{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-238:instruments","from_node_id":"-134:data"},{"to_node_id":"-196:instruments","from_node_id":"-134:data"},{"to_node_id":"-196:features","from_node_id":"-191:data"},{"to_node_id":"-203:features","from_node_id":"-191:data"},{"to_node_id":"-153:features","from_node_id":"-191:data"},{"to_node_id":"-183:features","from_node_id":"-191:data"},{"to_node_id":"-160:features","from_node_id":"-191:data"},{"to_node_id":"-203:input_data","from_node_id":"-196:data"},{"to_node_id":"-211:input_1","from_node_id":"-203:data"},{"to_node_id":"-214:input_1","from_node_id":"-211:data_1"},{"to_node_id":"-228:data2","from_node_id":"-214:data"},{"to_node_id":"-153:instruments","from_node_id":"-144:data"},{"to_node_id":"-292:instruments","from_node_id":"-144:data"},{"to_node_id":"-160:input_data","from_node_id":"-153:data"},{"to_node_id":"-312:input_1","from_node_id":"-160:data"},{"to_node_id":"-199:data","from_node_id":"-175:data"},{"to_node_id":"-199:model","from_node_id":"-183:model"},{"to_node_id":"-292:options_data","from_node_id":"-199:predictions"},{"to_node_id":"-235:input_data","from_node_id":"-228:data"},{"to_node_id":"-183:training_ds","from_node_id":"-235:data"},{"to_node_id":"-228:data1","from_node_id":"-238:data"},{"to_node_id":"-175:input_data","from_node_id":"-312:data_1"}],"nodes":[{"node_id":"-134","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-12-31","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-134"}],"output_ports":[{"name":"data","node_id":"-134"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-191","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# #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\nbuy_condition=where((close_1/close_2>1.15),1,0)\nsell_condition=where((close_1/close_2<1.15),1,0)\n# ta_macd_macd_12_26_9_0/adjust_factor_0\n# ta_macd_macdsignal_12_26_9_0/adjust_factor_0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-191"}],"output_ports":[{"name":"data","node_id":"-191"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-196","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"200","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-196"},{"name":"features","node_id":"-196"}],"output_ports":[{"name":"data","node_id":"-196"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-203","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-203"},{"name":"features","node_id":"-203"}],"output_ports":[{"name":"data","node_id":"-203"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-211","module_id":"BigQuantSpace.filtet_st_stock.filtet_st_stock-v7","parameters":[],"input_ports":[{"name":"input_1","node_id":"-211"}],"output_ports":[{"name":"data_1","node_id":"-211"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-214","module_id":"BigQuantSpace.filter_delist_stocks.filter_delist_stocks-v3","parameters":[],"input_ports":[{"name":"input_1","node_id":"-214"}],"output_ports":[{"name":"data","node_id":"-214"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-144","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2022-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-06-08","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-144"}],"output_ports":[{"name":"data","node_id":"-144"}],"cacheable":true,"seq_num":13,"comment":"","comment_collapsed":true},{"node_id":"-153","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"2022-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-06-01","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":90,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-153"},{"name":"features","node_id":"-153"}],"output_ports":[{"name":"data","node_id":"-153"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true},{"node_id":"-160","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-160"},{"name":"features","node_id":"-160"}],"output_ports":[{"name":"data","node_id":"-160"}],"cacheable":true,"seq_num":15,"comment":"","comment_collapsed":true},{"node_id":"-175","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-175"},{"name":"features","node_id":"-175"}],"output_ports":[{"name":"data","node_id":"-175"}],"cacheable":true,"seq_num":18,"comment":"","comment_collapsed":true},{"node_id":"-183","module_id":"BigQuantSpace.stock_ranker_train.stock_ranker_train-v6","parameters":[{"name":"learning_algorithm","value":"排序","type":"Literal","bound_global_parameter":null},{"name":"number_of_leaves","value":30,"type":"Literal","bound_global_parameter":null},{"name":"minimum_docs_per_leaf","value":1000,"type":"Literal","bound_global_parameter":null},{"name":"number_of_trees","value":20,"type":"Literal","bound_global_parameter":null},{"name":"learning_rate","value":0.1,"type":"Lite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#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/develop/datasource/deprecated/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/develop/bigexpr/usage.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, -5) / shift(open, -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, label)\n","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SHA","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-238"}],"output_ports":[{"name":"data","node_id":"-238"}],"cacheable":true,"seq_num":22,"comment":"","comment_collapsed":true},{"node_id":"-292","module_id":"BigQuantSpace.trade.trade-v4","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":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.hold_days = 5\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\n # 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.hold_days # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.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.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 not is_staging and cash_for_sell > 0:\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 # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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[2022-06-10 12:27:01.557699] INFO: moduleinvoker: instruments.v2 开始运行..
