st股票过滤问题


(zhudan) #1

默认模板策略里加入股票过滤st模块,运行的时候,17年3月买入了一只股票,第二天就st,并且跌停,卖不出去,之后停牌了一个多月。但是复牌后没有自动卖出股票,是什么原因?后面又出现了这种情况,导致策略一直满仓了一年多 而且不卖股票,也买不进股票, 怎么回事啊?


(达达) #2

st股票触及了停牌,或者可能会连续跌停一字板无法卖出。目前平台默认一字板和跌停无法卖出的,有可能最后直接退市了。您可以考虑在策略代码中对名称中含有’st’的股进行专门的每日判断卖出,但是能否卖出也要看给不给机会。


(zhudan) #3

问个比较弱智的问题,会不会是因为策略已经过滤掉了st股票,所以买入时还没st,但是第二天被st了,但是之前已经过滤掉了,所以不会出现在卖出列表中?还有,哎,我这编程水平实在是太弱了,实在是不知道如何对卖出策略进行判断,比如 :涨停就不卖、 st股票就第二天开盘卖出 等等。 是需要新加入模块吗 还是直接在某个模块里修改呢?如何修改,还请大哥指点指点。


(达达) #4

是有你说的这个问题存在,所以你可以在每日的交易逻辑对持仓股票判断检查一下如果是股票名称含有st或者退市就马上卖出,如果下一日无法卖出就在后面继续每日卖出。在初始化函数中指定了limit条件单时以开盘价成交,这样卖出st股票的时候与普通卖出时一个用了开盘价一个用了收盘价。涨停的股票通过判断是否涨停在盘前处理函数中判断,如果涨停就取消卖单。

