超参寻优出现模块未定义


(kilmjin) #1

用超参寻优模块时,出现


模型里有m9啊,是什么原因啊?


(iQuant) #2

您好,方便分享策略至社区吗?我们帮您检查一下。


(kilmjin) #3
克隆策略

    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    In [1]:
    # 本代码由可视化策略环境自动生成 2019年10月11日 10:42
    # 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()#读取上一个模块的数据
        ins= m9.data.read_pickle()['instruments']
        start = m9.data.read_pickle()['start_date']
        end = m9.data.read_pickle()['end_date']
        #获取代码的股票名称
        df_name=D.history_data(instruments=ins, start_date=start, end_date=end,
                   fields=['name'])
        df_merge=pd.merge(df,df_name,on=['instrument','date'])
        #过滤掉含有‘退’字的股票
        df_filter=df_merge[~(df_merge.name.str.contains('退'))]
        data_1 = DataSource.write_df(df_filter)
        return Outputs(data_1=data_1, 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 m5_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m4_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 = 0.5
        context.options['hold_days'] = 5
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m4_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')]
        sell = 4
        buy = 1
    
        # 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, 3* cash_avg)
        #cash_for_buy = min(context.portfolio.cash, context.portfolio.portfolio_value)
        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()}
        #print(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 True:
            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]))])))
            instruments12 = list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities )])
            position_prediction = ranker_prediction[ranker_prediction.instrument.isin(instruments12)]
            instruments = list(position_prediction.instrument[position_prediction.score <sell])
            #instruments = list(equities)
            #instruments = list(position_prediction.instrument[position_prediction.score < 0.2])
            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
        rank_buy = ranker_prediction[ranker_prediction.score>buy]
        #rank_buy = ranker_prediction[ranker_prediction.score>1.12]
        buy_instruments1 = list(rank_buy.instrument)
        #print(list(ranker_prediction.date)[0],ranker_prediction.score)
        #buy_instruments1 = list(ranker_prediction.instrument[ranker_prediction.score>1.16])
        buy_scores1 = list(rank_buy.score)
        buy_instruments = buy_instruments1[:np.where(len(buy_instruments1)>2,2,len(buy_instruments1))]
        buy_scores = buy_scores1[:len(buy_instruments)]
        buy_cash_weights = buy_scores/np.sum(buy_scores)
        #buy_cash_weights = T.norm([1/math.log(i+2) for i in range(0,len(buy_instruments))])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        if any(buy_instruments):
            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)
                today_price = data.current(symbol(instrument), ['amount', 'volume'])
                buy_price =today_price['amount'] / today_price['volume']
                buy_amount = int(round(cash/(buy_price*100)))
                #print(buy_amount)
            #context.order_lots(symbol(instrument),1)
                if buy_amount > 0: 
                    context.order_lots(symbol(instrument),buy_amount)
           # if cash > 0:
             #   context.order_value(context.symbol(instrument), cash)
    # 回测引擎:准备数据,只执行一次
    def m4_prepare_bigquant_run(context):
        pass
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2005-01-01',
        'm1.end_date': '2016-01-01',
        '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)-1)*100
    
    # 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, 60)
    
    # 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,
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2016-01-02'),
        'm9.end_date': T.live_run_param('trading_date', '2019-09-11'),
        'm9.market': 'CN_STOCK_A',
        'm9.instrument_list': '',
        'm9.max_count': 0,
    
        'm3': 'M.input_features.v1',
        'm3.features': """
    ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),20)
    
    
    
    
    """,
    
        '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': 400,
    
        '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,
    
        'm13': 'M.dropnan.v1',
        'm13.input_data': T.Graph.OutputPort('m7.data'),
    
        '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': 400,
    
        '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,
    
        'm5': 'M.cached.v3',
        'm5.input_1': T.Graph.OutputPort('m18.data'),
        'm5.run': m5_run_bigquant_run,
        'm5.post_run': m5_post_run_bigquant_run,
        'm5.input_ports': '',
        'm5.params': '{}',
        'm5.output_ports': '',
    
        'm14': 'M.dropnan.v1',
        'm14.input_data': T.Graph.OutputPort('m5.data_1'),
    
        'm11': 'M.filter_stockcode.v2',
        'm11.input_1': T.Graph.OutputPort('m14.data'),
        'm11.start': '688',
    
