AI量化策略开发进阶

可视化
bigstudio
标签: #<Tag:0x00007f5205078588> #<Tag:0x00007f52050783d0>

(iQuant) #1

本文主要介绍在AI可视化模板策略上的一些尝试,加入一些更为复杂的逻辑,最终提高策略开发能力达到进阶水平。

不知道大家是否有这样的疑惑,在按照平台给出的模板开发了策略以后,不知道从哪些角度提升策略效果,只知道增删一些因子不断调试。本文以一个实际的策略案例,希望大家能够掌握以下几点:

  • 修改训练集、测试集时间
  • 通过表达式引擎构建衍生因子
  • 修改数据标注
  • 通过某因子过滤数据集
  • 自定义模块
  • 加入固定百分比止损

我们依次对每点进行阐释:

  1. 修改训练集、测试集时间
     
    金融市场不同时期具有不同的市场风格,比如15年之前,中小创涨得不错,17年以来白马股表现喜人。因此我们可以通过修改开始结束日期来确定不同的训练集和测试集。如图1所示。
    1
    $$图1:修改日期获取不同的数据集$$

  2. 通过表达式引擎构建衍生因子
     
    构建衍生因子属于特征工程内容,平台的表达式引擎能够快速支持大量复杂的衍生因子。当我们评估一个人身体状况时,如果给了身高、体重数据,其实并不难很好地进行判断,但是如果我们构建一个新的特征——身高体重比(身高除以比重),此时对身体状况的判断可能有所帮助,而且这个特征对模型的贡献得分应该大于身高和体重。此外,数据也需要进行一些处理,比如销售净利率特征(fs_net_profit_margin_0),该特征能够表示公司的盈利能力,但是不同的行业周期、不同的季度变动是很大的,因此一家公司盈利能力怎么样可能还需考虑其所在的行业,因此销售净利率/所在行业平均销售盈利率这一衍生特征可能更好。更多表达式引擎的运用可以参考:bigexpr

fs_net_profit_margin_0/group_mean(industry_sw_level1_0, fs_net_profit_margin_0)

$$经行业调整过的销售净利特征率$$
将“经行业调整过的销售净利特征率”也加入 输入特征列表 模块

  1. 修改数据标注
     
    数据标注非常重要,重要性并不亚于特征抽取,也称构建因子。如果抽取特征大多为价量特征,那么最好是通过短期的收益率来标注股票。如果抽取的特征是财务报表中的数据,比如市盈率、净资产收益率、资产负债率,因为这类因子更新很慢,一个季度才会有所调整,因此需用长期的数据,例如未来90天收益率对股票进行标注。因此数据标注和特征抽取紧密相连,具有逻辑的一致性。这里我们的标注是未来90天收益率。
    shift(close, -90) / shift(open, -1)

%E6%95%B0%E6%8D%AE%E6%A0%87%E6%B3%A8
$$图2:修改数据标注$$

  1. 通过某因子过滤数据集
     
    平台目前提供了一个数据过滤模块,以便快速对数据集进行过滤。当我们需要根据某个特征来过滤时,首先应该在 输入特征列表 这个模块输入该特征。这里我们以过滤掉上市不足120个自然日的股票举例。
    %E6%95%B0%E6%8D%AE%E8%BF%87%E6%BB%A4
    $$图3:过滤数据集$$

  2. 自定义模块
     
    虽然在画布的左侧有不少数据和模块可供选择(平台已封装好,可直接使用),但是机器学习和量化研究会面临各种各样的需求,因此不能单单靠封装好了的模块,在此我们可以使用 自定义模块。以去除数据集里面创业板的股票举例说明。


    因为创业板的股票以3开头,所以将股票代码以3开头的股票去掉即可,关于自定义模块如何使用可以参考:BigStudio使用文档介绍(五)
    $$图4:自定义模块$$

  3. 加入固定百分比止损
     
    虽然我们的模板策略是AI量化选股策略,但是我们依然可以将其与人工逻辑信号相结合,比如加入止盈止损、修改资金分配、加入大盘择时等等,这些都是在 Trade(交易/回测)模块里的主函数(handle_data)里实现。这里我们简单加一个固定百分比止损的例子。具体方法是将止损的代码加入Trade(交易/回测)模块主函数。如图4:


    $$图5:百分比止损$$

策略回测结果见图5:


$$图6:策略回测曲线$$

代码如下,欢迎克隆!