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":"-404: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":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-404:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-411:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-418:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-425:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"DestinationInputPortId":"-149:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-1918:options_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"DestinationInputPortId":"-418:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-1918:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-86:data"},{"DestinationInputPortId":"-411:input_data","SourceOutputPortId":"-404:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-411:data"},{"DestinationInputPortId":"-425:input_data","SourceOutputPortId":"-418:data"},{"DestinationInputPortId":"-152:input_1","SourceOutputPortId":"-425:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","SourceOutputPortId":"-149:data_1"},{"DestinationInputPortId":"-86:input_data","SourceOutputPortId":"-152:data_1"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2015-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# #号开始的表示注释\n# 0. 每行一个,顺序执行,从第二个开始,可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html\n# 添加benchmark_前缀,可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_\n\n# 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)\nshift(close, -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":"# #号开始的表示注释\n# 多个特征,每行一个,可以包含基础特征和衍生特征\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-bdf8-104b137c8601-43","ModuleId":"BigQuantSpace.stock_ranker_train.stock_ranker_train-v5","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":"m_lazy_run","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"training_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"test_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"base_model","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43"}],"OutputPortsInternal":[{"Name":"model","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43","OutputType":null},{"Name":"feature_gains","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43","OutputType":null},{"Name":"m_lazy_run","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":6,"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":"2015-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2017-01-01","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":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-86","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-86"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-86","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":14,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-404","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":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-404"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-404"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-404","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-411","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":"-411"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-411"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-411","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-418","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":0,"ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-418"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-418"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-418","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":17,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-425","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":"-425"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-425"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-425","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":18,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-149","ModuleId":"BigQuantSpace.filtet_st_stock.filtet_st_stock-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-149"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-149","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":28,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-152","ModuleId":"BigQuantSpace.filtet_st_stock.filtet_st_stock-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-152"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-152","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":29,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-1918","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","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 ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n today = data.current_dt.strftime('%Y-%m-%d')\n\n # 1. 资金分配\n # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金\n # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)\n is_staging = context.trading_day_index < context.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 equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n\n # 记录持仓中st的股票\n st_stock_list = []\n name_df = context.name_df\n name_today = name_df[name_df.date==today]\n for instrument in equities:\n name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]\n # 如果股票状态变为了st 则卖出\n if 'ST' in name_instrument or '退' in name_instrument:\n # 指定一个limit_price,此时会以开盘价成交,这是由于初始化函数中改写了下单价格\n context.order_target(context.symbol(instrument), 0, limit_price=1.0)\n st_stock_list.append(instrument)\n cash_for_sell -= positions[instrument]\n if st_stock_list!=[]:\n print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')\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 price_limit_status = context.price_limit_status\n status_today = price_limit_status[price_limit_status.date==today]\n for instrument in instruments:\n # 如果是st股票已经卖过了,就跳过\n if instrument in st_stock_list:\n continue\n # 如果涨停就跳过股票\n status_instrument = status_today[status_today.instrument==instrument]['price_limit_status'].values[0]\n if status_instrument>2:\n continue\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 buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[: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 if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n\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 # 获取股票名称 用于过滤st和退市股\n context.name_df = DataSource('instruments_CN_STOCK_A').read()\n # 获取涨跌停状态\n context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])\n","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\n # 系统已经设置了默认的交易手续费和滑点,要修改手续费可使用如下函数\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 设置买入的股票数量,这里买入预测股票列表排名靠前的5只\n stock_count = 5\n # 每只的股票的权重,如下的权重分配会使得靠前的股票分配多一点的资金,[0.339160, 0.213986, 0.169580, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 5\n\n from zipline.finance.slippage import SlippageModel\n class FixedPriceSlippage(SlippageModel):\n def process_order(self, data, order, bar_volume=0, trigger_check_price=0):\n if order.limit is None:\n price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell\n price = data.current(order.asset, price_field)\n else:\n price = data.current(order.asset, self._price_field_buy)\n # 返回希望成交的价格和数量\n return (price, order.amount)\n # 设置price_field,默认是开盘买入,收盘卖出\n context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')\n context.set_slippage(us_equities=context.fix_slippage)","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"def bigquant_run(context, data):\n df_price_limit_status=context.price_limit_status.set_index('date')\n today=data.current_dt.strftime('%Y-%m-%d')\n # 得到当前未完成订单\n for orders in get_open_orders().values():\n # 循环,撤销订单\n for _order in orders:\n ins=str(_order.sid.symbol)\n if data.can_trade(_order.sid):\n #判断一下如果昨日涨停,则取消卖单\n if df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status.ix[today]>2 and _order.amount<0:\n cancel_order(_order)\n print(today,'尾盘涨停取消卖单',ins) ","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":"后复权","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":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-1918"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-1918"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-1918"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-1918"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-1918"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-1918","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":4,"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='211,64,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='70,183,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='765,21,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='611,670,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='244,356,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='833,771,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1074,127,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-84' Position='435,580,200,200'/><NodePosition Node='-86' Position='1082,537,200,200'/><NodePosition Node='-404' Position='381,188,200,200'/><NodePosition Node='-411' Position='385,280,200,200'/><NodePosition Node='-418' Position='1078,236,200,200'/><NodePosition Node='-425' Position='1081,327,200,200'/><NodePosition Node='-149' Position='316,486,200,200'/><NodePosition Node='-152' Position='1081,433,200,200'/><NodePosition Node='-1918' Position='977,913,200,200'/></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":true}
    In [50]:
    # 本代码由可视化策略环境自动生成 2019年3月12日 13:13
    # 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:每日数据处理函数,每天执行一次
    def m4_handle_data_bigquant_run(context, data):
        # 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')]
        today = data.current_dt.strftime('%Y-%m-%d')
    
        # 1. 资金分配
        # 平均持仓时间是hold_days,每日都将买入股票,每日预期使用 1/hold_days 的资金
        # 实际操作中,会存在一定的买入误差,所以在前hold_days天,等量使用资金;之后,尽量使用剩余资金(这里设置最多用等量的1.5倍)
        is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / 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()}
        equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
    
        # 记录持仓中st的股票
        st_stock_list = []
        name_df = context.name_df
        name_today = name_df[name_df.date==today]
        for instrument in equities:
            name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]
            # 如果股票状态变为了st 则卖出
            if 'ST' in name_instrument or '退' in name_instrument:
                # 指定一个limit_price,此时会以开盘价成交,这是由于初始化函数中改写了下单价格
                context.order_target(context.symbol(instrument), 0, limit_price=1.0)
                st_stock_list.append(instrument)
                cash_for_sell -= positions[instrument]
        if st_stock_list!=[]:
            print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')
     
