策略分享03

策略分享
标签: #<Tag:0x00007f8c7b99a8e0>

(zxc7573316672) #1
克隆策略

    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-107:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-779:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-3661:features_ds","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-1038:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-819:input_data","SourceOutputPortId":"-107:data"},{"DestinationInputPortId":"-1038:training_ds","SourceOutputPortId":"-648:data"},{"DestinationInputPortId":"-161:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-142:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-837:input_data","SourceOutputPortId":"-161:data"},{"DestinationInputPortId":"-1871:input_ds","SourceOutputPortId":"-187:data"},{"DestinationInputPortId":"-1038:predict_ds","SourceOutputPortId":"-187:data"},{"DestinationInputPortId":"-2908:input_1","SourceOutputPortId":"-779:data"},{"DestinationInputPortId":"-779:data2","SourceOutputPortId":"-819:data"},{"DestinationInputPortId":"-2911:input_1","SourceOutputPortId":"-837:data"},{"DestinationInputPortId":"-2982:input_data","SourceOutputPortId":"-2908:data_1"},{"DestinationInputPortId":"-1288:input_data","SourceOutputPortId":"-2911:data_1"},{"DestinationInputPortId":"-161:features","SourceOutputPortId":"-1206:data"},{"DestinationInputPortId":"-837:features","SourceOutputPortId":"-1206:data"},{"DestinationInputPortId":"-1877:data2","SourceOutputPortId":"-1871:data"},{"DestinationInputPortId":"-344:input_ds","SourceOutputPortId":"-1877:data"},{"DestinationInputPortId":"-107:features","SourceOutputPortId":"-3661:data"},{"DestinationInputPortId":"-819:features","SourceOutputPortId":"-3661:data"},{"DestinationInputPortId":"-1206:features_ds","SourceOutputPortId":"-3661:data"},{"DestinationInputPortId":"-142:options_data","SourceOutputPortId":"-175:data_1"},{"DestinationInputPortId":"-175:input_1","SourceOutputPortId":"-344:sorted_data"},{"DestinationInputPortId":"-1877:data1","SourceOutputPortId":"-1038:predictions"},{"DestinationInputPortId":"-223:input_data","SourceOutputPortId":"-2982:data"},{"DestinationInputPortId":"-172:input_data","SourceOutputPortId":"-1288:data"},{"DestinationInputPortId":"-648:input_data","SourceOutputPortId":"-223:data"},{"DestinationInputPortId":"-187:input_data","SourceOutputPortId":"-172:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# #号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html\n# 添加benchmark_前缀,可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_\n\n# 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)\nshift(close, -23)/shift(close, -1)\n\n# 极值处理:用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n# 将分数映射到分类,这里使用20个分类\nall_wbins(label, 20)\n\n# 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)\nwhere(shift(high, -1) == shift(low, -1), NaN, 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#号开始的表示注释,注释需单独一行\n# 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征\n#f15 = mean(close_1, 5)\n#f05 = mean(close_0, 5)\n#f110 = mean(close_1, 10)\n#f010 = mean(close_0, 10)\nclose_0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-3661"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-3661","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"过滤条件所需的特征","CommentCollapsed":false},{"Id":"-175","ModuleId":"BigQuantSpace.cached.cached-v3","ModuleParameters":[{"Name":"run","Value":"# 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\ndef bigquant_run(input_1):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read_df()\n df = pd.DataFrame(df)\n df = df.groupby(['date','score'], as_index = True, sort = False).apply(lambda x: x.sort_values('ranker', ascending = False))\n df = df.reset_index(drop=True)\n data_1 = DataSource.write_df(df)\n \n return Outputs(data_1=data_1)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"post_run","Value":"# 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。\ndef bigquant_run(outputs):\n return 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    In [1]:
    # 本代码由可视化策略环境自动生成 2020年11月11日 10:55
    # 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 m17_run_bigquant_run(input_1):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read_df()
        df = pd.DataFrame(df)
        df = df.groupby(['date','score'], as_index = True, sort = False).apply(lambda x: x.sort_values('ranker', ascending = False))
        df = df.reset_index(drop=True)
        data_1 = DataSource.write_df(df)
       
        return Outputs(data_1=data_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 m17_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 = 160
        # 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 = [1/stock_count for i in range(stock_count)]
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.007
        context.options['hold_days'] = 2
    def m19_handle_data_bigquant_run(context, data):
        #------------------------START:加入下面if的两行代码到之前到主函数的最前部分-------------------
        # 相隔几天(以3天举例)运行一下handle_data函数
        if context.trading_day_index % 22 != 0:
            return 
        #------------------------END:加上这两句代码在主函数就能实现隔几天运行---------------------
    
         # 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/2# / 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StockRanker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StockRanker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)
    
    g = T.Graph({
    
        'm1': 'M.instruments.v2',
        'm1.start_date': '2016-01-01',
        'm1.end_date': '2019-01-01',
        '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, -23)/shift(close, -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': '000905.HIX',
        'm2.drop_na_label': True,
        'm2.cast_label_int': True,
    
