{"description":"实验创建于2018/6/27","graph":{"edges":[{"to_node_id":"-353:instruments","from_node_id":"-51:data"},{"to_node_id":"-370:instruments","from_node_id":"-51:data"},{"to_node_id":"-353:features","from_node_id":"-59:data"},{"to_node_id":"-360:features","from_node_id":"-59:data"},{"to_node_id":"-1372:input_1","from_node_id":"-353:data"},{"to_node_id":"-865:input_data","from_node_id":"-360:data"},{"to_node_id":"-531:input_data","from_node_id":"-390:sorted_data"},{"to_node_id":"-370:options_data","from_node_id":"-531:data"},{"to_node_id":"-93:input_data","from_node_id":"-865:data"},{"to_node_id":"-390:input_ds","from_node_id":"-93:data"},{"to_node_id":"-443:input_data","from_node_id":"-220:data"},{"to_node_id":"-220:instruments","from_node_id":"-226:data"},{"to_node_id":"-220:features","from_node_id":"-234:data"},{"to_node_id":"-443:features","from_node_id":"-234:data"},{"to_node_id":"-2883:input_1","from_node_id":"-443:data"},{"to_node_id":"-360:input_data","from_node_id":"-1372:data"},{"to_node_id":"-2883:input_2","from_node_id":"-2864:data"},{"to_node_id":"-2864:instruments","from_node_id":"-2870:data"},{"to_node_id":"-2864:features","from_node_id":"-2878:data"},{"to_node_id":"-1372:input_2","from_node_id":"-2883:data"}],"nodes":[{"node_id":"-51","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2020-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-12-31","type":"Literal","bound_global_parameter":"交易日期"},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-51"}],"output_ports":[{"name":"data","node_id":"-51"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-59","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"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\nshouyi=(close_0-open_1)/open_1\namount_zf=amount_0/amount_1\namount_bl=amount_0/avg_amount_180\nzgzzf=high_0/close_1\nzhangf=(close_0-open_0)/close_1\ndbzs_zhangf=(close_0-open_0)/open_0 - zs_zhangf\ndbzs_return=return_0 - zs_return_0\nzhangf_max=max((close_0-open_0)/open_0,(close_0-close_1)/close_1)\nzhenf=(high_0-low_0)/close_1\nreturn0=return_0\nreturn1=shift(return_0,1)\nreturn3=return_3\nmax10=ts_max(close_0,10)\nmax10d=ts_argmax(close_0,10)\nmin10=ts_min(close_0,10)\nmin10d=ts_argmin(close_0,10)\npriceHighBl10=close_0/max10\npriceLowBl10=close_0/min10\nmax30=ts_max(close_0,30)\nmax30d=ts_argmax(close_0,30)\nmin30=ts_min(close_0,30)\nmin30d=ts_argmin(close_0,30)\npriceHighBl30=close_0/max30\npriceLowBl30=close_0/min30\nzs_huiluo\nzs_return_0\nzs_return_1\nzs_priceHighBl10\nzs_priceLowBl10\nzs_priceHighBl30\nzs_priceLowBl30\ndbzs_priceHighBl10=priceHighBl10-zs_priceHighBl10\ndbzs_priceLowBl10=priceLowBl10-zs_priceLowBl10\ndbzs_priceHighBl30=priceHighBl30-zs_priceHighBl30\ndbzs_priceLowBl30=priceLowBl30-zs_priceLowBl30\nmf_net_pct_l_0\nmf_net_pct_m_0\nmf_net_pct_main_0\nmf_net_pct_s_0\nmf_net_pct_xl_0\nopen_0\nzhangfMax5=ts_max(zhangf,5)\n#当前10天最低价与前10天最低价比值\nlow10bl=min10/shift(min10,10)\n#前10天的最低值与前20天的最高值比值\nhigh20chu10bl=shift(max10,20)/shift(min10,10)\n#当前价格与10天前，15日内的最低价的比值\ncloseOldLow25bl=close_0/shift(ts_min(close_0,15),10)\n#10天前，15日内的最低价与25天前15天的最高价比值\ncloseOldLow25_15bl=shift(ts_min(close_0,15),25)/shift(ts_min(close_0,15),10)\nddzb=mf_net_pct_l_0+mf_net_pct_main_0+mf_net_pct_xl_0\ndbzb30=sum(ddzb,30)\nzlcy=ts_max(max(mf_net_pct_l_0,mf_net_pct_main_0,mf_net_pct_xl_0),5)\n#前10天至40天的振幅\nhpbl30_old=ts_max(shift(close_0,10),30)/ts_min(shift(close_0,10),30)\n#最近10天的振幅\nhpbl10=ts_max(close_0,10)/ts_min(close_0,10)\nisHasZhangt=ts_max(where((return_0>1.09)&(close_0==high_0),1,0),10)\nisHasDiet=ts_max(where((return_0<0.91)&(close_0==low_0),1,0),10)\nmc1=where((zhangf_max>0.05)&(amount_zf<1),1,0)\nmc2=where(close_0<mean(close_0,5),1,0)\nmc3=where((return_0<1)&(close_0<open_0),1,0)\nmc4=where((zhenf>0.08)&(return_0<1.025),1,0)\n#连续下跌天数\nisXiaDie0=where(return_0<1,1,0)\nlxxd_1d=where(sum(isXiaDie0,1)==1,1,0)\nlxxd_2d=where(sum(isXiaDie0,2)==2,2,0)\nlxxd_3d=where(sum(isXiaDie0,3)==3,3,0)\nlxxd_4d=where(sum(isXiaDie0,4)==4,4,0)\nlxxd_5d=where(sum(isXiaDie0,5)==5,5,0)\nlxxd_6d=where(sum(isXiaDie0,6)==6,6,0)\nlxxd_8d=where(sum(isXiaDie0,8)==8,8,0)\nlxxd_10d=where(sum(isXiaDie0,10)==10,10,0)\nlxxd_days=max(lxxd_1d,lxxd_2d,lxxd_3d,lxxd_4d,lxxd_5d,lxxd_6d,lxxd_8d,lxxd_10d)\n#连续上涨天数\nisShangZ0=where(return_0>1,1,0)\nlxsz_1d=where(sum(isShangZ0,1)==1,1,0)\nlxsz_2d=where(sum(isShangZ0,2)==2,2,0)\nlxsz_3d=where(sum(isShangZ0,3)==3,3,0)\nlxsz_4d=where(sum(isShangZ0,4)==4,4,0)\nlxsz_5d=where(sum(isShangZ0,5)==5,5,0)\nlxsz_6d=where(sum(isShangZ0,6)==6,6,0)\nlxsz_8d=where(sum(isShangZ0,8)==8,8,0)\nlxsz_10d=where(sum(isShangZ0,10)==10,10,0)\nlxsz_days=max(lxsz_1d,lxsz_2d,lxsz_3d,lxsz_4d,lxsz_5d,lxsz_6d,lxsz_8d,lxsz_10d)\njx5d=mean(close_0,5)\nisLxsz=where((jx5d/shift(jx5d,5)>1)&(shift(jx5d,5)/shift(jx5d,10)>1),1,0)\n#与值数涨幅的相关性\nzszf_xgx_1=where((zs_return_0>1)&(return_0<1),1,0)\nmaxZhangf5=ts_max(zhangf,5)\nmaxZhangf5d=ts_argmax(zhangf,5)\nmaxZhangfOpen=where(maxZhangf5d>3,open_0,where(maxZhangf5d>2,shift(open_0,1),where(maxZhangf5d>1,shift(open_0,2),where(maxZhangf5d>0,shift(open_0,3),0))))\nzhangf_low_bl=close_0/maxZhangfOpen\nminZhangfLow=maxZhangfOpen/min(open_1,close_1,open_0)-1\n#20年，胜率0.6，盈亏比2.91；18年3年胜率0.52，盈亏比1.94\nmy1=where((abs(return0-1.02)<0.015)&(priceHighBl10==1)&(priceLowBl10<1.08)&(abs(priceHighBl30-0.9)<0.05)&(abs(priceLowBl30-1.12)<0.05)&(amount_bl>3)&(amount_zf>1)&(dbzs_return>0)&(return0>return1)&((return3+zgzzf)<2.1),1,0)\n#20年，胜率0.65，盈亏比2.51；18年3年，0.71，1.39，97支；\nmy3=where((return0>1)&(return1>1)&(zhangf>0)&(return3<0.97)&(priceHighBl10<0.88)&(priceLowBl10<1.07)&(priceHighBl30==priceHighBl10)&(priceLowBl30==priceLowBl10)&(dbzs_return>0)&(ddzb>0.05),1,0)\n#18年3年，0.65胜，1.39盈亏比\nmy6=where((abs(return0-1.02)<0.01)&(return0>return1)&(lxsz_days==2)&((return0+return1)<2.04)&(return3<1)&(priceHighBl10<0.93)&(priceLowBl10<1.12)&(priceHighBl30==priceHighBl10)&(priceLowBl30==priceLowBl10)&(dbzs_return>0),1,0)\n#18年3年，0.57胜，1.96盈亏比\nmy8=where((return0>return1)&(zhangf>0)&(zhenf<0.025)&(abs(return1-0.99)<0.005)&(priceLowBl10<1.05)&(abs(max10d-7)<3)&(abs(priceHighBl30-0.95)<0.05)&(priceLowBl30<1.15)&(zlcy>0.05)&(amount_bl>1.3)&(max30d<10),1,0)\n#20年155支，胜率0.57，盈亏比1.76；18年3年，胜率0.5，盈亏比1.42\nmy11=where((lxsz_days>2)&(return3<1.06)&(dbzs_return>0)&(priceHighBl10==1)&(priceLowBl10<1.06)&(abs(priceHighBl30-0.9)<0.05)&(abs(priceLowBl30-priceLowBl10)<0.05)&(abs(dbzs_priceHighBl10-0.01)<0.01)&(ts_min(amount_zf,2)>1)&(ddzb>0.05),1,0)\n#按策略优质度排序\nyzd1=where(my3==1,10,0)\nyzd2=where(my6==1,8,0)\nyzd3=where(my8==1,6,0)\nyzd4=where(my1==1,4,0)\nyzd5=where(my11==1,2,0)\nyzd=yzd1+yzd2+yzd3+yzd4+yzd5\nmy=max(my1,my3,my6,my8,my11)\nbuy_condition=where(my==1,1,0)\nsell_condition=where(mc2==3,1,0)\n#gltj=where((shouyi>0.08)&(shouyi<0.2)&(shift(dbzs_return,2)>0)&(shift(dbzs_priceLowBl10,2)<0.05)&(shift(dbzs_priceLowBl30,2)<1.15),1,0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-59"}],"output_ports":[{"name":"data","node_id":"-59"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-353","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"60","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-353"},{"name":"features","node_id":"-353"}],"output_ports":[{"name":"data","node_id":"-353"}],"cacheable":false,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-360","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":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-360"},{"name":"features","node_id":"-360"}],"output_ports":[{"name":"data","node_id":"-360"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-370","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\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 #context.set_commission(PerOrder(buy_cost=0.00001, sell_cost=0.0001, min_cost=1))\n \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 context.