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0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-45"}],"output_ports":[{"name":"data","node_id":"-45"}],"cacheable":true,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-37","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-37"},{"name":"features","node_id":"-37"}],"output_ports":[{"name":"data","node_id":"-37"}],"cacheable":true,"seq_num":10,"comment":"","comment_collapsed":true},{"node_id":"-50","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"yz_1 == True and yz_2 == True and yz_3 == True and yz_4 == True and yz_5 == True and yz_6 == True","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-50"}],"output_ports":[{"name":"data","node_id":"-50"},{"name":"left_data","node_id":"-50"}],"cacheable":true,"seq_num":12,"comment":"","comment_collapsed":true},{"node_id":"-6837","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"yz_1 = where(mean(close_0, 5) >= mean(close_0, 10), 1, 0)\nyz_2 = where(mean(close_0, 10) >= mean(close_0, 20), 1, 0)\nyz_3 = where(close_0 >= mean(close_0, 5), 1, 0)\nyz_4 = where(high_0 >= ts_max(high_0, 5), 1, 0)\nyz_5 = where(zf1 <= 0.04, 1, 0)\nyz_6 = where((close_0 - ts_min(close_0,20))/ts_min(close_0,20) < 0.4, 1, 0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-6837"}],"output_ports":[{"name":"data","node_id":"-6837"}],"cacheable":true,"seq_num":20,"comment":"","comment_collapsed":true},{"node_id":"-6842","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-6842"},{"name":"features","node_id":"-6842"}],"output_ports":[{"name":"data","node_id":"-6842"}],"cacheable":true,"seq_num":23,"comment":"","comment_collapsed":true},{"node_id":"-6851","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"yz_1 == True and yz_2 == True and yz_3 == True and yz_4 == True and yz_5 == True and yz_6 == True","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-6851"}],"output_ports":[{"name":"data","node_id":"-6851"},{"name":"left_data","node_id":"-6851"}],"cacheable":true,"seq_num":24,"comment":"","comment_collapsed":true},{"node_id":"-773","module_id":"BigQuantSpace.standardlize.standardlize-v8","parameters":[{"name":"columns_input","value":"label","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-773"},{"name":"input_2","node_id":"-773"}],"output_ports":[{"name":"data","node_id":"-773"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true},{"node_id":"-141","module_id":"BigQuantSpace.trade.trade-v4","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 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 = 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","type":"Literal","bound_global_parameter":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 #获取当日日期\n today_date = data.current_dt.strftime('%Y-%m-%d')\n \n #大盘风控模块,读取风控数据 \n benckmark_risk=context.benckmark_risk.loc[today_date].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 position_all = context.portfolio.positions.keys()\n for i in position_all:\n context.order_target(i, 0)\n print(today_date,'大盘风控止损触发,全仓卖出')\n return\n \n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\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) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n \n #--------------------------START:持有固定天数卖出(不含建仓期)---------------\n current_stopdays_stock = [] \n today = data.current_dt\n today_date = data.current_dt.strftime('%Y-%m-%d')\n # 不是建仓期(在前hold_days属于建仓期)\n if not is_staging:\n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n if len(equities)>0:\n for i in equities:\n sid = equities[i].sid # 交易标的\n stock_cost=equities[i].cost_basis # 成本价\n stock_market_price = data.current(context.symbol(i), 'price') # 最新市场价格\n last_sale_date = equities[i].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 total_of_profit = stock_market_price/stock_cost - 1 # 持仓收益\n highest_price_since_buy = data.history(context.symbol(i), 'close', hold_days, '1d').max() # 建仓以来的最高价\n # 日均收益小于 9.7% 就全部卖出\n if total_of_profit/hold_days < 0.97:\n context.order_target_percent(sid, 0)\n current_stopdays_stock.append(i)\n # 当日下跌 直接卖出\n if i not in current_stopdays_stock and highest_price_since_buy and highest_price_since_buy > stock_market_price:\n context.order_target_percent(sid, 0)\n current_stopdays_stock.append(i)\n #if len(current_stopdays_stock)>0: \n #print(today_date,'日均收益小于9.7% 就全部卖出',current_stopdays_stock)\n #-------------------------------END:持有固定天数卖出-------------------------- \n \n \n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n for instrument in instruments:\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 not in current_stopdays_stock:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n else:\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments_tmp = list(ranker_prediction.instrument)\n #防止卖出后再次买入\n