        # 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)])))
            price_limit_status = context.price_limit_status
            status_today = price_limit_status[price_limit_status.date==today]
            for instrument in instruments:
                # 如果是st股票已经卖过了,就跳过
                if instrument in st_stock_list:
                    continue
                # 如果涨停就跳过股票
                status_instrument = status_today[status_today.instrument==instrument]['price_limit_status'].values[0]
                if status_instrument>2:
                    continue
                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
        buy_cash_weights = context.stock_weights
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        for i, instrument in enumerate(buy_instruments):
            cash = cash_for_buy * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 0:
                context.order_value(context.symbol(instrument), cash)
    
    
    # 回测引擎:准备数据,只执行一次
    def m4_prepare_bigquant_run(context):
        # 获取股票名称 用于过滤st和退市股
        context.name_df = DataSource('instruments_CN_STOCK_A').read()
        # 获取涨跌停状态
        context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])
    
    # 回测引擎:初始化函数,只执行一次
    def m4_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 = 5
        # 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 = 0.2
        context.options['hold_days'] = 5
    
        from zipline.finance.slippage import SlippageModel
        class FixedPriceSlippage(SlippageModel):
            def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
                if order.limit is None:
                    price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                    price = data.current(order.asset, price_field)
                else:
                    price = data.current(order.asset, self._price_field_buy)
                # 返回希望成交的价格和数量
                return (price, order.amount)
        # 设置price_field,默认是开盘买入,收盘卖出
        context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')
        context.set_slippage(us_equities=context.fix_slippage)
    def m4_before_trading_start_bigquant_run(context, data):
        df_price_limit_status=context.price_limit_status.set_index('date')
        today=data.current_dt.strftime('%Y-%m-%d')
        # 得到当前未完成订单
        for orders in get_open_orders().values():
            # 循环,撤销订单
            for _order in orders:
                ins=str(_order.sid.symbol)
                if data.can_trade(_order.sid):
                    #判断一下如果昨日涨停,则取消卖单
                    if  df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status.ix[today]>2 and _order.amount<0:
                        cancel_order(_order)
                        print(today,'尾盘涨停取消卖单',ins)                     
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2015-01-01',
        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/data_history_data.html
    #   添加benchmark_前缀,可使用对应的benchmark数据
    # 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
    
    # 计算收益:5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
    shift(close, -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
    # 多个特征,每行一个,可以包含基础特征和衍生特征
    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=0
    )
    
    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
    )
    
    m28 = M.filtet_st_stock.v2(
        input_1=m7.data
    )
    
    m13 = M.dropnan.v1(
        input_data=m28.data_1
    )
    
    m6 = M.stock_ranker_train.v5(
        training_ds=m13.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,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        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=0
    )
    
    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
    )
    
    m29 = M.filtet_st_stock.v2(
        input_1=m18.data
    )
    
    m14 = M.dropnan.v1(
        input_data=m29.data_1
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m4 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m4_handle_data_bigquant_run,
        prepare=m4_prepare_bigquant_run,
        initialize=m4_initialize_bigquant_run,
        before_trading_start=m4_before_trading_start_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='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark=''
    )
    