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 # 每只股票的权重平均分配\n context.stock_weights = 1/context.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\n","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 today = data.current_dt.strftime('%Y-%m-%d')\n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n stock_now = len(equities); #获取当前持仓股票数量\n stock_count = context.stock_count\n \n # 按日期过滤得到今日的预测数据\n # 加载预测数据\n df = context.options['data'].read_df()\n df_today = df[df.date == data.current_dt.strftime('%Y-%m-%d')]\n df_today.set_index('instrument')\n \n \n now_stock = []\n sell_stock = []\n \n try:\n buy_list = context.daily_buy_stock[today]\n except:\n buy_list = []\n\n \n # 1. 资金分配\n #is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天） \n #stock_cash = context.portfolio.portfolio_value/stock_count\n #cash_avg = context.portfolio.portfolio_value\n #cash_for_buy = min(context.portfolio.cash, stock_cash)\n #cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n \n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n \n \n #if not is_staging :\n if 1==1 : \n if len(equities) > 0:\n for i in equities.keys():\n last_sale_date = equities[i].last_sale_date\t# 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 >= context.options['hold_days'] and i not in buy_list :\n print('日期:',today,'卖出2:',i)\n context.order_target(context.symbol(i), 0)\n sell_stock.append(i)\n stock_now = stock_now -1\n #print('日期：', today, '股票：', i, ' 卖出')\n \n# 3. 生成买入订单\n buy_num = stock_count - stock_now\n #if is_staging :\n # buy_num = 1\n if len(buy_list)>0:\n print('日期：', today, '选出股票数量：', len(buy_list))\n if buy_num>0 and len(buy_list)>0 :\n # 不再买入已经轮仓卖出和移动止损的股票,以防止出现空头持仓\n buy_instruments = [i for i in buy_list if i not in now_stock][:buy_num]\n cash_for_buy = context.portfolio.cash/len(buy_instruments)\n for i, instrument in enumerate(buy_instruments):\n current_price = data.current(context.symbol(instrument), 'price')\n \n if cash_for_buy>0 and data.can_trade(context.symbol(instrument)): \n amount = math.floor(cash_for_buy / current_price / 100) * 100\n context.order(context.symbol(instrument), amount)\n #if(instrument=='002735.SZA'):\n print('日期:',today,'买入:',instrument)\n else :\n print('日期:',today,'无资金或不能交易未买入:',instrument)","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 # 加载预测数据\n df = context.options['data'].read_df()\n # 函数：求满足开仓条件的股票列表\n def open_pos_con(df):\n return list(df[df['buy_condition']>0].instrument)\n # 函数：求满足平仓条件的股票列表\n def close_pos_con(df):\n return list(df[df['sell_condition']>0].instrument)\n \n # 每日卖出股票的数据框\n context.daily_sell_stock= df.groupby('date').apply(close_pos_con) \n # 每日买入股票的数据框\n context.daily_buy_stock= df.groupby('date').apply(open_pos_con) \n\n\n \n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。\ndef bigquant_run(context, data):\n pass\n","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":"后复权","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":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-370"},{"name":"options_data","node_id":"-370"},{"name":"history_ds","node_id":"-370"},{"name":"benchmark_ds","node_id":"-370"},{"name":"trading_calendar","node_id":"-370"}],"output_ports":[{"name":"raw_perf","node_id":"-370"}],"cacheable":false,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-390","module_id":"BigQuantSpace.sort.sort-v4","parameters":[{"name":"sort_by","value":"yzd","type":"Literal","bound_global_parameter":null},{"name":"group_by","value":"date","type":"Literal","bound_global_parameter":null},{"name":"keep_columns","value":"--","type":"Literal","bound_global_parameter":null},{"name":"ascending","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-390"},{"name":"sort_by_ds","node_id":"-390"}],"output_ports":[{"name":"sorted_data","node_id":"-390"}],"cacheable":true,"seq_num":9,"comment":"","comment_collapsed":true},{"node_id":"-531","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"my==1","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":"-531"}],"output_ports":[{"name":"data","node_id":"-531"},{"name":"left_data","node_id":"-531"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-865","module_id":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","parameters":[{"name":"index_constituent_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"board_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22displayValue%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"industry_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%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#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\nzs_zhangf=(close-open)/open\nzs_huiluo=(high-close)/close\nzs_huishen=(close-low)/low\nzs_zhenf=(high-low)/shift(close,1)\nzs_volume_zf=volume/shift(volume,1)\nzs_return_0=close/shift(close,1)\nzs_return_1=shift(zs_return_0,1)\nzs_return_2=shift(zs_return_0,2)\nzs_max10=ts_max(close,10)\n#zs_max10d=ts_argmax(close,10)\nzs_max30=ts_max(close,30)\n#zs_max30d=ts_argmax(close,30)\nzs_min10=ts_min(close,10)\n#zs_min10d=ts_argmin(close,10)\nzs_min30=ts_min(close,30)\n#zs_min30d=ts_argmin(close,30)\nzs_priceHighBl10=close/zs_max10\nzs_priceLowBl10=close/zs_min10\nzs_priceHighBl30=close/zs_max30\nzs_priceLowBl30=close/zs_min30","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-234"}],"output_ports":[{"name":"data","node_id":"-234"}],"cacheable":true,"seq_num":12,"comment":"","comment_collapsed":true},{"node_id":"-443","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":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-443"},{"name":"features","node_id":"-443"}],"output_ports":[{"name":"data","node_id":"-443"}],"cacheable":true,"seq_num":13,"comment":"","comment_collapsed":true},{"node_id":"-1372","module_id":"BigQuantSpace.data_join.data_join-v3","parameters":[{"name":"on","value":"date","type":"Literal","bound_global_parameter":null},{"name":"how","value":"left","type":"Literal","bound_global_parameter":null},{"name":"sort","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-1372"},{"name":"input_2","node_id":"-1372"}],"output_ports":[{"name":"data","node_id":"-1372"}],"cacheable":true,"seq_num":14,"comment":"","comment_collapsed":true},{"node_id":"-2864","module_id":"BigQuantSpace.use_datasource.use_datasource-v1","parameters":[{"name":"datasource_id","value":"bar1d_index_CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-2864"},{"name":"features","node_id":"-2864"}],"output_ports":[{"name":"data","node_id":"-2864"}],"cacheable":true,"seq_num":17,"comment":"","comment_collapsed":true},{"node_id":"-2870","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2020-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-30","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"000002.HIX","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":0,"type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"-2870"}],"output_ports":[{"name":"data","node_id":"-2870"}],"cacheable":true,"seq_num":18,"comment":"","comment_collapsed":true},{"node_id":"-2878","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# 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# 本代码由可视化策略环境自动生成 2022年1月21日 15:56
# 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 m8_initialize_bigquant_run(context):

