超参寻优使用简介

可视化
教程
超参优化
标签: #<Tag:0x00007f20ca83f648> #<Tag:0x00007f20ca83ef90> #<Tag:0x00007f20ca83e310>

(iQuant) #1

超参寻优模块简介

最近,我们上线一个新的模块——超参优化模块,它可以帮助大家对我们平台上的机器学习模型进行超参数优化,让你的收益更上一层楼,接下来就让我给大家介绍下。

超参寻优理论简介

在机器学习里,我们本质上是对损失函数进行最优化的过程。过程类似下面的曲面,算法试图去寻找损失曲面的全局最小值,当然损失曲面实际中不一定是凸曲面,
可能会更加凹凸不平,存在多个局部高低点。

loss

我们还是回到主题,讲述的重点在于超参数寻优的意义。当我们损失曲面给定的时候,我们寻找最优点的路径可能会有一些模型以为的超参数来确定。形象的比喻,
如下图,不同的超参数可能对应这一条不同的寻优路径,比如当我们控制学习率的时候,模型每一步权重更新的部长就会不一样,这样可能导致寻优路径产生根本的差异,
尤其是在高维空间下。

模块使用介绍

在介绍了超参优化的原理后,就来介绍我们提供的超参寻优模块工具。

首先打开可视化策略,然后在左边的高级优化下面找到超参搜索,并把它拖进来:

hyperModule

单击模块后,我们能够在右边看到模块的属性,包括:

  • 超参数输入
  • 评分函数
  • 参数搜索方法
  • 搜索迭代次数
  • 并行运行作业数

超参数输入

在这里构造参数搜索空间。在超参数输入里,我们只需要指定需要调优的参数名,已经参数的搜索空间就ok了,下面给出了示例:

def bigquant_run():
    param_grid = {}

    # 在这里设置需要调优的参数备选
    param_grid['m6.number_of_trees'] = [5, 10, 20]

    return param_grid

切换到代码模式,可以参考所有可以调优的参数,甚至包括算法和算法的版本都可以修改。

评分函数

评分函数是用来评价一组参数好坏的指标,下面我们给出了一个示例,以回测最终的夏普比作为评分函数:

def bigquant_run(result):

    score = result.get('m19').read_raw_perf()['sharpe'].tail(1)[0]

    return score

参数搜索算法

参数搜索算法有两个可选项:

  • 网格搜索
  • 随机搜索

网格搜索是指给定参数组合后,遍历所有的排列组合。随机搜索指的是每次从所有的排列组合中,随机抽出一组参数,在具有很多参数的情况下,随机搜索会更有效率。

搜索迭代次数

在随机搜索的情况下,最多迭代的次数。

并行运行作业数

指用多少个线程同时搜索所有参数空间

示例策略

克隆策略

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cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 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\n stock_count = 5\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 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    In [1]:
    # 本代码由可视化策略环境自动生成 2018年11月11日 13:09
    # 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: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()}
    
        # 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
    
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2010-01-01',
        'm1.end_date': '2015-01-01',
        '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/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, -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': """# #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
    """,
    
        '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': 0,
    
        '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',
    
        '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,
    
        'm13': 'M.dropnan.v1',
        'm13.input_data': T.Graph.OutputPort('m7.data'),
    
        'm6': 'M.stock_ranker_train.v5',
        'm6.training_ds': T.Graph.OutputPort('m13.data'),
        'm6.features': T.Graph.OutputPort('m3.data'),
        'm6.learning_algorithm': '排序',
        'm6.number_of_leaves': 30,
        'm6.minimum_docs_per_leaf': 1000,
        'm6.number_of_trees': 20,
        'm6.learning_rate': 0.1,
        'm6.max_bins': 1023,
        'm6.feature_fraction': 1,
        'm6.m_lazy_run': False,
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2015-01-01'),
        'm9.end_date': T.live_run_param('trading_date', '2017-01-01'),
        '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': 0,
    
        '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',
    
        'm14': 'M.dropnan.v1',
        'm14.input_data': T.Graph.OutputPort('m18.data'),
    
        'm8': 'M.stock_ranker_predict.v5',
        'm8.model': T.Graph.OutputPort('m6.model'),
        'm8.data': T.Graph.OutputPort('m14.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.handle_data': m19_handle_data_bigquant_run,
        'm19.prepare': m19_prepare_bigquant_run,
        'm19.initialize': m19_initialize_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': '后复权',
        'm19.product_type': '股票',
        'm19.plot_charts': True,
        'm19.backtest_only': False,
    })
    
