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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, (1 if is_staging else 1.5) * cash_avg)\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.portfolio.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,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\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. 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bigquant_run(\n bq_graph,\n inputs,\n trading_days_market='CN', # 使用那个市场的交易日历, TODO\n train_instruments_mid='m1', # 训练数据 证券代码列表 模块id\n test_instruments_mid='m9', # 测试数据 证券代码列表 模块id\n predict_mid='m8', # 预测 模块id\n trade_mid='m19', # 回测 模块id\n model_train_mid='m6', # 模型训练 模块id\n start_date='2015-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2022-09-06'), # 数据结束日期\n train_update_days=250, # 更新周期,按交易日计算,每多少天更新一次\n train_update_days_for_live=None, #模拟实盘模式下的更新周期,按交易日计算,每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期,可以设置这个参数\n train_data_min_days=250, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数\n train_data_max_days=750, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期\n rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制\n):\n \n def merge_datasources(input_1):\n # 合并多个滚动回测的预测数据\n df_list = [ds[0].read_df().set_index('date').loc[ds[1]:].reset_index() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': df['date'].min().strftime('%Y-%m-%d'),\n 'end_date': df['date'].max().strftime('%Y-%m-%d'),\n 'instruments': list(set(df['instrument'])),\n }\n return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))\n \n def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):\n # 是否实盘模式\n tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])\n is_live_run = T.live_run_param('trading_date', None) is not None\n\n if is_live_run and train_update_days_for_live:\n train_update_days = train_update_days_for_live\n\n rollings = []\n train_end_date = train_data_min_days\n while train_end_date < len(tdays):\n if train_data_max_days is not None and train_data_max_days > 0:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = 0\n rollings.append({\n 'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),\n 'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),\n 'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),\n 'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),\n })\n train_end_date += train_update_days\n\n if not rollings:\n raise Exception('没有滚动需要执行,请检查配置')\n\n if is_live_run and rolling_count_for_live:\n rollings = rollings[-rolling_count_for_live:]\n\n return rollings\n\n g = bq_graph\n\n rolling_dates = gen_rolling_dates(\n trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)\n print('====='*5, '全部滚动的时间区间为:', rolling_dates)\n\n # 训练和预测\n results = []\n for rolling in rolling_dates: # 回测区间\n for nt in [10,20,30]: # number_of_trees\n for lr in [0.1,0.2]: # learning_of_rate\n parameters = {}\n # parameters[trade_mid + '.__enabled__'] = False # 将回测模块先注释\n parameters[model_train_mid + '.number_of_trees'] = nt\n parameters[model_train_mid + '.learning_rate'] = lr\n \n parameters[model_train_mid + '.plot_charts'] = False # 不绘制特征重要性图\n parameters[trade_mid + '.plot_charts'] = False # 不绘制单次滚动回测的图\n parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']\n parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']\n parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']\n parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']\n print('本次滚动回测的参数为:', nt, lr, rolling, parameters)\n \n output = {'参数详情':(nt,lr), 'graph_data':g.run(parameters)} # 将参数和图的结果保存下来\n results.append(output)\n \n # 整理预测数据\n performance_df = pd.DataFrame()\n\n for r in results:\n para_info = r['参数详情']\n graph_data = r['graph_data']\n test_end = graph_data[test_instruments_mid].data.read()['end_date']\n performance = graph_data[trade_mid].raw_perf.read()['sharpe'].tail(1).values[0] # 选择夏普比最大的参数组合\n\n tmp = pd.DataFrame({'test_end':[test_end], 'para_info':[para_info], 'performance':[performance]}) # 单次结果\n performance_df = performance_df.append(tmp)\n performance_df.index = range(len(performance_df))\n fine_paras_df = performance_df.groupby('test_end').apply(lambda x:x.sort_values('performance', ascending=False).head(1)) \n\n # 下列两行代码的目的:上一次滚动的最佳参数应用在下一次的预测集,因此需要关联起来,以此去找到图结果(graph_data)\n para_lst = fine_paras_df.para_info.tolist()[ :fine_paras_df.shape[0]-1] \n test_end_lst = fine_paras_df.test_end.tolist()[-1*(fine_paras_df.shape[0]-1): ] \n\n\n fine_para_ix = []\n for s in range(len(para_lst)):\n test_dt = test_end_lst[s]\n para = para_lst[s]\n select_df = performance_df[(performance_df['test_end'] == test_dt) & (performance_df['para_info'] == para)]\n ix = select_df.index.values[0]\n fine_para_ix.append(ix)\n print('----', ix )\n all_results = np.array(results) # 转换成array 便于批量抽取某些位置的元素\n fine_results = [r['graph_data'] for r in all_results[fine_para_ix]]\n \n # 合并预测结果并回测\n mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in fine_results])\n parameters = {}\n parameters['*.__enabled__'] = False # 将所有模块的状态设置为不运行\n parameters[trade_mid + '.__enabled__'] = True # 启动回测模块,只运行该模块\n parameters[trade_mid + '.instruments'] = mx.instrument_data\n parameters[trade_mid + '.options_data'] = mx.data\n parameters[trade_mid + '.plot_charts'] = True # 需要绘制全部结果图\n print('====='*5, '合并预测结果并回测!')\n\n trade = g.run(parameters)\n return {'rollings': results, 'fine_results': fine_results, 'trade': trade}\n","type":"Literal","bound_global_parameter":null},{"name":"run_now","value":"True","type":"Literal","bound_global_parameter":null},{"name":"bq_graph","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"bq_graph_port","node_id":"-970"},{"name":"input_1","node_id":"-970"},{"name":"input_2","node_id":"-970"},{"name":"input_3","node_id":"-970"}],"output_ports":[{"name":"result","node_id":"-970"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='208,64,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='70,183,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='541.5498046875,-129.69119453430176,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='541,561.4617919921875,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' 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    In [ ]:
    # 本代码由可视化策略环境自动生成 2022年9月8日 08:29
    # 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': '2014-01-02',
        'm1.end_date': '2015-01-09',
        '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.HIX',
        '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': 90,
    
