{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-281:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-295:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"to_node_id":"-109:input_1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-281:data"},{"to_node_id":"-259:input_1","from_node_id":"-295:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"-129:data"},{"to_node_id":"-129:input_data","from_node_id":"-147:data_1"},{"to_node_id":"-206:data1","from_node_id":"-147:data_2"},{"to_node_id":"-228:input_1","from_node_id":"-505:data"},{"to_node_id":"-758:input","from_node_id":"-327:data"},{"to_node_id":"-228:input_2","from_node_id":"-327:data"},{"to_node_id":"-1481:instruments","from_node_id":"-758:instrument_list"},{"to_node_id":"-249:data2","from_node_id":"-1415:data_1"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-281:input_data","from_node_id":"-206:data"},{"to_node_id":"-206:data2","from_node_id":"-228:data_1"},{"to_node_id":"-249:data1","from_node_id":"-228:data_2"},{"to_node_id":"-295:input_data","from_node_id":"-249:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","from_node_id":"-259:data_1"},{"to_node_id":"-1481:options_data","from_node_id":"-109:data_1"},{"to_node_id":"-109:input_2","from_node_id":"-121:data_1"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# 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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# 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, 10)\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":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"True","type":"Literal","bound_global_parameter":null},{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-129"}],"output_ports":[{"name":"data","node_id":"-129"}],"cacheable":true,"seq_num":10,"comment":"","comment_collapsed":true},{"node_id":"-147","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n df = DataSource(\"bar1d_CN_CONBOND\").read(start_date=\"2017-06-01\", end_date=\"2020-06-30\")\n df2 = df.drop(['close'],axis = 1)\n data_1 = DataSource.write_df(df)\n data_2 = DataSource.write_df(df2)\n return Outputs(data_1=data_1, data_2=data_2, data_3=None)\n","type":"Literal","bound_global_parameter":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 outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-147"},{"name":"input_2","node_id":"-147"},{"name":"input_3","node_id":"-147"}],"output_ports":[{"name":"data_1","node_id":"-147"},{"name":"data_2","node_id":"-147"},{"name":"data_3","node_id":"-147"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-505","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"market_performance_CN_CONBOND","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2017-06-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-06-30","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-505"},{"name":"features","node_id":"-505"}],"output_ports":[{"name":"data","node_id":"-505"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-327","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"market_performance_CN_CONBOND","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"2020-06-30","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-30","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-327"},{"name":"features","node_id":"-327"}],"output_ports":[{"name":"data","node_id":"-327"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-758","module_id":"BigQuantSpace.trade_data_generation.trade_data_generation-v1","parameters":[{"name":"category","value":"CN_STOCK","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input","node_id":"-758"}],"output_ports":[{"name":"history_data","node_id":"-758"},{"name":"instrument_list","node_id":"-758"},{"name":"calendar","node_id":"-758"}],"cacheable":false,"seq_num":12,"comment":"","comment_collapsed":true},{"node_id":"-1415","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n df = DataSource(\"bar1d_CN_CONBOND\").read(start_date=\"2020-06-30\", end_date=\"2021-06-30\")\n df = df.drop(['close'],axis = 1)\n data_1 = DataSource.write_df(df)\n data_2 = DataSource.write_pickle(df)\n return Outputs(data_1=data_1, data_2=data_2, data_3=None)\n","type":"Literal","bound_global_parameter":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 outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-1415"},{"name":"input_2","node_id":"-1415"},{"name":"input_3","node_id":"-1415"}],"output_ports":[{"name":"data_1","node_id":"-1415"},{"name":"data_2","node_id":"-1415"},{"name":"data_3","node_id":"-1415"}],"cacheable":true,"seq_num":15,"comment":"","comment_collapsed":true},{"node_id":"-1481","module_id":"BigQuantSpace.hftrade.hftrade-v2","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 print(context.options)\n # 加载预测数据\n context.ranker_prediction = context.options.get('data').read()['data']\n print(context.ranker_prediction)\n context.param = context.options['data'].read()[\"param\"]\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 = context.param[\"stock_count\"]\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 = 0.4\n context.hold_days = context.param[\"hold_days\"]\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 交易引擎:每个单位时间开盘前调用一次。