        'm10': 'M.features_short.v1',
        'm10.input_1': T.Graph.OutputPort('m3.data'),
    
        'm6': 'M.stock_ranker_train.v5',
        'm6.training_ds': T.Graph.OutputPort('m13.data'),
        'm6.features': T.Graph.OutputPort('m10.data_1'),
        'm6.learning_algorithm': '排序',
        'm6.number_of_leaves': 30,
        'm6.minimum_docs_per_leaf': 1000,
        'm6.number_of_trees': 50,
        'm6.learning_rate': 0.1,
        'm6.max_bins': 1023,
        'm6.feature_fraction': 1,
        'm6.m_lazy_run': False,
    
        'm8': 'M.stock_ranker_predict.v5',
        'm8.model': T.Graph.OutputPort('m6.model'),
        'm8.data': T.Graph.OutputPort('m11.data_1'),
        'm8.m_lazy_run': False,
    
        'm4': 'M.trade.v4',
        'm4.instruments': T.Graph.OutputPort('m9.data'),
        'm4.options_data': T.Graph.OutputPort('m8.predictions'),
        'm4.start_date': '',
        'm4.end_date': '',
        'm4.initialize': m4_initialize_bigquant_run,
        'm4.handle_data': m4_handle_data_bigquant_run,
        'm4.prepare': m4_prepare_bigquant_run,
        'm4.volume_limit': 0,
        'm4.order_price_field_buy': 'open',
        'm4.order_price_field_sell': 'close',
        'm4.capital_base': 100000,
        'm4.auto_cancel_non_tradable_orders': True,
        'm4.data_frequency': 'daily',
        'm4.price_type': '后复权',
        'm4.product_type': '股票',
        'm4.plot_charts': True,
        'm4.backtest_only': False,
        'm4.benchmark': '',
    })
    
    # g.run({})
    
    
    def m12_param_grid_builder_bigquant_run():
        param_grid = {}
    
        # 在这里设置需要调优的参数备选
        param_grid['m3.features'] = [
            """
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
            """,
            """
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),10)
            """,
            """
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),15)
            """,
            """
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),20)
            """,
            """
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),25)
            """,
            """
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),30)
            """
        ]
        param_grid['m6.number_of_trees'] = [20,30,40,50]
        param_grid['m6.learning_rate'] = [0.05,0.1]
        param_grid['m6.number_of_leaves'] = [30]
        param_grid['m6.minimum_docs_per_leaf'] = [1000,1500]
        return param_grid
    
    def m12_scoring_bigquant_run(result):
        score = result.get('m4').read_raw_perf()['algorithm_period_return'].tail(1)[0]
    
        return score
    
    
    m12 = M.hyper_parameter_search.v1(
        param_grid_builder=m12_param_grid_builder_bigquant_run,
        scoring=m12_scoring_bigquant_run,
        search_algorithm='网格搜索',
        search_iterations=10,
        workers=2,
        worker_distributed_run=True,
        worker_silent=True,
        run_now=True,
        bq_graph=g
    )
    
    Fitting 1 folds for each of 96 candidates, totalling 96 fits
    [Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.
    [CV] m6.learning_rate=0.05, m6.minimum_docs_per_leaf=1000, m3.features=
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
            , m6.number_of_trees=20, m6.number_of_leaves=30 
    [CV] m6.learning_rate=0.05, m6.minimum_docs_per_leaf=1000, m3.features=
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
            , m6.number_of_trees=30, m6.number_of_leaves=30 
    
    ('error_help error: ', AttributeError("'NoneType' object has no attribute 'get'",))
    [CV]  m6.learning_rate=0.05, m6.minimum_docs_per_leaf=1000, m3.features=
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
            , m6.number_of_trees=30, m6.number_of_leaves=30, score=-inf, total= 1.7min
    [CV] m6.learning_rate=0.05, m6.minimum_docs_per_leaf=1000, m3.features=
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
            , m6.number_of_trees=40, m6.number_of_leaves=30 
    [Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:  1.7min
    
    ('error_help error: ', AttributeError("'NoneType' object has no attribute 'get'",))
    [CV]  m6.learning_rate=0.05, m6.minimum_docs_per_leaf=1000, m3.features=
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
            , m6.number_of_trees=20, m6.number_of_leaves=30, score=-inf, total= 1.9min
    [CV] m6.learning_rate=0.05, m6.minimum_docs_per_leaf=1000, m3.features=
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
            , m6.number_of_trees=50, m6.number_of_leaves=30 
    [Parallel(n_jobs=2)]: Done   2 tasks      | elapsed:  1.9min
    