        'm3': 'M.input_features.v1',
        'm3.features': """Alpha1 = mean(mf_net_amount_main_0, 90)
    Alpha2 = mean(mf_net_amount_l_0, 60)
    Alpha3 = rank_swing_volatility_120_0
    Alpha4 = std(avg_turn_30, 180)
    Alpha5 = std(mean(deal_number_0, 90), 90)
    Alpha6 = std(mean(deal_number_0, 180), 180)
    Alpha7 = mean(mf_net_pct_s_0, 90)
    Alpha8 = mean(mf_net_pct_s_0, 30)
    Alpha9 = (rank(correlation(delay((open_0 - close_0), 1), close_0, 200)) + rank((open_0 - close_0)))
    Alpha10 = std(mean(turn_0, 120), 120)
    Alpha11 = std(mean(volume_0, 180), 180)
    Alpha12 = std(mean(volume_0, 90), 90)
    Alpha13 = mean(mf_net_amount_main_0, 60)
    Alpha14 = mean(mf_net_amount_l_0, 90)
    Alpha15 = mean(mf_net_amount_l_0, 120)""",
    
        'm20': 'M.input_features.v1',
        'm20.features_ds': T.Graph.OutputPort('m3.data'),
        'm20.features': """
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    #f15 = mean(close_1, 5)
    #f05 = mean(close_0, 5)
    #f110 = mean(close_1, 10)
    #f010 = mean(close_0, 10)
    close_0""",
    
        'm15': 'M.general_feature_extractor.v7',
        'm15.instruments': T.Graph.OutputPort('m1.data'),
        'm15.features': T.Graph.OutputPort('m20.data'),
        'm15.start_date': '',
        'm15.end_date': '',
        'm15.before_start_days': 600,
    
        'm24': 'M.derived_feature_extractor.v3',
        'm24.input_data': T.Graph.OutputPort('m15.data'),
        'm24.features': T.Graph.OutputPort('m20.data'),
        'm24.date_col': 'date',
        'm24.instrument_col': 'instrument',
        'm24.drop_na': False,
        'm24.remove_extra_columns': False,
        'm24.user_functions': {},
    
        'm10': 'M.join.v3',
        'm10.data1': T.Graph.OutputPort('m2.data'),
        'm10.data2': T.Graph.OutputPort('m24.data'),
        'm10.on': 'date,instrument',
        'm10.how': 'inner',
        'm10.sort': False,
    
        'm6': 'M.filtet_st_stock.v7',
        'm6.input_1': T.Graph.OutputPort('m10.data'),
    
        'm4': 'M.filter.v3',
        'm4.input_data': T.Graph.OutputPort('m6.data_1'),
        'm4.expr': 'close_0 > 0',
        'm4.output_left_data': False,
    
        'm9': 'M.chinaa_stock_filter.v1',
        'm9.input_data': T.Graph.OutputPort('m4.data'),
        'm9.index_constituent_cond': ['中证800'],
        'm9.board_cond': ['全部'],
        'm9.industry_cond': ['全部'],
        'm9.st_cond': ['全部'],
        'm9.delist_cond': ['全部'],
        'm9.output_left_data': False,
    
        'm5': 'M.dropnan.v2',
        'm5.input_data': T.Graph.OutputPort('m9.data'),
    
        'm12': 'M.input_features.v1',
        'm12.features_ds': T.Graph.OutputPort('m20.data'),
        'm12.features': """#每档排序指标,默认从大到小排序,若想从小到大排序,在前面加负号-
    ranker = close_0/mean(close_0, 5)
    
    #过滤条件
    #f15 = mean(close_1, 5)
    #f05 = mean(close_0, 5)
    #f110 = mean(close_1, 10)
    #f010 = mean(close_0, 10)
    
    
    """,
    
        'm16': 'M.instruments.v2',
        'm16.start_date': T.live_run_param('trading_date', '2019-01-01'),
        'm16.end_date': T.live_run_param('trading_date', '2020-11-10'),
        'm16.market': 'CN_STOCK_A',
        'm16.instrument_list': '',
        'm16.max_count': 0,
    
        'm18': 'M.general_feature_extractor.v7',
        'm18.instruments': T.Graph.OutputPort('m16.data'),
        'm18.features': T.Graph.OutputPort('m12.data'),
        'm18.start_date': '',
        'm18.end_date': '',
        'm18.before_start_days': 600,
    
        'm26': 'M.derived_feature_extractor.v3',
        'm26.input_data': T.Graph.OutputPort('m18.data'),
        'm26.features': T.Graph.OutputPort('m12.data'),
        'm26.date_col': 'date',
        'm26.instrument_col': 'instrument',
        'm26.drop_na': False,
        'm26.remove_extra_columns': False,
        'm26.user_functions': {},
    