\ndef bigquant_run(context, data):\n # 盘前处理,订阅行情等\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_tick","value":"# 交易引擎:tick数据处理函数,每个tick执行一次\ndef bigquant_run(context, tick):\n pass\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\n try:\n print('处理data',data)\n context.ranker_prediction = context.options.get('data').read()['data']\n # 相隔几天(hold_days)进行一下换仓\n if context.trading_day_index % context.hold_days != 0:\n return \n\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 # 目前持仓\n positions = {e: p.amount * p.last_sale_price for e, p in context.portfolio.positions.items()}\n # 权重\n buy_cash_weights = context.stock_weights\n # 今日买入股票列表\n stock_to_buy = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n # 持仓上限\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n\n # 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\n stock_hold_now = [equity for equity in context.portfolio.positions ]\n # 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\n no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]\n # 需要卖出的股票\n stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]\n\n # 卖出\n for stock in stock_to_sell:\n # 如果该股票停牌,则没法成交。因此需要用can_trade方法检查下该股票的状态\n # 如果返回真值,则可以正常下单,否则会出错\n # 因为stock是字符串格式,我们用symbol方法将其转化成平台可以接受的形式:Equity格式\n if data.can_trade(context.symbol(stock)):\n # 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,\n # 即卖出全部股票,可参考回测文档\n if context.order_target_percent(context.symbol(stock), 0) != 0:\n print('sell_context.symbol',context.symbol(stock))\n\n # 如果当天没有买入的股票,就返回\n if len(stock_to_buy) == 0:\n return\n\n # 买入\n for i, instrument in enumerate(stock_to_buy):\n cash = context.portfolio.portfolio_value * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 500:\n if context.order_value(context.symbol(instrument), cash) !=0:\n print('fail_order_value',cash) \n except Exception as e:\n logger.exception(\"限价单下买单失败%s,%s\",context,e)\n ","type":"Literal","bound_global_parameter":null},{"name":"handle_trade","value":"# 交易引擎:成交回报处理函数,每个成交发生时执行一次\ndef bigquant_run(context, trade):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_order","value":"# 交易引擎:委托回报处理函数,每个委托变化时执行一次\ndef bigquant_run(context, order):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"after_trading","value":"# 交易引擎:盘后处理函数,每日盘后执行一次\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"真实价格","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"可转债","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"8","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.HIX","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"disable_cache","value":"True","type":"Literal","bound_global_parameter":null},{"name":"replay_bdb","value":"True","type":"Literal","bound_global_parameter":null},{"name":"show_debug_info","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-1481"},{"name":"options_data","node_id":"-1481"},{"name":"history_ds","node_id":"-1481"},{"name":"benchmark_ds","node_id":"-1481"}],"output_ports":[{"name":"raw_perf","node_id":"-1481"}],"cacheable":false,"seq_num":21,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"name":"data2","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-206","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"-206"},{"name":"data2","node_id":"-206"}],"output_ports":[{"name":"data","node_id":"-206"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-228","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"def del_input(input_1):\n df = input_1.read_df()\n df = df.drop([\"trigger_cond_item_desc\",'revise_item_desc','trigger_item_desc'],axis=1)\n df = df.dropna(axis=0, how='all', thresh=None, subset=None, inplace=False)\n print(df.columns)\n #df.sort_values(by=['date','double_low'],axis=0,ascending=True,inplace = True)\n df = df.sort_index(axis = 1)\n df = df.reset_index(drop = True)\n df = df.groupby('date').head(10)\n #print(df[['double_low','date']])\n data_1 = DataSource.write_df(df)\n return data_1\n# 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, input_2, input_3):\n data_1 = del_input(input_1)\n data_2 = del_input(input_2)\n return Outputs(data_1=data_1, data_2=data_2, data_3=None)","type":"Literal","bound_global_parameter":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 outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-228"},{"name":"input_2","node_id":"-228"},{"name":"input_3","node_id":"-228"}],"output_ports":[{"name":"data_1","node_id":"-228"},{"name":"data_2","node_id":"-228"},{"name":"data_3","node_id":"-228"}],"cacheable":false,"seq_num":23,"comment":"","comment_collapsed":true},{"node_id":"-249","module_id":"BigQuantSpace.join.join-v3","parameters":[{"name":"on","value":"date,instrument","type":"Literal","bound_global_parameter":null},{"name":"how","value":"inner","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"data1","node_id":"-249"},{"name":"data2","node_id":"-249"}],"output_ports":[{"name":"data","node_id":"-249"}],"cacheable":true,"seq_num":25,"comment":"","comment_collapsed":true},{"node_id":"-259","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read_df()\n #print(df.columns)\n #df = df1[['date','instrument','close','label']]\n #df = df.drop([\"trigger_cond_item_desc\",'revise_item_desc','trigger_item_desc'],axis=1)\n df = df.dropna(axis=0, how='all', thresh=None, subset=None, inplace=False)\n print(df.columns)\n df.sort_values(by=['date','double_low'],axis=0,ascending=True,inplace = True)\n df = df.sort_index(axis = 1)\n df = df.reset_index(drop = True)\n df = df.groupby('date').head(10)\n #print(df[['double_low','date']])\n data_1 = DataSource.write_df(df)\n data_2 = DataSource.write_pickle(df)\n return Outputs(data_1=data_1, data_2=data_2, data_3=None)","type":"Literal","bound_global_parameter":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 outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-259"},{"name":"input_2","node_id":"-259"},{"name":"input_3","node_id":"-259"}],"output_ports":[{"name":"data_1","node_id":"-259"},{"name":"data_2","node_id":"-259"},{"name":"data_3","node_id":"-259"}],"cacheable":false,"seq_num":13,"comment":"","comment_collapsed":true},{"node_id":"-109","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n df = input_1.read()\n param = input_2.read()\n \n data = {\n \"param\": param,\n \"data\": df\n }\n data_1 = DataSource.write_pickle(data)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","type":"Literal","bound_global_parameter":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 outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-109"},{"name":"input_2","node_id":"-109"},{"name":"input_3","node_id":"-109"}],"output_ports":[{"name":"data_1","node_id":"-109"},{"name":"data_2","node_id":"-109"},{"name":"data_3","node_id":"-109"}],"cacheable":true,"seq_num":11,"comment":"合并数据和Trade参数","comment_collapsed":true},{"node_id":"-121","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"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, input_2, input_3, stock_count, hold_days):\n # 示例代码如下。在这里编写您的代码\n param = {\n \"stock_count\": stock_count,\n \"hold_days\": hold_days\n }\n data_1 = DataSource.write_pickle(param)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","type":"Literal","bound_global_parameter":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 outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{\n \"stock_count\": 3,\n \"hold_days\": 4 \n}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-121"},{"name":"input_2","node_id":"-121"},{"name":"input_3","node_id":"-121"}],"output_ports":[{"name":"data_1","node_id":"-121"},{"name":"data_2","node_id":"-121"},{"name":"data_3","node_id":"-121"}],"cacheable":true,"seq_num":14,"comment":"暴露Trade的参数","comment_collapsed":true},{"node_id":"-16105","module_id":"BigQuantSpace.hyper_parameter_search.hyper_parameter_search-v1","parameters":[{"name":"param_grid_builder","value":"def bigquant_run():\n import itertools\n param_grid = {}\n \n period_list = [5,6,120] \n \n # 在这里设置需要调优的参数备选\n feature_list = [\n '''\n double_low = close + bond_prem_ratio\n remain_size\n rank_swing_volatility_5 = nanstd((high-low)/pre_close, {0})*sqrt(200)*100\n '''.format(period) for period in period_list\n ]\n param_grid[\"m14.params\"] = [\n \"\"\"{\"stock_count\": 3, \"hold_days\": 3}\"\"\",\n \"\"\"{\"stock_count\": 4, \"hold_days\": 4}\"\"\",\n ]\n param_grid['m3.features'] = feature_list\n return param_grid","type":"Literal","bound_global_parameter":null},{"name":"scoring","value":"def bigquant_run(result):\n # 评分:收益/最大回撤\n score = result.get('m21').read_raw_perf()['sharpe'].tail(1)[0]\n return {'score': score}\n","type":"Literal","bound_global_parameter":null},{"name":"search_algorithm","value":"网格搜索","type":"Literal","bound_global_parameter":null},{"name":"search_iterations","value":10,"type":"Literal","bound_global_parameter":null},{"name":"random_state","value":"","type":"Literal","bound_global_parameter":null},{"name":"workers","value":1,"type":"Literal","bound_global_parameter":null},{"name":"worker_distributed_run","value":"False","type":"Literal","bound_global_parameter":null},{"name":"worker_silent","value":"False","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":"-16105"},{"name":"input_1","node_id":"-16105"},{"name":"input_2","node_id":"-16105"},{"name":"input_3","node_id":"-16105"}],"output_ports":[{"name":"result","node_id":"-16105"}],"cacheable":false,"seq_num":9,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position 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Position='1121,-57,200,200'/><node_position Node='-259' Position='911,394,200,200'/><node_position Node='-109' Position='1269,643,200,200'/><node_position Node='-121' Position='1577,522,200,200'/><node_position Node='-16105' Position='116,574,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}
[2022-06-01 16:50:03.310050] INFO: moduleinvoker: input_features.v1 开始运行..
