超参搜索模块与自定义运行模块结合使用

策略分享
标签: #<Tag:0x00007fcf66d6ded0>

(华尔街的猫) #1

在策略开发过程中,要实现参数寻优和多因子等多个变量进行批量并行计算,就需要同时使用超参搜索模块与自定义运行模块。本文为大家介绍两模块结合使用的具体方法。

一、超参搜索模块

1. 超参数输入
在模块中输入自己设定的几个不同参数值,如设置运算树参数,代码案例:

代码示例
def bigquant_run():
    param_grid = {}
    param_grid['m4.number_of_trees'] = [4, 8]
    return param_grid

2. 评分函数
设定评分标准,在此计算夏普比率,作为最后的返回值。

代码示例
def bigquant_run(result):
    score = result.get('m19').read_raw_perf()['sharpe'].tail(1)[0]
    return score

3. 设置网格搜索

4. 设置并行运行作业数量
注意:取消勾选即时执行,如图
%E6%8D%95%E8%8E%B7

二、自定义运行模块

首先需要对参数列表parameters_list定义,在这个例子中定义了三个参数’pe_ttm_0’,‘shift(close_0,5)/close_0’,‘pb_lf_0’,分别是滚动市盈率、5日前与当日收盘值之比、市净率。

代码示例
def bigquant_run(bq_graph, inputs):
    g = bq_graph
    parameters_list = []
    features =['pe_ttm_0', 'shift(close_0,5)/close_0','pb_lf_0']
    for feature in features:
        parameters = {'m3.features':feature}
        parameters_list.append({'parameters': parameters})
    results = T.parallel_map(g.run, parameters_list, max_workers=1, backend='multiprocessing',remote_run=False, silent=False)
    return results

三、自定义Python模块

此处将每个参数和因子的score值以列表形式输出至result.csv文件中,结果可直接在左侧文件栏中查看。

  • 注意:超参数模块和自定义运行模块如想同时运行,需要将模块进行链接。如图
    1
克隆策略

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    In [15]:
    # 本代码由可视化策略环境自动生成 2020年8月20日 18:54
    # 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 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
    
    # 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.portfolio.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.portfolio.positions.items()}
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities)])))
    
            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
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2010-01-01',
        'm1.end_date': '2011-01-01',
        'm1.market': 'CN_STOCK_A',
        'm1.instrument_list': '',
        'm1.max_count': 0,
    
        'm2': 'M.advanced_auto_labeler.v2',
        'm2.instruments': T.Graph.OutputPort('m1.data'),
        'm2.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
    #   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/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)
    """,
        'm2.start_date': '',
        'm2.end_date': '',
        'm2.benchmark': '000300.SHA',
        'm2.drop_na_label': True,
        'm2.cast_label_int': True,
    
        'm3': 'M.input_features.v1',
        'm3.features': """pb_lf_0
    """,
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m3.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 20,
    
        'm16': 'M.derived_feature_extractor.v3',
        'm16.input_data': T.Graph.OutputPort('m15.data'),
        'm16.features': T.Graph.OutputPort('m3.data'),
        'm16.date_col': 'date',
        'm16.instrument_col': 'instrument',
        'm16.drop_na': False,
        'm16.remove_extra_columns': False,
    
        'm7': 'M.join.v3',
        'm7.data1': T.Graph.OutputPort('m2.data'),
        'm7.data2': T.Graph.OutputPort('m16.data'),
        'm7.on': 'date,instrument',
        'm7.how': 'inner',
        'm7.sort': False,
    
