系统克隆策略模板,换因子训练报错

新手专区
标签: #<Tag:0x00007ff19254b2b8>

(sycdup) #1
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":"-404:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-411:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-8068:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-318:features_ds","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-173:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-329:data1","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":"-224:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-182:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62: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":"-177:input_data","SourceOutputPortId":"-425:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"-8068:model"},{"DestinationInputPortId":"-224:features","SourceOutputPortId":"-148:data"},{"DestinationInputPortId":"-182:input_2","SourceOutputPortId":"-184:data"},{"DestinationInputPortId":"-202:data2","SourceOutputPortId":"-189:data"},{"DestinationInputPortId":"-189:data2","SourceOutputPortId":"-196:data"},{"DestinationInputPortId":"-490:input_ds","SourceOutputPortId":"-202:data"},{"DestinationInputPortId":"-189:data1","SourceOutputPortId":"-224:data"},{"DestinationInputPortId":"-418:features","SourceOutputPortId":"-318:data"},{"DestinationInputPortId":"-425:features","SourceOutputPortId":"-318:data"},{"DestinationInputPortId":"-329:data2","SourceOutputPortId":"-323:data"},{"DestinationInputPortId":"-202:data1","SourceOutputPortId":"-329:data"},{"DestinationInputPortId":"-1918:options_data","SourceOutputPortId":"-490:sorted_data"},{"DestinationInputPortId":"-8068:training_ds","SourceOutputPortId":"-173:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-177:data"},{"DestinationInputPortId":"-323:input_ds","SourceOutputPortId":"-177:data"},{"DestinationInputPortId":"-196:input_ds","SourceOutputPortId":"-182: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":"2018-01-01","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\nZLX=ta_ema((close_0-ts_min(low_0,19))/(ts_max(high_0,19)-ts_min(low_0,19)),21)-0.5\nCZX=ta_ema((close_0-ts_min(low_0,19))/(ts_max(high_0,19)-ts_min(low_0,19)),5)-0.5\n(CZX-ZLX)>0 \nZLX<0.1\n\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":"2018-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2020-01-01","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":"-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":"-1918","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\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 = 3\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 = 0.6\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":"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 # 获取当前持仓\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n \n today = data.current_dt.strftime('%Y-%m-%d')\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 == today]\n \n #大盘风控模块,读取风控数据 \n benckmark_risk=ranker_prediction['bm_0'].values[0]\n\n #当risk为1时,市场有风险,全部平仓,不再执行其它操作\n if benckmark_risk > 0:\n for instrument in positions.keys():\n context.order_target(context.symbol(instrument), 0)\n print(today,'大盘风控止损触发,全仓卖出')\n return\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 \n \n # 2. 根据需要加入移动止赢止损模块、固定天数卖出模块、ST或退市股卖出模块\n stock_sold = [] # 记录卖出的股票,防止多次卖出出现空单\n \n #------------------------START:止赢止损模块(含建仓期)---------------\n current_stopwin_stock=[]\n current_stoploss_stock = [] \n positions_cost={e.symbol:p.cost_basis for e,p in context.portfolio.positions.items()}\n if len(positions)>0:\n for instrument in positions.keys():\n stock_cost=positions_cost[instrument] \n stock_market_price=data.current(context.symbol(instrument),'price') \n # 赚9%且为可交易状态就止盈\n if stock_market_price/stock_cost-1>=0.09 and data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument),0)\n cash_for_sell -= positions[instrument]\n current_stopwin_stock.append(instrument)\n # 亏5%并且为可交易状态就止损\n if stock_market_price/stock_cost-1 <= -0.05 and data.can_trade(context.symbol(instrument)): \n context.order_target_percent(context.symbol(instrument),0)\n cash_for_sell -= positions[instrument]\n current_stoploss_stock.append(instrument)\n if len(current_stopwin_stock)>0:\n print(today,'止盈股票列表',current_stopwin_stock)\n stock_sold += current_stopwin_stock\n if len(current_stoploss_stock)>0:\n print(today,'止损股票列表',current_stoploss_stock)\n stock_sold += current_stoploss_stock\n #--------------------------END: 止赢止损模块--------------------------\n \n #--------------------------START:持有固定天数卖出(不含建仓期)-----------\n current_stopdays_stock = []\n positions_lastdate = {e.symbol:p.last_sale_date for e,p in context.portfolio.positions.items()}\n # 不是建仓期(在前hold_days属于建仓期)\n if not is_staging:\n for instrument in positions.keys():\n #如果上面的止盈止损已经卖出过了,就不要重复卖出以防止产生空单\n if instrument in stock_sold:\n continue\n # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数,比如datetime.timedelta(5)\n if data.current_dt - positions_lastdate[instrument]>=datetime.timedelta(5) and data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument), 0)\n current_stopdays_stock.append(instrument)\n