[2022-06-01 16:50:03.352473] INFO: moduleinvoker: input_features.v1 运行完成[0.042438s].
[2022-06-01 16:50:03.365192] INFO: moduleinvoker: cached.v3 开始运行..
[2022-06-01 16:50:04.662490] INFO: moduleinvoker: cached.v3 运行完成[1.297303s].
[2022-06-01 16:50:04.675551] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..
[2022-06-01 16:50:05.077206] INFO: 自动标注(任意数据源): 开始标注 ..
[2022-06-01 16:50:05.633861] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.958302s].
[2022-06-01 16:50:05.645080] INFO: moduleinvoker: use_datasource.v1 开始运行..
[2022-06-01 16:50:06.344509] INFO: moduleinvoker: use_datasource.v1 运行完成[0.699431s].
[2022-06-01 16:50:06.350072] INFO: moduleinvoker: use_datasource.v1 开始运行..
[2022-06-01 16:50:06.356998] INFO: moduleinvoker: 命中缓存
[2022-06-01 16:50:06.358756] INFO: moduleinvoker: use_datasource.v1 运行完成[0.008693s].
[2022-06-01 16:50:06.373439] INFO: moduleinvoker: trade_data_generation.v1 开始运行..
[2022-06-01 16:50:06.601238] INFO: moduleinvoker: trade_data_generation.v1 运行完成[0.227791s].
[2022-06-01 16:50:06.612379] INFO: moduleinvoker: cached.v3 开始运行..
[2022-06-01 16:50:07.116644] INFO: moduleinvoker: cached.v3 运行完成[0.504273s].
[2022-06-01 16:50:07.127947] INFO: moduleinvoker: join.v3 开始运行..
[2022-06-01 16:50:09.597861] INFO: join: /data, 行数=6568/7510, 耗时=0.22339s
[2022-06-01 16:50:09.655332] INFO: join: 最终行数: 6568
[2022-06-01 16:50:09.696617] INFO: moduleinvoker: join.v3 运行完成[2.56866s].
[2022-06-01 16:50:09.707200] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2022-06-01 16:50:09.881863] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.002s
[2022-06-01 16:50:09.907539] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.024s
[2022-06-01 16:50:10.063789] INFO: derived_feature_extractor: /data, 6568
[2022-06-01 16:50:10.127747] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.420539s].
[2022-06-01 16:50:10.136544] INFO: moduleinvoker: join.v3 开始运行..
[2022-06-01 16:50:10.616702] INFO: join: /data, 行数=6465/6568, 耗时=0.138967s
[2022-06-01 16:50:10.651059] INFO: join: 最终行数: 6465
[2022-06-01 16:50:10.659349] INFO: moduleinvoker: join.v3 运行完成[0.522806s].
[2022-06-01 16:50:10.670693] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..
[2022-06-01 16:50:10.771550] INFO: StockRanker: 特征预处理 ..
[2022-06-01 16:50:10.806314] INFO: StockRanker: prepare data: training ..
[2022-06-01 16:50:10.928744] INFO: StockRanker训练: d84374c2 准备训练: 6465 行数
[2022-06-01 16:50:10.930963] INFO: StockRanker训练: AI模型训练,将在6465*3=1.94万数据上对模型训练进行2轮迭代训练。预计将需要1~2分钟。请耐心等待。
[2022-06-01 16:50:11.137996] INFO: StockRanker训练: 正在训练 ..
[2022-06-01 16:50:11.202939] INFO: StockRanker训练: 任务状态: Pending
[2022-06-01 16:50:21.260911] INFO: StockRanker训练: 任务状态: Running
[2022-06-01 16:51:21.524114] INFO: StockRanker训练: 00:01:01.5603839, finished iteration 1
[2022-06-01 16:51:21.525749] INFO: StockRanker训练: 00:01:01.5684970, finished iteration 2
[2022-06-01 16:51:21.527032] INFO: StockRanker训练: 任务状态: Succeeded
[2022-06-01 16:51:21.743422] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[71.072723s].