        'm5': 'M.dropnan.v2',
        'm5.input_data': T.Graph.OutputPort('m7.data'),
    
        'm4': 'M.stock_ranker_train.v6',
        'm4.training_ds': T.Graph.OutputPort('m5.data'),
        'm4.features': T.Graph.OutputPort('m3.data'),
        'm4.learning_algorithm': '排序',
        'm4.number_of_leaves': 30,
        'm4.minimum_docs_per_leaf': 1000,
        'm4.number_of_trees': 10,
        'm4.learning_rate': 0.1,
        'm4.max_bins': 1023,
        'm4.feature_fraction': 1,
        'm4.data_row_fraction': 1,
        'm4.ndcg_discount_base': 1,
        'm4.m_lazy_run': False,
    
        'm9': 'M.instruments.v2',
        'm9.start_date': '2012-01-01',
        'm9.end_date': '2012-10-01',
        'm9.market': 'CN_STOCK_A',
        'm9.instrument_list': '',
        'm9.max_count': 0,
    
        'm17': 'M.general_feature_extractor.v7',
        'm17.instruments': T.Graph.OutputPort('m9.data'),
        'm17.features': T.Graph.OutputPort('m3.data'),
        'm17.start_date': '',
        'm17.end_date': '',
        'm17.before_start_days': 20,
    
        'm18': 'M.derived_feature_extractor.v3',
        'm18.input_data': T.Graph.OutputPort('m17.data'),
        'm18.features': T.Graph.OutputPort('m3.data'),
        'm18.date_col': 'date',
        'm18.instrument_col': 'instrument',
        'm18.drop_na': False,
        'm18.remove_extra_columns': False,
    
        'm10': 'M.dropnan.v2',
        'm10.input_data': T.Graph.OutputPort('m18.data'),
    
        'm8': 'M.stock_ranker_predict.v5',
        'm8.model': T.Graph.OutputPort('m4.model'),
        'm8.data': T.Graph.OutputPort('m10.data'),
        'm8.m_lazy_run': False,
    
        'm19': 'M.trade.v4',
        'm19.instruments': T.Graph.OutputPort('m9.data'),
        'm19.options_data': T.Graph.OutputPort('m8.predictions'),
        'm19.start_date': '',
        'm19.end_date': '',
        'm19.initialize': m19_initialize_bigquant_run,
        'm19.handle_data': m19_handle_data_bigquant_run,
        'm19.prepare': m19_prepare_bigquant_run,
        'm19.volume_limit': 0.025,
        'm19.order_price_field_buy': 'open',
        'm19.order_price_field_sell': 'close',
        'm19.capital_base': 1000000,
        'm19.auto_cancel_non_tradable_orders': True,
        'm19.data_frequency': 'daily',
        'm19.price_type': '真实价格',
        'm19.product_type': '股票',
        'm19.plot_charts': True,
        'm19.backtest_only': False,
        'm19.benchmark': '000300.SHA',
    })
    
    # g.run({})
    
    
    def m6_param_grid_builder_bigquant_run():
        param_grid = {}
        param_grid['m4.number_of_trees'] = [4, 8]
        return param_grid
    
    def m6_scoring_bigquant_run(result):
        score = result.get('m19').read_raw_perf()['sharpe'].tail(1)[0]
    
        return score
    
    def m11_run_bigquant_run(bq_graph, inputs):
        
        g = bq_graph
        parameters_list = []
        features =['pe_ttm_0', 'shift(close_0,5)/close_0','pb_lf_0']
        
        for feature in features:
            parameters = {'m3.features':feature}
            parameters_list.append({'parameters': parameters})
    
        results = T.parallel_map(g.run, parameters_list, max_workers=1, backend='multiprocessing',remote_run=False, silent=False)
        return results
    
