复制链接
克隆策略

    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-215:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-119:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-132:features_ds","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-137:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"-209:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"DestinationInputPortId":"-231:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-250:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-172:input_1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-119:training_ds","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-86:data"},{"DestinationInputPortId":"-222:input_data","SourceOutputPortId":"-215:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"-222:data"},{"DestinationInputPortId":"-238:input_data","SourceOutputPortId":"-231:data"},{"DestinationInputPortId":"-143:input_data","SourceOutputPortId":"-238:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"-119:model"},{"DestinationInputPortId":"-215:features","SourceOutputPortId":"-132:data"},{"DestinationInputPortId":"-222:features","SourceOutputPortId":"-132:data"},{"DestinationInputPortId":"-231:features","SourceOutputPortId":"-132:data"},{"DestinationInputPortId":"-238:features","SourceOutputPortId":"-132:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","SourceOutputPortId":"-137:data"},{"DestinationInputPortId":"-86:input_data","SourceOutputPortId":"-143:data"},{"DestinationInputPortId":"-172:input_2","SourceOutputPortId":"-170:data"},{"DestinationInputPortId":"-216:input_ds","SourceOutputPortId":"-209:data"},{"DestinationInputPortId":"-250:options_data","SourceOutputPortId":"-216:sorted_data"},{"DestinationInputPortId":"-209:data2","SourceOutputPortId":"-172:data_1"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2015-02-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-05-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# #号开始的表示注释\n# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html\n# 添加benchmark_前缀，可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_\n\n# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)\nshift(close, -2) / 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\nrank_return_10/rank_return_30\navg_turn_15/turn_0\nta_cci_14_0\nreturn_0\nturn_5/avg_turn_10\nswing_volatility_10_0/swing_volatility_60_0\nrank_amount_10/rank_amount_30\nlist_days_0\nmf_net_amount_xl_0\navg_turn_5\nturn_0/avg_turn_5\nrank_return_20\nrank_return_5/rank_return_20\navg_amount_5\nrank_avg_turn_5","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":"2019-10-23","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2020-03-09","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":"-215","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"60","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-215"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-215"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-215","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-222","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-222"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-222"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-222","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":16,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-231","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"60","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-231"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-231"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-231","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":17,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-238","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-238"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-238"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-238","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":18,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-250","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\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 = 1\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 = (1,0)\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 = 1\n context.options['hold_days'] = 1\n #用于判断奇偶交易\n context.datecont = 0\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n if context.datecont == 0:\n context.datecont = 1\n else:\n context.datecont = 0\n \n positions = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n \n #大盘风控模块，读取风控数据\n today = data.current_dt.strftime('%Y-%m-%d')\n \n #----------------大盘风控模块，读取风控数据------------------\n risk = 0\n today = data.current_dt.strftime('%Y-%m-%d')\n bm_ret0=ranker_prediction.bm_ret0.values[0]\n bm_ret1=ranker_prediction.bm_ret1.values[0]\n bm_ret2=ranker_prediction.bm_ret2.values[0]\n bm_ret3=ranker_prediction.bm_ret3.values[0]\n bm_risk_v0=ranker_prediction.bm_risk_v0.values[0]\n bm_risk_v1=ranker_prediction.bm_risk_v1.values[0]\n bm_risk_v2=ranker_prediction.bm_risk_v2.values[0]\n \n if bm_ret0 < 0.001:\n if bm_risk_v0 > 0:\n print(today,'大盘放量下跌,全仓卖出')\n risk = 1\n elif bm_ret1 < 0.001 and bm_ret2 < 0.002:\n print(today,'大盘连续下跌,全仓卖出')\n risk = 1\n if bm_ret3 < -0.02:\n print(today,'大盘三日下跌超过2%,全仓卖出')\n risk = 1\n if bm_ret0 > 0.01:\n if (bm_risk_v0 + bm_risk_v1) < 0:\n print(today,'大盘缩量上涨,全仓卖出')\n risk = 1\n \n if risk == 1:\n \n if len(positions)>0:\n # 全部卖出后返回\n for instrument in positions:\n if data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument), 0)\n return # 风控卖出后直接使用return结束当日交易，后续轮仓逻辑不再执行\n #---------------------大盘风控结束--------------------------------------\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 #------------------------------------------卖出模块START--------------------------------------------\n if len(positions) > 0:\n for instrument in positions.keys():\n last_sale_date = positions[instrument].last_sale_date #上次交易日期\n delta_days = data.current_dt - last_sale_date \n hold_days = delta_days.days #持仓天数\n if hold_days >= 0:\n context.order_target(context.symbol(instrument), 0)\n #-------------------------------------------卖出模块END---------------------------------------------\n \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\n for i, instrument in enumerate(buy_instruments):\n try:\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 context.datecont == 1:\n # 获取今天和昨天的成交量\n volume_since_buy = data.history(context.symbol(instrument), 'volume', 3, '1d')\n close_price = data.current(context.symbol(instrument), 'close') #当收盘价\n high_price = data.current(context.symbol(instrument), 'high') #当天最高价\n if ((volume_since_buy[2]/volume_since_buy[1] < 2.5) or (high_price/close_price<1.05)) and volume_since_buy[2]/volume_since_buy[0] > 1:\n current_price = data.current(context.symbol(instrument), 'price')\n amount = math.floor(cash / current_price - cash / current_price % 100)\n context.order(context.symbol(instrument), amount)\n return\n else:\n print('today = ',today,'instrument = ',instrument)\n except:\n print('today = ',today,'instrument = ',instrument)","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":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"volume_limit","Value":0.025,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_buy","Value":"open","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"order_price_field_sell","Value":"close","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"capital_base","Value":1000000,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"auto_cancel_non_tradable_orders","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"data_frequency","Value":"daily","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"price_type","Value":"真实价格","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"product_type","Value":"股票","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"plot_charts","Value":"True","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"backtest_only","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"benchmark","Value":"000300.SHA","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"options_data","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"history_ds","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"benchmark_ds","NodeId":"-250"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"trading_calendar","NodeId":"-250"}],"OutputPortsInternal":[{"Name":"raw_perf","NodeId":"-250","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":19,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-119","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":"-119"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-119"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"test_ds","NodeId":"-119"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"base_model","NodeId":"-119"}],"OutputPortsInternal":[{"Name":"model","NodeId":"-119","OutputType":null},{"Name":"feature_gains","NodeId":"-119","OutputType":null},{"Name":"m_lazy_run","NodeId":"-119","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":4,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-132","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\nclose_0\nhigh_1\nopen_0\nlow_0\nst_status_0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-132"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-132","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":5,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-137","ModuleId":"BigQuantSpace.filter.filter-v3","ModuleParameters":[{"Name":"expr","Value":"st_status_0==0 and low_0>high_1 and close_0>open_0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-137"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-137","OutputType":null},{"Name":"left_data","NodeId":"-137","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":10,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-143","ModuleId":"BigQuantSpace.filter.filter-v3","ModuleParameters":[{"Name":"expr","Value":"st_status_0==0 and low_0>high_1+0.02 and close_0>open_0","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"output_left_data","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-143"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-143","OutputType":null},{"Name":"left_data","NodeId":"-143","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":11,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-170","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\nret_1=close/shift(close,1)\nret_3=close/shift(close,3)\nvolumepct_1=volume/shift(volume,1)\nbm_ret0=ret_1\nbm_ret1=shift(bm_ret0,1)\nbm_ret2=shift(bm_ret0,2)\nbm_ret3=ret_3\nbm_risk_v0=volumepct_1\nbm_risk_v1=shift(bm_risk_v0,1)\nbm_risk_v2=shift(bm_risk_v0,2)","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"-170"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-170","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":20,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-209","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":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"-209"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"-209"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-209","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":25,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-216","ModuleId":"BigQuantSpace.sort.sort-v4","ModuleParameters":[{"Name":"sort_by","Value":"date,position","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"group_by","Value":"--","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":"-216"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"sort_by_ds","NodeId":"-216"}],"OutputPortsInternal":[{"Name":"sorted_data","NodeId":"-216","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":26,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-172","ModuleId":"BigQuantSpace.index_feature_extract.index_feature_extract-v3","ModuleParameters":[{"Name":"before_days","Value":100,"ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"index","Value":"000001.HIX","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_1","NodeId":"-172"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_2","NodeId":"-172"}],"OutputPortsInternal":[{"Name":"data_1","NodeId":"-172","OutputType":null},{"Name":"data_2","NodeId":"-172","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":6,"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='744,0,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='249,375,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='886,780,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1071,127,200,200'/><NodePosition Node='287d2cb0-f53c-4101-bdf8-104b137c8601-84' Position='411,584,200,200'/><NodePosition Node='-86' Position='1069,543,200,200'/><NodePosition Node='-215' Position='381,188,200,200'/><NodePosition Node='-222' Position='385,280,200,200'/><NodePosition Node='-231' Position='1078,236,200,200'/><NodePosition Node='-238' Position='1081,327,200,200'/><NodePosition Node='-250' Position='1166,1067,200,200'/><NodePosition Node='-119' Position='618,694,200,200'/><NodePosition Node='-132' Position='570,87,200,200'/><NodePosition Node='-137' Position='331,479,200,200'/><NodePosition Node='-143' Position='1074,441,200,200'/><NodePosition Node='-170' Position='1484,601,200,200'/><NodePosition Node='-209' Position='1107,883,200,200'/><NodePosition Node='-216' Position='1170,970,200,200'/><NodePosition Node='-172' Position='1384,754,200,200'/></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 2020年3月11日 10:29
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