[2022-06-01 16:51:21.756402] INFO: moduleinvoker: cached.v3 开始运行..
[2022-06-01 16:51:22.371877] INFO: moduleinvoker: cached.v3 运行完成[0.615485s].
[2022-06-01 16:51:22.381892] INFO: moduleinvoker: join.v3 开始运行..
[2022-06-01 16:51:23.450867] INFO: join: /data, 行数=1234/268823, 耗时=0.965136s
[2022-06-01 16:51:23.485357] INFO: join: 最终行数: 1234
[2022-06-01 16:51:23.495604] INFO: moduleinvoker: join.v3 运行完成[1.11371s].
[2022-06-01 16:51:23.503183] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2022-06-01 16:51:23.561672] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s
[2022-06-01 16:51:23.586897] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.024s
[2022-06-01 16:51:23.646392] INFO: derived_feature_extractor: /data, 1234
[2022-06-01 16:51:23.701457] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.198262s].
[2022-06-01 16:51:23.711090] INFO: moduleinvoker: cached.v3 开始运行..
[2022-06-01 16:51:23.824428] INFO: moduleinvoker: cached.v3 运行完成[0.113348s].
[2022-06-01 16:51:23.837723] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..
[2022-06-01 16:51:23.988328] INFO: StockRanker预测: /data ..
[2022-06-01 16:51:24.064732] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[0.227003s].
[2022-06-01 16:51:24.078570] INFO: moduleinvoker: cached.v3 开始运行..
[2022-06-01 16:51:24.110984] INFO: moduleinvoker: cached.v3 运行完成[0.032421s].
[2022-06-01 16:51:24.124272] INFO: moduleinvoker: cached.v3 开始运行..
[2022-06-01 16:51:24.208061] INFO: moduleinvoker: cached.v3 运行完成[0.08379s].
[2022-06-01 16:51:24.251630] INFO: moduleinvoker: hfbacktest.v1 开始运行..
[2022-06-01 16:51:24.257639] INFO: hfbacktest: passed-in daily_data_ds:None
[2022-06-01 16:51:24.259422] INFO: hfbacktest: passed-in minute_data_ds:None
[2022-06-01 16:51:24.260779] INFO: hfbacktest: passed-in tick_data_ds:None
[2022-06-01 16:51:24.263018] INFO: hfbacktest: passed-in each_data_ds:None
[2022-06-01 16:51:24.264209] INFO: hfbacktest: passed-in dominant_data_ds:None
[2022-06-01 16:51:24.265341] INFO: hfbacktest: passed-in benchmark_data_ds:None
[2022-06-01 16:51:24.266327] INFO: hfbacktest: passed-in trading_calendar_ds:None
[2022-06-01 16:51:24.267366] INFO: hfbacktest: biglearning V1.4.11
[2022-06-01 16:51:24.268368] INFO: hfbacktest: bigtrader v1.9.6 2022-05-31
[2022-06-01 16:51:24.285821] INFO: hfbacktest: strategy callbacks:{'on_init': , 'on_stop': , 'on_start': , 'handle_data': , 'handle_tick': , 'handle_trade': , 'handle_order': }
[2022-06-01 16:51:24.295922] INFO: hfbacktest: begin reading history data, 2020-06-30 00:00:00~2020-12-30, disable_cache:True, replay_bdb:1
[2022-06-01 16:51:24.297364] INFO: hfbacktest: reading benchmark data 2020-06-29 00:00:00~2020-12-30...
[2022-06-01 16:51:24.467592] INFO: hfbacktest: reading daily data 2019-06-25 00:00:00~2020-12-30...
