{"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":"-360:input_data","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":"-795:input_data","from_node_id":"-93:data"},{"to_node_id":"-801:input_data","from_node_id":"-789:data"},{"to_node_id":"-789:input_data","from_node_id":"-795:data"}],"nodes":[{"node_id":"-51","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2021-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-01-06","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":"\n#昨日MACD.dea-dif在 0到-0.1之间，调整幅度不大\nta_macd_dea(close_1/adjust_factor_1)\nta_macd_dif(close_1/adjust_factor_1)\n#死叉前后两天（昨天，前天）收盘价必须一跌一涨\nclose_2\nclose_1\n\n#昨天ma5死叉ma10\ncon1=ta_ma(close_1, 5)-ta_ma(close_1, 10)<0\n#前天ma5在ma10之上\ncon2=ta_ma(close_2, 5)-ta_ma(close_2, 10)>0\n#ma20上升\ncon3=ta_ma(close_1, 20)-ta_ma(close_2, 20)>0\n#ma30上升\ncon4=ta_ma(close_1, 30)-ta_ma(close_2, 30)>0\n#前天成交量ma5大于ma10\ncon5=ta_ma(volume_2, 5)-ta_ma(volume_2, 10)>0\n#昨天成交量ma5*1.2大于ma10\ncon6=1.2*ta_ma(volume_1, 5)-ta_ma(volume_1, 10)>0\ncon7=ta_macd_dif(close_1)>0#昨天macd.dif大于0\ncon8=ta_macd_dea(close_1)>0#昨天macd.dea大于0\n#昨日MACD.dea-dif在 0到-0.1之间，调整幅度不大\ncon9=ta_macd_dea(close_1)-ta_macd_dif(close_1)<0\ncon10=ta_macd_dea(close_1)-ta_macd_dif(close_1)>-0.1\n#死叉前后两天（昨天，前天）收盘价必须一跌一涨\ncon11=(close_2>close_1)| (close_1>close_2)\ncon12=((open_0)<(close_1))\n#\nmy1=where((con1)&(con2)&(con3)&(con4)&(con5)&(con6),1,0)\n#\nmy2=where((con7)&(con8)&(con9)&(con10),1,0)\n#\nmy3=where((con11),1,0)\n#\nmy4=where((con12),1,0)\n#按策略优质度排序\nyzd1=where(my1==1,8,0)\nyzd2=where(my2==1,6,0)\nyzd3=where(my3==1,4,0)\nyzd4=where(my4==1,2,0)\nyzd=yzd1+yzd2+yzd3+yzd4\nmy=min(my1,my2,my3,my4)\nbuy_condition=where(my==1,1,0)\nsell_condition=where(my<1,1,0)\n# yzd=yzd1+yzd2+yzd3\n# my=my1+my2+my3\n# buy_condition=where(my==3,1,0)\n# sell_condition=where(my<3,1,0)\n","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":"False","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'] = 0\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 print(buy_list)\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","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":"open","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":"","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%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22displayValue%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%9B%BD%E9%98%B2%E5%86%9B%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22displayValue%22%3A%22%E5%AE%B6%E7%94%A8%E7%94%B5%E5%99%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E6%9D%90%E6%96%99%2F%E5%BB%BA%E7%AD%91%E5%BB%BA%E6%9D%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22displayValue%22%3A%22%E5%BB%BA%E7%AD%91%E8%A3%85%E9%A5%B0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22displayValue%22%3A%22%E6%88%BF%E5%9C%B0%E4%BA%A7%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22displayValue%22%3A%22%E6%9C%89%E8%89%B2%E9%87%91%E5%B1%9E%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%9C%BA%E6%A2%B0%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E6%B1%BD%E8%BD%A6%2F%E4%BA%A4%E8%BF%90%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22displayValue%22%3A%22%E7%94%B5%E5%AD%90%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22displayValue%22%3A%22%E7%94%B5%E6%B0%94%E8%AE%BE%E5%A4%87%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22displayValue%22%3A%22%E7%BA%BA%E7%BB%87%E6%9C%8D%E8%A3%85%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22displayValue%22%3A%22%E7%BB%BC%E5%90%88%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22displayValue%22%3A%22%E8%AE%A1%E7%AE%97%E6%9C%BA%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22displayValue%22%3A%22%E8%BD%BB%E5%B