buy_instruments=[k for k in buy_instruments_tmp if k not in current_stopdays_stock][: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 current_price = data.current(context.symbol(instrument), 'price')\n amount = math.floor(cash / current_price / 100) * 100\n context.order(context.symbol(instrument), amount)\n","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎:准备数据,只执行一次\ndef bigquant_run(context):\n #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度\n # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值),其中context.start_date和context.end_date是回测指定的起始时间和终止时间\n start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d') \n benckmark_data=D.history_data(instruments=['000001.SZA'], start_date=start_date, end_date=context.end_date,fields=['close'])\n #计算指数5日涨幅\n benckmark_data['ret5']=benckmark_data['close']/benckmark_data['close'].shift(5)-1\n #计算大盘风控条件,如果5日涨幅小于-10%则设置风险状态risk为1,否则为0\n benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.1,0,0)\n #修改日期格式为字符串(便于在handle中使用字符串日期索引来查看每日的风险状态)\n benckmark_data['date']=benckmark_data['date'].apply(lambda x:x.strftime('%Y-%m-%d'))\n #设置日期为索引\n benckmark_data.set_index('date',inplace=True)\n #把风控序列输出给全局变量context.benckmark_risk\n context.benckmark_risk=benckmark_data[['risk']]","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":0.025,"type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":"100000","type":"Literal","bound_global_parameter":null},{"name":"auto_cancel_non_tradable_orders","value":"True","type":"Literal","bound_global_parameter":null},{"name":"data_frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"真实价格","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SHA","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-141"},{"name":"options_data","node_id":"-141"},{"name":"history_ds","node_id":"-141"},{"name":"benchmark_ds","node_id":"-141"},{"name":"trading_calendar","node_id":"-141"}],"output_ports":[{"name":"raw_perf","node_id":"-141"}],"cacheable":false,"seq_num":26,"comment":"","comment_collapsed":true},{"node_id":"-10798","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"date 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[2021-09-13 10:08:20.774373] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-09-13 10:08:20.784867] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.786458] INFO: moduleinvoker: instruments.v2 运行完成[0.012098s].
[2021-09-13 10:08:20.793754] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2021-09-13 10:08:20.815099] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.817854] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.024094s].
[2021-09-13 10:08:20.826554] INFO: moduleinvoker: standardlize.v8 开始运行..
[2021-09-13 10:08:20.833858] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.835471] INFO: moduleinvoker: standardlize.v8 运行完成[0.008942s].
[2021-09-13 10:08:20.839468] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-09-13 10:08:20.845351] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.847179] INFO: moduleinvoker: input_features.v1 运行完成[0.007711s].
[2021-09-13 10:08:20.861214] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-09-13 10:08:20.868112] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.869601] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.008402s].
[2021-09-13 10:08:20.877459] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-09-13 10:08:20.887386] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.888908] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.011455s].
[2021-09-13 10:08:20.896235] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-09-13 10:08:20.907318] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.910105] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.013851s].
[2021-09-13 10:08:20.918275] INFO: moduleinvoker: instruments.v2 开始运行..
[2021-09-13 10:08:20.927944] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.929969] INFO: moduleinvoker: instruments.v2 运行完成[0.011688s].
[2021-09-13 10:08:20.942762] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2021-09-13 10:08:20.953668] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.955612] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.012867s].
[2021-09-13 10:08:20.963557] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2021-09-13 10:08:20.979911] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.981496] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.017944s].
[2021-09-13 10:08:20.988228] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-09-13 10:08:20.994982] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:20.996718] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.008491s].
[2021-09-13 10:08:21.001533] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-09-13 10:08:21.035129] INFO: moduleinvoker: input_features.v1 运行完成[0.033597s].