    # 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))
    #context.set_commission(PerOrder(buy_cost=0.00001, sell_cost=0.0001, min_cost=1))
    
    # 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
    context.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 = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 每只股票的权重平均分配
    context.stock_weights = 1/context.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


# 回测引擎：每日数据处理函数，每天执行一次
def m8_handle_data_bigquant_run(context, data):
    today = data.current_dt.strftime('%Y-%m-%d')
    equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    stock_now = len(equities); #获取当前持仓股票数量
    stock_count = context.stock_count
    
    # 按日期过滤得到今日的预测数据
    # 加载预测数据
    df = context.options['data'].read_df()
    df_today = df[df.date == data.current_dt.strftime('%Y-%m-%d')]
    df_today.set_index('instrument')
    
    
    now_stock = []
    sell_stock = []
       
    try:
        buy_list = context.daily_buy_stock[today]
    except:
        buy_list = []

    
    # 1. 资金分配
    #is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天） 
    #stock_cash = context.portfolio.portfolio_value/stock_count
    #cash_avg = context.portfolio.portfolio_value
    #cash_for_buy = min(context.portfolio.cash,  stock_cash)
    #cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}
    
            
    #if not is_staging :
    if 1==1 :    
        if len(equities) > 0:
            for i in equities.keys():
                last_sale_date = equities[i].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 >= context.options['hold_days'] and i not in buy_list :
                    print('日期:',today,'卖出2:',i)
                    context.order_target(context.symbol(i), 0)
                    sell_stock.append(i)
                    stock_now = stock_now -1
                    #print('日期：', today, '股票：', i, ' 卖出')
                 
# 3. 生成买入订单
    buy_num = stock_count - stock_now
    #if is_staging :
    #    buy_num = 1
    if len(buy_list)>0:
        print('日期：', today, '选出股票数量：', len(buy_list))
    if buy_num>0 and len(buy_list)>0 :
        # 不再买入已经轮仓卖出和移动止损的股票,以防止出现空头持仓
        buy_instruments = [i for i in buy_list if i not in now_stock][:buy_num]
        cash_for_buy = context.portfolio.cash/len(buy_instruments)
        for i, instrument in enumerate(buy_instruments):
            current_price = data.current(context.symbol(instrument), 'price')
            
            if cash_for_buy>0 and data.can_trade(context.symbol(instrument)):           
                amount = math.floor(cash_for_buy / current_price / 100) * 100
                context.order(context.symbol(instrument), amount)
                #if(instrument=='002735.SZA'):
                print('日期:',today,'买入:',instrument)
            else :
                print('日期:',today,'无资金或不能交易未买入:',instrument)
# 回测引擎：准备数据，只执行一次
def m8_prepare_bigquant_run(context):
    # 加载预测数据
    df = context.options['data'].read_df()
    # 函数：求满足开仓条件的股票列表
    def open_pos_con(df):
        return list(df[df['buy_condition']>0].instrument)
    # 函数：求满足平仓条件的股票列表
    def close_pos_con(df):
        return list(df[df['sell_condition']>0].instrument)
    
    # 每日卖出股票的数据框
    context.daily_sell_stock= df.groupby('date').apply(close_pos_con)  
    # 每日买入股票的数据框
    context.daily_buy_stock= df.groupby('date').apply(open_pos_con)  


    

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m8_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2020-01-01',
    end_date=T.live_run_param('trading_date', '2021-12-31'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = 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
shouyi=(close_0-open_1)/open_1
amount_zf=amount_0/amount_1
amount_bl=amount_0/avg_amount_180
zgzzf=high_0/close_1
zhangf=(close_0-open_0)/close_1
dbzs_zhangf=(close_0-open_0)/open_0 - zs_zhangf
dbzs_return=return_0 - zs_return_0
zhangf_max=max((close_0-open_0)/open_0,(close_0-close_1)/close_1)
zhenf=(high_0-low_0)/close_1
return0=return_0
return1=shift(return_0,1)
return3=return_3
max10=ts_max(close_0,10)
max10d=ts_argmax(close_0,10)
min10=ts_min(close_0,10)
min10d=ts_argmin(close_0,10)
priceHighBl10=close_0/max10
priceLowBl10=close_0/min10
max30=ts_max(close_0,30)
max30d=ts_argmax(close_0,30)
min30=ts_min(close_0,30)
min30d=ts_argmin(close_0,30)
priceHighBl30=close_0/max30
priceLowBl30=close_0/min30
zs_huiluo
zs_return_0
zs_return_1
zs_priceHighBl10
zs_priceLowBl10
zs_priceHighBl30
zs_priceLowBl30
dbzs_priceHighBl10=priceHighBl10-zs_priceHighBl10
dbzs_priceLowBl10=priceLowBl10-zs_priceLowBl10
dbzs_priceHighBl30=priceHighBl30-zs_priceHighBl30
dbzs_priceLowBl30=priceLowBl30-zs_priceLowBl30
mf_net_pct_l_0
mf_net_pct_m_0
mf_net_pct_main_0
mf_net_pct_s_0
mf_net_pct_xl_0
open_0
zhangfMax5=ts_max(zhangf,5)
#当前10天最低价与前10天最低价比值
low10bl=min10/shift(min10,10)
#前10天的最低值与前20天的最高值比值
high20chu10bl=shift(max10,20)/shift(min10,10)
#当前价格与10天前，15日内的最低价的比值
closeOldLow25bl=close_0/shift(ts_min(close_0,15),10)
#10天前，15日内的最低价与25天前15天的最高价比值
closeOldLow25_15bl=shift(ts_min(close_0,15),25)/shift(ts_min(close_0,15),10)
ddzb=mf_net_pct_l_0+mf_net_pct_main_0+mf_net_pct_xl_0
dbzb30=sum(ddzb,30)
zlcy=ts_max(max(mf_net_pct_l_0,mf_net_pct_main_0,mf_net_pct_xl_0),5)
#前10天至40天的振幅
hpbl30_old=ts_max(shift(close_0,10),30)/ts_min(shift(close_0,10),30)
#最近10天的振幅
hpbl10=ts_max(close_0,10)/ts_min(close_0,10)
isHasZhangt=ts_max(where((return_0>1.09)&(close_0==high_0),1,0),10)
isHasDiet=ts_max(where((return_0<0.91)&(close_0==low_0),1,0),10)
mc1=where((zhangf_max>0.05)&(amount_zf<1),1,0)
mc2=where(close_0<mean(close_0,5),1,0)
mc3=where((return_0<1)&(close_0<open_0),1,0)
mc4=where((zhenf>0.08)&(return_0<1.025),1,0)
#连续下跌天数
isXiaDie0=where(return_0<1,1,0)
lxxd_1d=where(sum(isXiaDie0,1)==1,1,0)
lxxd_2d=where(sum(isXiaDie0,2)==2,2,0)
lxxd_3d=where(sum(isXiaDie0,3)==3,3,0)
lxxd_4d=where(sum(isXiaDie0,4)==4,4,0)
lxxd_5d=where(sum(isXiaDie0,5)==5,5,0)
lxxd_6d=where(sum(isXiaDie0,6)==6,6,0)
lxxd_8d=where(sum(isXiaDie0,8)==8,8,0)
lxxd_10d=where(sum(isXiaDie0,10)==10,10,0)
lxxd_days=max(lxxd_1d,lxxd_2d,lxxd_3d,lxxd_4d,lxxd_5d,lxxd_6d,lxxd_8d,lxxd_10d)
#连续上涨天数
isShangZ0=where(return_0>1,1,0)
lxsz_1d=where(sum(isShangZ0,1)==1,1,0)
lxsz_2d=where(sum(isShangZ0,2)==2,2,0)
lxsz_3d=where(sum(isShangZ0,3)==3,3,0)
lxsz_4d=where(sum(isShangZ0,4)==4,4,0)
lxsz_5d=where(sum(isShangZ0,5)==5,5,0)
lxsz_6d=where(sum(isShangZ0,6)==6,6,0)
lxsz_8d=where(sum(isShangZ0,8)==8,8,0)
lxsz_10d=where(sum(isShangZ0,10)==10,10,0)
lxsz_days=max(lxsz_1d,lxsz_2d,lxsz_3d,lxsz_4d,lxsz_5d,lxsz_6d,lxsz_8d,lxsz_10d)
jx5d=mean(close_0,5)
isLxsz=where((jx5d/shift(jx5d,5)>1)&(shift(jx5d,5)/shift(jx5d,10)>1),1,0)
#与值数涨幅的相关性
zszf_xgx_1=where((zs_return_0>1)&(return_0<1),1,0)
maxZhangf5=ts_max(zhangf,5)
maxZhangf5d=ts_argmax(zhangf,5)
maxZhangfOpen=where(maxZhangf5d>3,open_0,where(maxZhangf5d>2,shift(open_0,1),where(maxZhangf5d>1,shift(open_0,2),where(maxZhangf5d>0,shift(open_0,3),0))))
zhangf_low_bl=close_0/maxZhangfOpen
minZhangfLow=maxZhangfOpen/min(open_1,close_1,open_0)-1
#20年，胜率0.6，盈亏比2.91；18年3年胜率0.52，盈亏比1.94
my1=where((abs(return0-1.02)<0.015)&(priceHighBl10==1)&(priceLowBl10<1.08)&(abs(priceHighBl30-0.9)<0.05)&(abs(priceLowBl30-1.12)<0.05)&(amount_bl>3)&(amount_zf>1)&(dbzs_return>0)&(return0>return1)&((return3+zgzzf)<2.1),1,0)
#20年，胜率0.65，盈亏比2.51；18年3年，0.71，1.39，97支；
my3=where((return0>1)&(return1>1)&(zhangf>0)&(return3<0.97)&(priceHighBl10<0.88)&(priceLowBl10<1.07)&(priceHighBl30==priceHighBl10)&(priceLowBl30==priceLowBl10)&(dbzs_return>0)&(ddzb>0.05),1,0)
#18年3年，0.65胜，1.39盈亏比
my6=where((abs(return0-1.02)<0.01)&(return0>return1)&(lxsz_days==2)&((return0+return1)<2.04)&(return3<1)&(priceHighBl10<0.93)&(priceLowBl10<1.12)&(priceHighBl30==priceHighBl10)&(priceLowBl30==priceLowBl10)&(dbzs_return>0),1,0)
#18年3年，0.57胜，1.96盈亏比
my8=where((return0>return1)&(zhangf>0)&(zhenf<0.025)&(abs(return1-0.99)<0.005)&(priceLowBl10<1.05)&(abs(max10d-7)<3)&(abs(priceHighBl30-0.95)<0.05)&(priceLowBl30<1.15)&(zlcy>0.05)&(amount_bl>1.3)&(max30d<10),1,0)
#20年155支，胜率0.57，盈亏比1.76；18年3年，胜率0.5，盈亏比1.42
my11=where((lxsz_days>2)&(return3<1.06)&(dbzs_return>0)&(priceHighBl10==1)&(priceLowBl10<1.06)&(abs(priceHighBl30-0.9)<0.05)&(abs(priceLowBl30-priceLowBl10)<0.05)&(abs(dbzs_priceHighBl10-0.01)<0.01)&(ts_min(amount_zf,2)>1)&(ddzb>0.05),1,0)
#按策略优质度排序
yzd1=where(my3==1,10,0)
yzd2=where(my6==1,8,0)
yzd3=where(my8==1,6,0)
yzd4=where(my1==1,4,0)
yzd5=where(my11==1,2,0)
yzd=yzd1+yzd2+yzd3+yzd4+yzd5
my=max(my1,my3,my6,my8,my11)
buy_condition=where(my==1,1,0)
sell_condition=where(mc2==3,1,0)
#gltj=where((shouyi>0.08)&(shouyi<0.2)&(shift(dbzs_return,2)>0)&(shift(dbzs_priceLowBl10,2)<0.05)&(shift(dbzs_priceLowBl30,2)<1.15),1,0)"""
)