[2022-06-01 16:51:24.691693] INFO: hfbacktest: cached_benchmark_ds:DataSource(98bce71a0f9d4988aff3dac1d8e562ccT)
[2022-06-01 16:51:24.693594] INFO: hfbacktest: cached_daily_ds:None
[2022-06-01 16:51:24.695264] INFO: hfbacktest: cached_minute_ds:None
[2022-06-01 16:51:24.696683] INFO: hfbacktest: cached_tick_ds:None
[2022-06-01 16:51:24.697752] INFO: hfbacktest: cached_each_ds:None
[2022-06-01 16:51:24.698790] INFO: hfbacktest: dominant_data_ds:None
[2022-06-01 16:51:24.699804] INFO: hfbacktest: read history data done, call run_backtest()
[2022-06-01 16:51:25.983463] ERROR: moduleinvoker: module name: hfbacktest, module version: v1, trackeback: OverflowError: cannot convert float infinity to integer
[2022-06-01 16:51:25.992394] ERROR: moduleinvoker: module name: hftrade, module version: v2, trackeback: OverflowError: cannot convert float infinity to integer
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
'date', 'conversion_price', 'name', 'trading_code',
'conversion_chg_pct_week', 'bond_prem_ratio', 'equ_name',
'redemption_price', 'close_equ', 'total_size', 'pure_bond_prem_ratio',
'pure_bond_ratio', 'close'],
dtype='object')
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
'date', 'conversion_price', 'name', 'trading_code',
'conversion_chg_pct_week', 'bond_prem_ratio', 'equ_name',
'redemption_price', 'close_equ', 'total_size', 'pure_bond_prem_ratio',
'pure_bond_ratio', 'close'],
dtype='object')
bigcharts-data-start/{"__type":"tabs","__id":"bigchart-362314b94ef842b08463a27d9100f758"}/bigcharts-data-end
Index(['instrument', 'date', 'pre_close', 'name_x', 'open', 'high', 'low',
'deal_number', 'volume', 'amount', 'accrued_interest',
'yield_to_maturity', 'vwap', 'gross_close', 'net_close',
'bond_prem_ratio', 'close', 'close_equ', 'conversion_chg_pct',
'conversion_chg_pct_week', 'conversion_price', 'equ_name',
'equ_trading_code', 'name_y', 'pure_bond_prem_ratio', 'pure_bond_ratio',
'redemption_price', 'remain_size', 'total_size', 'trading_code',
'double_low', 'rank_swing_volatility_5'],
dtype='object')
2022-06-01 16:51:24.720449 init history datas...
2022-06-01 16:51:24.721263 init history datas done.
2022-06-01 16:51:24.727520 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2020-06-30 ~ 2020-12-30
2022-06-01 16:51:24.727815 run_backtest() running...
2022-06-01 16:51:24.738595 initial contracts len=0
2022-06-01 16:51:24.738763 backtest inited.
{'data': DataSource(388af68346ec4f2aad8c6d7ef909631dT)}
date instrument score position
0 2020-06-30 110034.HCB 0.024629 1
1 2020-06-30 110041.HCB 0.024629 2
2 2020-06-30 110052.HCB 0.024629 3
3 2020-06-30 110033.HCB 0.024629 4
4 2020-06-30 110064.HCB 0.024629 5
... ... ... ... ...
1229 2020-12-30 127014.ZCB -0.003492 6
1230 2020-12-30 123017.ZCB -0.003492 7
1231 2020-12-30 123081.ZCB -0.311603 8
1232 2020-12-30 128036.ZCB -0.311603 9
1233 2020-12-30 127008.ZCB -0.311603 10
[1234 rows x 4 columns]
2022-06-01 16:51:24.795039 backtest transforming 1d, bars=1...