克隆策略

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    In [21]:
    # 本代码由可视化策略环境自动生成 2019年4月22日 15:06
    # 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 m5_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_df()
        print('起始数据量:',len(df))
        condi = df['instrument'].map(lambda x: x[0] != '3')
        df = df[condi]
        print('去除创业板后数据量:',len(df))
        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 m5_post_run_bigquant_run(outputs):
        return outputs
    
    # 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):
        # 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()}
        
        #----------------------------------------------止损模块START-----------------------------------------------------
        date = data.current_dt.strftime('%Y-%m-%d')
        costs = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}
        # 新建当日止损股票列表是为了handle_data策略逻辑分布不再对该股票进行判断
        current_stoploss_stock = []
        if len(costs) > 0:
            for i in costs.keys():
                stock_cost = costs[i]
                stock_market_price = data.current(context.symbol(i), 'price')
                # 亏5%就止损
                if (stock_market_price - stock_cost) / stock_cost <= -0.25:
                    context.order_target_percent(context.symbol(i),0)
                    current_stoploss_stock.append(i)
                    print('日期:',date,'股票:',i,'出现止损情况')
        #-----------------------------------------------止损模块END-----------------------------------------------------
        
        # 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 m19_prepare_bigquant_run(context):
        pass
    
    # 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 = 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
    
    
    m1 = M.instruments.v2(
        start_date='2009-01-01',
        end_date='2015-01-01',
        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, -90) / 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)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = 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
    return_5
    return_10
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    list_days_0
    fs_net_profit_margin_0/group_mean(industry_sw_level1_0, fs_net_profit_margin_0)""",
        m_cached=False
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m4 = M.filter.v3(
        input_data=m13.data,
        expr=' list_days_0 >=120',
        output_left_data=False
    )
    
    m5 = M.cached.v3(
        input_1=m4.data,
        run=m5_run_bigquant_run,
        post_run=m5_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m5.data_1,
        features=m3.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,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        initialize=m19_initialize_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'
    )
    