m5 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=60,
    m_cached=False
)

m10 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2050-12-30',
    market='CN_STOCK_A',
    instrument_list='000002.HIX',
    max_count=0
)

m12 = 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
zs_zhangf=(close-open)/open
zs_huiluo=(high-close)/close
zs_huishen=(close-low)/low
zs_zhenf=(high-low)/shift(close,1)
zs_volume_zf=volume/shift(volume,1)
zs_return_0=close/shift(close,1)
zs_return_1=shift(zs_return_0,1)
zs_return_2=shift(zs_return_0,2)
zs_max10=ts_max(close,10)
#zs_max10d=ts_argmax(close,10)
zs_max30=ts_max(close,30)
#zs_max30d=ts_argmax(close,30)
zs_min10=ts_min(close,10)
#zs_min10d=ts_argmin(close,10)
zs_min30=ts_min(close,30)
#zs_min30d=ts_argmin(close,30)
zs_priceHighBl10=close/zs_max10
zs_priceLowBl10=close/zs_min10
zs_priceHighBl30=close/zs_max30
zs_priceLowBl30=close/zs_min30"""
)

m4 = M.use_datasource.v1(
    instruments=m10.data,
    features=m12.data,
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='',
    end_date=''
)

m13 = M.derived_feature_extractor.v3(
    input_data=m4.data,
    features=m12.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=True,
    user_functions={}
)

m18 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2020-12-30',
    market='CN_STOCK_A',
    instrument_list='000002.HIX',
    max_count=0
)

m19 = 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
close
"""
)

m17 = M.use_datasource.v1(
    instruments=m18.data,
    features=m19.data,
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='',
    end_date=''
)

m20 = M.data_join.v3(
    input_1=m13.data,
    input_2=m17.data,
    on='date',
    how='left',
    sort=False
)

m14 = M.data_join.v3(
    input_1=m5.data,
    input_2=m20.data,
    on='date',
    how='left',
    sort=False
)

m7 = M.derived_feature_extractor.v3(
    input_data=m14.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=True,
    user_functions={}
)

m6 = M.chinaa_stock_filter.v1(
    input_data=m7.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m11 = M.dropnan.v2(
    input_data=m6.data
)

m9 = M.sort.v4(
    input_ds=m11.data,
    sort_by='yzd',
    group_by='date',
    keep_columns='--',
    ascending=False
)

m3 = M.filter.v3(
    input_data=m9.sorted_data,
    expr='my==1',
    output_left_data=False
)

m8 = M.trade.v4(
    instruments=m1.data,
    options_data=m3.data,
    start_date='',
    end_date='',
    initialize=m8_initialize_bigquant_run,
    handle_data=m8_handle_data_bigquant_run,
    prepare=m8_prepare_bigquant_run,
    before_trading_start=m8_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=''
)

[2022-01-21 15:48:55.557743] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-01-21 15:48:55.598838] INFO: moduleinvoker: 命中缓存

[2022-01-21 15:48:55.600663] INFO: moduleinvoker: instruments.v2 运行完成[0.042939s].

[2022-01-21 15:48:55.609371] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-01-21 15:48:55.618876] INFO: moduleinvoker: 命中缓存

[2022-01-21 15:48:55.620471] INFO: moduleinvoker: input_features.v1 运行完成[0.011083s].

[2022-01-21 15:48:55.642109] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-01-21 15:48:55.683280] WARNING: bigdatasource: cannot find filed [zs_return_0] table in field_table_map!

[2022-01-21 15:48:55.684937] WARNING: bigdatasource: cannot find filed [zs_return_1] table in field_table_map!

[2022-01-21 15:48:55.686206] WARNING: bigdatasource: cannot find filed [zs_zhangf] table in field_table_map!

[2022-01-21 15:48:55.687350] WARNING: bigdatasource: cannot find filed [zs_priceHighBl30] table in field_table_map!

[2022-01-21 15:48:55.688596] WARNING: bigdatasource: cannot find filed [zs_priceHighBl10] table in field_table_map!

[2022-01-21 15:48:55.689748] WARNING: bigdatasource: cannot find filed [zs_huiluo] table in field_table_map!

[2022-01-21 15:48:55.690898] WARNING: bigdatasource: cannot find filed [zs_priceLowBl10] table in field_table_map!

[2022-01-21 15:48:55.692051] WARNING: bigdatasource: cannot find filed [zs_priceLowBl30] table in field_table_map!

[2022-01-21 15:49:00.097894] WARNING: bigdatasource: unknown fields: ['zs_return_0', 'zs_zhangf', 'zs_priceHighBl10', 'zs_priceLowBl30', 'zs_return_1', 'zs_huiluo', 'zs_priceHighBl30', 'zs_priceLowBl10']

[2022-01-21 15:49:00.332688] INFO: 基础特征抽取: 年份 2019, 特征行数=156001

[2022-01-21 15:49:00.337936] WARNING: bigdatasource: cannot find filed [zs_return_0] table in field_table_map!

[2022-01-21 15:49:00.339614] WARNING: bigdatasource: cannot find filed [zs_return_1] table in field_table_map!

[2022-01-21 15:49:00.340956] WARNING: bigdatasource: cannot find filed [zs_zhangf] table in field_table_map!

[2022-01-21 15:49:00.342088] WARNING: bigdatasource: cannot find filed [zs_priceHighBl30] table in field_table_map!

[2022-01-21 15:49:00.343198] WARNING: bigdatasource: cannot find filed [zs_priceHighBl10] table in field_table_map!

[2022-01-21 15:49:00.344342] WARNING: bigdatasource: cannot find filed [zs_huiluo] table in field_table_map!

[2022-01-21 15:49:00.345451] WARNING: bigdatasource: cannot find filed [zs_priceLowBl10] table in field_table_map!

[2022-01-21 15:49:00.346480] WARNING: bigdatasource: cannot find filed [zs_priceLowBl30] table in field_table_map!

[2022-01-21 15:49:09.190482] WARNING: bigdatasource: unknown fields: ['zs_return_0', 'zs_zhangf', 'zs_priceHighBl10', 'zs_priceLowBl30', 'zs_return_1', 'zs_huiluo', 'zs_priceHighBl30', 'zs_priceLowBl10']

[2022-01-21 15:49:10.475339] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2022-01-21 15:49:10.481828] WARNING: bigdatasource: cannot find filed [zs_return_0] table in field_table_map!

[2022-01-21 15:49:10.484167] WARNING: bigdatasource: cannot find filed [zs_return_1] table in field_table_map!

[2022-01-21 15:49:10.486126] WARNING: bigdatasource: cannot find filed [zs_zhangf] table in field_table_map!

