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{"description":"实验创建于2017/8/26","graph":{"edges":[{"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-bdf8-104b137c8601-43: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":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","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":"-228:input_1","from_node_id":"-147:data_2"},{"to_node_id":"-1481:instruments","from_node_id":"-758:instrument_list"},{"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":"-228:data_1"},{"to_node_id":"-295:input_data","from_node_id":"-228:data_2"},{"to_node_id":"-1481:options_data","from_node_id":"-109:data_1"},{"to_node_id":"-109:input_2","from_node_id":"-121:data_1"},{"to_node_id":"-228:input_2","from_node_id":"-221:data_2"},{"to_node_id":"-758:input","from_node_id":"-221:data_2"}],"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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5)*sqrt(200)*100","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43","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":"3","type":"Literal","bound_global_parameter":null},{"name":"minimum_docs_per_leaf","value":"100","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":"Literal","bound_global_parameter":null},{"name":"max_bins","value":1023,"type":"Literal","bound_global_parameter":null},{"name":"feature_fraction","value":1,"type":"Literal","bound_global_parameter":null},{"name":"data_row_fraction","value":1,"type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"ndcg_discount_base","value":1,"type":"Literal","bound_global_parameter":null},{"name":"m_lazy_run","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"training_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"features","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"test_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"base_model","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"}],"output_ports":[{"name":"model","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"feature_gains","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"name":"m_lazy_run","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"}],"cacheable":false,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","module_id":"BigQuantSpace.stock_ranker_predict.stock_ranker_predict-v5","parameters":[{"name":"m_lazy_run","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"model","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"output_ports":[{"name":"predictions","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"name":"m_lazy_run","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-281","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":"-281"},{"name":"features","node_id":"-281"}],"output_ports":[{"name":"data","node_id":"-281"}],"cacheable":true,"seq_num":16,"comment":"","comment_collapsed":true},{"node_id":"-295","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":"-295"},{"name":"features","node_id":"-295"}],"output_ports":[{"name":"data","node_id":"-295"}],"cacheable":true,"seq_num":18,"comment":"","comment_collapsed":true},{"node_id":"-129","module_id":"BigQuantSpace.auto_labeler_on_datasource.auto_labeler_on_datasource-v1","parameters":[{"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/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,start_date_input,end_date_input):\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=start_date_input, end_date=end_date_input)\n df1 = DataSource(\"market_performance_CN_CONBOND\").read(start_date=start_date_input, end_date=end_date_input)\n df2 = df.drop(['close'],axis = 1)\n df3 = pd.merge(df1,df2,on=['date','instrument'],how='inner')\n data_1 = DataSource.write_df(df3)\n data_2 = DataSource.write_df(df3)\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":"{\"start_date_input\":\"2017-06-01\",\n\"end_date_input\":\"2019-11-01\"}","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":"-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":"-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('初始化函数开始:')\n print('context.options',context.options)\n # 加载预测数据\n context.ranker_prediction = context.options.get('data').read()['data']\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=0.0003, 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 print('初始化函数结束')\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 print('订阅行情前',data)\n context.subscribe(context.instruments)\n print('订阅行情后',data)\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":"import math\n# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n print('当前日期data.current_dt',data.current_dt)\n print('data',data)\n try:\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 print(\"<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\")\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 print('卖出order target percent',context.symbol(stock))\n print('卖出结果',context.order_target_percent(context.symbol(stock), 0))\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 print('买入列表',stock_to_buy)\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 cash = int(math.floor(cash))\n print('买入order_value',context.symbol(instrument),' cash ',cash)\n print(context.order_value(context.symbol(instrument), cash))\n except Exception as e:\n print('抛出异常',e)","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":"80","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":"False","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":"-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":"-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 print('m11.input1',input_1)\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\": 4,\n \"hold_days\": 5 \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\": 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    In [2]:
    # 本代码由可视化策略环境自动生成 2022年6月27日 23:07
    # 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 m2_run_bigquant_run(input_1, input_2, input_3,start_date_input,end_date_input):
        # 示例代码如下。在这里编写您的代码
        df = DataSource("bar1d_CN_CONBOND").read(start_date=start_date_input, end_date=end_date_input)
        df1 = DataSource("market_performance_CN_CONBOND").read(start_date=start_date_input, end_date=end_date_input)
        df2 = df.drop(['close'],axis = 1)
        df3 = pd.merge(df1,df2,on=['date','instrument'],how='inner')
        data_1 = DataSource.write_df(df3)
        data_2 = DataSource.write_df(df3)
        return Outputs(data_1=data_1, data_2=data_2, data_3=None)
    