7%A5%E5%88%B6%E9%80%A0%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22displayValue%22%3A%22%E9%80%9A%E4%BF%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%87%87%E6%8E%98%22%2C%22displayValue%22%3A%22%E9%87%87%E6%8E%98%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%92%A2%E9%93%81%22%2C%22displayValue%22%3A%22%E9%92%A2%E9%93%81%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22displayValue%22%3A%22%E9%93%B6%E8%A1%8C%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%93%B6%E9%87%91%E8%9E%8D%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22displayValue%22%3A%22%E9%A3%9F%E5%93%81%E9%A5%AE%E6%96%99%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"st_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%E6%AD%A3%E5%B8%B8%22%2C%22displayValue%22%3A%22%E6%AD%A3%E5%B8%B8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22ST%22%2C%22displayValue%22%3A%22ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22*ST%22%2C%22displayValue%22%3A%22*ST%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22displayValue%22%3A%22%E6%9A%82%E5%81%9C%E4%B8%8A%E5%B8%82%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"delist_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%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%80%80%E5%B8%82%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22displayValue%22%3A%22%E9%9D%9E%E9%80%80%E5%B8%82%22%2C%22selected%22%3Atrue%7D%5D%7D","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":"-865"}],"output_ports":[{"name":"data","node_id":"-865"},{"name":"left_data","node_id":"-865"}],"cacheable":true,"seq_num":6,"comment":"","comment_collapsed":true},{"node_id":"-93","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-93"},{"name":"features","node_id":"-93"}],"output_ports":[{"name":"data","node_id":"-93"}],"cacheable":true,"seq_num":11,"comment":"","comment_collapsed":true},{"node_id":"-789","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"(con7==1) & (con8==1) & (con9==1) & (con10==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":"-789"}],"output_ports":[{"name":"data","node_id":"-789"},{"name":"left_data","node_id":"-789"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-795","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"(con1==1) & (con2==1) & (con3==1) & (con4==1) & (con5==1) & (con6==1 ) &(date=='2021-01-05')","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":"-795"}],"output_ports":[{"name":"data","node_id":"-795"},{"name":"left_data","node_id":"-795"}],"cacheable":true,"seq_num":10,"comment":"","comment_collapsed":true},{"node_id":"-801","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"con11==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":"-801"}],"output_ports":[{"name":"data","node_id":"-801"},{"name":"left_data","node_id":"-801"}],"cacheable":true,"seq_num":12,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-51' Position='132,7,200,200'/><node_position Node='-59' Position='478,81,200,200'/><node_position Node='-353' Position='257,236,200,200'/><node_position Node='-360' Position='239,338,200,200'/><node_position Node='-370' Position='478,838,200,200'/><node_position Node='-390' Position='288,618,200,200'/><node_position Node='-531' Position='301,706,200,200'/><node_position Node='-865' Position='261,433,200,200'/><node_position Node='-93' Position='269,519,200,200'/><node_position Node='-789' Position='757.935546875,507.0030517578125,200,200'/><node_position Node='-795' Position='644.5262451171875,405.88720703125,200,200'/><node_position Node='-801' Position='764.9951782226562,612.9430541992188,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2022年3月19日 20:06
# 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'] = 0