    起始数据量: 2698219
    去除创业板后数据量: 2444039
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-66cf240bb46f41f0b39bc0ce7c0b196b"}/bigcharts-data-end
    日期: 2015-06-26 股票: 000611.SZA 出现止损情况
    日期: 2015-06-29 股票: 000611.SZA 出现止损情况
    日期: 2015-06-30 股票: 000611.SZA 出现止损情况
    日期: 2015-07-01 股票: 000611.SZA 出现止损情况
    日期: 2015-07-01 股票: 002352.SZA 出现止损情况
    日期: 2015-07-02 股票: 600242.SHA 出现止损情况
    日期: 2015-07-02 股票: 600071.SHA 出现止损情况
    日期: 2015-07-02 股票: 000560.SZA 出现止损情况
    日期: 2015-07-02 股票: 000611.SZA 出现止损情况
    日期: 2015-07-02 股票: 000025.SZA 出现止损情况
    日期: 2015-07-03 股票: 600071.SHA 出现止损情况
    日期: 2015-07-03 股票: 000018.SZA 出现止损情况
    日期: 2015-07-03 股票: 000560.SZA 出现止损情况
    日期: 2015-07-03 股票: 600242.SHA 出现止损情况
    日期: 2015-07-03 股票: 000025.SZA 出现止损情况
    日期: 2015-07-03 股票: 000611.SZA 出现止损情况
    日期: 2015-07-06 股票: 600071.SHA 出现止损情况
    日期: 2015-07-06 股票: 000018.SZA 出现止损情况
    日期: 2015-07-06 股票: 000560.SZA 出现止损情况
    日期: 2015-07-06 股票: 000020.SZA 出现止损情况
    日期: 2015-07-06 股票: 600242.SHA 出现止损情况
    日期: 2015-07-06 股票: 000025.SZA 出现止损情况
    日期: 2015-07-06 股票: 000611.SZA 出现止损情况
    日期: 2015-07-07 股票: 600071.SHA 出现止损情况
    日期: 2015-07-07 股票: 000018.SZA 出现止损情况
    日期: 2015-07-07 股票: 000560.SZA 出现止损情况
    日期: 2015-07-07 股票: 000020.SZA 出现止损情况
    日期: 2015-07-07 股票: 600242.SHA 出现止损情况
    日期: 2015-07-07 股票: 000025.SZA 出现止损情况
    日期: 2015-07-07 股票: 000611.SZA 出现止损情况
    日期: 2015-07-08 股票: 600071.SHA 出现止损情况
    日期: 2015-07-08 股票: 000018.SZA 出现止损情况
    日期: 2015-07-08 股票: 000560.SZA 出现止损情况
    日期: 2015-07-08 股票: 000020.SZA 出现止损情况
    日期: 2015-07-08 股票: 600242.SHA 出现止损情况
    日期: 2015-07-08 股票: 000025.SZA 出现止损情况
    日期: 2015-07-08 股票: 600883.SHA 出现止损情况
    日期: 2015-07-08 股票: 000611.SZA 出现止损情况
    日期: 2015-07-09 股票: 600242.SHA 出现止损情况
    日期: 2015-07-09 股票: 000560.SZA 出现止损情况
    日期: 2015-07-09 股票: 000020.SZA 出现止损情况
    日期: 2015-07-10 股票: 000560.SZA 出现止损情况
    日期: 2015-07-10 股票: 000020.SZA 出现止损情况
    日期: 2015-07-10 股票: 600242.SHA 出现止损情况
    日期: 2015-07-13 股票: 000560.SZA 出现止损情况
    日期: 2015-07-13 股票: 000020.SZA 出现止损情况
    日期: 2015-07-14 股票: 000560.SZA 出现止损情况
    日期: 2015-07-14 股票: 000020.SZA 出现止损情况
    日期: 2015-07-15 股票: 000560.SZA 出现止损情况
    日期: 2015-07-15 股票: 000020.SZA 出现止损情况
    日期: 2015-07-16 股票: 000560.SZA 出现止损情况
    日期: 2015-07-16 股票: 000020.SZA 出现止损情况
    日期: 2015-07-17 股票: 000560.SZA 出现止损情况
    日期: 2015-07-17 股票: 000020.SZA 出现止损情况
    日期: 2015-07-20 股票: 000560.SZA 出现止损情况
    日期: 2015-07-21 股票: 000560.SZA 出现止损情况
    日期: 2015-07-22 股票: 000560.SZA 出现止损情况
    日期: 2015-07-23 股票: 000560.SZA 出现止损情况
    日期: 2015-07-24 股票: 000560.SZA 出现止损情况
    日期: 2015-07-27 股票: 000560.SZA 出现止损情况
    日期: 2015-08-24 股票: 600619.SHA 出现止损情况
    日期: 2015-08-24 股票: 000564.SZA 出现止损情况
    日期: 2015-08-25 股票: 600619.SHA 出现止损情况
    日期: 2015-08-25 股票: 000564.SZA 出现止损情况
    日期: 2015-08-25 股票: 600841.SHA 出现止损情况
    日期: 2015-08-26 股票: 600071.SHA 出现止损情况
    日期: 2015-08-26 股票: 000564.SZA 出现止损情况
    日期: 2015-09-01 股票: 600163.SHA 出现止损情况
    日期: 2015-09-02 股票: 600163.SHA 出现止损情况
    日期: 2016-01-12 股票: 600841.SHA 出现止损情况
    日期: 2016-01-14 股票: 600071.SHA 出现止损情况
    日期: 2016-06-14 股票: 600230.SHA 出现止损情况
    
    • 收益率146.51%
    • 年化收益率59.35%
    • 基准收益率-6.33%
    • 阿尔法0.5
    • 贝塔0.69
    • 夏普比率1.47
    • 胜率0.6
    • 盈亏比0.9
    • 收益波动率33.64%
    • 信息比率0.12
    • 最大回撤50.32%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-66bf21b7e4f24049af913b091d711629"}/bigcharts-data-end

    社区干货与精选整理(持续更新中...)
    (yangziriver) #4

    image
    #亏5%就止损
    应该是亏25%就止损吧?