    2015-01-13 尾盘涨停取消卖单 300380.SZA
    2015-01-15 持仓出现st股/退市股 ['601918.SHA', '000693.SZA'] 进行卖出处理
    2015-01-16 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
    2015-01-19 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
    2015-01-20 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
    2015-01-21 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
    2015-02-03 尾盘涨停取消卖单 002657.SZA
    2015-02-11 尾盘涨停取消卖单 300378.SZA
    2015-02-16 尾盘涨停取消卖单 300378.SZA
    2015-03-05 尾盘涨停取消卖单 300166.SZA
    2015-03-16 尾盘涨停取消卖单 000626.SZA
    2015-04-02 持仓出现st股/退市股 ['600306.SHA'] 进行卖出处理
    2015-04-03 尾盘涨停取消卖单 300345.SZA
    2015-04-03 尾盘涨停取消卖单 300266.SZA
    2015-04-16 尾盘涨停取消卖单 002100.SZA
    2015-04-17 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
    2015-05-13 持仓出现st股/退市股 ['600546.SHA'] 进行卖出处理
    2015-05-21 尾盘涨停取消卖单 002531.SZA
    2015-05-21 尾盘涨停取消卖单 002275.SZA
    2015-06-01 尾盘涨停取消卖单 600006.SHA
    2015-06-01 尾盘涨停取消卖单 600774.SHA
    2015-06-05 持仓出现st股/退市股 ['000504.SZA'] 进行卖出处理
    2015-06-12 尾盘涨停取消卖单 600559.SHA
    2015-06-17 尾盘涨停取消卖单 300085.SZA
    2015-06-23 尾盘涨停取消卖单 300248.SZA
    2015-06-23 尾盘涨停取消卖单 300208.SZA
    2015-06-30 尾盘涨停取消卖单 300216.SZA
    2015-07-07 持仓出现st股/退市股 ['600375.SHA'] 进行卖出处理
    2015-07-08 持仓出现st股/退市股 ['600375.SHA'] 进行卖出处理
    2015-07-09 尾盘涨停取消卖单 600893.SHA
    2015-07-09 尾盘涨停取消卖单 600375.SHA
    2015-07-09 尾盘涨停取消卖单 000514.SZA
    2015-07-09 持仓出现st股/退市股 ['600375.SHA'] 进行卖出处理
    2015-07-10 尾盘涨停取消卖单 600375.SHA
    2015-07-10 持仓出现st股/退市股 ['000913.SZA', '600721.SHA', '600375.SHA'] 进行卖出处理
    2015-07-13 尾盘涨停取消卖单 000913.SZA
    2015-07-13 尾盘涨停取消卖单 600721.SHA
    2015-07-13 尾盘涨停取消卖单 600375.SHA
    2015-07-13 持仓出现st股/退市股 ['000913.SZA', '600721.SHA', '600375.SHA'] 进行卖出处理
    2015-07-17 尾盘涨停取消卖单 603030.SHA
    2015-07-17 尾盘涨停取消卖单 002197.SZA
    2015-07-17 尾盘涨停取消卖单 600119.SHA
    2015-07-20 尾盘涨停取消卖单 600105.SHA
    2015-07-21 尾盘涨停取消卖单 300222.SZA
    2015-07-21 尾盘涨停取消卖单 600986.SHA
    2015-08-04 尾盘涨停取消卖单 000409.SZA
    2015-08-04 尾盘涨停取消卖单 600986.SHA
    2015-08-07 尾盘涨停取消卖单 300359.SZA
    2015-08-07 尾盘涨停取消卖单 300075.SZA
    2015-08-12 尾盘涨停取消卖单 300208.SZA
    2015-08-27 尾盘涨停取消卖单 600790.SHA
    2015-08-27 尾盘涨停取消卖单 300348.SZA
    2015-08-27 尾盘涨停取消卖单 300380.SZA
    2015-08-28 尾盘涨停取消卖单 002062.SZA
    2015-08-28 尾盘涨停取消卖单 600198.SHA
    2015-08-28 尾盘涨停取消卖单 000567.SZA
    2015-08-28 尾盘涨停取消卖单 300012.SZA
    2015-08-28 尾盘涨停取消卖单 300053.SZA
    2015-08-31 持仓出现st股/退市股 ['000504.SZA'] 进行卖出处理
    2015-09-01 持仓出现st股/退市股 ['000504.SZA'] 进行卖出处理
    2015-09-08 尾盘涨停取消卖单 300252.SZA
    2015-09-09 尾盘涨停取消卖单 600702.SHA
    2015-09-11 尾盘涨停取消卖单 002268.SZA
    2015-09-11 尾盘涨停取消卖单 002161.SZA
    2015-09-16 尾盘涨停取消卖单 300277.SZA
    2015-09-16 尾盘涨停取消卖单 002161.SZA
    2015-09-21 持仓出现st股/退市股 ['601918.SHA'] 进行卖出处理
    2015-09-24 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
    2015-09-30 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
    2015-10-22 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
    2015-11-04 尾盘涨停取消卖单 002197.SZA
    2015-11-06 尾盘涨停取消卖单 002197.SZA
    2015-11-06 尾盘涨停取消卖单 300310.SZA
    2015-12-09 持仓出现st股/退市股 ['000037.SZA'] 进行卖出处理
    2015-12-14 尾盘涨停取消卖单 600257.SHA
    2016-01-08 持仓出现st股/退市股 ['600701.SHA'] 进行卖出处理
    2016-01-14 尾盘涨停取消卖单 300364.SZA
    2016-01-14 尾盘涨停取消卖单 300149.SZA
    2016-01-14 尾盘涨停取消卖单 300377.SZA
    2016-01-18 尾盘涨停取消卖单 300377.SZA
    2016-01-19 尾盘涨停取消卖单 300312.SZA
    2016-01-21 持仓出现st股/退市股 ['000633.SZA'] 进行卖出处理
    2016-01-22 持仓出现st股/退市股 ['000856.SZA', '000629.SZA'] 进行卖出处理
    2016-01-25 持仓出现st股/退市股 ['600866.SHA'] 进行卖出处理
    2016-01-26 持仓出现st股/退市股 ['600866.SHA'] 进行卖出处理
    2016-02-02 尾盘涨停取消卖单 300078.SZA
    2016-02-04 持仓出现st股/退市股 ['000606.SZA', '000504.SZA'] 进行卖出处理
    2016-02-05 持仓出现st股/退市股 ['000504.SZA'] 进行卖出处理
    2016-02-16 尾盘涨停取消卖单 000566.SZA
    2016-03-01 尾盘涨停取消卖单 600978.SHA
    2016-03-21 尾盘涨停取消卖单 002388.SZA
    2016-04-06 尾盘涨停取消卖单 300023.SZA
    2016-04-15 尾盘涨停取消卖单 300028.SZA
    2016-05-16 尾盘涨停取消卖单 300081.SZA
    2016-06-22 尾盘涨停取消卖单 300201.SZA
    2016-07-08 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
    2016-07-11 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
    2016-07-12 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
    2016-07-13 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
    2016-07-14 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
    2016-07-15 尾盘涨停取消卖单 600556.SHA
    2016-07-15 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
    2016-07-20 尾盘涨停取消卖单 002219.SZA
    2016-07-28 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
    2016-08-09 尾盘涨停取消卖单 600084.SHA
    2016-08-10 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
    2016-08-15 尾盘涨停取消卖单 300428.SZA
    2016-09-05 尾盘涨停取消卖单 000652.SZA
    2016-10-10 尾盘涨停取消卖单 300451.SZA
    2016-10-21 尾盘涨停取消卖单 000935.SZA
    2016-12-07 尾盘涨停取消卖单 600671.SHA
    