        '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'),
    
        'm6': 'M.stock_ranker_train.v6',
        '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': 2,
        'm6.learning_rate': 0.1,
        'm6.max_bins': 1023,
        'm6.feature_fraction': 1,
        'm6.data_row_fraction': 1,
        'm6.plot_charts': True,
        'm6.ndcg_discount_base': 1,
        'm6.m_lazy_run': False,
    
        'm9': 'M.instruments.v2',
        'm9.start_date': T.live_run_param('trading_date', '2015-01-12'),
        'm9.end_date': T.live_run_param('trading_date', '2016-01-19'),
        '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': 90,
    
        '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,
    
        '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.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.HIX',
    })
    
    # g.run({})
    
    
    def m4_run_bigquant_run(
        bq_graph,
        inputs,
        trading_days_market='CN', # 使用那个市场的交易日历, TODO
        train_instruments_mid='m1', # 训练数据 证券代码列表 模块id
        test_instruments_mid='m9', # 测试数据 证券代码列表 模块id
        predict_mid='m8', # 预测 模块id
        trade_mid='m19', # 回测 模块id
        model_train_mid='m6', # 模型训练 模块id
        start_date='2015-01-01', # 数据开始日期
        end_date=T.live_run_param('trading_date', '2022-09-06'), # 数据结束日期
        train_update_days=250, # 更新周期,按交易日计算,每多少天更新一次
        train_update_days_for_live=None, #模拟实盘模式下的更新周期,按交易日计算,每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期,可以设置这个参数
        train_data_min_days=250, # 最小数据天数,按交易日计算,所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数
        train_data_max_days=750, # 最大数据天数,按交易日计算,0,表示没有限制,否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期
        rolling_count_for_live=1, #实盘模式下滚动次数,模拟实盘模式下,取最后多少次滚动。一般在模拟实盘模式下,只用到最后一次滚动训练的模型,这里可以设置为1;如果你的滚动训练数据时间段很短,以至于期间可能没有训练数据,这里可以设置大一点。0表示没有限制
    ):
        
        def merge_datasources(input_1):
            # 合并多个滚动回测的预测数据
            df_list = [ds[0].read_df().set_index('date').loc[ds[1]:].reset_index() for ds in input_1]
            df = pd.concat(df_list)
            instrument_data = {
                'start_date': df['date'].min().strftime('%Y-%m-%d'),
                'end_date': df['date'].max().strftime('%Y-%m-%d'),
                'instruments': list(set(df['instrument'])),
            }
            return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))
        