# 回测引擎：初始化函数，只执行一次
def m19_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = context.options['data'].read_df()

    # 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
    # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
    stock_count = 1
    # 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 = (1,0)
    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 = 1
    context.options['hold_days'] = 1
    #用于判断奇偶交易
    context.datecont = 0

# 回测引擎：每日数据处理函数，每天执行一次
def m19_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]

    if context.datecont == 0:
        context.datecont = 1
    else:
        context.datecont = 0
    
    positions = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    
    #大盘风控模块，读取风控数据
    today = data.current_dt.strftime('%Y-%m-%d')
    
     #----------------大盘风控模块，读取风控数据------------------
    risk = 0
    today = data.current_dt.strftime('%Y-%m-%d')
    bm_ret0=ranker_prediction.bm_ret0.values[0]
    bm_ret1=ranker_prediction.bm_ret1.values[0]
    bm_ret2=ranker_prediction.bm_ret2.values[0]
    bm_ret3=ranker_prediction.bm_ret3.values[0]
    bm_risk_v0=ranker_prediction.bm_risk_v0.values[0]
    bm_risk_v1=ranker_prediction.bm_risk_v1.values[0]
    bm_risk_v2=ranker_prediction.bm_risk_v2.values[0]
    
    if bm_ret0 < 0.001:
        if bm_risk_v0 > 0:
            print(today,'大盘放量下跌,全仓卖出')
            risk = 1
        elif bm_ret1 < 0.001 and bm_ret2 < 0.002:
            print(today,'大盘连续下跌,全仓卖出')
            risk = 1
        if bm_ret3 < -0.02:
            print(today,'大盘三日下跌超过2%,全仓卖出')
            risk = 1
    if bm_ret0 > 0.01:
        if (bm_risk_v0 + bm_risk_v1) < 0:
            print(today,'大盘缩量上涨,全仓卖出')
            risk = 1
            
    if risk == 1:
        
        if len(positions)>0:
            # 全部卖出后返回
            for instrument in positions:
                if data.can_trade(context.symbol(instrument)):
                    context.order_target_percent(context.symbol(instrument), 0)
        return # 风控卖出后直接使用return结束当日交易，后续轮仓逻辑不再执行
    #---------------------大盘风控结束--------------------------------------
    