cash_for_sell -= positions[instrument]\n if len(current_stopdays_stock)>0: \n print(today,'固定天数卖出列表',current_stopdays_stock)\n stock_sold += current_stopdays_stock\n #------------------------- END:持有固定天数卖出-----------------------\n \n #-------------------------- START: ST和退市股卖出 --------------------- \n st_stock_list = []\n for instrument in positions.keys():\n try:\n instrument_name = ranker_prediction[ranker_prediction.instrument==instrument].name.values[0]\n # 如果股票状态变为了st或者退市 则卖出\n if 'ST' in instrument_name or '退' in instrument_name:\n if instrument in stock_sold:\n continue\n if data.can_trade(context.symbol(instrument)):\n context.order_target(context.symbol(instrument), 0)\n st_stock_list.append(instrument)\n cash_for_sell -= positions[instrument]\n except:\n continue\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 stock_sold += st_stock_list\n\n #-------------------------- END: ST和退市股卖出 --------------------- \n \n \n # 3. 生成轮仓卖出订单: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 positions)])))\n for instrument in instruments:\n # 如果资金够了就不卖出了\n if cash_for_sell <= 0:\n break\n #防止多个止损条件同时满足,出现多次卖出产生空单\n if instrument in stock_sold:\n continue\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n stock_sold.append(instrument)\n\n # 4. 生成轮仓买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n # 计算今日跌停的股票\n dt_list = list(ranker_prediction[ranker_prediction.price_limit_status_0==1].instrument)\n # 计算今日ST/退市的股票\n st_list = list(ranker_prediction[ranker_prediction.name.str.contains('ST')|ranker_prediction.name.str.contains('退')].instrument)\n # 计算所有禁止买入的股票池\n banned_list = stock_sold+dt_list+st_list\n buy_cash_weights = context.stock_weights\n buy_instruments=[k for k in list(ranker_prediction.instrument) if k not in banned_list][: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 price = data.current(context.symbol(instrument), 'price') # 最新价格\n stock_num = np.floor(cash/price/100)*100 # 向下取整\n context.order(context.symbol(instrument), stock_num) # 整百下单\n\n \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 pass","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"def bigquant_run(context, data):\n # 获取涨跌停状态数据\n df_price_limit_status = context.ranker_prediction.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 try:\n #判断一下如果当日涨停,则取消卖单\n if df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status_0.ix[today]>2 and _order.amount<0:\n cancel_order(_order)\n print(today,'尾盘涨停取消卖单',ins) \n except:\n continue","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":"100000","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},{"Id":"-8068","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":"-8068"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-8068"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"test_ds","NodeId":"-8068"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"base_model","NodeId":"-8068"}],"OutputPortsInternal":[{"Name":"model","NodeId":"-8068","OutputType":null},{"Name":"feature_gains","NodeId":"-8068","OutputType":null},{"Name":"m_lazy_run","NodeId":"-8068","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-148","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\nname","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-148"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-148","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-184","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\nbm_0 = where(close/shift(close,5)-1<-0.05,1,0)","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-184"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-184","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":24,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-189","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"left","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"-189"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"-189"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-189","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-196","ModuleId":"BigQuantSpace.select_columns.select_columns-v3","ModuleParameters":[{"Name":"columns","Value":"date,bm_0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"reverse_select","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_ds","NodeId":"-196"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"columns_ds","NodeId":"-196"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-196","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-202","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date,instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"left","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"-202"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"-202"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-202","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":27,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-224","ModuleId":"BigQuantSpace.use_datasource.use_datasource-v1","ModuleParameters":[{"Name":"datasource_id","Value":"instruments_CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-224"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