        def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):
            # 是否实盘模式
            tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])
            is_live_run = T.live_run_param('trading_date', None) is not None
    
            if is_live_run and train_update_days_for_live:
                train_update_days = train_update_days_for_live
    
            rollings = []
            train_end_date = train_data_min_days
            while train_end_date < len(tdays):
                if train_data_max_days is not None and train_data_max_days > 0:
                    train_start_date = max(train_end_date - train_data_max_days, 0)
                else:
                    train_start_date = 0
                rollings.append({
                    'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),
                    'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),
                    'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),
                    'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),
                })
                train_end_date += train_update_days
    
            if not rollings:
                raise Exception('没有滚动需要执行,请检查配置')
    
            if is_live_run and rolling_count_for_live:
                rollings = rollings[-rolling_count_for_live:]
    
            return rollings
    
        g = bq_graph
    
        rolling_dates = gen_rolling_dates(
            trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)
        print('====='*5, '全部滚动的时间区间为:', rolling_dates)
    
        # 训练和预测
        results = []
        for rolling in rolling_dates: # 回测区间
            for nt in [10,20,30]: # number_of_trees
                for lr in [0.1,0.2]: # learning_of_rate
                    parameters = {}
                    # parameters[trade_mid + '.__enabled__'] = False # 将回测模块先注释
                    parameters[model_train_mid + '.number_of_trees'] = nt
                    parameters[model_train_mid + '.learning_rate'] = lr
                    
                    parameters[model_train_mid + '.plot_charts'] = False # 不绘制特征重要性图
                    parameters[trade_mid + '.plot_charts'] = False # 不绘制单次滚动回测的图
                    parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']
                    parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']
                    parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']
                    parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
                    print('本次滚动回测的参数为:', nt, lr, rolling, parameters)
                    
                    output = {'参数详情':(nt,lr), 'graph_data':g.run(parameters)} # 将参数和图的结果保存下来
                    results.append(output)
                    
        # 整理预测数据
        performance_df = pd.DataFrame()
    
        for r in results:
            para_info = r['参数详情']
            graph_data = r['graph_data']
            test_end = graph_data[test_instruments_mid].data.read()['end_date']
            performance = graph_data[trade_mid].raw_perf.read()['sharpe'].tail(1).values[0] # 选择夏普比最大的参数组合
    
            tmp = pd.DataFrame({'test_end':[test_end], 'para_info':[para_info], 'performance':[performance]}) # 单次结果
            performance_df = performance_df.append(tmp)
        performance_df.index = range(len(performance_df))
        fine_paras_df = performance_df.groupby('test_end').apply(lambda x:x.sort_values('performance', ascending=False).head(1))    
    
        # 下列两行代码的目的:上一次滚动的最佳参数应用在下一次的预测集,因此需要关联起来,以此去找到图结果(graph_data)
        para_lst = fine_paras_df.para_info.tolist()[ :fine_paras_df.shape[0]-1] 
        test_end_lst = fine_paras_df.test_end.tolist()[-1*(fine_paras_df.shape[0]-1): ] 
    
    
        fine_para_ix = []
        for s in range(len(para_lst)):
            test_dt = test_end_lst[s]
            para = para_lst[s]
            select_df = performance_df[(performance_df['test_end'] == test_dt) & (performance_df['para_info'] == para)]
            ix = select_df.index.values[0]
            fine_para_ix.append(ix)
            print('----', ix )
        all_results = np.array(results) # 转换成array 便于批量抽取某些位置的元素
        fine_results = [r['graph_data'] for r in all_results[fine_para_ix]]
         
        # 合并预测结果并回测
        mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in fine_results])
        parameters = {}
        parameters['*.__enabled__'] = False # 将所有模块的状态设置为不运行
        parameters[trade_mid + '.__enabled__'] = True # 启动回测模块,只运行该模块
        parameters[trade_mid + '.instruments'] = mx.instrument_data
        parameters[trade_mid + '.options_data'] = mx.data
        parameters[trade_mid + '.plot_charts'] = True # 需要绘制全部结果图
        print('====='*5, '合并预测结果并回测!')
    
        trade = g.run(parameters)
        return {'rollings': results, 'fine_results': fine_results, 'trade': trade}
    
    
    m4 = M.hyper_rolling_train.v1(
        run=m4_run_bigquant_run,
        run_now=True,
        bq_graph=g
    )