# 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]
        print(buy_list)
        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='2021-01-01',
    end_date=T.live_run_param('trading_date', '2021-01-06'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""
#昨日MACD.dea-dif在 0到-0.1之间，调整幅度不大
ta_macd_dea(close_1/adjust_factor_1)
ta_macd_dif(close_1/adjust_factor_1)
#死叉前后两天（昨天，前天）收盘价必须一跌一涨
close_2
close_1

#昨天ma5死叉ma10
con1=ta_ma(close_1, 5)-ta_ma(close_1, 10)<0
#前天ma5在ma10之上
con2=ta_ma(close_2, 5)-ta_ma(close_2, 10)>0
#ma20上升
con3=ta_ma(close_1, 20)-ta_ma(close_2, 20)>0
#ma30上升
con4=ta_ma(close_1, 30)-ta_ma(close_2, 30)>0
#前天成交量ma5大于ma10
con5=ta_ma(volume_2, 5)-ta_ma(volume_2, 10)>0
#昨天成交量ma5*1.2大于ma10
con6=1.2*ta_ma(volume_1, 5)-ta_ma(volume_1, 10)>0
con7=ta_macd_dif(close_1)>0#昨天macd.dif大于0
con8=ta_macd_dea(close_1)>0#昨天macd.dea大于0
#昨日MACD.dea-dif在 0到-0.1之间，调整幅度不大
con9=ta_macd_dea(close_1)-ta_macd_dif(close_1)<0
con10=ta_macd_dea(close_1)-ta_macd_dif(close_1)>-0.1
#死叉前后两天（昨天，前天）收盘价必须一跌一涨
con11=(close_2>close_1)| (close_1>close_2)
con12=((open_0)<(close_1))
#
my1=where((con1)&(con2)&(con3)&(con4)&(con5)&(con6),1,0)
#
my2=where((con7)&(con8)&(con9)&(con10),1,0)
#
my3=where((con11),1,0)
#
my4=where((con12),1,0)
#按策略优质度排序
yzd1=where(my1==1,8,0)
yzd2=where(my2==1,6,0)
yzd3=where(my3==1,4,0)
yzd4=where(my4==1,2,0)
yzd=yzd1+yzd2+yzd3+yzd4
my=min(my1,my2,my3,my4)
buy_condition=where(my==1,1,0)
sell_condition=where(my<1,1,0)
# yzd=yzd1+yzd2+yzd3
# my=my1+my2+my3
# buy_condition=where(my==3,1,0)
# sell_condition=where(my<3,1,0)
"""
)

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

m7 = M.derived_feature_extractor.v3(
    input_data=m5.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    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,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=100000,
    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=''
)

m10 = M.filter.v3(
    input_data=m11.data,
    expr='(con1==1) & (con2==1) & (con3==1) & (con4==1) & (con5==1) & (con6==1 ) &(date==\'2021-01-05\')',
    output_left_data=False
)

m4 = M.filter.v3(
    input_data=m10.data,
    expr='(con7==1) & (con8==1) & (con9==1) & (con10==1)',
    output_left_data=False
)

m12 = M.filter.v3(
    input_data=m4.data,
    expr='con11==1',
    output_left_data=False
)

[2022-03-19 19:59:37.769425] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-03-19 19:59:37.776823] INFO: moduleinvoker: 命中缓存

[2022-03-19 19:59:37.778195] INFO: moduleinvoker: instruments.v2 运行完成[0.008775s].

[2022-03-19 19:59:37.781698] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-03-19 19:59:37.790539] INFO: moduleinvoker: 命中缓存

[2022-03-19 19:59:37.791841] INFO: moduleinvoker: input_features.v1 运行完成[0.010135s].

[2022-03-19 19:59:37.804799] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-03-19 19:59:39.734612] INFO: 基础特征抽取: 年份 2020, 特征行数=179288

[2022-03-19 19:59:41.703059] INFO: 基础特征抽取: 年份 2021, 特征行数=12364

[2022-03-19 19:59:41.758646] INFO: 基础特征抽取: 总行数: 191652

[2022-03-19 19:59:41.760714] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[3.955923s].

[2022-03-19 19:59:41.768348] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-03-19 19:59:45.726082] INFO: derived_feature_extractor: 提取完成 ta_macd_dea(close_1/adjust_factor_1), 3.527s

[2022-03-19 19:59:49.828085] INFO: derived_feature_extractor: 提取完成 ta_macd_dif(close_1/adjust_factor_1), 4.100s

[2022-03-19 19:59:57.087216] INFO: derived_feature_extractor: 提取完成 con1=ta_ma(close_1, 5)-ta_ma(close_1, 10)<0, 7.258s

[2022-03-19 20:00:04.984641] INFO: derived_feature_extractor: 提取完成 con2=ta_ma(close_2, 5)-ta_ma(close_2, 10)>0, 7.896s

[2022-03-19 20:00:13.094198] INFO: derived_feature_extractor: 提取完成 con3=ta_ma(close_1, 20)-ta_ma(close_2, 20)>0, 8.108s

[2022-03-19 20:00:20.760883] INFO: derived_feature_extractor: 提取完成 con4=ta_ma(close_1, 30)-ta_ma(close_2, 30)>0, 7.665s