-224"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-224","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":22,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-318","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\nprice_limit_status_0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-318"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-318","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":6,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-323","ModuleId":"BigQuantSpace.select_columns.select_columns-v3","ModuleParameters":[{"Name":"columns","Value":"date,instrument,price_limit_status_0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"reverse_select","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_ds","NodeId":"-323"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"columns_ds","NodeId":"-323"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-323","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":10,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-329","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":"-329"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"-329"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-329","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":11,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-490","ModuleId":"BigQuantSpace.sort.sort-v4","ModuleParameters":[{"Name":"sort_by","Value":"position","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"group_by","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"keep_columns","Value":"--","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"ascending","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_ds","NodeId":"-490"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"sort_by_ds","NodeId":"-490"}],"OutputPortsInternal":[{"Name":"sorted_data","NodeId":"-490","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":12,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-173","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-173"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-173"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-173","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":19,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-177","ModuleId":"BigQuantSpace.dropnan.dropnan-v2","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-177"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-177"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-177","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":21,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-182","ModuleId":"BigQuantSpace.index_feature_extract.index_feature_extract-v3","ModuleParameters":[{"Name":"before_days","Value":100,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"index","Value":"000300.HIX","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-182"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-182"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-182","OutputType":null},{"Name":"data_2","NodeId":"-182","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":23,"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='184,72,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='79,197,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='599,-1,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='192,375,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='612,690,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='921,111,200,200'/><NodePosition Node='-404' Position='381,188,200,200'/><NodePosition Node='-411' Position='333,283,200,200'/><NodePosition Node='-418' Position='856,274,200,200'/><NodePosition Node='-425' Position='830,369,200,200'/><NodePosition Node='-1918' Position='1043,887,200,200'/><NodePosition Node='-8068' Position='507,571,200,200'/><NodePosition Node='-148' Position='1111,428,200,200'/><NodePosition Node='-184' Position='1230,330,200,200'/><NodePosition Node='-189' Position='1179,599,200,200'/><NodePosition Node='-196' Position='1448,517,200,200'/><NodePosition Node='-202' Position='1062,748,200,200'/><NodePosition Node='-224' Position='1158,505,200,200'/><NodePosition Node='-318' Position='692,185,200,200'/><NodePosition Node='-323' Position='882,589,200,200'/><NodePosition Node='-329' Position='918,668,200,200'/><NodePosition Node='-490' Position='1076,816,200,200'/><NodePosition Node='-173' Position='249,488,200,200'/><NodePosition Node='-177' Position='822,479,200,200'/><NodePosition Node='-182' Position='1407,431,200,200'/></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 [1]:
    # 本代码由可视化策略环境自动生成 2021年1月13日 22:47
    # 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 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 = 3
        # 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 = 0.6
        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)
    # 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):
        # 获取当前持仓
        positions = {e.symbol: p.amount * p.last_sale_price
                     for e, p in context.portfolio.positions.items()}
        
