聚烯烃次日预测，本周预测最佳模型计算

config_juxiting.py

@@ -86,6 +86,11 @@ bdwdname = [
     '次日'
 ]
 
+# 数据库预测结果表八大维度列名
+price_columns = [
+    'day_price', 'week_price', 'second_week_price', 'next_week_price',
+    'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
+]
 
 modelsindex = [{
     'NHITS': 'SELF0000077',

lib/dataread.py

@@ -51,6 +51,7 @@ global_config = {
     'baicangidnamedict': None,  # 百川id名称映射
     'modelsindex': None,  # 模型索引
     'bdwdname': None,
+    'price_columns': None,
 
     # 模型参数
     'data_set': None,                  # 数据集名称
@@ -746,6 +747,25 @@ def calculate_kdj(data, n=9):
     # data = data.dropna()
     return data
 
+# 计算因子与预测目标的相关性
+
+
+def calculate_correlation(df):
+    try:
+        yy = df['y']
+        # 去掉ds y
+        df = df.drop(columns=['ds', 'y'])
+        # 计算相关系数
+        corr = df.corrwith(yy)
+        # 输出相关系数
+        print(corr)
+        # 保存结果
+        corr.to_csv(os.path.join(config.dataset, '相关系数.csv'))
+    except Exception as e:
+        config.logger.info('计算相关系数错误：', e)
+    finally:
+        config.logger.info('计算相关系数完成')
+
 
 def check_column(df, col_name, two_months_ago):
     '''
@@ -1080,6 +1100,7 @@ def datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, datecol='date', end_t
     if add_kdj:
         df = calculate_kdj(df)
 
+    calculate_correlation(df=df)
     featureAnalysis(df, dataset=dataset, y=y)
     return df
 

lib/pydantic_models.py

@@ -40,3 +40,39 @@ class PredictionResult(BaseModel):
     create_date: Optional[datetime] = None
     update_user: Optional[str] = None
     update_date: Optional[datetime] = None
+
+
+class PpPredictionResult(BaseModel):
+    feature_factor_frequency: str
+    strategy_id: int
+    oil_code: Optional[str] = 'PP'
+    oil_name: Optional[str] = 'PP期货'
+    data_date: Optional[datetime] = None
+    market_price: Optional[Decimal] = None
+    day_price: Optional[Decimal] = None
+    week_price: Optional[Decimal] = None
+    second_week_price: Optional[Decimal] = None
+    next_week_price: Optional[Decimal] = None
+    next_month_price: Optional[Decimal] = None
+    next_february_price: Optional[Decimal] = None
+    next_march_price: Optional[Decimal] = None
+    next_april_price: Optional[Decimal] = None
+    model_evaluation_id: int
+    model_id: int
+    tenant_code: Optional[str] = None
+    reserved_str1: Optional[str] = None
+    reserved_str2: Optional[str] = None
+    reserved_str3: Optional[str] = None
+    reserved_str4: Optional[str] = None
+    reserved_str5: Optional[str] = None
+    reserved_num1: Optional[Decimal] = None
+    reserved_num2: Optional[Decimal] = None
+    reserved_num3: Optional[Decimal] = None
+    reserved_num4: Optional[Decimal] = None
+    reserved_num5: Optional[Decimal] = None
+    version_num: Decimal = Decimal(1)
+    delete_flag: str = '0'
+    create_user: Optional[str] = None
+    create_date: Optional[datetime] = None
+    update_user: Optional[str] = None
+    update_date: Optional[datetime] = None

lib/tools.py

@@ -37,7 +37,7 @@ import time
 import logging
 from dotenv import load_dotenv
 
-from lib.pydantic_models import PredictionResult
+from lib.pydantic_models import PredictionResult, PpPredictionResult
 load_dotenv()
 
 global logger
@@ -642,6 +642,105 @@ def get_week_date(end_time):
     return create_dates, ds_dates
 