[2022-03-19 20:00:28.218443] INFO: derived_feature_extractor: 提取完成 con5=ta_ma(volume_2, 5)-ta_ma(volume_2, 10)>0, 7.456s

[2022-03-19 20:00:35.996791] INFO: derived_feature_extractor: 提取完成 con6=1.2*ta_ma(volume_1, 5)-ta_ma(volume_1, 10)>0, 7.777s

[2022-03-19 20:00:40.178145] INFO: derived_feature_extractor: 提取完成 con7=ta_macd_dif(close_1)>0#昨天macd.dif大于0, 4.179s

[2022-03-19 20:00:43.813720] INFO: derived_feature_extractor: 提取完成 con8=ta_macd_dea(close_1)>0#昨天macd.dea大于0, 3.634s

[2022-03-19 20:00:51.579050] INFO: derived_feature_extractor: 提取完成 con9=ta_macd_dea(close_1)-ta_macd_dif(close_1)<0, 7.764s

[2022-03-19 20:00:59.029948] INFO: derived_feature_extractor: 提取完成 con10=ta_macd_dea(close_1)-ta_macd_dif(close_1)>-0.1, 7.449s

[2022-03-19 20:00:59.032979] INFO: derived_feature_extractor: 提取完成 con11=(close_2>close_1)| (close_1>close_2), 0.001s

