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liurui 2024-11-14 10:21:25 +08:00
parent d61259d3b9
commit 11270ead85
3 changed files with 27 additions and 12 deletions
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34
lib/dataread.py
View File

@ -436,6 +436,11 @@ def calculate_kdj(data, n=9):
def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y',dataset='dataset',delweekenday=False,add_kdj=False,is_timefurture=False):
'''
原油特征数据处理函数，
接收的是两个df，一个是指标数据，一个是指标列表
输出的是一个df，包含ds，y，指标列
'''
df = df_zhibiaoshuju.copy()
if end_time == '':
end_time = datetime.datetime.now().strftime('%Y-%m-%d')
@ -457,6 +462,11 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
two_months_ago = current_date - timedelta(days=40)
def check_column(col_name):
'''
判断两月不更新指标
输入：列名
输出：True or False
'''
if 'ds' in col_name or 'y' in col_name:
return False
df_check_column = df[['ds',col_name]]
@ -469,7 +479,6 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
return corresponding_date < two_months_ago
columns_to_drop = df.columns[df.columns.map(check_column)].tolist()
df = df.drop(columns = columns_to_drop)
logger.info(f'删除两月不更新特征后数据量：{df.shape}')
# 删除预测列空值的行
@ -481,7 +490,7 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
# 去掉指标列表中的columns_to_drop的行
df_zhibiaoliebiao = df_zhibiaoliebiao[df_zhibiaoliebiao['指标名称'].isin(df.columns.tolist())]
df_zhibiaoliebiao.to_csv(os.path.join(dataset,'特征处理后的指标名称及分类.csv'),index=False)
# 频度分析
# 数据频度分析
featurePindu(dataset=dataset)
# 向上填充
df = df.ffill()
@ -491,26 +500,35 @@ def datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y'
# 删除周六日的数据
if delweekenday:
df = df[df['ds'].dt.weekday < 5]
# kdj指标
if add_kdj:
df = calculate_kdj(df)
# 衍生时间特征
if is_timefurture:
df = addtimecharacteristics(df=df,dataset=dataset)
# 特征分析
featureAnalysis(df,dataset=dataset,y=y)
return df
def datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time='',y='y',dataset='dataset',delweekenday=False,add_kdj=False,is_timefurture=False):
'''
聚烯烃特征数据处理函数，
接收的是两个df，一个是指标数据，一个是指标列表
输出的是一个df，包含ds，y，指标列
'''
df = df_zhibiaoshuju.copy()
if end_time == '':
end_time = datetime.datetime.now().strftime('%Y-%m-%d')
# date转为pddate
df.rename(columns={datecol:'ds'},inplace=True)
# 指定列统一减少数值
df[offsite_col] = df[offsite_col]-offsite
# 预测列为avg_cols的均值
df[y] = df[avg_cols].mean(axis=1)
print(df[['ds',y]+avg_cols].head(20))
# 去掉多余的列avg_cols
df = df.drop(columns=avg_cols)
# 重命名预测列
df.rename(columns={y:'y'},inplace=True)
# 按时间顺序排列
@ -521,10 +539,10 @@ def datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,datecol='date',end_time
# 获取小于等于当前日期的数据
df = df[df['ds'] <= end_time]
logger.info(f'删除两月不更新特征前数据量：{df.shape}')
# 去掉近最后数据对应的日期在两月以前的列，删除近2月的数据是常熟的列
# 去掉近最后数据对应的日期在两月以前的列，删除近2月的数据是常数的列
current_date = datetime.datetime.now()
two_months_ago = current_date - timedelta(days=40)
# 检查两月不更新的特征
def check_column(col_name):
if 'ds' in col_name or 'y' in col_name:
return False

3
lib/tools.py
View File

@ -441,8 +441,5 @@ class SQLiteHandler:
else:
print(f"Column '{column_name}' already exists in table '{table_name}'.")
if __name__ == '__main__':
print('This is a tool, not a script.')

2
main.py → main_yuanyou.py
View File

@ -26,7 +26,6 @@ def predict_main():
)
# 获取数据
if is_eta:
# eta数据
logger.info('从eta获取数据...')
signature = BinanceAPI(APPID, SECRET)
etadata = EtaReader(signature=signature,
@ -48,6 +47,7 @@ def predict_main():
df = datachuli(df_zhibiaoshuju,df_zhibiaoliebiao,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time)
else:
# 读取数据
logger.info('读取本地数据：'+os.path.join(dataset,data_set))
df = getdata(filename=os.path.join(dataset,data_set),y=y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time) # 原始数据，未处理