[2022-01-21 15:49:10.487789] WARNING: bigdatasource: cannot find filed [zs_priceHighBl30] table in field_table_map!

[2022-01-21 15:49:10.489357] WARNING: bigdatasource: cannot find filed [zs_priceHighBl10] table in field_table_map!

[2022-01-21 15:49:10.490969] WARNING: bigdatasource: cannot find filed [zs_huiluo] table in field_table_map!

[2022-01-21 15:49:10.492716] WARNING: bigdatasource: cannot find filed [zs_priceLowBl10] table in field_table_map!

[2022-01-21 15:49:10.494356] WARNING: bigdatasource: cannot find filed [zs_priceLowBl30] table in field_table_map!

[2022-01-21 15:49:18.362157] WARNING: bigdatasource: unknown fields: ['zs_return_0', 'zs_zhangf', 'zs_priceHighBl10', 'zs_priceLowBl30', 'zs_return_1', 'zs_huiluo', 'zs_priceHighBl30', 'zs_priceLowBl10']

[2022-01-21 15:49:19.702701] INFO: 基础特征抽取: 年份 2021, 特征行数=1061527

[2022-01-21 15:49:19.862638] INFO: 基础特征抽取: 总行数: 2163489

[2022-01-21 15:49:19.872404] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[24.230296s].

[2022-01-21 15:49:19.911035] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-01-21 15:49:19.935236] INFO: moduleinvoker: instruments.v2 运行完成[0.024199s].

[2022-01-21 15:49:19.940833] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-01-21 15:49:19.949579] INFO: moduleinvoker: 命中缓存

[2022-01-21 15:49:19.951264] INFO: moduleinvoker: input_features.v1 运行完成[0.010469s].

[2022-01-21 15:49:19.963318] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-01-21 15:49:20.247851] INFO: moduleinvoker: use_datasource.v1 运行完成[0.284537s].

[2022-01-21 15:49:20.265866] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-01-21 15:49:20.325574] INFO: derived_feature_extractor: 提取完成 zs_zhangf=(close-open)/open, 0.002s

[2022-01-21 15:49:20.328672] INFO: derived_feature_extractor: 提取完成 zs_huiluo=(high-close)/close, 0.002s

[2022-01-21 15:49:20.331411] INFO: derived_feature_extractor: 提取完成 zs_huishen=(close-low)/low, 0.001s

[2022-01-21 15:49:20.336743] INFO: derived_feature_extractor: 提取完成 zs_zhenf=(high-low)/shift(close,1), 0.004s

[2022-01-21 15:49:20.341900] INFO: derived_feature_extractor: 提取完成 zs_volume_zf=volume/shift(volume,1), 0.004s

[2022-01-21 15:49:20.346544] INFO: derived_feature_extractor: 提取完成 zs_return_0=close/shift(close,1), 0.003s

[2022-01-21 15:49:20.350462] INFO: derived_feature_extractor: 提取完成 zs_return_1=shift(zs_return_0,1), 0.003s

[2022-01-21 15:49:20.354266] INFO: derived_feature_extractor: 提取完成 zs_return_2=shift(zs_return_0,2), 0.003s

[2022-01-21 15:49:20.360992] INFO: derived_feature_extractor: 提取完成 zs_max10=ts_max(close,10), 0.006s

[2022-01-21 15:49:20.366639] INFO: derived_feature_extractor: 提取完成 zs_max30=ts_max(close,30), 0.004s

[2022-01-21 15:49:20.372178] INFO: derived_feature_extractor: 提取完成 zs_min10=ts_min(close,10), 0.004s

[2022-01-21 15:49:20.377467] INFO: derived_feature_extractor: 提取完成 zs_min30=ts_min(close,30), 0.004s

[2022-01-21 15:49:20.379849] INFO: derived_feature_extractor: 提取完成 zs_priceHighBl10=close/zs_max10, 0.001s

[2022-01-21 15:49:20.382105] INFO: derived_feature_extractor: 提取完成 zs_priceLowBl10=close/zs_min10, 0.001s

[2022-01-21 15:49:20.384305] INFO: derived_feature_extractor: 提取完成 zs_priceHighBl30=close/zs_max30, 0.001s

[2022-01-21 15:49:20.386463] INFO: derived_feature_extractor: 提取完成 zs_priceLowBl30=close/zs_min30, 0.001s

[2022-01-21 15:49:20.430914] INFO: derived_feature_extractor: /data, 2930

[2022-01-21 15:49:20.478868] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.212993s].

[2022-01-21 15:49:20.484303] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-01-21 15:49:20.494667] INFO: moduleinvoker: 命中缓存

[2022-01-21 15:49:20.496101] INFO: moduleinvoker: instruments.v2 运行完成[0.0118s].

[2022-01-21 15:49:20.508481] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-01-21 15:49:20.532902] INFO: moduleinvoker: 命中缓存

[2022-01-21 15:49:20.534603] INFO: moduleinvoker: input_features.v1 运行完成[0.026135s].

[2022-01-21 15:49:20.541682] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-01-21 15:49:20.551883] INFO: moduleinvoker: 命中缓存

[2022-01-21 15:49:20.553424] INFO: moduleinvoker: use_datasource.v1 运行完成[0.011748s].

[2022-01-21 15:49:20.595469] INFO: moduleinvoker: data_join.v3 开始运行..

[2022-01-21 15:49:20.831758] INFO: moduleinvoker: data_join.v3 运行完成[0.236298s].

[2022-01-21 15:49:20.841398] INFO: moduleinvoker: data_join.v3 开始运行..

[2022-01-21 15:49:29.456002] INFO: moduleinvoker: data_join.v3 运行完成[8.614603s].

[2022-01-21 15:49:29.466699] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-01-21 15:49:40.615667] INFO: derived_feature_extractor: 提取完成 shouyi=(close_0-open_1)/open_1, 0.005s

[2022-01-21 15:49:40.624256] INFO: derived_feature_extractor: 提取完成 amount_zf=amount_0/amount_1, 0.006s

[2022-01-21 15:49:40.637658] INFO: derived_feature_extractor: 提取完成 amount_bl=amount_0/avg_amount_180, 0.012s

[2022-01-21 15:49:40.643590] INFO: derived_feature_extractor: 提取完成 zgzzf=high_0/close_1, 0.004s

[2022-01-21 15:49:40.650016] INFO: derived_feature_extractor: 提取完成 zhangf=(close_0-open_0)/close_1, 0.005s

[2022-01-21 15:49:40.659431] INFO: derived_feature_extractor: 提取完成 dbzs_zhangf=(close_0-open_0)/open_0 - zs_zhangf, 0.008s

[2022-01-21 15:49:40.667728] INFO: derived_feature_extractor: 提取完成 dbzs_return=return_0 - zs_return_0, 0.007s

[2022-01-21 15:49:40.718400] INFO: derived_feature_extractor: 提取完成 zhangf_max=max((close_0-open_0)/open_0,(close_0-close_1)/close_1), 0.049s

[2022-01-21 15:49:40.725077] INFO: derived_feature_extractor: 提取完成 zhenf=(high_0-low_0)/close_1, 0.004s

[2022-01-21 15:49:40.729142] INFO: derived_feature_extractor: 提取完成 return0=return_0, 0.002s

[2022-01-21 15:49:41.013589] INFO: derived_feature_extractor: 提取完成 return1=shift(return_0,1), 0.283s

[2022-01-21 15:49:41.018800] INFO: derived_feature_extractor: 提取完成 return3=return_3, 0.004s

[2022-01-21 15:49:42.588312] INFO: derived_feature_extractor: 提取完成 max10=ts_max(close_0,10), 1.568s

[2022-01-21 15:49:49.075768] INFO: derived_feature_extractor: 提取完成 max10d=ts_argmax(close_0,10), 6.486s

[2022-01-21 15:49:50.577033] INFO: derived_feature_extractor: 提取完成 min10=ts_min(close_0,10), 1.500s

[2022-01-21 15:49:57.102500] INFO: derived_feature_extractor: 提取完成 min10d=ts_argmin(close_0,10), 6.524s

[2022-01-21 15:49:57.115398] INFO: derived_feature_extractor: 提取完成 priceHighBl10=close_0/max10, 0.011s

[2022-01-21 15:49:57.128022] INFO: derived_feature_extractor: 提取完成 priceLowBl10=close_0/min10, 0.011s

[2022-01-21 15:49:58.695313] INFO: derived_feature_extractor: 提取完成 max30=ts_max(close_0,30), 1.566s

[2022-01-21 15:50:05.040130] INFO: derived_feature_extractor: 提取完成 max30d=ts_argmax(close_0,30), 6.343s

[2022-01-21 15:50:06.551071] INFO: derived_feature_extractor: 提取完成 min30=ts_min(close_0,30), 1.509s

[2022-01-21 15:50:12.961422] INFO: derived_feature_extractor: 提取完成 min30d=ts_argmin(close_0,30), 6.409s

[2022-01-21 15:50:12.979006] INFO: derived_feature_extractor: 提取完成 priceHighBl30=close_0/max30, 0.016s