    # 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 m12_run_bigquant_run(input_1, input_2, input_3):
        a1=input_1[0].cv_results_['mean_train_score']
        a2=input_1[1].cv_results_['mean_train_score']
        a3=input_1[2].cv_results_['mean_train_score']
        data=numpy.array([a1,a2,a3])
        index=['pe_ttm_0','shift(close_0,5)/close_0','pb_lf_0']
        columns=['number_of_trees=4','number_of_trees=8']
        df=pd.DataFrame(data=data,columns=columns,index=index)
        df.to_csv('result.csv')
        return Outputs(data_1=input_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 m12_post_run_bigquant_run(outputs):
        return outputs
    
    
    m6 = M.hyper_parameter_search.v1(
        param_grid_builder=m6_param_grid_builder_bigquant_run,
        scoring=m6_scoring_bigquant_run,
        search_algorithm='网格搜索',
        search_iterations=10,
        workers=5,
        worker_distributed_run=False,
        worker_silent=True,
        run_now=False,
        bq_graph=g
    )
    
    m11 = M.hyper_run.v1(
        bq_graph_port=m6.result,
        run=m11_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    m12 = M.cached.v3(
        input_1=m11.result,
        run=m12_run_bigquant_run,
        post_run=m12_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports='',
        m_cached=False
    )
    
    [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
    [CV] m4.number_of_trees=4 ............................................
    [CV] m4.number_of_trees=8 ............................................
    
    Fitting 1 folds for each of 2 candidates, totalling 2 fits
    [Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.
    
    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-0ef03d727c8944f7a70e1ca97877a5b3"}/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-d4266a80193e47ed8a5aff6e10e268a9"}/bigcharts-data-end
    • 收益率-4.01%
    • 年化收益率-5.51%
    • 基准收益率-2.24%
    • 阿尔法-0.02
    • 贝塔0.81
    • 夏普比率-0.24
    • 胜率0.5
    • 盈亏比1.0
    • 收益波动率24.04%
    • 信息比率-0.01
    • 最大回撤22.01%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-1bf98e020a714e3e8d36a2e7fb8d105a"}/bigcharts-data-end
    • 收益率6.29%
    • 年化收益率8.81%
    • 基准收益率-2.24%
    • 阿尔法0.11
    • 贝塔0.78
    • 夏普比率0.35
    • 胜率0.53
    • 盈亏比1.02
    • 收益波动率22.66%
    • 信息比率0.05
    • 最大回撤24.9%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-00b7cd5d0bfc46f9a5521b510038bab3"}/bigcharts-data-end
    [CV] . m4.number_of_trees=4, score=-0.23896662832222307, total=   1.8s
    
    [Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    1.9s
    [CV] ... m4.number_of_trees=8, score=0.3549665229903602, total=   2.5s
    [Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    2.6s remaining:    0.0s
    [Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    2.6s finished
    
    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-c96b76de1c064fccaa227e162907b2c8"}/bigcharts-data-end
    • 收益率6.29%
    • 年化收益率8.81%
    • 基准收益率-2.24%
    • 阿尔法0.11
    • 贝塔0.78
    • 夏普比率0.35
    • 胜率0.53
    • 盈亏比1.02
    • 收益波动率22.66%
    • 信息比率0.05
    • 最大回撤24.9%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-4e711865e4084f579996b146c03b7351"}/bigcharts-data-end
    [Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    5.8s remaining:    0.0s
    [CV] m4.number_of_trees=4 ............................................
    [CV] m4.number_of_trees=8 ............................................
    
    Fitting 1 folds for each of 2 candidates, totalling 2 fits
    [Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.
    
    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-141b48e6840d4598916b1f741f6d9c32"}/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-aeae890bb71347b4a696373be4717d06"}/bigcharts-data-end
    • 收益率31.15%
    • 年化收益率45.57%
    • 基准收益率-2.24%
    • 阿尔法0.42
    • 贝塔1.02
    • 夏普比率1.49
    • 胜率0.54
    • 盈亏比1.19
    • 收益波动率25.38%
    • 信息比率0.18
    • 最大回撤13.14%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-e2e66ae20a084d2fb147854b9765e97f"}/bigcharts-data-end
    • 收益率3.8%
    • 年化收益率5.29%
    • 基准收益率-2.24%
    • 阿尔法0.11
    • 贝塔1.04
    • 夏普比率0.22
    • 胜率0.54
    • 盈亏比0.95
    • 收益波动率28.9%
    • 信息比率0.03
    • 最大回撤23.44%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-8a456b6c85dc40c6aa468df24237f8a8"}/bigcharts-data-end
    [CV] .. m4.number_of_trees=8, score=0.22081275874265358, total=   3.0s
    [CV] ... m4.number_of_trees=4, score=1.4906014574585014, total=   3.2s
    [Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    3.8s
    [Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    3.9s remaining:    0.0s
    [Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    3.9s finished
    