    # 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)
    
   #------------------------------------------卖出模块START--------------------------------------------
    if len(positions) > 0:
        for instrument in positions.keys():
            last_sale_date = positions[instrument].last_sale_date   #上次交易日期
            delta_days = data.current_dt - last_sale_date  
            hold_days = delta_days.days #持仓天数
            if hold_days >= 0:
                context.order_target(context.symbol(instrument), 0)
    #-------------------------------------------卖出模块END---------------------------------------------
    
    
    # 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):
        try:
            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 context.datecont == 1:
                # 获取今天和昨天的成交量
                volume_since_buy = data.history(context.symbol(instrument), 'volume', 3, '1d')
                close_price = data.current(context.symbol(instrument), 'close')  #当收盘价
                high_price = data.current(context.symbol(instrument), 'high')  #当天最高价
                if ((volume_since_buy[2]/volume_since_buy[1] < 2.5) or (high_price/close_price<1.05)) and volume_since_buy[2]/volume_since_buy[0] > 1:
                    current_price = data.current(context.symbol(instrument), 'price')
                    amount = math.floor(cash / current_price - cash / current_price % 100)
                    context.order(context.symbol(instrument), amount)
                    return
                else:
                    print('today = ',today,'instrument = ',instrument)
        except:
            print('today = ',today,'instrument = ',instrument)
# 回测引擎：准备数据，只执行一次
def m19_prepare_bigquant_run(context):
    pass

m1 = M.instruments.v2(
    start_date='2015-02-01',
    end_date='2019-05-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    label_expr="""# #号开始的表示注释
# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
#   添加benchmark_前缀，可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_

# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -2) / 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
rank_return_10/rank_return_30
avg_turn_15/turn_0
ta_cci_14_0
return_0
turn_5/avg_turn_10
swing_volatility_10_0/swing_volatility_60_0
rank_amount_10/rank_amount_30
list_days_0
mf_net_amount_xl_0
avg_turn_5
turn_0/avg_turn_5
rank_return_20
rank_return_5/rank_return_20
avg_amount_5
rank_avg_turn_5"""
)

m5 = 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
close_0
high_1
open_0
low_0
st_status_0"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m5.data,
    start_date='',
    end_date='',
    before_start_days=60
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m5.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
)

m10 = M.filter.v3(
    input_data=m7.data,
    expr='st_status_0==0 and low_0>high_1 and close_0>open_0',
    output_left_data=False
)

m13 = M.dropnan.v1(
    input_data=m10.data
)

m4 = M.stock_ranker_train.v6(
    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,
    data_row_fraction=1,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2019-10-23'),
    end_date=T.live_run_param('trading_date', '2020-03-09'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m5.data,
    start_date='',
    end_date='',
    before_start_days=60
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m5.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m11 = M.filter.v3(
    input_data=m18.data,
    expr='st_status_0==0 and low_0>high_1+0.02 and close_0>open_0',
    output_left_data=False
)

m14 = M.dropnan.v1(
    input_data=m11.data
)

m8 = M.stock_ranker_predict.v5(
    model=m4.model,
    data=m14.data,
    m_lazy_run=False
)

m20 = 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
ret_1=close/shift(close,1)
ret_3=close/shift(close,3)
volumepct_1=volume/shift(volume,1)
bm_ret0=ret_1
bm_ret1=shift(bm_ret0,1)
bm_ret2=shift(bm_ret0,2)
bm_ret3=ret_3
bm_risk_v0=volumepct_1
bm_risk_v1=shift(bm_risk_v0,1)
bm_risk_v2=shift(bm_risk_v0,2)"""
)

m6 = M.index_feature_extract.v3(
    input_1=m9.data,
    input_2=m20.data,
    before_days=100,
    index='000001.HIX'
)