[2022-03-19 20:00:59.035179] INFO: derived_feature_extractor: 提取完成 con12=((open_0)
[2022-03-19 20:00:59.038212] INFO: derived_feature_extractor: 提取完成 my1=where((con1)&(con2)&(con3)&(con4)&(con5)&(con6),1,0), 0.002s
[2022-03-19 20:00:59.040941] INFO: derived_feature_extractor: 提取完成 my2=where((con7)&(con8)&(con9)&(con10),1,0), 0.002s
[2022-03-19 20:00:59.062778] INFO: derived_feature_extractor: 提取完成 my3=where((con11),1,0), 0.002s
[2022-03-19 20:00:59.065723] INFO: derived_feature_extractor: 提取完成 my4=where((con12),1,0), 0.002s
[2022-03-19 20:00:59.068090] INFO: derived_feature_extractor: 提取完成 yzd1=where(my1==1,8,0), 0.001s
[2022-03-19 20:00:59.070306] INFO: derived_feature_extractor: 提取完成 yzd2=where(my2==1,6,0), 0.001s
[2022-03-19 20:00:59.073326] INFO: derived_feature_extractor: 提取完成 yzd3=where(my3==1,4,0), 0.002s
[2022-03-19 20:00:59.076342] INFO: derived_feature_extractor: 提取完成 yzd4=where(my4==1,2,0), 0.002s
[2022-03-19 20:00:59.079351] INFO: derived_feature_extractor: 提取完成 yzd=yzd1+yzd2+yzd3+yzd4, 0.002s
[2022-03-19 20:00:59.083988] INFO: derived_feature_extractor: 提取完成 my=min(my1,my2,my3,my4), 0.003s
[2022-03-19 20:00:59.087037] INFO: derived_feature_extractor: 提取完成 buy_condition=where(my==1,1,0), 0.002s
[2022-03-19 20:00:59.089815] INFO: derived_feature_extractor: 提取完成 sell_condition=where(my<1,1,0), 0.001s
[2022-03-19 20:00:59.570291] INFO: derived_feature_extractor: /y_2020, 179288
[2022-03-19 20:00:59.733086] INFO: derived_feature_extractor: /y_2021, 12364
[2022-03-19 20:00:59.873859] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[78.105499s].
[2022-03-19 20:00:59.882431] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2022-03-19 20:01:00.905414] INFO: A股股票过滤: 过滤 /y_2020, 123161/0/179288
[2022-03-19 20:01:01.231017] INFO: A股股票过滤: 过滤 /y_2021, 8464/0/12364
[2022-03-19 20:01:01.328825] INFO: A股股票过滤: 过滤完成, 131625 + 0
[2022-03-19 20:01:01.360158] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[1.47772s].
[2022-03-19 20:01:01.368311] INFO: moduleinvoker: dropnan.v2 开始运行..
[2022-03-19 20:01:01.629308] INFO: dropnan: /y_2020, 30562/123161
[2022-03-19 20:01:01.711451] INFO: dropnan: /y_2021, 8380/8464
[2022-03-19 20:01:01.766997] INFO: dropnan: 行数: 38942/131625
[2022-03-19 20:01:01.772651] INFO: moduleinvoker: dropnan.v2 运行完成[0.404337s].
[2022-03-19 20:01:01.778700] INFO: moduleinvoker: sort.v4 开始运行..
[2022-03-19 20:01:02.147050] INFO: moduleinvoker: sort.v4 运行完成[0.368354s].
[2022-03-19 20:01:02.155977] INFO: moduleinvoker: filter.v3 开始运行..
[2022-03-19 20:01:02.187718] INFO: filter: 使用表达式 my==1 过滤
[2022-03-19 20:01:02.394582] INFO: filter: 过滤 /data, 11/0/38942
[2022-03-19 20:01:02.417804] INFO: moduleinvoker: filter.v3 运行完成[0.261821s].
[2022-03-19 20:01:02.484376] INFO: moduleinvoker: backtest.v8 开始运行..
[2022-03-19 20:01:02.488995] INFO: backtest: biglearning backtest:V8.6.2
[2022-03-19 20:01:02.541152] INFO: backtest: product_type:stock by specified
[2022-03-19 20:01:02.668398] INFO: moduleinvoker: cached.v2 开始运行..
[2022-03-19 20:01:02.677652] INFO: moduleinvoker: 命中缓存
[2022-03-19 20:01:02.679533] INFO: moduleinvoker: cached.v2 运行完成[0.011161s].
[2022-03-19 20:01:03.503631] INFO: algo: TradingAlgorithm V1.8.7
[2022-03-19 20:01:03.662321] INFO: algo: trading transform...
[2022-03-19 20:01:03.956550] INFO: Performance: Simulated 3 trading days out of 3.
[2022-03-19 20:01:03.958333] INFO: Performance: first open: 2021-01-04 09:30:00+00:00
[2022-03-19 20:01:03.959300] INFO: Performance: last close: 2021-01-06 15:00:00+00:00
[2022-03-19 20:01:21.664508] INFO: moduleinvoker: backtest.v8 运行完成[19.180128s].
[2022-03-19 20:01:21.666371] INFO: moduleinvoker: trade.v4 运行完成[19.241462s].
[2022-03-19 20:01:21.692690] INFO: moduleinvoker: filter.v3 开始运行..
[2022-03-19 20:01:21.735031] INFO: filter: 使用表达式 (con1==1) & (con2==1) & (con3==1) & (con4==1) & (con5==1) & (con6==1 ) &(date=='2021-01-05') 过滤
[2022-03-19 20:01:21.969331] INFO: filter: 过滤 /y_2020, 0/0/30562
[2022-03-19 20:01:22.106000] INFO: filter: 过滤 /y_2021, 6/0/8380
[2022-03-19 20:01:22.187813] INFO: moduleinvoker: filter.v3 运行完成[0.495114s].
[2022-03-19 20:01:22.214261] INFO: moduleinvoker: filter.v3 开始运行..
[2022-03-19 20:01:22.266254] INFO: filter: 使用表达式 (con7==1) & (con8==1) & (con9==1) & (con10==1) 过滤
[2022-03-19 20:01:22.461858] INFO: filter: 过滤 /y_2021, 3/0/6
[2022-03-19 20:01:22.504905] INFO: moduleinvoker: filter.v3 运行完成[0.290654s].
[2022-03-19 20:01:22.516499] INFO: moduleinvoker: filter.v3 开始运行..
[2022-03-19 20:01:22.538425] INFO: filter: 使用表达式 con11==1 过滤
[2022-03-19 20:01:22.717852] INFO: filter: 过滤 /y_2021, 3/0/3
[2022-03-19 20:01:22.788911] INFO: moduleinvoker: filter.v3 运行完成[0.272388s].

日期： 2021-01-05 选出股票数量： 2
['002309.SZA', '603321.SHA']
日期: 2021-01-05 买入: 002309.SZA
日期: 2021-01-06 卖出2: 002309.SZA
日期： 2021-01-06 选出股票数量： 1
['002284.SZA']
日期: 2021-01-06 买入: 002284.SZA

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-55a9248359fb4cb08205ea4878794988"}/bigcharts-data-end