.v1(
        shape='10,5',
        batch_shape='',
        dtype='float32',
        sparse=False,
        name=''
    )
    
    m10 = M.dl_layer_conv1d.v1(
        inputs=m6.data,
        filters=32,
        kernel_size='5',
        strides='1',
        padding='valid',
        dilation_rate=1,
        activation='relu',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m12 = M.dl_layer_maxpooling1d.v1(
        inputs=m10.data,
        pool_size=1,
        padding='valid',
        name=''
    )
    
    m32 = M.dl_layer_conv1d.v1(
        inputs=m12.data,
        filters=32,
        kernel_size='3',
        strides='1',
        padding='valid',
        dilation_rate=1,
        activation='relu',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m33 = M.dl_layer_maxpooling1d.v1(
        inputs=m32.data,
        pool_size=1,
        padding='valid',
        name=''
    )
    
    m28 = M.dl_layer_globalmaxpooling1d.v1(
        inputs=m33.data,
        name=''
    )
    
    m30 = M.dl_layer_dense.v1(
        inputs=m28.data,
        units=1,
        activation='linear',
        use_bias=True,
        kernel_initializer='glorot_uniform',
        bias_initializer='Zeros',
        kernel_regularizer='None',
        kernel_regularizer_l1=0,
        kernel_regularizer_l2=0,
        bias_regularizer='None',
        bias_regularizer_l1=0,
        bias_regularizer_l2=0,
        activity_regularizer='None',
        activity_regularizer_l1=0,
        activity_regularizer_l2=0,
        kernel_constraint='None',
        bias_constraint='None',
        name=''
    )
    
    m34 = M.dl_model_init.v1(
        inputs=m6.data,
        outputs=m30.data
    )
    
    m5 = M.dl_model_train.v1(
        input_model=m34.data,
        training_data=m4.data_1,
        optimizer='RMSprop',
        loss='mean_squared_error',
        metrics='mae',
        batch_size=10240,
        epochs=0,
        custom_objects=m5_custom_objects_bigquant_run,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m11 = M.dl_model_predict.v1(
        trained_model=m5.data,
        input_data=m8.data_1,
        batch_size=1024,
        n_gpus=0,
        verbose='2:每个epoch输出一行记录'
    )
    
    m24 = M.cached.v3(
        input_1=m11.data,
        input_2=m45.data_1,
        run=m24_run_bigquant_run,
        post_run=m24_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m42 = M.sort.v4(
        input_ds=m24.data_1,
        sort_by='date',
        group_by='--',
        keep_columns='--',
        ascending=True
    )
    
    m2 = M.trade.v4(
        instruments=m9.data,
        options_data=m42.sorted_data,
        start_date='',
        end_date='',
        initialize=m2_initialize_bigquant_run,
        handle_data=m2_handle_data_bigquant_run,
        prepare=m2_prepare_bigquant_run,
        before_trading_start=m2_before_trading_start_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        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='000300.HIX'
    )
    
    17/17 - 0s
    DataSource(f253e9d3e7cf494b9d6195f18c0a02bdT)
    
    • 收益率-0.75%
    • 年化收益率-0.59%
    • 基准收益率2.2%
    • 阿尔法-0.01
    • 贝塔0.51
    • 夏普比率-0.05
    • 胜率0.63
    • 盈亏比0.81
    • 收益波动率22.37%
    • 信息比率-0.0
    • 最大回撤22.74%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-0195ba899bc0487e90de25384f2ab3bb"}/bigcharts-data-end