        'm7': 'M.filtet_st_stock.v7',
        'm7.input_1': T.Graph.OutputPort('m26.data'),
    
        'm8': 'M.filter.v3',
        'm8.input_data': T.Graph.OutputPort('m7.data_1'),
        'm8.expr': 'close_0 > 0',
        'm8.output_left_data': False,
    
        'm11': 'M.chinaa_stock_filter.v1',
        'm11.input_data': T.Graph.OutputPort('m8.data'),
        'm11.index_constituent_cond': ['中证800'],
        'm11.board_cond': ['全部'],
        'm11.industry_cond': ['全部'],
        'm11.st_cond': ['全部'],
        'm11.delist_cond': ['全部'],
        'm11.output_left_data': False,
    
        'm22': 'M.dropnan.v2',
        'm22.input_data': T.Graph.OutputPort('m11.data'),
    
        'm13': 'M.select_columns.v3',
        'm13.input_ds': T.Graph.OutputPort('m22.data'),
        'm13.columns': 'date,instrument,ranker',
        'm13.reverse_select': False,
    
        'm23': 'M.stock_ranker.v2',
        'm23.training_ds': T.Graph.OutputPort('m5.data'),
        'm23.features': T.Graph.OutputPort('m3.data'),
        'm23.predict_ds': T.Graph.OutputPort('m22.data'),
        'm23.learning_algorithm': '排序',
        'm23.number_of_leaves': 40,
        'm23.minimum_docs_per_leaf': 2000,
        'm23.number_of_trees': 15,
        'm23.learning_rate': 0.1,
        'm23.max_bins': 1023,
        'm23.feature_fraction': 1,
        'm23.data_row_fraction': 1,
        'm23.ndcg_discount_base': 1,
        'm23.slim_data': True,
    
        'm14': 'M.join.v3',
        'm14.data1': T.Graph.OutputPort('m23.predictions'),
        'm14.data2': T.Graph.OutputPort('m13.data'),
        'm14.on': 'date,instrument',
        'm14.how': 'inner',
        'm14.sort': False,
    
        'm21': 'M.sort.v4',
        'm21.input_ds': T.Graph.OutputPort('m14.data'),
        'm21.sort_by': 'position',
        'm21.group_by': 'date',
        'm21.keep_columns': '--',
        'm21.ascending': True,
    
        'm17': 'M.cached.v3',
        'm17.input_1': T.Graph.OutputPort('m21.sorted_data'),
        'm17.run': m17_run_bigquant_run,
        'm17.post_run': m17_post_run_bigquant_run,
        'm17.input_ports': 'input_1',
        'm17.params': '{}',
        'm17.output_ports': '',
    
        'm19': 'M.trade.v4',
        'm19.instruments': T.Graph.OutputPort('m16.data'),
        'm19.options_data': T.Graph.OutputPort('m17.data_1'),
        'm19.start_date': '',
        'm19.end_date': '',
        'm19.initialize': m19_initialize_bigquant_run,
        'm19.handle_data': m19_handle_data_bigquant_run,
        'm19.volume_limit': 0,
        'm19.order_price_field_buy': 'close',
        'm19.order_price_field_sell': 'close',
        'm19.capital_base': 200000000,
        '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': '000905.HIX',
    })
    
    # g.run({})
    
    
    def m25_param_grid_builder_bigquant_run():
        param_grid = {}
    
        # 在这里设置需要调优的参数备选
        param_grid['m23.minimum_docs_per_leaf'] = [1000, 2000, 3000]
        param_grid['m23.number_of_leaves'] = [10, 20, 30, 40]
        param_grid['m23.number_of_trees'] = [10, 15, 20]
        param_grid['m23.learning_rate'] = [0.05, 0.1, 0.2]
    
        return param_grid
    
    def m25_scoring_bigquant_run(result):
        score = result.get('m19').read_raw_perf()['sharpe'].tail(1)[0]
    
        return score
    
    
    m25 = M.hyper_parameter_search.v1(
        param_grid_builder=m25_param_grid_builder_bigquant_run,
        scoring=m25_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
    )
    
    列: ['date', 'instrument', 'ranker']
    /data: 369353
    
    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-8f94914e602f405cbfc8016bd39305c0"}/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-10e4afead7da49dbb43b388ab042bf02"}/bigcharts-data-end
    • 收益率29.22%
    • 年化收益率15.47%
    • 基准收益率54.07%
    • 阿尔法0.03
    • 贝塔0.35
    • 夏普比率1.22
    • 胜率0.54
    • 盈亏比1.38
    • 收益波动率9.73%
    • 信息比率-0.05
    • 最大回撤7.22%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-89ce85f5fe3b4d1389bf52fd5c9b9e3e"}/bigcharts-data-end
    In [2]:
    #m4.predictions.read_all_df().to_csv('3.csv')
    
    In [3]:
    #m21.sorted_data.read_all_df()
    
    In [4]:
    #m4.predictions.read_df().to_csv('1.csv')
    #dt.to_csv('3.csv')