    # g.run({})
    
    
    def m20_param_grid_builder_bigquant_run():
        param_grid = {}
    
        # 在这里设置需要调优的参数备选
        param_grid['m6.number_of_trees'] = [5, 10, 20]
    
        return param_grid
    
    def m20_scoring_bigquant_run(result):
        score = result.get('m19').read_raw_perf()['sharpe'].tail(1)[0]
    
        return score
    
    
    m20 = M.hyper_parameter_search.v1(
        param_grid_builder=m20_param_grid_builder_bigquant_run,
        scoring=m20_scoring_bigquant_run,
        search_algorithm='网格搜索',
        search_iterations=10,
        workers=1,
        worker_distributed_run=True,
        worker_silent=True,
        run_now=True,
        bq_graph=g
    )
    
    Fitting 1 folds for each of 3 candidates, totalling 3 fits
    [Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
    [CV] m6.number_of_trees=5 ............................................
    [CV] ... m6.number_of_trees=5, score=0.8200535615725041, total=  10.8s
    [Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   10.9s remaining:    0.0s
    [CV] m6.number_of_trees=10 ...........................................
    [CV] .. m6.number_of_trees=10, score=1.4404307211678549, total=  21.0s
    [Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:   32.1s remaining:    0.0s
    [CV] m6.number_of_trees=20 ...........................................
    [CV] ... m6.number_of_trees=20, score=1.914699392161583, total=  10.4s
    [Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   42.8s remaining:    0.0s
    [Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:   42.8s finished
    
    In [5]:
    m20.result.best_params_
    
    Out[5]:
    {'m6.number_of_trees': 20}
    In [6]:
    m20.result.best_score_
    
    Out[6]:
    1.914699392161583

    可以像Quantopian,在一个策略中存储多个回测结果?
    (alexanderjs) #2

    [2018-12-02 00:32:57.847851] ERROR: bigquant: job_id: ad7a6f02f58611e8b0230a580a81041f, 获取打包运算返回结果失败

    这个是什么错误?


    (冰柠檬) #3

    你好 问一下 这些参数的名称有具体的api给出字段名吗?比如 我想设置学习率是[0.1,0,5,1] 设置没叶节点最小样本数 等参数 这些字段名字我在哪里查看呢?


    (iQuant) #5

    您可以打开代码模式 会发现所有模块的参数都有个编号
    如图所示:
    image
    比如学习率对应的参数是’m6.learning_rate’

    对于策略的评价指标你可以首先通过回测模块的raw_perf结果中读取策略累计收益cum_return和基准累计收益bm_cum_return数据存在df这个DataFrame中,如下计算策略的日收益率和基准的每日收益率:

    df['daily_return'] =(1+df['cum_return']).pct_change().fillna(0)
    df['benchmark_daily_return'] =(1+df['bm_cum_return']).pct_change().fillna(0)
    

    夏普比率、最大回撤和累计收益您就可以通过empyrical库使用如下的示例函数get_stats计算,比如计算返回max_drawdown

    #策略评价函数
    import empyrical
    def get_stats(results):
        return_ratio  = empyrical.cum_returns_final(results.daily_return)
        annual_return_ratio  = empyrical.annual_return(results.daily_return,period='daily')
        sharp_ratio = empyrical.sharpe_ratio(results.daily_return,0.04/252)
        return_volatility = empyrical.annual_volatility(results.daily_return)
        max_drawdown  = empyrical.max_drawdown(results.daily_return)
        calmar_ratio = empyrical.calmar_ratio(results.daily_return)
        sortino_ratio = empyrical.sortino_ratio(results.daily_return)
        alpha = empyrical.alpha(results.daily_return, results.benchmark_daily_return, risk_free=0.04)
        beta = empyrical.beta(results.daily_return, results.benchmark_daily_return, risk_free=0.04)
        return max_drawdown
    

    最后,可以记录比对这个max_drawdown指标


    (冰柠檬) #6

    嗯嗯 谢谢