克隆策略

    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-215:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-215:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-222:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-231:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-238:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-118:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-132:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-250:options_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"DestinationInputPortId":"-231:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-250:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-222:input_data","SourceOutputPortId":"-215:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-222:data"},{"DestinationInputPortId":"-238:input_data","SourceOutputPortId":"-231:data"},{"DestinationInputPortId":"-128:input_data","SourceOutputPortId":"-238:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"-118:model"},{"DestinationInputPortId":"-118:training_ds","SourceOutputPortId":"-132:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-136:data"},{"DestinationInputPortId":"-136:input_data","SourceOutputPortId":"-128:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2018-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2021-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# #号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/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","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na_label","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"cast_label_int","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"\n# #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\nreturn_5\nreturn_10\nreturn_20\navg_amount_0/avg_amount_5\navg_amount_5/avg_amount_20\nrank_avg_amount_0/rank_avg_amount_5\nrank_avg_amount_5/rank_avg_amount_10\nrank_return_0\nrank_return_5\nrank_return_10\nrank_return_0/rank_return_5\nrank_return_5/rank_return_10\npe_ttm_0\n","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date,instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"inner","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","ModuleId":"BigQuantSpace.stock_ranker_predict.stock_ranker_predict-v5","ModuleParameters":[{"Name":"m_lazy_run","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"model","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"OutputPortsInternal":[{"Name":"predictions","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","OutputType":null},{"Name":"m_lazy_run","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2021-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2021-02-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":9,"IsPartOfPartialRun":null,"Comment":"预测数据,用于回测和模拟","CommentCollapsed":false},{"Id":"-215","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":90,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-215"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-215"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-215","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-222","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-222"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-222"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-222","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-231","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":90,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-231"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-231"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-231","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":17,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-238","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-238"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-238"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-238","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":18,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-250","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":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 # 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 = 2\n stock_count_start = 10\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 context.stock_weights = [1 / stock_count] * stock_count\n# context.stock_weights_start = T.norm([1 / math.log(i + 2) for i in range(0, stock_count_start)])\n context.stock_weights_start = [1 / stock_count_start] * stock_count_start\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.1\n context.stock_stand = 10\n context.options['hold_days'] = 1\n","ValueType":"Literal","LinkedGlobalParameter":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 today = data.current_dt.strftime('%Y-%m-%d')\n ranker_prediction = context.ranker_prediction[context.ranker_prediction.date == today]\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.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n buy_cash_weights = context.stock_weights\n if is_staging:\n buy_cash_weights = context.stock_weights_start\n equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n \n stock_sell_num = len(positions) - context.stock_stand + len(context.stock_weights)\n print(f\"今日 {today} 卖出 {stock_sell_num} 只 >>>>>>> {len(positions)}, {len(context.stock_weights)}\")\n for instrument in instruments[:stock_sell_num]:\n print(f\"卖出 ------ {today}, {instrument}\")\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n instruments = list(ranker_prediction.instrument[ranker_prediction.instrument.apply(lambda x: x not in equities)])\n buy_instruments = list(instruments[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * 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 print(f\"{today}买入{instrument}, {cash}\")\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n