2022-06-01 16:51:24.795290 transform start_trading_day=2020-06-30 00:00:00, simulation period=2020-06-30 ~ 2020-12-30
2022-06-01 16:51:24.795325 transform source=None, before_start_days=8
2022-06-01 16:51:24.795354 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f6659263ad0>
处理data BarDatas(current_dt:2020-06-30 15:00:00)
处理data BarDatas(current_dt:2020-07-01 15:00:00)
处理data BarDatas(current_dt:2020-07-02 15:00:00)
处理data BarDatas(current_dt:2020-07-03 15:00:00)
处理data BarDatas(current_dt:2020-07-06 15:00:00)
2022-06-01 16:51:25.028421 market open send order=OrderReq(bkt000,110034.HCB,'1','0',0,121.8199,U,0,strategy,2020-07-06 15:00:00) failed err=-108,委托数量错误
2022-06-01 16:51:25.028802 market open send order=OrderReq(bkt000,113011.HCB,'1','0',0,128.5399,U,0,strategy,2020-07-06 15:00:00) failed err=-108,委托数量错误
处理data BarDatas(current_dt:2020-07-07 15:00:00)
处理data BarDatas(current_dt:2020-07-08 15:00:00)
处理data BarDatas(current_dt:2020-07-09 15:00:00)
处理data BarDatas(current_dt:2020-07-10 15:00:00)
2022-06-01 16:51:25.149732 market open send order=OrderReq(bkt000,110048.HCB,'1','0',0,115.73,U,0,strategy,2020-07-10 15:00:00) failed err=-108,委托数量错误
处理data BarDatas(current_dt:2020-07-13 15:00:00)
处理data BarDatas(current_dt:2020-07-14 15:00:00)
处理data BarDatas(current_dt:2020-07-15 15:00:00)
处理data BarDatas(current_dt:2020-07-16 15:00:00)
2022-06-01 16:51:25.248887 market open send order=OrderReq(bkt000,110052.HCB,'1','0',0,112.05,U,0,strategy,2020-07-16 15:00:00) failed err=-108,委托数量错误
处理data BarDatas(current_dt:2020-07-17 15:00:00)
处理data BarDatas(current_dt:2020-07-20 15:00:00)
处理data BarDatas(current_dt:2020-07-21 15:00:00)
处理data BarDatas(current_dt:2020-07-22 15:00:00)
处理data BarDatas(current_dt:2020-07-23 15:00:00)
处理data BarDatas(current_dt:2020-07-24 15:00:00)
处理data BarDatas(current_dt:2020-07-27 15:00:00)
处理data BarDatas(current_dt:2020-07-28 15:00:00)
2022-06-01 16:51:25.499833 market open send order=OrderReq(bkt000,113026.HCB,'1','0',0,101.23,U,0,strategy,2020-07-28 15:00:00) failed err=-108,委托数量错误
2022-06-01 16:51:25.500214 market open send order=OrderReq(bkt000,113525.HCB,'1','0',0,122.9499,U,0,strategy,2020-07-28 15:00:00) failed err=-108,委托数量错误
处理data BarDatas(current_dt:2020-07-29 15:00:00)
处理data BarDatas(current_dt:2020-07-30 15:00:00)
处理data BarDatas(current_dt:2020-07-31 15:00:00)
处理data BarDatas(current_dt:2020-08-03 15:00:00)
处理data BarDatas(current_dt:2020-08-04 15:00:00)
处理data BarDatas(current_dt:2020-08-05 15:00:00)
处理data BarDatas(current_dt:2020-08-06 15:00:00)
处理data BarDatas(current_dt:2020-08-07 15:00:00)
2022-06-01 16:51:25.797407 market open send order=OrderReq(bkt000,110057.HCB,'1','0',0,120.37,U,0,strategy,2020-08-07 15:00:00) failed err=-108,委托数量错误
处理data BarDatas(current_dt:2020-08-10 15:00:00)
处理data BarDatas(current_dt:2020-08-11 15:00:00)
处理data BarDatas(current_dt:2020-08-12 15:00:00)
处理data BarDatas(current_dt:2020-08-13 15:00:00)
2022-06-01 16:51:25.886427 market open send order=OrderReq(bkt000,110041.HCB,'1','0',0,113.61,U,0,strategy,2020-08-13 15:00:00) failed err=-108,委托数量错误
处理data BarDatas(current_dt:2020-08-14 15:00:00)
处理data BarDatas(current_dt:2020-08-17 15:00:00)
处理data BarDatas(current_dt:2020-08-18 15:00:00)
处理data BarDatas(current_dt:2020-08-19 15:00:00)
2022-06-01 16:51:25.982837 strategy strategy exception:Traceback (most recent call last):
File "bigtrader/strategy/engine.py", line 714, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func
File "bigtrader/strategy/strategy_base.py", line 2161, in bigtrader2.bigtrader.strategy.strategy_base.StrategyBase.call_handle_market_open
File "bigtrader/finance/account_engine.py", line 2456, in bigtrader2.bigtrader.finance.account_engine.AccountEngine.send_order
File "bigtrader/finance/account_engine.py", line 2354, in bigtrader2.bigtrader.finance.account_engine.AccountEngine._check_order_cash_or_adjust
OverflowError: cannot convert float infinity to integer
---------------------------------------------------------------------------
OverflowError Traceback (most recent call last)
<ipython-input-3-92df467ea664> in <module>
373 )
374
--> 375 m21 = M.hftrade.v2(
376 instruments=m12.instrument_list,
377 options_data=m11.data_1,
OverflowError: cannot convert float infinity to integer