    ('error_help error: ', AttributeError("'NoneType' object has no attribute 'get'",))
    [CV]  m6.learning_rate=0.05, m6.minimum_docs_per_leaf=1000, m3.features=
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
            , m6.number_of_trees=50, m6.number_of_leaves=30, score=-inf, total=  25.3s
    [CV] m6.learning_rate=0.05, m6.minimum_docs_per_leaf=1500, m3.features=
            ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
            , m6.number_of_trees=20, m6.number_of_leaves=30 
    [Parallel(n_jobs=2)]: Done   3 tasks      | elapsed:  2.4min
    [2019-10-11 10:41:05.785660] ERROR root: Internal Python error in the inspect module.
    Below is the traceback from this internal error.
    
    Traceback (most recent call last):
      File "/usr/local/python3/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2910, in run_code
        exec(code_obj, self.user_global_ns, self.user_ns)
      File "<ipython-input-1-07c75cbadd3c>", line 306, in <module>
        bq_graph=g
      File "module2/common/modulemanagerv2.py", line 103, in biglearning.module2.common.modulemanagerv2.BigQuantModuleVersion.__call__
      File "module2/common/moduleinvoker.py", line 295, in biglearning.module2.common.moduleinvoker.module_invoke
      File "module2/common/moduleinvoker.py", line 206, in biglearning.module2.common.moduleinvoker._invoke_with_cache
      File "module2/common/moduleinvoker.py", line 163, in biglearning.module2.common.moduleinvoker._module_run
      File "module2/modules/hyper_parameter_search/v1/__init__.py", line 133, in biglearning.module2.modules.hyper_parameter_search.v1.__init__.bigquant_run
      File "module2/modules/hyper_parameter_search/v1/__init__.py", line 66, in biglearning.module2.modules.hyper_parameter_search.v1.__init__._run
      File "/usr/local/python3/lib/python3.5/site-packages/sklearn/model_selection/_search.py", line 726, in fit
        self._run_search(evaluate_candidates)
      File "/usr/local/python3/lib/python3.5/site-packages/sklearn/model_selection/_search.py", line 1195, in _run_search
        evaluate_candidates(ParameterGrid(self.param_grid))
      File "/usr/local/python3/lib/python3.5/site-packages/sklearn/model_selection/_search.py", line 715, in evaluate_candidates
        cv.split(X, y, groups)))
      File "/usr/local/python3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 930, in __call__
        self.retrieve()
      File "/usr/local/python3/lib/python3.5/site-packages/sklearn/externals/joblib/parallel.py", line 833, in retrieve
        self._output.extend(job.get(timeout=self.timeout))
      File "/usr/local/python3/lib/python3.5/multiprocessing/pool.py", line 638, in get
        self.wait(timeout)
      File "/usr/local/python3/lib/python3.5/multiprocessing/pool.py", line 635, in wait
        self._event.wait(timeout)
      File "/usr/local/python3/lib/python3.5/threading.py", line 549, in wait
        signaled = self._cond.wait(timeout)
      File "/usr/local/python3/lib/python3.5/threading.py", line 293, in wait
        waiter.acquire()
    KeyboardInterrupt
    
    During handling of the above exception, another exception occurred:
    
    Traceback (most recent call last):
      File "/usr/local/python3/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 1828, in showtraceback
        stb = value._render_traceback_()
    AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'
    
    During handling of the above exception, another exception occurred:
    