pass\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":0.025,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"close","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":1000000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"真实价格","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-250"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-250","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":19,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-118","ModuleId":"BigQuantSpace.stock_ranker_train.stock_ranker_train-v6","ModuleParameters":[{"Name":"learning_algorithm","Value":"排序","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"number_of_leaves","Value":"30","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"minimum_docs_per_leaf","Value":1000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"number_of_trees","Value":20,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"learning_rate","Value":0.1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_bins","Value":1023,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"feature_fraction","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_row_fraction","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"ndcg_discount_base","Value":1,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"m_lazy_run","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"training_ds","NodeId":"-118"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-118"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"test_ds","NodeId":"-118"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"base_model","NodeId":"-118"}],"OutputPortsInternal":[{"Name":"model","NodeId":"-118","OutputType":null},{"Name":"feature_gains","NodeId":"-118","OutputType":null},{"Name":"m_lazy_run","NodeId":"-118","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-132","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-132"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-132"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-132","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-136","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-136"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-136"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-136","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":10,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-128","ModuleId":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","ModuleParameters":[{"Name":"index_constituent_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"board_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22displayValue%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"industry_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22displayValue%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22displayValue%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22displayValue%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22displayValue%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22displayValue%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22displayValue%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22displayValue%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22displayValue%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22displayValue%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22displayValue%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%87%87%E6%8E%98%22%2C%22displayValue%22%3A%22%E9%87%87%E6%8E%98%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%92%A2%E9%93%81%22%2C%22displayValue%22%3A%22%E9%92%A2%E9%93%81%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22displayValue%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22displayValue%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"st_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22displayValue%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"delist_cond","Value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22selected%22%3Atrue%7D%5D%7D","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-128"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-128","OutputType":null},{"Name":"left_data","NodeId":"-128","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":11,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true}],"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='213,65,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='70,183,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='740,18,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='249,375,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='731,658,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1074,124,200,200'/><NodePosition Node='-215' Position='381,188,200,200'/><NodePosition Node='-222' Position='385,280,200,200'/><NodePosition Node='-231' Position='1078,236,200,200'/><NodePosition Node='-238' Position='1081,327,200,200'/><NodePosition Node='-250' Position='750.1343994140625,784,200,200'/><NodePosition Node='-118' Position='638,561,200,200'/><NodePosition Node='-132' Position='376,467,200,200'/><NodePosition Node='-136' Position='1070,476,200,200'/><NodePosition Node='-128' Position='1077,401,200,200'/></NodePositions><NodeGroups /></DataV1>"},"IsDraft":true,"ParentExperimentId":null,"WebService":{"IsWebServiceExperiment":false,"Inputs":[],"Outputs":[],"Parameters":[{"Name":"交易日期","Value":"","ParameterDefinition":{"Name":"交易日期","FriendlyName":"交易日期","DefaultValue":"","ParameterType":"String","HasDefaultValue":true,"IsOptional":true,"ParameterRules":[],"HasRules":false,"MarkupType":0,"CredentialDescriptor":null}}],"WebServiceGroupId":null,"SerializedClientData":"<?xml version='1.0' encoding='utf-16'?><DataV1 xmlns:xsd='http://www.w3.org/2001/XMLSchema' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance'><Meta /><NodePositions></NodePositions><NodeGroups /></DataV1>"},"DisableNodesUpdate":false,"Category":"user","Tags":[],"IsPartialRun":false}
    In [2]:
    # 本代码由可视化策略环境自动生成 2021年6月24日16:47
    # 本代码单元只能在可视化模式下编辑。您也可以拷贝代码,粘贴到新建的代码单元或者策略,然后修改。
    