    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-c3e5b5d634dc424d91512845b500a509"}/bigcharts-data-end
    • 收益率31.15%
    • 年化收益率45.57%
    • 基准收益率-2.24%
    • 阿尔法0.42
    • 贝塔1.02
    • 夏普比率1.49
    • 胜率0.54
    • 盈亏比1.19
    • 收益波动率25.38%
    • 信息比率0.18
    • 最大回撤13.14%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-52a59114fdf94db3971f8f5eab6e5168"}/bigcharts-data-end
    [Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   11.7s remaining:    0.0s
    [CV] m4.number_of_trees=4 ............................................
    [CV] m4.number_of_trees=8 ............................................
    
    Fitting 1 folds for each of 2 candidates, totalling 2 fits
    [Parallel(n_jobs=5)]: Using backend MultiprocessingBackend with 5 concurrent workers.
    
    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-71771358151549d3b5e58027737c8a01"}/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-59d27d867d2e4027a567d59a9895bb53"}/bigcharts-data-end
    • 收益率-8.31%
    • 年化收益率-11.33%
    • 基准收益率-2.24%
    • 阿尔法-0.09
    • 贝塔0.84
    • 夏普比率-0.49
    • 胜率0.49
    • 盈亏比0.99
    • 收益波动率24.35%
    • 信息比率-0.03
    • 最大回撤27.29%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-cab75440b9824027a4fceb0c3e4aa473"}/bigcharts-data-end
    [CV] . m4.number_of_trees=8, score=-0.49330727483953474, total=   1.7s
    [Parallel(n_jobs=5)]: Done   1 tasks      | elapsed:    1.8s
    
    • 收益率11.79%
    • 年化收益率16.68%
    • 基准收益率-2.24%
    • 阿尔法0.18
    • 贝塔0.77
    • 夏普比率0.66
    • 胜率0.52
    • 盈亏比1.15
    • 收益波动率22.95%
    • 信息比率0.07
    • 最大回撤20.47%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-5d6faa4e7b544a02b682a3404c0f868c"}/bigcharts-data-end
    [CV] .... m4.number_of_trees=4, score=0.657894609342273, total=   3.0s
    [Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    3.1s remaining:    0.0s
    [Parallel(n_jobs=5)]: Done   2 out of   2 | elapsed:    3.1s finished
    
    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-867bdf33f40a4f6a9e1e803b54e73e3d"}/bigcharts-data-end
    • 收益率11.79%
    • 年化收益率16.68%
    • 基准收益率-2.24%
    • 阿尔法0.18
    • 贝塔0.77
    • 夏普比率0.66
    • 胜率0.52
    • 盈亏比1.15
    • 收益波动率22.95%
    • 信息比率0.07
    • 最大回撤20.47%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-12ec39b9b83549f88b57cc9f650b1040"}/bigcharts-data-end
    [Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   17.0s remaining:    0.0s
    [Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   17.0s finished
    

    (冰柠檬) #2

    这个是单个特征和单个算法的参数的结合,可以训练的时候使用多个特征,改变多个算法参数值进行训练吗?如何实现呢?


    (冰柠檬) #3

    多个算法参数可以了,多个特征如何设置


    (adhaha111) #4

    您好,直接将每次要运行因子放在同一个列表就行:


    (冰柠檬) #5

    好的 谢谢解答