sphinxrascal168的博客

一种循环取数的方法

#生成rownumdf1 = df1.sort_values(by=['ts_code','trade_date'],ascending=True)df1['rownum'] = df1['trade_date'].groupby(df1['ts_code']).rank(ascending=False)#tscode组内排序,按日期降序SELECT t_time, code, name, CL, row_number(...

1、从控制面板 进入 插看设备和打印机2、选中一个打印机，查看打印服务器属性，在安全选项卡中，把管理服务器权限的√去掉3、则此时，端口和驱动程序下的添加、删除、配置等按钮变灰，无法安装打印机，重新把√选中则可以恢复...

import lightgbm as lgbimport shapmodel = lgb.LGBMClassifier().fit(X,y)explainer = shap.Explainer(model)shap_value = explainer(X)shap.plots.waterfall(shap_value[100],max_display=40)shap.plots.bar(shap_value,max_display=30)

#################开始训练模型###########################train_data = df4.loc[(df1['rownum_W']>=sst) & (df1['rownum_W']<=101)]train_data = train_data.fillna(0)#处理以下target的字段类型，不然后面有问题train_data['target'] = train_data['target'].astype('int')data_

第10行df1['c_ma30'] = TA.SMA(df1,30,column='close')#原来的跟21重合了，搞错了第13#21日均线的斜率是否大于0，来判断是否盘整等df1['ma21xielv_d0_flag'] = np.where(df1['ma21xielv']>=0,1,0)df1['d21_0_count'] = ta.SUM(df1['ma21xielv_d0_flag'], timeperiod=15)#盘平df1['d21_0_count'] =

#####对当日涨跌进行分段df1['pct_chg_cate'] = np.select([((df1['pct_chg']>=4.9999)), ((df1['pct_chg'] <4.9999) & (df1['pct_chg']>=1.5001)), ((df1['pct_chg'] <(1.5001)) & (df1['pc...

第13行#是否长上影，绝对长度和相对长度都有要求df1['K_l_up_flag'] = np.where((df1['shiti_up']>50) & (df1['shiti_up_len']>3),1,0)#是否长下影df1['K_l_low_flag'] = np.where((df1['shiti_low']>50) & (df1['shiti_low_len']>3),1,0)#是否上下影都长df1['K_l_lu_flag'] = np.wh

#以今天的上线浮动12%，看是否盘整df1['c_up_12per'] = df1['close'].shift(14)*1.12df1['c_down_12per'] = df1['close'].shift(14)*0.88df1['c_shitf1_nei_flag'] = np.where((df1['close'].shift(1)> df1['c_down_12per']) & (df1['close'].shift(1)< df1['c_up_12per']),1..

var_ok = list(set(['shizi_panzheng_count','zhenfu_count_W','l30_c_xielv_W','l30_c_xielv_count_W','h30_c_xielv_count_W','RSI_2_W','bian_2_UP_count_W','yuanhu_count_W','h30_c_xielv_W','suoliang_flag_count_W','fangliang_flag_count_W','xie_l_up_count_W','hengc

# X=X.values# y=y.values#更改为bor特征选择器# import sklearn# from lightgbm.sklearn import LGBMClassifier# from sklearn.ensemble import RandomForestClassifier# from boruta import BorutaPy# rf = LGBMClassifier(num_leaves=60,max_depth=7,n_estimators=1200,lea

原因： 没有启动MySQL服务------------更多是删除某个文件库表时导致的错误解决方案：以管理员身份打开cmd命令行窗口，使用cd命令将目录切换为你MySQL的bin目录下，输入 net start mysql 启动MySQL服务即可解决此问题！！！电脑中搜索服务找到MySQL服务，启动它！管理员身份命令行窗口打开Windows快捷键： Win + X + A如果找不到MySQL服务，需要安装MySQL服务。解决的方法是用管理员的方式打开mysql安装目录bin。执行mysqld.e

#提前缩小数据量df_t = df1.loc[(df1['rownum_W'] <= (sst+25))]t = df_t.groupby(['rownum_W'],as_index=False).ts_code.count()row_liu = t['rownum_W'].loc[t.ts_code>12000]df1 = pd.DataFrame()for row in row_liu: df_for = df_t[df_t.rownum_W==row] df1 ...

df1['honglv_flag'] = np.where(df1['close']>=df1['open'],1,0)df1_week_hong = pd.DataFrame(df1.groupby(['ts_code','year','week'],as_index=False).honglv_flag.sum())df_weekv = pd.merge(df_weekv,df_week_hong,how = 'inner',on=['ts_code','year','week'])

#基础信息，主要要用到换手率\PE\PB等sqlcmd2 = "select * from a_hisdata.basic order by ts_code,trade_date ASC"df2 = pd.read_sql(sqlcmd2, dbconn)df2 = df2.sort_values(by=['ts_code','trade_date'],ascending=True)#填充空值df2 = df2.fillna(value=9999)df2['trade_date'] = df2[

20210716结束后在sst=11时 group1 在0.9 group2 在0.6在sst=10时 group1 在0.6 group2 在0.2在sst= 9时 group1 在0.6 group2 在0.3在sst= 8时 group1 在 group2 在在sst= 7时 group1 在 group2 在在sst= 6时 group1 在 group2 在在sst= 5时 grou...

data=df_gn[df_gn.trade_date==‘20210427’].sample(50)for i in list(set(df_gn.trade_date))[1:2]:print(i)dd=df_gn[(df_gn.trade_date==i) & (data.vol>=20000)].sample(50)data=data.append(dd)