    • 收益率299.99%
    • 年化收益率104.59%
    • 基准收益率-6.33%
    • 阿尔法0.8
    • 贝塔0.99
    • 夏普比率1.76
    • 胜率0.62
    • 盈亏比0.92
    • 收益波动率44.7%
    • 信息比率0.16
    • 最大回撤42.65%

    000511退市股票数据问题
    (zhudan) #5
    非常感谢。虽然不太能看懂,但是填入因子后,收益率提升了不少,回测的时候,巨慢,比普通的策略慢十几倍,不知道为什么。
    而且仔细看了一下输出的日志,有很多 虽然提示出现st股票卖出,但是对照当日行情,该股票并未st,有的甚至是涨百分之十,而且几天内还对股票持续买入。股票过了一段时间确实被st了,但是当时应该没有st,为什么会这样呢?
     另外,如果想在买入订单里加上,前一日跌停就取消订单,该如何修改?

    (youke) #6

    m9 = M.instruments.v2(
    start_date=T.live_run_param(‘trading_date’, ‘2015-01-01’),
    end_date=T.live_run_param(‘trading_date’, ‘2017-01-01’),
    market=‘CN_STOCK_A’,
    instrument_list=’’,
    max_count=0
    )

    我把时间段改成2016-01-01到2019-03-01就出错了
    IndexError Traceback (most recent call last)
    in ()
    276 plot_charts=True,
    277 backtest_only=False,
    –> 278 benchmark=’’
    279 )

    in m4_handle_data_bigquant_run(context, data)
    26 name_today = name_df[name_df.date==today]
    27 for instrument in equities:
    —> 28 name_instrument = name_today[name_today.instrument==instrument][‘name’].values[0]
    29 # 如果股票状态变为了st 则卖出
    30 if ‘ST’ in name_instrument or ‘退’ in name_instrument:

    IndexError: index 0 is out of bounds for axis 0 with size 0

    这是怎么回事?


    (youke) #7

    除了过滤ST,我还想过滤连续跌停的,遇到连续跌停,不管是不是ST,直接过滤掉,这样如何写代码?