[2022-01-21 15:50:12.991113] INFO: derived_feature_extractor: 提取完成 priceLowBl30=close_0/min30, 0.011s

[2022-01-21 15:50:13.002611] INFO: derived_feature_extractor: 提取完成 dbzs_priceHighBl10=priceHighBl10-zs_priceHighBl10, 0.010s

[2022-01-21 15:50:13.016029] INFO: derived_feature_extractor: 提取完成 dbzs_priceLowBl10=priceLowBl10-zs_priceLowBl10, 0.011s

[2022-01-21 15:50:13.030955] INFO: derived_feature_extractor: 提取完成 dbzs_priceHighBl30=priceHighBl30-zs_priceHighBl30, 0.013s

[2022-01-21 15:50:13.045639] INFO: derived_feature_extractor: 提取完成 dbzs_priceLowBl30=priceLowBl30-zs_priceLowBl30, 0.013s

[2022-01-21 15:50:14.590581] INFO: derived_feature_extractor: 提取完成 zhangfMax5=ts_max(zhangf,5), 1.543s

[2022-01-21 15:50:14.848741] INFO: derived_feature_extractor: 提取完成 low10bl=min10/shift(min10,10), 0.257s

[2022-01-21 15:50:15.388060] INFO: derived_feature_extractor: 提取完成 high20chu10bl=shift(max10,20)/shift(min10,10), 0.538s

[2022-01-21 15:50:17.232611] INFO: derived_feature_extractor: 提取完成 closeOldLow25bl=close_0/shift(ts_min(close_0,15),10), 1.842s

[2022-01-21 15:50:20.839346] INFO: derived_feature_extractor: 提取完成 closeOldLow25_15bl=shift(ts_min(close_0,15),25)/shift(ts_min(close_0,15),10), 3.605s

[2022-01-21 15:50:20.846445] INFO: derived_feature_extractor: 提取完成 ddzb=mf_net_pct_l_0+mf_net_pct_main_0+mf_net_pct_xl_0, 0.005s

[2022-01-21 15:50:22.312061] INFO: derived_feature_extractor: 提取完成 dbzb30=sum(ddzb,30), 1.464s

[2022-01-21 15:50:23.871538] INFO: derived_feature_extractor: 提取完成 zlcy=ts_max(max(mf_net_pct_l_0,mf_net_pct_main_0,mf_net_pct_xl_0),5), 1.558s

[2022-01-21 15:50:27.634271] INFO: derived_feature_extractor: 提取完成 hpbl30_old=ts_max(shift(close_0,10),30)/ts_min(shift(close_0,10),30), 3.761s

[2022-01-21 15:50:30.773921] INFO: derived_feature_extractor: 提取完成 hpbl10=ts_max(close_0,10)/ts_min(close_0,10), 3.138s

[2022-01-21 15:50:32.366384] INFO: derived_feature_extractor: 提取完成 isHasZhangt=ts_max(where((return_0>1.09)&(close_0==high_0),1,0),10), 1.591s

[2022-01-21 15:50:33.850955] INFO: derived_feature_extractor: 提取完成 isHasDiet=ts_max(where((return_0<0.91)&(close_0==low_0),1,0),10), 1.483s

[2022-01-21 15:50:33.861586] INFO: derived_feature_extractor: 提取完成 mc1=where((zhangf_max>0.05)&(amount_zf<1),1,0), 0.009s