        today = data.current_dt.strftime('%Y-%m-%d')
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == today]
        
        #大盘风控模块,读取风控数据    
        benckmark_risk=ranker_prediction['bm_0'].values[0]
    
        #当risk为1时,市场有风险,全部平仓,不再执行其它操作
        if benckmark_risk > 0:
            for instrument in positions.keys():
                context.order_target(context.symbol(instrument), 0)
            print(today,'大盘风控止损触发,全仓卖出')
            return
        
        # 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)
       
        
        # 2. 根据需要加入移动止赢止损模块、固定天数卖出模块、ST或退市股卖出模块
        stock_sold = [] # 记录卖出的股票,防止多次卖出出现空单
        
        #------------------------START:止赢止损模块(含建仓期)---------------
        current_stopwin_stock=[]
        current_stoploss_stock = []   
        positions_cost={e.symbol:p.cost_basis for e,p in context.portfolio.positions.items()}
        if len(positions)>0:
            for instrument in positions.keys():
                stock_cost=positions_cost[instrument]  
                stock_market_price=data.current(context.symbol(instrument),'price')  
                # 赚9%且为可交易状态就止盈
                if stock_market_price/stock_cost-1>=0.09 and data.can_trade(context.symbol(instrument)):
                    context.order_target_percent(context.symbol(instrument),0)
                    cash_for_sell -= positions[instrument]
                    current_stopwin_stock.append(instrument)
                # 亏5%并且为可交易状态就止损
                if stock_market_price/stock_cost-1 <= -0.05 and data.can_trade(context.symbol(instrument)):   
                    context.order_target_percent(context.symbol(instrument),0)
                    cash_for_sell -= positions[instrument]
                    current_stoploss_stock.append(instrument)
            if len(current_stopwin_stock)>0:
                print(today,'止盈股票列表',current_stopwin_stock)
                stock_sold += current_stopwin_stock
            if len(current_stoploss_stock)>0:
                print(today,'止损股票列表',current_stoploss_stock)
                stock_sold += current_stoploss_stock
        #--------------------------END: 止赢止损模块--------------------------
        
        #--------------------------START:持有固定天数卖出(不含建仓期)-----------
        current_stopdays_stock = []
        positions_lastdate = {e.symbol:p.last_sale_date for e,p in context.portfolio.positions.items()}
        # 不是建仓期(在前hold_days属于建仓期)
        if not is_staging:
            for instrument in positions.keys():
                #如果上面的止盈止损已经卖出过了,就不要重复卖出以防止产生空单
                if instrument in stock_sold:
                    continue
                # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数,比如datetime.timedelta(5)
                if data.current_dt - positions_lastdate[instrument]>=datetime.timedelta(5) and data.can_trade(context.symbol(instrument)):
                    context.order_target_percent(context.symbol(instrument), 0)
                    current_stopdays_stock.append(instrument)
                    cash_for_sell -= positions[instrument]
            if len(current_stopdays_stock)>0:        
                print(today,'固定天数卖出列表',current_stopdays_stock)
                stock_sold += current_stopdays_stock
        #-------------------------  END:持有固定天数卖出-----------------------
        
        #-------------------------- START: ST和退市股卖出 ---------------------  
        st_stock_list = []
        for instrument in positions.keys():
            try:
                instrument_name = ranker_prediction[ranker_prediction.instrument==instrument].name.values[0]
                # 如果股票状态变为了st或者退市 则卖出
                if 'ST' in instrument_name or '退' in instrument_name:
                    if instrument in stock_sold:
                        continue
                    if data.can_trade(context.symbol(instrument)):
                        context.order_target(context.symbol(instrument), 0)
                        st_stock_list.append(instrument)
                        cash_for_sell -= positions[instrument]
            except:
                continue
        if st_stock_list!=[]:
            print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')    
            stock_sold += st_stock_list
    
        #-------------------------- END: ST和退市股卖出 --------------------- 
        
        
        # 3. 生成轮仓卖出订单: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 positions)])))
            for instrument in instruments:
                # 如果资金够了就不卖出了
                if cash_for_sell <= 0:
                    break
                #防止多个止损条件同时满足,出现多次卖出产生空单
                if instrument in stock_sold:
                    continue
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                stock_sold.append(instrument)
    
        # 4. 生成轮仓买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        # 计算今日跌停的股票
        dt_list = list(ranker_prediction[ranker_prediction.price_limit_status_0==1].instrument)
        # 计算今日ST/退市的股票
        st_list = list(ranker_prediction[ranker_prediction.name.str.contains('ST')|ranker_prediction.name.str.contains('退')].instrument)
        # 计算所有禁止买入的股票池
        banned_list = stock_sold+dt_list+st_list
        buy_cash_weights = context.stock_weights
        buy_instruments=[k for k in list(ranker_prediction.instrument) if k not in banned_list][: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:
                price = data.current(context.symbol(instrument), 'price')  # 最新价格
                stock_num = np.floor(cash/price/100)*100  # 向下取整
                context.order(context.symbol(instrument), stock_num) # 整百下单
    