m25 = M.join.v3(
    data1=m8.predictions,
    data2=m6.data_1,
    on='date',
    how='left',
    sort=False
)

m26 = M.sort.v4(
    input_ds=m25.data,
    sort_by='date,position',
    group_by='--',
    keep_columns='--',
    ascending=True
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m26.sorted_data,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)
    设置测试数据集，查看训练迭代过程的NDCG
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-3ce514266f4c4b02869c0ab8f7ab45b1"}/bigcharts-data-end
    • 收益率240.7%
    • 年化收益率2772.24%
    • 基准收益率2.6%
    • 阿尔法3.51
    • 贝塔0.77
    • 夏普比率5.37
    • 胜率0.75
    • 盈亏比1.65
    • 收益波动率66.35%
    • 信息比率0.34
    • 最大回撤9.89%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-fc7c3b9f27be43aba201e43da00a0267"}/bigcharts-data-end
    In [2]:
    m6.data_1.read()
    Out[2]:
    date bm_ret0 bm_ret1 bm_ret2 bm_ret3 bm_risk_v0 bm_risk_v1 bm_risk_v2
    0 2019-07-15 NaN NaN NaN NaN NaN NaN NaN
    1 2019-07-16 0.998447 NaN NaN NaN 0.786263 NaN NaN
    2 2019-07-17 0.997983 0.998447 NaN NaN 1.135192 0.786263 NaN
    3 2019-07-18 0.989591 0.997983 0.998447 0.986062 1.001719 1.135192 0.786263
    4 2019-07-19 1.007936 0.989591 0.997983 0.995433 0.997203 1.001719 1.135192
    5 2019-07-22 0.987270 1.007936 0.989591 0.984747 1.208774 0.997203 1.001719
    6 2019-07-23 1.004493 0.987270 1.007936 0.999575 0.715708 1.208774 0.997203
    7 2019-07-24 1.008046 1.004493 0.987270 0.999684 1.200566 0.715708 1.208774
    8 2019-07-25 1.004817 1.008046 1.004493 1.017453 0.938411 1.200566 0.715708
    9 2019-07-26 1.002445 1.004817 1.008046 1.015378 0.956474 0.938411 1.200566
    10 2019-07-29 0.998800 1.002445 1.004817 1.006065 0.918535 0.956474 0.938411
    11 2019-07-30 1.003853 0.998800 1.002445 1.005099 1.158796 0.918535 0.956474
    12 2019-07-31 0.993282 1.003853 0.998800 0.995913 0.951023 1.158796 0.918535
    13 2019-08-01 0.991905 0.993282 1.003853 0.989037 1.009482 0.951023 1.158796
    14 2019-08-02 0.985929 0.991905 0.993282 0.971378 1.298467 1.009482 0.951023
    15 2019-08-05 0.983841 0.985929 0.991905 0.962145 0.905516 1.298467 1.009482
    16 2019-08-06 0.984427 0.983841 0.985929 0.954891 1.380507 0.905516 1.298467
    17 2019-08-07 0.996804 0.984427 0.983841 0.965424 0.682391 1.380507 0.905516
    18 2019-08-08 1.009345 0.996804 0.984427 0.990451 0.948153 0.682391 1.380507
    19 2019-08-09 0.992915 1.009345 0.996804 0.998991 0.991312 0.948153 0.682391
    20 2019-08-12 1.014503 0.992915 1.009345 1.016728 0.887501 0.991312 0.948153
    21 2019-08-13 0.993700 1.014503 0.992915 1.000969 0.995083 0.887501 0.991312
    22 2019-08-14 1.004167 0.993700 1.014503 1.012311 1.100050 0.995083 0.887501
    23 2019-08-15 1.002450 1.004167 0.993700 1.000285 1.060810 1.100050 0.995083
    24 2019-08-16 1.002850 1.002450 1.004167 1.009497 0.972575 1.060810 1.100050
    25 2019-08-19 1.020990 1.002850 1.002450 1.026409 1.443958 0.972575 1.060810