    
    # 回测引擎:初始化函数,只执行一次
    def m19_initialize_bigquant_run(context):
        # 加载预测数据
        context.ranker_prediction = context.options['data'].read_df()
        # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数
        context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
        # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)
        # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只
        stock_count = 2
        stock_count_start = 10
        # 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)])
        context.stock_weights = [1 / stock_count] * stock_count
    #     context.stock_weights_start = T.norm([1 / math.log(i + 2) for i in range(0, stock_count_start)])
        context.stock_weights_start = [1 / stock_count_start] * stock_count_start
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.1
        context.stock_stand = 10
        context.options['hold_days'] = 1
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m19_handle_data_bigquant_run(context, data):
        # 按日期过滤得到今日的预测数据
        today = data.current_dt.strftime('%Y-%m-%d')
        ranker_prediction = context.ranker_prediction[context.ranker_prediction.date == today]
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
        cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.portfolio.positions.items()}
        buy_cash_weights = context.stock_weights
        if is_staging:
            buy_cash_weights = context.stock_weights_start
        equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
    
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities)])))
            
            stock_sell_num = len(positions) - context.stock_stand + len(context.stock_weights)
            print(f"今日 {today} 卖出 {stock_sell_num} 只 >>>>>>> {len(positions)}, {len(context.stock_weights)}")
            for instrument in instruments[:stock_sell_num]:
                print(f"卖出 ------ {today}, {instrument}")
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        instruments = list(ranker_prediction.instrument[ranker_prediction.instrument.apply(lambda x: x not in equities)])
        buy_instruments = list(instruments[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            print(f"{today}买入{instrument}, {cash}")
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m19_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2018-01-01',
        end_date='2021-01-01',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m2 = M.advanced_auto_labeler.v2(
        instruments=m1.data,
        label_expr="""# #号开始的表示注释
    # 0. 每行一个,顺序执行,从第二个开始,可以使用label字段
    # 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -5) / shift(open, -1)
    
    # 极值处理:用1%和99%分位的值做clip
    clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
    
    # 将分数映射到分类,这里使用20个分类
    all_wbins(label, 20)
    
    # 过滤掉一字涨停的情况 (设置label为NaN,在后续处理和训练中会忽略NaN的label)
    where(shift(high, -1) == shift(low, -1), NaN, label)
    """,
        start_date='',
        end_date='',
        benchmark='000300.SHA',
        drop_na_label=True,
        cast_label_int=True
    )
    
    m3 = M.input_features.v1(
        features="""
    # #号开始的表示注释,注释需单独一行
    # 多个特征,每行一个,可以包含基础特征和衍生特征,特征须为本平台特征
    return_5
    return_10
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m5 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m5.data,
        features=m3.data,
        learning_algorithm='排序',
        number_of_leaves=30,
        minimum_docs_per_leaf=1000,
        number_of_trees=20,
        learning_rate=0.1,
        max_bins=1023,
        feature_fraction=1,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2021-01-01'),
        end_date=T.live_run_param('trading_date', '2021-02-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m11 = M.chinaa_stock_filter.v1(
        input_data=m18.data,
        index_constituent_cond=['全部'],
        board_cond=['全部'],
        industry_cond=['全部'],
        st_cond=['正常'],
        delist_cond=['非退市'],
        output_left_data=False
    )
    
    m10 = M.dropnan.v2(
        input_data=m11.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m10.data,
        m_lazy_run=False
    )
    
    m19 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        initialize=m19_initialize_bigquant_run,
        handle_data=m19_handle_data_bigquant_run,
        prepare=m19_prepare_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark='000300.SHA'
    )
    