    Traceback (most recent call last):
      File "/usr/local/python3/lib/python3.5/site-packages/IPython/core/ultratb.py", line 1090, in get_records
        return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)
      File "/usr/local/python3/lib/python3.5/site-packages/IPython/core/ultratb.py", line 311, in wrapped
        return f(*args, **kwargs)
      File "/usr/local/python3/lib/python3.5/site-packages/IPython/core/ultratb.py", line 345, in _fixed_getinnerframes
        records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))
      File "/usr/local/python3/lib/python3.5/inspect.py", line 1459, in getinnerframes
        frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)
      File "/usr/local/python3/lib/python3.5/inspect.py", line 1417, in getframeinfo
        filename = getsourcefile(frame) or getfile(frame)
      File "/usr/local/python3/lib/python3.5/inspect.py", line 677, in getsourcefile
        if getattr(getmodule(object, filename), '__loader__', None) is not None:
      File "/usr/local/python3/lib/python3.5/inspect.py", line 723, in getmodule
        os.path.realpath(f)] = module.__name__
      File "/usr/local/python3/lib/python3.5/posixpath.py", line 373, in realpath
        path, ok = _joinrealpath(filename[:0], filename, {})
      File "/usr/local/python3/lib/python3.5/posixpath.py", line 407, in _joinrealpath
        if not islink(newpath):
      File "/usr/local/python3/lib/python3.5/posixpath.py", line 161, in islink
        st = os.lstat(path)
    KeyboardInterrupt
    
    ---------------------------------------------------------------------------

    (达达) #4

    在自定义模块中使用了m9,但是流程中没有,建议用去除退市股模块替代,另外采用别名处理时,没有加因子名缩写,修改后如下所示。建议没次先少跑一些组合测试一下,以防止意外退出/内存不足等问题。

    克隆策略

      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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.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, 3* cash_avg)\n #cash_for_buy = min(context.portfolio.cash, context.portfolio.portfolio_value)\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 #print(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 True:\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 instruments12 = list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities )])\n position_prediction = ranker_prediction[ranker_prediction.instrument.isin(instruments12)]\n instruments = list(position_prediction.instrument[position_prediction.score <sell])\n #instruments = list(equities)\n #instruments = list(position_prediction.instrument[position_prediction.score < 0.2])\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n #cash_for_sell -= positions[instrument]\n #if cash_for_sell <= 0:\n #break\n\n # 3. 生成买入订单:按StockRanker预测的排序,买入前面的stock_count只股票\n #buy_cash_weights = context.stock_weights\n rank_buy = ranker_prediction[ranker_prediction.score>buy]\n #rank_buy = ranker_prediction[ranker_prediction.score>1.12]\n buy_instruments1 = list(rank_buy.instrument)\n #print(list(ranker_prediction.date)[0],ranker_prediction.score)\n #buy_instruments1 = list(ranker_prediction.instrument[ranker_prediction.score>1.16])\n 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      In [ ]:
      # 本代码由可视化策略环境自动生成 2019年10月11日 14:51
      # 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 m4_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 = 0.5
          context.options['hold_days'] = 5
      
      # 回测引擎:每日数据处理函数,每天执行一次
      def m4_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')]
          sell = 4
          buy = 1
      
          # 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, 3* cash_avg)
          #cash_for_buy = min(context.portfolio.cash, context.portfolio.portfolio_value)
          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()}
          #print(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 True:
              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]))])))
              instruments12 = list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                      lambda x: x in equities )])
              position_prediction = ranker_prediction[ranker_prediction.instrument.isin(instruments12)]
              instruments = list(position_prediction.instrument[position_prediction.score <sell])
              #instruments = list(equities)
              #instruments = list(position_prediction.instrument[position_prediction.score < 0.2])
              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
          rank_buy = ranker_prediction[ranker_prediction.score>buy]
          #rank_buy = ranker_prediction[ranker_prediction.score>1.12]
          buy_instruments1 = list(rank_buy.instrument)
          #print(list(ranker_prediction.date)[0],ranker_prediction.score)
          #buy_instruments1 = list(ranker_prediction.instrument[ranker_prediction.score>1.16])
          buy_scores1 = list(rank_buy.score)
          buy_instruments = buy_instruments1[:np.where(len(buy_instruments1)>2,2,len(buy_instruments1))]
          buy_scores = buy_scores1[:len(buy_instruments)]
          buy_cash_weights = buy_scores/np.sum(buy_scores)
          #buy_cash_weights = T.norm([1/math.log(i+2) for i in range(0,len(buy_instruments))])
          max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
          if any(buy_instruments):
              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)
                  today_price = data.current(symbol(instrument), ['amount', 'volume'])
                  buy_price =today_price['amount'] / today_price['volume']
                  buy_amount = int(round(cash/(buy_price*100)))
                  #print(buy_amount)
              #context.order_lots(symbol(instrument),1)
                  if buy_amount > 0: 
                      context.order_lots(symbol(instrument),buy_amount)
             # if cash > 0:
               #   context.order_value(context.symbol(instrument), cash)
      # 回测引擎:准备数据,只执行一次
      def m4_prepare_bigquant_run(context):
          pass
      
      g = T.Graph({
      
          'm1': 'M.instruments.v2',
          'm1.start_date': '2005-01-01',
          'm1.end_date': '2016-01-01',
          '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)-1)*100
      