    26 2019-08-20 0.998927 1.020990 1.002850 1.022801 0.884084 1.443958 0.972575
    27 2019-08-21 1.000114 0.998927 1.020990 1.020011 0.838370 0.884084 1.443958
    28 2019-08-22 1.001078 1.000114 0.998927 1.000118 0.938288 0.838370 0.884084
    29 2019-08-23 1.004852 1.001078 1.000114 1.006050 1.066690 0.938288 0.838370
    ... ... ... ... ... ... ... ... ...
    128 2020-01-20 1.006598 1.000460 0.994836 1.001861 1.106088 0.935729 1.005252
    129 2020-01-21 0.985902 1.006598 1.000460 0.992863 1.115621 1.106088 0.935729
    130 2020-01-22 1.002822 0.985902 1.006598 0.995207 0.953252 1.115621 1.106088
    131 2020-01-23 0.972482 1.002822 0.985902 0.961477 1.218493 0.953252 1.115621
    132 2020-02-03 0.922755 0.972482 1.002822 0.899895 0.791573 1.218493 0.953252
    133 2020-02-04 1.013355 0.922755 0.972482 0.909347 1.686085 0.791573 1.218493
    134 2020-02-05 1.012503 1.013355 0.922755 0.946770 0.850886 1.686085 0.791573
    135 2020-02-06 1.017182 1.012503 1.013355 1.043655 1.021405 0.850886 1.686085
    136 2020-02-07 1.003298 1.017182 1.012503 1.033297 0.978152 1.021405 0.850886
    137 2020-02-10 1.005050 1.003298 1.017182 1.025691 0.952986 0.978152 1.021405
    138 2020-02-11 1.003870 1.005050 1.003298 1.012267 0.912652 0.952986 0.978152
    139 2020-02-12 1.008693 1.003870 1.005050 1.017711 0.924081 0.912652 0.952986
    140 2020-02-13 0.992885 1.008693 1.003870 1.005392 1.104817 0.924081 0.912652
    141 2020-02-14 1.003763 0.992885 1.008693 1.005284 0.912104 1.104817 0.924081
    142 2020-02-17 1.022837 1.003763 0.992885 1.019380 1.249540 0.912104 1.104817
    143 2020-02-18 1.000452 1.022837 1.003763 1.027149 0.995108 1.249540 0.912104
    144 2020-02-19 0.996794 1.000452 1.022837 1.020018 1.011151 0.995108 1.249540
    145 2020-02-20 1.018402 0.996794 1.000452 1.015596 1.097073 1.011151 0.995108
    146 2020-02-21 1.003140 1.018402 0.996794 1.018324 1.054448 1.097073 1.011151
    147 2020-02-24 0.997225 1.003140 1.018402 1.018764 1.016109 1.054448 1.097073
    148 2020-02-25 0.994001 0.997225 1.003140 0.994355 1.192189 1.016109 1.054448
    149 2020-02-26 0.991663 0.994001 0.997225 0.982978 1.062105 1.192189 1.016109
    150 2020-02-27 1.001138 0.991663 0.994001 0.986836 0.747306 1.062105 1.192189
    151 2020-02-28 0.962884 1.001138 0.991663 0.955943 1.144621 0.747306 1.062105
    152 2020-03-02 1.031465 0.962884 1.001138 0.994311 0.915548 1.144621 0.747306
    153 2020-03-03 1.007393 1.031465 0.962884 1.000524 1.116446 0.915548 1.144621
    154 2020-03-04 1.006271 1.007393 1.031465 1.045607 0.861574 1.116446 0.915548
    155 2020-03-05 1.019926 1.006271 1.007393 1.033911 1.260621 0.861574 1.116446
    156 2020-03-06 0.987900 1.019926 1.006271 1.013904 0.812844 1.260621 0.861574
    157 2020-03-09 0.969939 0.987900 1.019926 0.977297 1.145001 0.812844 1.260621

    158 rows × 8 columns