    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-5946c4139ae14f4ca40738fc03c1e44e"}/bigcharts-data-end
    2021-01-04买入600131.SHA, 100000.0
    2021-01-04买入300916.SZA, 100000.0
    2021-01-04买入300650.SZA, 100000.0
    2021-01-04买入603815.SHA, 100000.0
    2021-01-04买入002765.SZA, 100000.0
    2021-01-04买入300318.SZA, 100000.0
    2021-01-04买入300668.SZA, 100000.0
    2021-01-04买入003018.SZA, 100000.0
    2021-01-04买入300276.SZA, 100000.0
    2021-01-04买入003015.SZA, 100000.0
    今日 2021-01-05 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-05, 300916.SZA
    卖出 ------ 2021-01-05, 003015.SZA
    2021-01-05买入600890.SHA, 2113.390952913149
    2021-01-05买入300135.SZA, 2113.390952913149
    今日 2021-01-06 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-06, 002765.SZA
    卖出 ------ 2021-01-06, 603815.SHA
    2021-01-06买入002168.SZA, 96766.6825601079
    2021-01-06买入002213.SZA, 96766.6825601079
    今日 2021-01-07 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-07, 600131.SHA
    卖出 ------ 2021-01-07, 003018.SZA
    2021-01-07买入002659.SZA, 93539.62367183868
    2021-01-07买入300514.SZA, 93539.62367183868
    今日 2021-01-08 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-08, 002659.SZA
    卖出 ------ 2021-01-08, 300650.SZA
    2021-01-08买入603655.SHA, 94831.0219647823
    2021-01-08买入000638.SZA, 94831.0219647823
    今日 2021-01-11 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-11, 600890.SHA
    卖出 ------ 2021-01-11, 002213.SZA
    2021-01-11买入300807.SZA, 91040.62886454645
    2021-01-11买入300819.SZA, 91040.62886454645
    今日 2021-01-12 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-12, 300276.SZA
    卖出 ------ 2021-01-12, 002168.SZA
    2021-01-12买入300781.SZA, 56421.36731458281
    2021-01-12买入688222.SHA, 56421.36731458281
    今日 2021-01-13 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-13, 300819.SZA
    卖出 ------ 2021-01-13, 300135.SZA
    2021-01-13买入300809.SZA, 86478.71000365482
    2021-01-13买入300491.SZA, 86478.71000365482
    今日 2021-01-14 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-14, 300318.SZA
    卖出 ------ 2021-01-14, 300668.SZA
    2021-01-14买入300727.SZA, 42972.35218785651
    2021-01-14买入603090.SHA, 42972.35218785651
    今日 2021-01-15 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-15, 300781.SZA
    卖出 ------ 2021-01-15, 603655.SHA
    2021-01-15买入600766.SHA, 85753.23279138125
    2021-01-15买入605358.SHA, 85753.23279138125
    今日 2021-01-18 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-18, 300491.SZA
    卖出 ------ 2021-01-18, 688222.SHA
    2021-01-18买入603222.SHA, 80711.95373566641
    2021-01-18买入600318.SHA, 80711.95373566641
    今日 2021-01-19 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-19, 300727.SZA
    卖出 ------ 2021-01-19, 605358.SHA
    2021-01-19买入600418.SHA, 79976.4952075766
    2021-01-19买入300589.SZA, 79976.4952075766
    今日 2021-01-20 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-20, 600318.SHA
    卖出 ------ 2021-01-20, 000638.SZA
    2021-01-20买入603617.SHA, 70397.30687714968
    2021-01-20买入688488.SHA, 70397.30687714968
    今日 2021-01-21 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-21, 300589.SZA
    卖出 ------ 2021-01-21, 600418.SHA
    2021-01-21买入603909.SHA, 85491.14229008491
    2021-01-21买入300598.SZA, 85491.14229008491
    今日 2021-01-22 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-22, 603617.SHA
    卖出 ------ 2021-01-22, 603222.SHA
    2021-01-22买入000813.SZA, 79529.82965174667
    2021-01-22买入600158.SHA, 79529.82965174667
    今日 2021-01-25 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-25, 603909.SHA
    卖出 ------ 2021-01-25, 600766.SHA
    2021-01-25买入000863.SZA, 70281.77422442229
    2021-01-25买入300292.SZA, 70281.77422442229
    今日 2021-01-26 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-26, 300809.SZA
    卖出 ------ 2021-01-26, 000863.SZA
    2021-01-26买入300824.SZA, 85874.68559659598
    2021-01-26买入002125.SZA, 85874.68559659598
    今日 2021-01-27 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-27, 603090.SHA
    卖出 ------ 2021-01-27, 600158.SHA
    2021-01-27买入300722.SZA, 82864.4529358226
    2021-01-27买入600616.SHA, 82864.4529358226
    今日 2021-01-28 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-28, 300824.SZA
    卖出 ------ 2021-01-28, 002125.SZA
    2021-01-28买入300645.SZA, 60896.56808169733
    2021-01-28买入603976.SHA, 60896.56808169733
    今日 2021-01-29 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-01-29, 000813.SZA
    卖出 ------ 2021-01-29, 688488.SHA
    2021-01-29买入600333.SHA, 81233.25258655881
    2021-01-29买入300223.SZA, 81233.25258655881
    今日 2021-02-01 卖出 2 只 >>>>>>> 10, 2
    卖出 ------ 2021-02-01, 300645.SZA
    卖出 ------ 2021-02-01, 600333.SHA
    2021-02-01买入002239.SZA, 76288.82851330709
    2021-02-01买入600316.SHA, 76288.82851330709
    
    • 收益率-10.7%
    • 年化收益率-74.29%
    • 基准收益率3.96%
    • 阿尔法-0.7
    • 贝塔-0.23
    • 夏普比率-3.31
    • 胜率0.32
    • 盈亏比1.1
    • 收益波动率39.55%
    • 信息比率-0.23
    • 最大回撤13.43%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-05fc5b72b9a24db389c35aab0e56215b"}/bigcharts-data-end