    # 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 m2_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m14_run_bigquant_run(input_1, input_2, input_3, stock_count, hold_days):
        # 示例代码如下。在这里编写您的代码
        param = {
            "stock_count": stock_count,
            "hold_days": hold_days
            }
        data_1 = DataSource.write_pickle(param)
        return Outputs(data_1=data_1, data_2=None, data_3=None)
    
    # 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 m14_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m1_run_bigquant_run(input_1, input_2, input_3,start_date_input,end_date_input):
        # 示例代码如下。在这里编写您的代码
        df = DataSource("bar1d_CN_CONBOND").read(start_date=start_date_input, end_date=end_date_input)
        df1 = DataSource("market_performance_CN_CONBOND").read(start_date=start_date_input, end_date=end_date_input)
        df2 = df.drop(['close'],axis = 1)
        df3 = pd.merge(df1,df2,on=['date','instrument'],how='inner')
        data_1 = DataSource.write_df(df3)
        data_2 = DataSource.write_df(df3)
        return Outputs(data_1=data_1, data_2=data_2, data_3=None)
    
    # 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 m1_post_run_bigquant_run(outputs):
        return outputs
    
    def del_input(input_1):
        df = input_1.read_df()
        df = df.drop(["trigger_cond_item_desc",'revise_item_desc','trigger_item_desc'],axis=1)
        df = df.dropna(axis=0, how='all', thresh=None, subset=None, inplace=False)
        print(df.columns)
        #df.sort_values(by=['date','double_low'],axis=0,ascending=True,inplace = True)
        df = df.sort_index(axis = 1)
        df = df.reset_index(drop = True)
        df = df.groupby('date').head(10)
        #print(df[['double_low','date']])
        data_1 = DataSource.write_df(df)
        return data_1
    # 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 m23_run_bigquant_run(input_1, input_2, input_3):
        data_1 = del_input(input_1)
        data_2 = del_input(input_2)
        return Outputs(data_1=data_1, data_2=data_2, data_3=None)
    # 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 m23_post_run_bigquant_run(outputs):
        return outputs
    
    # 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 m11_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        print('m11.input1',input_1)
        df = input_1.read()
        param = input_2.read()
        
        data = {
            "param": param,
            "data": df
        }
        data_1 = DataSource.write_pickle(data)
        return Outputs(data_1=data_1, data_2=None, data_3=None)
    
    # 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 m11_post_run_bigquant_run(outputs):
        return outputs
    
    # 回测引擎:初始化函数,只执行一次
    def m21_initialize_bigquant_run(context):
        print('初始化函数开始:')
        print('context.options',context.options)
        # 加载预测数据
        context.ranker_prediction = context.options.get('data').read()['data']
        context.param = context.options['data'].read()["param"]
        # 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=0.0003, 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 = context.param["stock_count"]
        # 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 = 0.4
        context.hold_days = context.param["hold_days"]
        print('初始化函数结束')
    
    # 交易引擎:每个单位时间开盘前调用一次。
    def m21_before_trading_start_bigquant_run(context, data):
        # 盘前处理,订阅行情等
        print('订阅行情前',data)
        context.subscribe(context.instruments)
        print('订阅行情后',data)
        pass
    
    # 交易引擎:tick数据处理函数,每个tick执行一次
    def m21_handle_tick_bigquant_run(context, tick):
        pass
    
    import math
    # 回测引擎:每日数据处理函数,每天执行一次
    def m21_handle_data_bigquant_run(context, data):
        print('当前日期data.current_dt',data.current_dt)
        print('data',data)
        try:
            context.ranker_prediction = context.options.get('data').read()['data']
            # 相隔几天(hold_days)进行一下换仓
            if context.trading_day_index % context.hold_days != 0:
                return 
    
