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    In [2]:
    # 本代码由可视化策略环境自动生成 2020年8月18日 14:12
    # 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):
    
        context.ranker_prediction = context.options['data'].read_df()
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
        context.stock_count = 30
        context.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):
        
        if context.trading_day_index % context.hold_days != 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')]
        
        stock_to_buy = list(ranker_prediction.instrument)[:context.stock_count]
        # 通过positions对象,使用列表生成式的方法获取目前持仓的股票列表
        stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
        # 继续持有的股票:调仓时,如果买入的股票已经存在于目前的持仓里,那么应继续持有
        no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
        # 需要卖出的股票
        stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
        
        assert type(stock_to_buy) == list, '格式类型不对!'
        
        
        # 卖出
        for stock in stock_to_sell:
            if data.can_trade(context.symbol(stock)):
                # order_target_percent是平台的一个下单接口,表明下单使得该股票的权重为0,
                #   即卖出全部股票,可参考回测文档
                context.order_target_percent(context.symbol(stock), 0)
        
        # 如果当天没有买入的股票,就返回
        if len(stock_to_buy) == 0:
            return
    
         
        context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, len(stock_to_buy))])
    
        # 买入
        c = 0
        for stock in stock_to_buy:
            
            if data.can_trade(context.symbol(stock)):
                # 下单使得某只股票的持仓权重达到weight,因为
                # weight大于0,因此是等权重买入
                context.order_target_percent(context.symbol(stock), context.stock_weights[c])
            c += 1
    # 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': '2014-01-01',
        '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': """sum(max(0,high_0-((high_1+low_1+close_1)/3)),26)/sum(max(0,((high_1+low_1+close_1)/3)-low_0),26)*100
    close_0
    open_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',
        '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,
    
        'm6': 'M.dropnan.v2',
        'm6.input_data': T.Graph.OutputPort('m7.data'),
    
        'm5': 'M.stock_ranker_train.v6',
        'm5.training_ds': T.Graph.OutputPort('m6.data'),
        'm5.features': T.Graph.OutputPort('m3.data'),
        'm5.learning_algorithm': '排序',
        'm5.number_of_leaves': 30,
        'm5.minimum_docs_per_leaf': 1000,
        'm5.number_of_trees': 20,
        'm5.learning_rate': 0.1,
        'm5.max_bins': 1023,
        'm5.feature_fraction': 1,
        'm5.data_row_fraction': 1,
        'm5.ndcg_discount_base': 1,
        'm5.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',
        '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('m5.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': 'open',
        'm19.capital_base': 3000000,
        '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,
        'm19.benchmark': '',
    })
    
    # g.run({})
    
    
    def m4_run_bigquant_run(bq_graph, inputs):
        features =['pe_ttm_0', 
              
                   'shift(close_0,5)/close_0','mean(close_0,10)/close_0'],['pe_ttm_0', 
              
                   'shift(close_0,2)/close_0','mean(close_0,20)/close_0']
    
        parameters_list = []
         
        for feature in features:
            parameters = {'m3.features':feature}
            parameters_list.append({'parameters': parameters})
        
        def run(parameters):
            try:
                print(parameters)
                return g.run(parameters)
            except Exception as e:
                print('ERROR --------', e)
                return None
     
        results = T.parallel_map(run, parameters_list, max_workers=2, remote_run=False, silent=False)  # 任务数  # 是否远程  # 
    
        return results
    
    
    m4 = M.hyper_run.v1(
        run=m4_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )
    
    [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
    [Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:  3.3min
    [Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:  3.3min remaining:    0.0s
    [Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:  3.3min finished
    
    In [3]:
    # 查看结果数量
    # len(m4.result)
    
    In [4]:
    # 查看第一个任务返回结果中的预测模块m8的预测结果前5条
    m4.result[0]['m8'].predictions.read_df().head()  # 每一个任务  每一个模块 的输入
    
    Out[4]:
    date instrument score position
    0 2015-01-16 600184.SHA 0.608532 1
    1 2015-01-16 600340.SHA 0.607597 2
    2 2015-01-16 600970.SHA 0.588423 3
    3 2015-01-16 002532.SZA 0.546666 4
    4 2015-01-16 002727.SZA 0.513908 5
    In [5]:
    # 绘制第一个任务返回结果中回测模块的绩效曲线
    m4.result[0]['m19'].display()
    
    • 收益率45.62%
    • 年化收益率21.42%
    • 基准收益率-6.33%
    • 阿尔法0.26
    • 贝塔1.02
    • 夏普比率0.61
    • 胜率0.55
    • 盈亏比0.95
    • 收益波动率41.29%
    • 信息比率0.07
    • 最大回撤50.57%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a6a7532ff15249bda2fb5382433fffe2"}/bigcharts-data-end

    这边我想传入多组因子进行并行计算回测,但是不知道如何对不同列表组合的因子进行传入

    (达达) #2

    参考学院教程 自定义运行模块