[2022-01-21 15:50:35.376619] INFO: derived_feature_extractor: 提取完成 mc2=where(close_0
[2022-01-21 15:50:35.386564] INFO: derived_feature_extractor: 提取完成 mc3=where((return_0<1)&(close_0
[2022-01-21 15:50:35.394552] INFO: derived_feature_extractor: 提取完成 mc4=where((zhenf>0.08)&(return_0<1.025),1,0), 0.007s
[2022-01-21 15:50:35.401791] INFO: derived_feature_extractor: 提取完成 isXiaDie0=where(return_0<1,1,0), 0.006s
[2022-01-21 15:50:36.929846] INFO: derived_feature_extractor: 提取完成 lxxd_1d=where(sum(isXiaDie0,1)==1,1,0), 1.527s
[2022-01-21 15:50:38.452045] INFO: derived_feature_extractor: 提取完成 lxxd_2d=where(sum(isXiaDie0,2)==2,2,0), 1.521s
[2022-01-21 15:50:39.944073] INFO: derived_feature_extractor: 提取完成 lxxd_3d=where(sum(isXiaDie0,3)==3,3,0), 1.490s
[2022-01-21 15:50:41.457573] INFO: derived_feature_extractor: 提取完成 lxxd_4d=where(sum(isXiaDie0,4)==4,4,0), 1.512s
[2022-01-21 15:50:42.916248] INFO: derived_feature_extractor: 提取完成 lxxd_5d=where(sum(isXiaDie0,5)==5,5,0), 1.457s
[2022-01-21 15:50:44.443660] INFO: derived_feature_extractor: 提取完成 lxxd_6d=where(sum(isXiaDie0,6)==6,6,0), 1.526s
[2022-01-21 15:50:45.936885] INFO: derived_feature_extractor: 提取完成 lxxd_8d=where(sum(isXiaDie0,8)==8,8,0), 1.492s
[2022-01-21 15:50:47.372920] INFO: derived_feature_extractor: 提取完成 lxxd_10d=where(sum(isXiaDie0,10)==10,10,0), 1.434s
[2022-01-21 15:50:47.437531] INFO: derived_feature_extractor: 提取完成 lxxd_days=max(lxxd_1d,lxxd_2d,lxxd_3d,lxxd_4d,lxxd_5d,lxxd_6d,lxxd_8d,lxxd_10d), 0.063s
[2022-01-21 15:50:47.445167] INFO: derived_feature_extractor: 提取完成 isShangZ0=where(return_0>1,1,0), 0.006s
[2022-01-21 15:50:48.990689] INFO: derived_feature_extractor: 提取完成 lxsz_1d=where(sum(isShangZ0,1)==1,1,0), 1.544s
[2022-01-21 15:50:50.504336] INFO: derived_feature_extractor: 提取完成 lxsz_2d=where(sum(isShangZ0,2)==2,2,0), 1.512s
[2022-01-21 15:50:51.986011] INFO: derived_feature_extractor: 提取完成 lxsz_3d=where(sum(isShangZ0,3)==3,3,0), 1.480s
[2022-01-21 15:50:53.438467] INFO: derived_feature_extractor: 提取完成 lxsz_4d=where(sum(isShangZ0,4)==4,4,0), 1.451s
[2022-01-21 15:50:54.931603] INFO: derived_feature_extractor: 提取完成 lxsz_5d=where(sum(isShangZ0,5)==5,5,0), 1.491s
[2022-01-21 15:50:56.416688] INFO: derived_feature_extractor: 提取完成 lxsz_6d=where(sum(isShangZ0,6)==6,6,0), 1.484s
[2022-01-21 15:50:57.906934] INFO: derived_feature_extractor: 提取完成 lxsz_8d=where(sum(isShangZ0,8)==8,8,0), 1.489s
[2022-01-21 15:50:59.392217] INFO: derived_feature_extractor: 提取完成 lxsz_10d=where(sum(isShangZ0,10)==10,10,0), 1.484s
[2022-01-21 15:50:59.461260] INFO: derived_feature_extractor: 提取完成 lxsz_days=max(lxsz_1d,lxsz_2d,lxsz_3d,lxsz_4d,lxsz_5d,lxsz_6d,lxsz_8d,lxsz_10d), 0.067s
[2022-01-21 15:51:01.116048] INFO: derived_feature_extractor: 提取完成 jx5d=mean(close_0,5), 1.653s
[2022-01-21 15:51:01.905223] INFO: derived_feature_extractor: 提取完成 isLxsz=where((jx5d/shift(jx5d,5)>1)&(shift(jx5d,5)/shift(jx5d,10)>1),1,0), 0.787s
[2022-01-21 15:51:01.920446] INFO: derived_feature_extractor: 提取完成 zszf_xgx_1=where((zs_return_0>1)&(return_0<1),1,0), 0.014s
[2022-01-21 15:51:03.543523] INFO: derived_feature_extractor: 提取完成 maxZhangf5=ts_max(zhangf,5), 1.622s
[2022-01-21 15:51:10.202555] INFO: derived_feature_extractor: 提取完成 maxZhangf5d=ts_argmax(zhangf,5), 6.657s
[2022-01-21 15:51:10.953070] INFO: derived_feature_extractor: 提取完成 maxZhangfOpen=where(maxZhangf5d>3,open_0,where(maxZhangf5d>2,shift(open_0,1),where(maxZhangf5d>1,shift(open_0,2),where(maxZhangf5d>0,shift(open_0,3),0)))), 0.749s
[2022-01-21 15:51:10.958405] INFO: derived_feature_extractor: 提取完成 zhangf_low_bl=close_0/maxZhangfOpen, 0.004s
[2022-01-21 15:51:11.004874] INFO: derived_feature_extractor: 提取完成 minZhangfLow=maxZhangfOpen/min(open_1,close_1,open_0)-1, 0.045s
[2022-01-21 15:51:11.027500] INFO: derived_feature_extractor: 提取完成 my1=where((abs(return0-1.02)<0.015)&(priceHighBl10==1)&(priceLowBl10<1.08)&(abs(priceHighBl30-0.9)<0.05)&(abs(priceLowBl30-1.12)<0.05)&(amount_bl>3)&(amount_zf>1)&(dbzs_return>0)&(return0>return1)&((return3+zgzzf)<2.1),1,0), 0.021s
[2022-01-21 15:51:11.046589] INFO: derived_feature_extractor: 提取完成 my3=where((return0>1)&(return1>1)&(zhangf>0)&(return3<0.97)&(priceHighBl10<0.88)&(priceLowBl10<1.07)&(priceHighBl30==priceHighBl10)&(priceLowBl30==priceLowBl10)&(dbzs_return>0)&(ddzb>0.05),1,0), 0.018s
[2022-01-21 15:51:11.110784] INFO: derived_feature_extractor: 提取完成 my6=where((abs(return0-1.02)<0.01)&(return0>return1)&(lxsz_days==2)&((return0+return1)<2.04)&(return3<1)&(priceHighBl10<0.93)&(priceLowBl10<1.12)&(priceHighBl30==priceHighBl10)&(priceLowBl30==priceLowBl10)&(dbzs_return>0),1,0), 0.063s
[2022-01-21 15:51:11.137487] INFO: derived_feature_extractor: 提取完成 my8=where((return0>return1)&(zhangf>0)&(zhenf<0.025)&(abs(return1-0.99)<0.005)&(priceLowBl10<1.05)&(abs(max10d-7)<3)&(abs(priceHighBl30-0.95)<0.05)&(priceLowBl30<1.15)&(zlcy>0.05)&(amount_bl>1.3)&(max30d<10),1,0), 0.025s
[2022-01-21 15:51:12.793863] INFO: derived_feature_extractor: 提取完成 my11=where((lxsz_days>2)&(return3<1.06)&(dbzs_return>0)&(priceHighBl10==1)&(priceLowBl10<1.06)&(abs(priceHighBl30-0.9)<0.05)&(abs(priceLowBl30-priceLowBl10)<0.05)&(abs(dbzs_priceHighBl10-0.01)<0.01)&(ts_min(amount_zf,2)>1)&(ddzb>0.05),1,0), 1.655s
[2022-01-21 15:51:12.802153] INFO: derived_feature_extractor: 提取完成 yzd1=where(my3==1,10,0), 0.007s
[2022-01-21 15:51:12.810310] INFO: derived_feature_extractor: 提取完成 yzd2=where(my6==1,8,0), 0.007s
[2022-01-21 15:51:12.818297] INFO: derived_feature_extractor: 提取完成 yzd3=where(my8==1,6,0), 0.007s
[2022-01-21 15:51:12.826341] INFO: derived_feature_extractor: 提取完成 yzd4=where(my1==1,4,0), 0.007s
[2022-01-21 15:51:12.834618] INFO: derived_feature_extractor: 提取完成 yzd5=where(my11==1,2,0), 0.007s
[2022-01-21 15:51:12.844171] INFO: derived_feature_extractor: 提取完成 yzd=yzd1+yzd2+yzd3+yzd4+yzd5, 0.008s
[2022-01-21 15:51:12.888925] INFO: derived_feature_extractor: 提取完成 my=max(my1,my3,my6,my8,my11), 0.043s
[2022-01-21 15:51:14.270350] INFO: derived_feature_extractor: 提取完成 buy_condition=where(my==1,1,0), 1.380s
[2022-01-21 15:51:14.276045] INFO: derived_feature_extractor: 提取完成 sell_condition=where(mc2==3,1,0), 0.004s
[2022-01-21 15:51:25.918980] INFO: derived_feature_extractor: /data, 2163489
[2022-01-21 15:51:34.815889] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[125.349184s].
[2022-01-21 15:51:34.829095] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2022-01-21 15:51:57.837297] INFO: A股股票过滤: 过滤 /data, 1491136/0/2163489
[2022-01-21 15:51:57.840553] INFO: A股股票过滤: 过滤完成, 1491136 + 0
[2022-01-21 15:51:57.878464] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[23.049349s].
[2022-01-21 15:51:57.891053] INFO: moduleinvoker: dropnan.v2 开始运行..
[2022-01-21 15:52:05.605004] INFO: dropnan: /data, 1336608/1491136
[2022-01-21 15:52:05.701632] INFO: dropnan: 行数: 1336608/1491136
[2022-01-21 15:52:05.716861] INFO: moduleinvoker: dropnan.v2 运行完成[7.825774s].
[2022-01-21 15:52:05.736277] INFO: moduleinvoker: sort.v4 开始运行..
[2022-01-21 15:52:18.704912] INFO: moduleinvoker: sort.v4 运行完成[12.968621s].
[2022-01-21 15:52:18.720258] INFO: moduleinvoker: filter.v3 开始运行..
[2022-01-21 15:52:18.736040] INFO: filter: 使用表达式 my==1 过滤
[2022-01-21 15:52:22.948044] INFO: filter: 过滤 /data, 848/0/1336608
[2022-01-21 15:52:22.973455] INFO: moduleinvoker: filter.v3 运行完成[4.253175s].
[2022-01-21 15:52:25.258651] INFO: moduleinvoker: backtest.v8 开始运行..
[2022-01-21 15:52:25.264400] INFO: backtest: biglearning backtest:V8.6.1
[2022-01-21 15:52:25.856745] INFO: backtest: product_type:stock by specified
[2022-01-21 15:52:25.955296] INFO: moduleinvoker: cached.v2 开始运行..
[2022-01-21 15:52:25.964594] INFO: moduleinvoker: 命中缓存
[2022-01-21 15:52:25.965923] INFO: moduleinvoker: cached.v2 运行完成[0.010645s].
[2022-01-21 15:52:28.860735] INFO: algo: TradingAlgorithm V1.8.6
[2022-01-21 15:52:29.762053] INFO: algo: trading transform...
[2022-01-21 15:52:50.630608] INFO: Performance: Simulated 486 trading days out of 486.
[2022-01-21 15:52:50.632358] INFO: Performance: first open: 2020-01-02 09:30:00+00:00
[2022-01-21 15:52:50.633468] INFO: Performance: last close: 2021-12-31 15:00:00+00:00
[2022-01-21 15:52:58.872819] INFO: moduleinvoker: backtest.v8 运行完成[33.614165s].
[2022-01-21 15:52:58.874430] INFO: moduleinvoker: trade.v4 运行完成[35.884717s].