      # 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, 60)
      
      # 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': """
      factor=ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),20)
      
      
      
      
      """,
      
          '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': 400,
      
          '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,
      
          'm13': 'M.dropnan.v1',
          'm13.input_data': T.Graph.OutputPort('m7.data'),
      
          'm10': 'M.features_short.v1',
          'm10.input_1': T.Graph.OutputPort('m3.data'),
      
          'm6': 'M.stock_ranker_train.v5',
          'm6.training_ds': T.Graph.OutputPort('m13.data'),
          'm6.features': T.Graph.OutputPort('m10.data_1'),
          'm6.learning_algorithm': '排序',
          'm6.number_of_leaves': 30,
          'm6.minimum_docs_per_leaf': 1000,
          'm6.number_of_trees': 50,
          'm6.learning_rate': 0.1,
          'm6.max_bins': 1023,
          'm6.feature_fraction': 1,
          'm6.m_lazy_run': False,
      
          'm19': 'M.instruments.v2',
          'm19.start_date': '2016-01-02',
          'm19.end_date': '2019-09-11',
          'm19.market': 'CN_STOCK_A',
          'm19.instrument_list': '',
          'm19.max_count': 0,
      
          'm17': 'M.general_feature_extractor.v7',
          'm17.instruments': T.Graph.OutputPort('m19.data'),
          'm17.features': T.Graph.OutputPort('m3.data'),
          'm17.start_date': '',
          'm17.end_date': '',
          'm17.before_start_days': 400,
      
          '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,
      
          'm9': 'M.filter_delist_stock.v4',
          'm9.input_1': T.Graph.OutputPort('m18.data'),
      
          'm14': 'M.dropnan.v1',
          'm14.input_data': T.Graph.OutputPort('m9.data_1'),
      
          'm11': 'M.filter_stockcode.v2',
          'm11.input_1': T.Graph.OutputPort('m14.data'),
          'm11.start': '688',
      
          'm8': 'M.stock_ranker_predict.v5',
          'm8.model': T.Graph.OutputPort('m6.model'),
          'm8.data': T.Graph.OutputPort('m11.data_1'),
          'm8.m_lazy_run': False,
      
          'm4': 'M.trade.v4',
          'm4.instruments': T.Graph.OutputPort('m19.data'),
          'm4.options_data': T.Graph.OutputPort('m8.predictions'),
          'm4.start_date': '',
          'm4.end_date': '',
          'm4.initialize': m4_initialize_bigquant_run,
          'm4.handle_data': m4_handle_data_bigquant_run,
          'm4.prepare': m4_prepare_bigquant_run,
          'm4.volume_limit': 0,
          'm4.order_price_field_buy': 'open',
          'm4.order_price_field_sell': 'close',
          'm4.capital_base': 100000,
          'm4.auto_cancel_non_tradable_orders': True,
          'm4.data_frequency': 'daily',
          'm4.price_type': '后复权',
          'm4.product_type': '股票',
          'm4.plot_charts': True,
          'm4.backtest_only': False,
          'm4.benchmark': '',
      })
      
      # 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['m3.features'] = [
              """
              factor=ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),5)
              """,
              """
              factor=ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),10)
              """,
              """
              factor=ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),15)
              """,
              """
              factor=ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),20)
              """,
              """
              factor=ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),25)
              """,
              """
              factor=ts_max((close_0-open_0)/max(high_0-close_1,high_0-low_0,close_1-low_0),30)
              """
          ]
          param_grid['m6.number_of_trees'] = [20,30,40,50]
          param_grid['m6.learning_rate'] = [0.05,0.1]
          param_grid['m6.number_of_leaves'] = [30]
          param_grid['m6.minimum_docs_per_leaf'] = [1000,1500]
          return param_grid
      
      
      def m20_scoring_bigquant_run(result):
          score = result.get('m4').read_raw_perf()['algorithm_period_return'].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
      )
      

      (kilmjin) #5

      非常感谢!