[2021-09-13 10:08:21.043765] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-09-13 10:08:31.680773] INFO: derived_feature_extractor: 提取完成 yz_1 = where(mean(close_0, 5) >= mean(close_0, 10), 1, 0), 3.150s
[2021-09-13 10:08:34.705716] INFO: derived_feature_extractor: 提取完成 yz_2 = where(mean(close_0, 10) >= mean(close_0, 20), 1, 0), 3.023s
[2021-09-13 10:08:36.180372] INFO: derived_feature_extractor: 提取完成 yz_3 = where(close_0 >= mean(close_0, 5), 1, 0), 1.472s
[2021-09-13 10:08:37.734895] INFO: derived_feature_extractor: 提取完成 yz_4 = where(high_0 >= ts_max(high_0, 5), 1, 0), 1.553s
[2021-09-13 10:08:37.741503] INFO: derived_feature_extractor: 提取完成 yz_5 = where(zf1 <= 0.04, 1, 0), 0.005s
[2021-09-13 10:08:40.847778] INFO: derived_feature_extractor: 提取完成 yz_6 = where((close_0 - ts_min(close_0,20))/ts_min(close_0,20) < 0.4, 1, 0), 3.105s
[2021-09-13 10:08:42.977957] INFO: derived_feature_extractor: /y_2019, 587293
[2021-09-13 10:08:46.602675] INFO: derived_feature_extractor: /y_2020, 870321
[2021-09-13 10:08:49.787460] INFO: derived_feature_extractor: /y_2021, 617504
[2021-09-13 10:08:50.566400] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[29.522619s].
[2021-09-13 10:08:50.575345] INFO: moduleinvoker: filter.v3 开始运行..
[2021-09-13 10:08:50.588740] INFO: filter: 使用表达式 yz_1 == True and yz_2 == True and yz_3 == True and yz_4 == True and yz_5 == True and yz_6 == True 过滤
[2021-09-13 10:08:51.219905] INFO: filter: 过滤 /y_2019, 43772/0/587293
[2021-09-13 10:08:52.114611] INFO: filter: 过滤 /y_2020, 64424/0/870321
[2021-09-13 10:08:52.762845] INFO: filter: 过滤 /y_2021, 41867/0/617504
[2021-09-13 10:08:52.798635] INFO: moduleinvoker: filter.v3 运行完成[2.223286s].
[2021-09-13 10:08:52.807072] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-09-13 10:08:52.965965] INFO: dropnan: /y_2019, 43772/43772
[2021-09-13 10:08:53.090244] INFO: dropnan: /y_2020, 64424/64424
[2021-09-13 10:08:53.188812] INFO: dropnan: /y_2021, 41867/41867
[2021-09-13 10:08:53.295360] INFO: dropnan: 行数: 150063/150063
[2021-09-13 10:08:53.301689] INFO: moduleinvoker: dropnan.v2 运行完成[0.494608s].
[2021-09-13 10:08:53.312264] INFO: moduleinvoker: input_features.v1 开始运行..
[2021-09-13 10:08:53.319126] INFO: moduleinvoker: 命中缓存
[2021-09-13 10:08:53.320567] INFO: moduleinvoker: input_features.v1 运行完成[0.008464s].
[2021-09-13 10:08:53.327885] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2021-09-13 10:09:27.155371] INFO: derived_feature_extractor: 提取完成 yz_1 = where(mean(close_0, 5) >= mean(close_0, 10), 1, 0), 10.490s
[2021-09-13 10:09:38.585734] INFO: derived_feature_extractor: 提取完成 yz_2 = where(mean(close_0, 10) >= mean(close_0, 20), 1, 0), 11.429s
[2021-09-13 10:09:44.290514] INFO: derived_feature_extractor: 提取完成 yz_3 = where(close_0 >= mean(close_0, 5), 1, 0), 5.703s
[2021-09-13 10:09:50.151247] INFO: derived_feature_extractor: 提取完成 yz_4 = where(high_0 >= ts_max(high_0, 5), 1, 0), 5.859s
[2021-09-13 10:09:50.163640] INFO: derived_feature_extractor: 提取完成 yz_5 = where(zf1 <= 0.04, 1, 0), 0.010s
[2021-09-13 10:10:01.564237] INFO: derived_feature_extractor: 提取完成 yz_6 = where((close_0 - ts_min(close_0,20))/ts_min(close_0,20) < 0.4, 1, 0), 11.399s
[2021-09-13 10:10:02.854853] INFO: derived_feature_extractor: /y_2009, 249804
[2021-09-13 10:10:04.750592] INFO: derived_feature_extractor: /y_2010, 413881
[2021-09-13 10:10:07.045133] INFO: derived_feature_extractor: /y_2011, 493517
[2021-09-13 10:10:09.515847] INFO: derived_feature_extractor: /y_2012, 548410
[2021-09-13 10:10:12.074672] INFO: derived_feature_extractor: /y_2013, 546947
[2021-09-13 10:10:14.740904] INFO: derived_feature_extractor: /y_2014, 554064
[2021-09-13 10:10:17.433839] INFO: derived_feature_extractor: /y_2015, 556950