            # 按日期过滤得到今日的预测数据
            ranker_prediction = context.ranker_prediction[
                context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
            # 目前持仓
            positions = {e: p.amount * p.last_sale_price for e, p in context.portfolio.positions.items()}
            # 权重
            buy_cash_weights = context.stock_weights
            # 今日买入股票列表
            stock_to_buy = list(ranker_prediction.instrument[:len(buy_cash_weights)])
            # 持仓上限
            max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
            print("<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<")
            # 通过positions对象,使用列表生成式的方法获取目前持仓的股票列表
            stock_hold_now = [equity for equity in context.portfolio.positions ]
            # 继续持有的股票:调仓时,如果买入的股票已经存在于目前的持仓里,那么应继续持有
            no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
            # 需要卖出的股票
            stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
    
            # 卖出
            for stock in stock_to_sell:
                # 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
                # 如果返回真值,则可以正常下单,否则会出错
                # 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
                if data.can_trade(context.symbol(stock)):
                    # order_target_percent是平台的一个下单接口,表明下单使得该股票的权重为0,
                    #   即卖出全部股票,可参考回测文档
                    print('卖出order target percent',context.symbol(stock))
                    print('卖出结果',context.order_target_percent(context.symbol(stock), 0))
    
            # 如果当天没有买入的股票,就返回
            if len(stock_to_buy) == 0:
                return
    
            # 买入
            print('买入列表',stock_to_buy)
            for i, instrument in enumerate(stock_to_buy):
                cash = context.portfolio.portfolio_value * 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 > 500:
                    cash = int(math.floor(cash))
                    print('买入order_value',context.symbol(instrument),' cash ',cash)
                    print(context.order_value(context.symbol(instrument), cash))
        except Exception as e:
            print('抛出异常',e)
    # 交易引擎:成交回报处理函数,每个成交发生时执行一次
    def m21_handle_trade_bigquant_run(context, trade):
        pass
    
    # 交易引擎:委托回报处理函数,每个委托变化时执行一次
    def m21_handle_order_bigquant_run(context, order):
        pass
    
    # 交易引擎:盘后处理函数,每日盘后执行一次
    def m21_after_trading_bigquant_run(context, data):
        pass
    
    
    g = T.Graph({
    
        '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
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100""",
    
        'm2': 'M.cached.v3',
        'm2.run': m2_run_bigquant_run,
        'm2.post_run': m2_post_run_bigquant_run,
        'm2.input_ports': '',
        'm2.params': """{"start_date_input":"2017-06-01",
    "end_date_input":"2019-11-01"}""",
        'm2.output_ports': '',
    
        'm10': 'M.auto_labeler_on_datasource.v1',
        'm10.input_data': T.Graph.OutputPort('m2.data_1'),
        'm10.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
    # 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, 10)
    
    # 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)
    """,
        'm10.drop_na_label': True,
        'm10.cast_label_int': True,
        'm10.date_col': 'date',
        'm10.instrument_col': 'instrument',
        'm10.user_functions': {},
    
        'm14': 'M.cached.v3',
        'm14.run': m14_run_bigquant_run,
        'm14.post_run': m14_post_run_bigquant_run,
        'm14.input_ports': '',
        'm14.params': """{
        "stock_count": 4,
        "hold_days": 5 
    }""",
        'm14.output_ports': '',
    
        'm1': 'M.cached.v3',
        'm1.run': m1_run_bigquant_run,
        'm1.post_run': m1_post_run_bigquant_run,
        'm1.input_ports': '',
        'm1.params': """{"start_date_input":"2021-01-05",
    "end_date_input":"2021-06-19"}""",
        'm1.output_ports': '',
    
        'm23': 'M.cached.v3',
        'm23.input_1': T.Graph.OutputPort('m2.data_2'),
        'm23.input_2': T.Graph.OutputPort('m1.data_2'),
        'm23.run': m23_run_bigquant_run,
        'm23.post_run': m23_post_run_bigquant_run,
        'm23.input_ports': '',
        'm23.params': '{}',
        'm23.output_ports': '',
        'm23.m_cached': False,
    