日期： 2020-01-02 选出股票数量： 1
日期: 2020-01-02 买入: 600844.SHA
日期： 2020-01-03 选出股票数量： 4
日期: 2020-01-06 卖出2: 600844.SHA
日期： 2020-01-06 选出股票数量： 1
日期: 2020-01-06 买入: 000552.SZA
日期： 2020-01-07 选出股票数量： 2
日期: 2020-01-08 卖出2: 000552.SZA
日期： 2020-01-13 选出股票数量： 1
日期: 2020-01-13 买入: 000831.SZA
日期： 2020-01-14 选出股票数量： 1
日期: 2020-01-15 卖出2: 000831.SZA
日期： 2020-02-04 选出股票数量： 1
日期: 2020-02-04 买入: 000776.SZA
日期： 2020-02-05 选出股票数量： 19
日期: 2020-02-06 卖出2: 000776.SZA
日期： 2020-02-06 选出股票数量： 44
日期: 2020-02-06 买入: 603878.SHA
日期： 2020-02-19 选出股票数量： 7
日期: 2020-02-19 买入: 002807.SZA
日期： 2020-02-20 选出股票数量： 9
日期: 2020-02-21 卖出2: 002807.SZA
日期： 2020-02-24 选出股票数量： 4
日期: 2020-02-24 买入: 600059.SHA
日期： 2020-02-25 选出股票数量： 8
日期: 2020-02-26 卖出2: 600059.SHA
日期： 2020-02-26 选出股票数量： 13
日期: 2020-02-26 买入: 002039.SZA
日期： 2020-02-27 选出股票数量： 3
日期: 2020-02-27 买入: 000528.SZA
日期： 2020-02-28 选出股票数量： 4
日期: 2020-03-02 卖出2: 000528.SZA
日期： 2020-03-02 选出股票数量： 1
日期: 2020-03-02 买入: 000698.SZA
日期： 2020-03-03 选出股票数量： 1
日期: 2020-03-04 卖出2: 000698.SZA
日期： 2020-03-04 选出股票数量： 4
日期: 2020-03-04 买入: 601179.SHA
日期： 2020-03-05 选出股票数量： 1
日期: 2020-03-06 卖出2: 601179.SHA
日期： 2020-03-09 选出股票数量： 2
日期: 2020-03-09 买入: 601111.SHA
日期: 2020-03-11 卖出2: 601111.SHA
日期： 2020-03-11 选出股票数量： 1
日期: 2020-03-11 买入: 601866.SHA
日期: 2020-03-13 卖出2: 601866.SHA
日期： 2020-03-17 选出股票数量： 2
日期: 2020-03-17 买入: 601339.SHA
日期： 2020-03-18 选出股票数量： 2
日期: 2020-03-19 卖出2: 601339.SHA
日期： 2020-03-19 选出股票数量： 3
日期: 2020-03-19 买入: 000958.SZA
日期： 2020-03-20 选出股票数量： 7
日期: 2020-03-20 买入: 603908.SHA
日期： 2020-03-23 选出股票数量： 1
日期: 2020-03-24 卖出2: 603908.SHA
日期： 2020-03-25 选出股票数量： 3
日期: 2020-03-25 买入: 600191.SHA
日期： 2020-03-26 选出股票数量： 4
日期: 2020-03-27 卖出2: 600191.SHA
日期： 2020-03-27 选出股票数量： 2
日期: 2020-03-27 买入: 000411.SZA
日期： 2020-04-01 选出股票数量： 5
日期: 2020-04-01 买入: 002490.SZA
日期： 2020-04-02 选出股票数量： 5
日期: 2020-04-03 卖出2: 002490.SZA
日期： 2020-04-03 选出股票数量： 2
日期: 2020-04-03 买入: 601607.SHA
日期： 2020-04-07 选出股票数量： 3
日期: 2020-04-07 买入: 000301.SZA
日期： 2020-04-08 选出股票数量： 3
日期: 2020-04-09 卖出2: 000301.SZA
日期： 2020-04-15 选出股票数量： 7
日期: 2020-04-15 买入: 600403.SHA
日期： 2020-04-16 选出股票数量： 5
日期: 2020-04-17 卖出2: 600403.SHA
日期： 2020-04-21 选出股票数量： 2
日期: 2020-04-21 买入: 002672.SZA
日期： 2020-04-22 选出股票数量： 5
日期: 2020-04-23 卖出2: 002672.SZA
日期： 2020-04-23 选出股票数量： 2
日期: 2020-04-23 买入: 600028.SHA
日期： 2020-04-24 选出股票数量： 5
日期: 2020-04-27 卖出2: 600028.SHA
日期： 2020-04-28 选出股票数量： 1
日期: 2020-04-28 买入: 002739.SZA
日期： 2020-04-29 选出股票数量： 3
日期: 2020-04-30 卖出2: 002739.SZA
日期： 2020-04-30 选出股票数量： 7
日期: 2020-04-30 买入: 603928.SHA
日期： 2020-05-06 选出股票数量： 9
日期: 2020-05-07 卖出2: 603928.SHA
日期： 2020-05-07 选出股票数量： 2
日期: 2020-05-07 买入: 002730.SZA
日期: 2020-05-11 卖出2: 002730.SZA
日期： 2020-05-13 选出股票数量： 1
日期: 2020-05-13 买入: 002880.SZA
日期: 2020-05-15 卖出2: 002880.SZA
日期： 2020-05-15 选出股票数量： 2
日期: 2020-05-15 买入: 002155.SZA
日期: 2020-05-19 卖出2: 002155.SZA
日期： 2020-05-19 选出股票数量： 1
日期: 2020-05-19 买入: 601966.SHA
日期： 2020-05-20 选出股票数量： 1
日期: 2020-05-21 卖出2: 601966.SHA
日期： 2020-05-21 选出股票数量： 3
日期: 2020-05-21 买入: 002774.SZA
日期： 2020-05-22 选出股票数量： 1
日期: 2020-05-25 卖出2: 002774.SZA
日期： 2020-05-26 选出股票数量： 2
日期: 2020-05-26 买入: 603863.SHA
日期： 2020-05-27 选出股票数量： 1
日期: 2020-05-28 卖出2: 603863.SHA
日期： 2020-05-28 选出股票数量： 5
日期: 2020-05-28 买入: 002521.SZA
日期： 2020-05-29 选出股票数量： 1
日期: 2020-05-29 买入: 002527.SZA
日期： 2020-06-01 选出股票数量： 2
日期: 2020-06-02 卖出2: 002527.SZA
日期： 2020-06-04 选出股票数量： 4
日期: 2020-06-04 买入: 000637.SZA
日期: 2020-06-08 卖出2: 000637.SZA
日期： 2020-06-09 选出股票数量： 1
日期: 2020-06-09 买入: 601188.SHA
日期: 2020-06-11 卖出2: 601188.SHA
日期： 2020-06-11 选出股票数量： 4
日期: 2020-06-11 买入: 002287.SZA
日期: 2020-06-15 卖出2: 601188.SHA
日期： 2020-06-16 选出股票数量： 4
日期: 2020-06-16 买入: 600606.SHA
日期： 2020-06-17 选出股票数量： 1
日期: 2020-06-18 卖出2: 600606.SHA
日期： 2020-06-18 选出股票数量： 3
日期: 2020-06-18 买入: 601228.SHA
日期: 2020-06-22 卖出2: 601228.SHA
日期： 2020-06-22 选出股票数量： 2
日期: 2020-06-22 买入: 002156.SZA
日期: 2020-06-24 卖出2: 002156.SZA
日期： 2020-06-30 选出股票数量： 2
日期: 2020-06-30 买入: 000850.SZA
日期： 2020-07-01 选出股票数量： 1
日期: 2020-07-02 卖出2: 000850.SZA
日期： 2020-07-03 选出股票数量： 1
日期: 2020-07-03 买入: 000678.SZA
日期: 2020-07-07 卖出2: 000678.SZA
日期： 2020-07-21 选出股票数量： 1
日期: 2020-07-21 买入: 000799.SZA
日期: 2020-07-23 卖出2: 000799.SZA
日期： 2020-07-28 选出股票数量： 6
日期: 2020-07-28 买入: 000921.SZA
日期: 2020-07-30 卖出2: 000921.SZA
日期： 2020-07-30 选出股票数量： 4
日期: 2020-07-30 买入: 603115.SHA
日期: 2020-08-03 卖出2: 603115.SHA
日期： 2020-08-11 选出股票数量： 1
日期: 2020-08-11 买入: 600009.SHA
日期： 2020-08-12 选出股票数量： 1
日期: 2020-08-13 卖出2: 600009.SHA
日期： 2020-08-13 选出股票数量： 1
日期: 2020-08-13 买入: 600067.SHA
日期： 2020-08-17 选出股票数量： 2
日期: 2020-08-17 买入: 600106.SHA
日期: 2020-08-19 卖出2: 600106.SHA
日期： 2020-08-19 选出股票数量： 6
日期: 2020-08-19 买入: 600871.SHA
日期： 2020-08-24 选出股票数量： 5
日期: 2020-08-24 买入: 603706.SHA
日期: 2020-08-26 卖出2: 603706.SHA
日期： 2020-08-28 选出股票数量： 1
日期: 2020-08-28 买入: 603078.SHA
日期： 2020-08-31 选出股票数量： 1
日期: 2020-09-01 卖出2: 603078.SHA
日期： 2020-09-01 选出股票数量： 1
日期: 2020-09-01 买入: 000685.SZA
日期： 2020-09-02 选出股票数量： 2
日期: 2020-09-02 买入: 002756.SZA
日期： 2020-09-03 选出股票数量： 1
日期: 2020-09-04 卖出2: 002756.SZA
日期： 2020-09-11 选出股票数量： 3
日期: 2020-09-11 买入: 603538.SHA
日期： 2020-09-14 选出股票数量： 5
日期: 2020-09-15 卖出2: 603538.SHA
日期： 2020-09-15 选出股票数量： 2
日期: 2020-09-15 买入: 600191.SHA
日期: 2020-09-17 卖出2: 600191.SHA
日期： 2020-09-18 选出股票数量： 1
日期: 2020-09-18 买入: 603580.SHA
日期： 2020-09-21 选出股票数量： 2
日期: 2020-09-22 卖出2: 603580.SHA
日期： 2020-09-22 选出股票数量： 1
日期: 2020-09-22 买入: 000950.SZA
日期： 2020-09-23 选出股票数量： 1
日期: 2020-09-24 卖出2: 000950.SZA
日期： 2020-09-30 选出股票数量： 1
日期: 2020-09-30 买入: 000040.SZA
日期： 2020-10-09 选出股票数量： 3
日期: 2020-10-12 卖出2: 000040.SZA
日期： 2020-10-15 选出股票数量： 1
日期: 2020-10-15 买入: 000898.SZA
日期： 2020-10-16 选出股票数量： 1
日期: 2020-10-19 卖出2: 000898.SZA
日期： 2020-10-19 选出股票数量： 1
日期: 2020-10-19 买入: 000695.SZA
日期： 2020-10-21 选出股票数量： 1
日期: 2020-10-21 买入: 600000.SHA
日期： 2020-10-22 选出股票数量： 2
日期: 2020-10-23 卖出2: 600000.SHA
日期： 2020-10-26 选出股票数量： 2
日期: 2020-10-26 买入: 000661.SZA
日期: 2020-10-28 卖出2: 000661.SZA
日期： 2020-10-28 选出股票数量： 1
日期: 2020-10-28 买入: 600500.SHA
日期： 2020-10-29 选出股票数量： 1
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日期： 2021-12-31 选出股票数量： 1
日期: 2021-12-31 买入: 601500.SHA

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