        
    
    
    # 回测引擎:准备数据,只执行一次
    def m4_prepare_bigquant_run(context):
        pass
    def m4_before_trading_start_bigquant_run(context, data):
        # 获取涨跌停状态数据
        df_price_limit_status = context.ranker_prediction.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)
                try:
                    #判断一下如果当日涨停,则取消卖单
                    if  df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status_0.ix[today]>2 and _order.amount<0:
                        cancel_order(_order)
                        print(today,'尾盘涨停取消卖单',ins) 
                except:
                    continue
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2018-01-01',
        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
    ZLX=ta_ema((close_0-ts_min(low_0,19))/(ts_max(high_0,19)-ts_min(low_0,19)),21)-0.5
    CZX=ta_ema((close_0-ts_min(low_0,19))/(ts_max(high_0,19)-ts_min(low_0,19)),5)-0.5
    (CZX-ZLX)>0 
    ZLX<0.1
    
    """
    )
    
    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
    )
    
    m19 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m5 = M.stock_ranker_train.v6(
        training_ds=m19.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
    )
    
    m6 = M.input_features.v1(
        features_ds=m3.data,
        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
    price_limit_status_0"""
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2018-01-01'),
        end_date=T.live_run_param('trading_date', '2020-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m6.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m6.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m21 = M.dropnan.v2(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m5.model,
        data=m21.data,
        m_lazy_run=False
    )
    
    m10 = M.select_columns.v3(
        input_ds=m21.data,
        columns='date,instrument,price_limit_status_0',
        reverse_select=False
    )
    
    m11 = M.join.v3(
        data1=m8.predictions,
        data2=m10.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m20 = 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
    name"""
    )
    
    m22 = M.use_datasource.v1(
        instruments=m9.data,
        features=m20.data,
        datasource_id='instruments_CN_STOCK_A',
        start_date='',
        end_date=''
    )
    
    m24 = 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
    bm_0 = where(close/shift(close,5)-1<-0.05,1,0)"""
    )
    
    m23 = M.index_feature_extract.v3(
        input_1=m9.data,
        input_2=m24.data,
        before_days=100,
        index='000300.HIX'
    )
    
    m26 = M.select_columns.v3(
        input_ds=m23.data_1,
        columns='date,bm_0',
        reverse_select=False
    )
    
    m25 = M.join.v3(
        data1=m22.data,
        data2=m26.data,
        on='date',
        how='left',
        sort=True
    )
    
    m27 = M.join.v3(
        data1=m11.data,
        data2=m25.data,
        on='date,instrument',
        how='left',
        sort=False
    )
    
    m12 = M.sort.v4(
        input_ds=m27.data,
        sort_by='position',
        group_by='date',
        keep_columns='--',
        ascending=True
    )
    
    m4 = M.trade.v4(
        instruments=m9.data,
        options_data=m12.sorted_data,
        start_date='',
        end_date='',
        initialize=m4_initialize_bigquant_run,
        handle_data=m4_handle_data_bigquant_run,
        prepare=m4_prepare_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=100000,
        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=''
    )
    
    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-89f5c8090658484e8dddcbfc395b8455"}/bigcharts-data-end
    列: ['date', 'instrument', 'price_limit_status_0']
    /y_2018: 681678
    /y_2019: 877552
    
    列: ['date', 'bm_0']
    /data: 552
    
    ---------------------------------------------------------------------------
    IndexError                                Traceback (most recent call last)
    <ipython-input-1-6893ce62c7c8> in <module>()
        414     plot_charts=True,
        415     backtest_only=False,
    --> 416     benchmark=''
        417 )
    
    <ipython-input-1-6893ce62c7c8> in m4_handle_data_bigquant_run(context, data)
         44 
         45     #大盘风控模块,读取风控数据
    ---> 46     benckmark_risk=ranker_prediction['bm_0'].values[0]
         47 
         48     #当risk为1时,市场有风险,全部平仓,不再执行其它操作
    
    IndexError: index 0 is out of bounds for axis 0 with size 0