[2022-06-10 12:27:01.565093] INFO: moduleinvoker: 命中缓存
[2022-06-10 12:27:01.566599] INFO: moduleinvoker: instruments.v2 运行完成[0.008911s].
[2022-06-10 12:27:01.576142] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2022-06-10 12:27:01.589953] INFO: moduleinvoker: 命中缓存
[2022-06-10 12:27:01.591722] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.015589s].
[2022-06-10 12:27:01.597020] INFO: moduleinvoker: input_features.v1 开始运行..
[2022-06-10 12:27:01.638494] INFO: moduleinvoker: input_features.v1 运行完成[0.041474s].
[2022-06-10 12:27:01.656621] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2022-06-10 12:27:04.500096] INFO: 基础特征抽取: 年份 2020, 特征行数=540554
[2022-06-10 12:27:07.617046] INFO: 基础特征抽取: 年份 2021, 特征行数=1061527
[2022-06-10 12:27:07.728197] INFO: 基础特征抽取: 总行数: 1602081
[2022-06-10 12:27:07.738816] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[6.0822s].
[2022-06-10 12:27:07.750211] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2022-06-10 12:27:10.813331] INFO: derived_feature_extractor: 提取完成 buy_condition=where((close_1/close_2>1.15),1,0), 0.021s
[2022-06-10 12:27:10.821930] INFO: derived_feature_extractor: 提取完成 sell_condition=where((close_1/close_2<1.15),1,0), 0.006s
[2022-06-10 12:27:11.881763] INFO: derived_feature_extractor: /y_2020, 540554
[2022-06-10 12:27:13.883331] INFO: derived_feature_extractor: /y_2021, 1061527
[2022-06-10 12:27:14.176878] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[6.426652s].
[2022-06-10 12:27:14.190865] INFO: moduleinvoker: filtet_st_stock.v7 开始运行..
[2022-06-10 12:27:17.085461] INFO: moduleinvoker: filtet_st_stock.v7 运行完成[2.894601s].
[2022-06-10 12:27:17.097314] INFO: moduleinvoker: filter_delist_stocks.v3 开始运行..
[2022-06-10 12:27:26.821432] INFO: moduleinvoker: filter_delist_stocks.v3 运行完成[9.724125s].
[2022-06-10 12:27:26.834552] INFO: moduleinvoker: join.v3 开始运行..
[2022-06-10 12:27:32.922427] INFO: join: /data, 行数=991606/1529265, 耗时=3.937096s
[2022-06-10 12:27:32.992034] INFO: join: 最终行数: 991606
[2022-06-10 12:27:33.002931] INFO: moduleinvoker: join.v3 运行完成[6.168395s].
[2022-06-10 12:27:33.012061] INFO: moduleinvoker: dropnan.v2 开始运行..
[2022-06-10 12:27:33.845861] INFO: dropnan: /data, 990685/991606
[2022-06-10 12:27:33.905770] INFO: dropnan: 行数: 990685/991606
[2022-06-10 12:27:33.921631] INFO: moduleinvoker: dropnan.v2 运行完成[0.909545s].
[2022-06-10 12:27:33.943580] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..
[2022-06-10 12:27:34.518240] INFO: StockRanker: 特征预处理 ..
[2022-06-10 12:27:34.630229] INFO: StockRanker: prepare data: training ..
[2022-06-10 12:27:34.728240] INFO: StockRanker: sort ..
[2022-06-10 12:27:45.188903] INFO: StockRanker训练: a63e28b0 准备训练: 990685 行数
[2022-06-10 12:27:45.190830] INFO: StockRanker训练: AI模型训练，将在990685*2=198.14万数据上对模型训练进行20轮迭代训练。预计将需要2～3分钟。请耐心等待。
[2022-06-10 12:27:45.457491] INFO: StockRanker训练: 正在训练 ..
[2022-06-10 12:27:45.504960] INFO: StockRanker训练: 任务状态: Pending
[2022-06-10 12:27:55.551290] INFO: StockRanker训练: 任务状态: Running
[2022-06-10 12:28:55.834782] INFO: StockRanker训练: 任务状态: Succeeded
[2022-06-10 12:28:55.846050] ERROR: moduleinvoker: module name: stock_ranker_train, module version: v6, trackeback: Exception: 模型训练失败：可能导致错误的原因是训练数据问题，请检查训练数据, err_code=1 (a63e28b0e87511eca7045a3246ee9563)
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Exception Traceback (most recent call last)
<ipython-input-25-d1098a0c7b19> in <module>
151 )
152
--> 153 m2 = M.stock_ranker_train.v6(
154 training_ds=m21.data,
155 features=m3.data,
Exception: 模型训练失败：可能导致错误的原因是训练数据问题，请检查训练数据, err_code=1 (a63e28b0e87511eca7045a3246ee9563)