        'm16': 'M.derived_feature_extractor.v3',
        'm16.input_data': T.Graph.OutputPort('m23.data_1'),
        '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('m10.data'),
        'm7.data2': T.Graph.OutputPort('m16.data'),
        'm7.on': 'date,instrument',
        'm7.how': 'inner',
        'm7.sort': False,
    
        'm6': 'M.stock_ranker_train.v6',
        'm6.training_ds': T.Graph.OutputPort('m7.data'),
        'm6.features': T.Graph.OutputPort('m3.data'),
        'm6.learning_algorithm': '排序',
        'm6.number_of_leaves': 3,
        'm6.minimum_docs_per_leaf': 100,
        'm6.number_of_trees': 20,
        '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,
        'm6.m_cached': False,
    
        'm18': 'M.derived_feature_extractor.v3',
        'm18.input_data': T.Graph.OutputPort('m23.data_2'),
        'm18.features': T.Graph.OutputPort('m3.data'),
        'm18.date_col': 'date',
        'm18.instrument_col': 'instrument',
        'm18.drop_na': False,
        'm18.remove_extra_columns': False,
    
        'm8': 'M.stock_ranker_predict.v5',
        'm8.model': T.Graph.OutputPort('m6.model'),
        'm8.data': T.Graph.OutputPort('m18.data'),
        'm8.m_lazy_run': False,
    
        'm11': 'M.cached.v3',
        'm11.input_1': T.Graph.OutputPort('m8.predictions'),
        'm11.input_2': T.Graph.OutputPort('m14.data_1'),
        'm11.run': m11_run_bigquant_run,
        'm11.post_run': m11_post_run_bigquant_run,
        'm11.input_ports': '',
        'm11.params': '{}',
        'm11.output_ports': '',
    
        'm12': 'M.trade_data_generation.v1',
        'm12.input': T.Graph.OutputPort('m1.data_2'),
        'm12.category': 'CN_STOCK',
        'm12.m_cached': False,
    
        'm21': 'M.hftrade.v2',
        'm21.instruments': T.Graph.OutputPort('m12.instrument_list'),
        'm21.options_data': T.Graph.OutputPort('m11.data_1'),
        'm21.start_date': '',
        'm21.end_date': '',
        'm21.initialize': m21_initialize_bigquant_run,
        'm21.before_trading_start': m21_before_trading_start_bigquant_run,
        'm21.handle_tick': m21_handle_tick_bigquant_run,
        'm21.handle_data': m21_handle_data_bigquant_run,
        'm21.handle_trade': m21_handle_trade_bigquant_run,
        'm21.handle_order': m21_handle_order_bigquant_run,
        'm21.after_trading': m21_after_trading_bigquant_run,
        'm21.capital_base': 1000000,
        'm21.frequency': 'daily',
        'm21.price_type': '真实价格',
        'm21.product_type': '可转债',
        'm21.before_start_days': '80',
        'm21.order_price_field_buy': 'open',
        'm21.order_price_field_sell': 'close',
        'm21.benchmark': '000300.HIX',
        'm21.plot_charts': True,
        'm21.disable_cache': False,
        'm21.replay_bdb': True,
        'm21.show_debug_info': True,
        'm21.backtest_only': False,
    })
    
    # g.run({})
    
    
    def m9_param_grid_builder_bigquant_run():
        import itertools
        param_grid = {}
        
        period_list = [5,6,120] 
        
        # 在这里设置需要调优的参数备选
        feature_list = [
        '''
        double_low = close + bond_prem_ratio
        remain_size
        rank_swing_volatility_5 = nanstd((high-low)/pre_close, {0})*sqrt(200)*100
        '''.format(period) for period in period_list
        ]
        param_grid["m14.params"] = [
            """{"stock_count": 3, "hold_days": 3}""",
            """{"stock_count": 4, "hold_days": 4}""",
        ]
        param_grid['m3.features'] = feature_list
        return param_grid
    def m9_scoring_bigquant_run(result):
        # 评分:收益/最大回撤
        score = result.get('m21').read_raw_perf()['sharpe'].tail(1)[0]
        return {'score': score}
    
    
    m9 = M.hyper_parameter_search.v1(
        param_grid_builder=m9_param_grid_builder_bigquant_run,
        scoring=m9_scoring_bigquant_run,
        search_algorithm='网格搜索',
        search_iterations=10,
        workers=1,
        worker_distributed_run=False,
        worker_silent=False,
        run_now=True,
        bq_graph=g
    )
    
    Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
           'date', 'conversion_price', 'name_x', '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', 'pre_close', 'name_y', 'open', 'high',
           'low', 'deal_number', 'volume', 'amount', 'accrued_interest',
           'yield_to_maturity', 'vwap', 'gross_close', 'net_close'],
          dtype='object')
    Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
           'date', 'conversion_price', 'name_x', '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', 'pre_close', 'name_y', 'open', 'high',
           'low', 'deal_number', 'volume', 'amount', 'accrued_interest',
           'yield_to_maturity', 'vwap', 'gross_close', 'net_close'],
          dtype='object')
    
    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-27274cfd9b8f4d2087ace9be3a6d7159"}/bigcharts-data-end
    m11.input1 DataSource(40d32d3b18ea48999be6fb2e1c2b7062T)
    
    2022-06-27 22:55:49.932538 init history datas... 
    2022-06-27 22:55:49.933923 init history datas done. 
    2022-06-27 22:55:49.946181 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2021-01-05 ~ 2021-06-18 
    2022-06-27 22:55:49.946755 run_backtest() running... 
    2022-06-27 22:55:49.987275 initial contracts len=0 
    2022-06-27 22:55:49.987535 backtest inited. 
    初始化函数开始:
    context.options {'data': DataSource(4ab7f0956b7c4dfba027981cd2dcb15dT)}
    初始化函数结束
    2022-06-27 22:55:50.051275 backtest transforming 1d, bars=1... 
    2022-06-27 22:55:50.051512 transform start_trading_day=2021-01-05 00:00:00, simulation period=2021-01-05 ~ 2021-06-18 
    2022-06-27 22:55:50.051560 transform source=None, before_start_days=80 
    2022-06-27 22:55:50.051611 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f809fd7c1e0> 
    订阅行情前 BarDatas(current_dt:2021-01-05 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-05 08:30:00)
    当前日期data.current_dt 2021-01-05 15:00:00
    data BarDatas(current_dt:2021-01-05 15:00:00)
    <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
    买入列表 ['110047.HCB', '110033.HCB', '128042.ZCB', '128129.ZCB']
    买入order_value Equity(381 [110047.HCB])  cash  390380
    0
    买入order_value Equity(386 [110033.HCB])  cash  246302
    0
    买入order_value Equity(267 [128042.ZCB])  cash  195190
    0
    买入order_value Equity(168 [128129.ZCB])  cash  168127
    0
    订阅行情前 BarDatas(current_dt:2021-01-06 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-06 08:30:00)
    当前日期data.current_dt 2021-01-06 15:00:00
    data BarDatas(current_dt:2021-01-06 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-07 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-07 08:30:00)
    当前日期data.current_dt 2021-01-07 15:00:00
    data BarDatas(current_dt:2021-01-07 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-08 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-08 08:30:00)
    当前日期data.current_dt 2021-01-08 15:00:00
    data BarDatas(current_dt:2021-01-08 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-11 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-11 08:30:00)
    当前日期data.current_dt 2021-01-11 15:00:00
    data BarDatas(current_dt:2021-01-11 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-12 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-12 08:30:00)
    当前日期data.current_dt 2021-01-12 15:00:00
    data BarDatas(current_dt:2021-01-12 15:00:00)
    <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
    卖出order target percent Equity(381 [110047.HCB])
    卖出结果 0
    卖出order target percent Equity(386 [110033.HCB])
    卖出结果 0
    卖出order target percent Equity(267 [128042.ZCB])
    卖出结果 0
    卖出order target percent Equity(168 [128129.ZCB])
    卖出结果 0
    买入列表 ['110065.HCB', '110034.HCB', '110051.HCB', '110056.HCB']
    买入order_value Equity(108 [110065.HCB])  cash  388693
    0
    买入order_value Equity(182 [110034.HCB])  cash  245238
    0
    买入order_value Equity(6 [110051.HCB])  cash  194346
    0
    买入order_value Equity(387 [110056.HCB])  cash  167401
    0
