BUG:【 按概念过滤股票】模块

策略分享
bug
标签: #<Tag:0x00007fc8287d2a10> #<Tag:0x00007fc8287d28a8>

(a1641181638) #1

因为之前【按指定概念选取数据】模块不能模糊匹配,按建议使用了【按概念过滤板块】。
但是做实验时候,发现两个问题:
1、有时候输出数据有问题。不能准确输出数据,检查数据发现并没有起到选中指定概念的作用【即使是模糊】。


2、加入这个模块之后,训练时间成倍成倍的增加。甚至很简单的比如【5G】概念,17-18训练,19-20TEST,ranker算法里的任意算法,都能训练一两个小时

克隆策略
In [7]:
#本版本,增加了股票过滤(概念股选择)、
#通过【“每日持仓分析”、“因子收益及风险分析”、“Brinson绩效归因”、“最近N日绩效评估”】进行绩效测试
#回测版本7天回测一次

    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    In [16]:
    # 本代码由可视化策略环境自动生成 2020年2月12日 17:10
    # 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。
    
    
    # 回测引擎:初始化函数,只执行一次
    def m6_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 = 3
        # 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.options['hold_days'] = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m6_handle_data_bigquant_run(context, data):
        #------------------------START:加入下面if的两行代码到之前到主函数的最前部分-------------------
        # 相隔几天(以5天举例)运行一下handle_data函数
        if context.trading_day_index % 5 != 0:
            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')]
    
        # 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)
        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,按机器学习算法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:按机器学习算法预测的排序,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 m6_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2018-01-01',
        end_date='2019-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m4 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# 我的夏普-股票自动标注版本
    #未来5交易日总资产日回报率的算数均值   除以   未来5交易日总资产日回报率的标准差
    #未来5交易日总资产日回报率的算数均值:  mean(((close_0 - open_0) / open_0),-5)
    #未来5交易日总资产日回报率的标准差:stddev(((close_0 - open_0) / open_0),-5)
    #assetDayReturn = (shift(close_0, -1) - shift(open_0, -1)) / shift(open_0, -1)
    #mean((shift(close_0, -1) - shift(open_0, -1)) / shift(open_0, -1),5) / stddev((shift(close_0, -1) - shift(open_0, -1)) / shift(open_0, -1),5)
    mean(((close - open) / open),-5) / stddev(((close - open) / open),-5)
    
    # 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10个分类
    all_wbins(label, 10)
    
    # 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
    )
    
    m2 = M.input_features.v1(
        features="""#分别代表
    #总市值
    market_cap_0  
    #市盈率
    pe_lyr_0  
    #市净率
    pb_lf_0
    #市销率
    ps_ttm_0  """
    )
    
    m25 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m2.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m26 = M.derived_feature_extractor.v3(
        input_data=m25.data,
        features=m2.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m5 = M.join.v3(
        data1=m4.data,
        data2=m26.data,
        on='date,instrument',
        how='inner',
        sort=True
    )
    
    m3 = M.filter_concepts.v1(
        input_1=m5.data,
        concept_str='医疗;'
    )
    
    m23 = M.dropnan.v1(
        input_data=m3.data_1
    )
    
    m9 = M.stock_ranker_train.v6(
        training_ds=m23.data,
        features=m2.data,
        learning_algorithm='logloss',
        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,
        m_lazy_run=False
    )
    
    m22 = M.instruments.v2(
        start_date='2019-01-01',
        end_date='2020-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m27 = M.general_feature_extractor.v7(
        instruments=m22.data,
        features=m2.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m28 = M.derived_feature_extractor.v3(
        input_data=m27.data,
        features=m2.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.filter_concepts.v1(
        input_1=m28.data,
        concept_str='医疗;'
    )
    
    m24 = M.dropnan.v1(
        input_data=m7.data_1
    )
    
    m21 = M.stock_ranker_predict.v5(
        model=m9.model,
        data=m24.data,
        m_lazy_run=False
    )
    
    m6 = M.trade.v4(
        instruments=m22.data,
        options_data=m21.predictions,
        start_date='',
        end_date='',
        initialize=m6_initialize_bigquant_run,
        handle_data=m6_handle_data_bigquant_run,
        prepare=m6_prepare_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='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__id":"bigchart-f86fa2d3cd374059958b65c67ad72a15","__type":"tabs"}/bigcharts-data-end
    • 收益率17.57%
    • 年化收益率18.19%
    • 基准收益率36.07%
    • 阿尔法-0.07
    • 贝塔0.78
    • 夏普比率0.67
    • 胜率0.55
    • 盈亏比1.23
    • 收益波动率25.29%
    • 信息比率-0.04
    • 最大回撤14.8%
    bigcharts-data-start/{"__id":"bigchart-74640f2bc6e54496a2f2332378c70efc","__type":"tabs"}/bigcharts-data-end
    In [ ]:
     
    

    (iQuant) #2

    收到您的反馈,我们先来检查一下,稍后给您回复。