    订阅行情前 BarDatas(current_dt:2021-01-13 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-13 08:30:00)
    当前日期data.current_dt 2021-01-13 15:00:00
    data BarDatas(current_dt:2021-01-13 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-14 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-14 08:30:00)
    当前日期data.current_dt 2021-01-14 15:00:00
    data BarDatas(current_dt:2021-01-14 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-15 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-15 08:30:00)
    当前日期data.current_dt 2021-01-15 15:00:00
    data BarDatas(current_dt:2021-01-15 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-18 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-18 08:30:00)
    当前日期data.current_dt 2021-01-18 15:00:00
    data BarDatas(current_dt:2021-01-18 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-19 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-19 08:30:00)
    当前日期data.current_dt 2021-01-19 15:00:00
    data BarDatas(current_dt:2021-01-19 15:00:00)
    <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
    卖出order target percent Equity(108 [110065.HCB])
    卖出结果 0
    卖出order target percent Equity(6 [110051.HCB])
    卖出结果 0
    卖出order target percent Equity(387 [110056.HCB])
    卖出结果 0
    买入列表 ['110034.HCB', '113036.HCB', '110038.HCB', '110045.HCB']
    买入order_value Equity(182 [110034.HCB])  cash  145528
    0
    买入order_value Equity(28 [113036.HCB])  cash  243631
    0
    买入order_value Equity(47 [110038.HCB])  cash  193073
    0
    买入order_value Equity(316 [110045.HCB])  cash  166304
    0
    订阅行情前 BarDatas(current_dt:2021-01-20 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-20 08:30:00)
    当前日期data.current_dt 2021-01-20 15:00:00
    data BarDatas(current_dt:2021-01-20 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-21 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-21 08:30:00)
    当前日期data.current_dt 2021-01-21 15:00:00
    data BarDatas(current_dt:2021-01-21 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-22 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-22 08:30:00)
    当前日期data.current_dt 2021-01-22 15:00:00
    data BarDatas(current_dt:2021-01-22 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-25 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-25 08:30:00)
    当前日期data.current_dt 2021-01-25 15:00:00
    data BarDatas(current_dt:2021-01-25 15:00:00)
    订阅行情前 BarDatas(current_dt:2021-01-26 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-26 08:30:00)
    当前日期data.current_dt 2021-01-26 15:00:00
    data BarDatas(current_dt:2021-01-26 15:00:00)
    <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
    卖出order target percent Equity(28 [113036.HCB])
    卖出结果 0
    卖出order target percent Equity(47 [110038.HCB])
    卖出结果 0
    卖出order target percent Equity(316 [110045.HCB])
    卖出结果 0
    买入列表 ['110047.HCB', '110077.HCB', '113024.HCB', '110034.HCB']
    买入order_value Equity(381 [110047.HCB])  cash  381043
    0
    买入order_value Equity(33 [110077.HCB])  cash  240411
    0
    买入order_value Equity(299 [113024.HCB])  cash  190521
    0
    订阅行情前 BarDatas(current_dt:2021-01-27 08:30:00)
    订阅行情后 BarDatas(current_dt:2021-01-27 08:30:00)
    2022-06-27 22:55:51.707928 strategy strategy exception:Traceback (most recent call last):
      File "bigtrader/strategy/engine.py", line 713, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func
      File "bigtrader/strategy/strategy_base.py", line 2188, in bigtrader2.bigtrader.strategy.strategy_base.StrategyBase.call_handle_continous_trading
      File "bigtrader/finance/account_engine.py", line 2457, in bigtrader2.bigtrader.finance.account_engine.AccountEngine.send_order
      File "bigtrader/finance/account_engine.py", line 2355, 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-2-d2d7234668d1> in <module>
        327 )
        328 
    --> 329 m21 = M.hftrade.v2(
        330     instruments=m12.instrument_list,
        331     options_data=m11.data_1,
    
    OverflowError: cannot convert float infinity to integer