 
+def get_bdwd_date(date=''):
+    '''
+    计算当前日期date对应的明天，五班后，下周日，下下周日，下月最后一天，下两月最后一天，下三月最后一天，下四月最后一天的日期
+
+    参数:
+        date (str): 输入的日期，格式为 '%Y-%m-%d'，默认为空字符串，表示当前日期
+
+    返回:
+        dict: 包含所需日期的字典，键分别为 'tomorrow', 'five_working_days_later', 'next_sunday', 
+              'next_next_sunday', 'next_month_last_day', 'next_two_months_last_day', 
+              'next_three_months_last_day', 'next_four_months_last_day'
+    '''
+    import datetime
+    if not date:
+        current_date = datetime.date.today()
+    else:
+        current_date = datetime.datetime.strptime(date, '%Y-%m-%d').date()
+
+    # 计算明天的日期
+    tomorrow = current_date + datetime.timedelta(days=1)
+
+    # 计算五班后的日期
+    five_working_days_later = current_date
+    working_days_count = 0
+    while working_days_count < 5:
+        five_working_days_later += datetime.timedelta(days=1)
+        if five_working_days_later.weekday() < 5:  # 周一到周五是工作日
+            working_days_count += 1
+
+     # 计算下周日的日期
+    days_to_next_sunday = (6 - current_date.weekday()) % 7
+    if days_to_next_sunday == 0:
+        days_to_next_sunday = 7
+    next_sunday = current_date + datetime.timedelta(days=days_to_next_sunday)
+
+    # 计算下下周周日的日期
+    next_next_sunday = next_sunday + datetime.timedelta(days=7)
+
+    # 计算下下下周周日的日期
+    next_next_next_sunday = next_next_sunday + datetime.timedelta(days=7)
+
+    # 计算下月最后一天的日期
+    next_month = current_date.replace(day=28) + datetime.timedelta(days=4)
+    next_month_last_day = next_month.replace(
+        day=1) - datetime.timedelta(days=1)
+
+    # 计算下两月最后一天的日期
+    next_two_months = next_month.replace(day=28) + datetime.timedelta(days=4)
+    next_two_months_last_day = next_two_months.replace(
+        day=1) - datetime.timedelta(days=1)
+
+    # 计算下三月最后一天的日期
+    next_three_months = next_two_months.replace(
+        day=28) + datetime.timedelta(days=4)
+    next_three_months_last_day = next_three_months.replace(
+        day=1) - datetime.timedelta(days=1)
+
+    # 计算下四月最后一天的日期
+    next_four_months = next_three_months.replace(
+        day=28) + datetime.timedelta(days=4)
+    next_four_months_last_day = next_four_months.replace(
+        day=1) - datetime.timedelta(days=1)
+
+    # 计算下五月最后一天的日期
+    next_five_months = next_four_months.replace(
+        day=28) + datetime.timedelta(days=4)
+    next_five_months_last_day = next_five_months.replace(
+        day=1) - datetime.timedelta(days=1)
+
+    return {
+        'tomorrow': tomorrow.strftime('%Y-%m-%d'),
+        'five_working_days_later': five_working_days_later.strftime('%Y-%m-%d'),
+        # 'next_sunday': next_sunday.strftime('%Y-%m-%d'),
+        'next_next_sunday': next_next_sunday.strftime('%Y-%m-%d'),
+        'next_next_next_sunday': next_next_next_sunday.strftime('%Y-%m-%d'),
+        # 'next_month_last_day': next_month_last_day.strftime('%Y-%m-%d'),
+        'next_two_months_last_day': next_two_months_last_day.strftime('%Y-%m-%d'),
+        'next_three_months_last_day': next_three_months_last_day.strftime('%Y-%m-%d'),
+        'next_four_months_last_day': next_four_months_last_day.strftime('%Y-%m-%d'),
+        'next_five_months_last_day': next_five_months_last_day.strftime('%Y-%m-%d'),
+    }
+
+
+def get_bdwd_price(date, true_price, global_config):
+    '''
+    计算当前日期date对应的明天，五班后，下周日，下下周日，下月最后一天，下两月最后一天，下三月最后一天，下四月最后一天的价格
+    '''
+    bdwd_price = {}
+    for wd in global_config['price_columns']:
+        if wd == 'day_price':
+            bdwd_price[wd] = true_price[true_price['ds'] == date][wd].values[0]
+        if wd == 'week_price':
+
+            bdwd_price[wd] = true_price[true_price['ds'] == date][wd].values[0]
+        true_price[wd] = pd.to_numeric(true_price[wd])
+
+    return
+
+
 class DeepSeek():
     def __init__(self):
         pass
@@ -674,6 +773,17 @@ class DeepSeek():
         return summary
 
 
+def get_model_id_name_dict(global_config):
+    '''
+    预测结果和模型表求子集得到模型名称
+    '''
+    tb = 'v_tbl_predict_models'
+    sql = f'select model_name,id from {tb} '
+    modelsname = global_config['db_mysql'].execute_query(sql)
+    model_id_name_dict = {row['id']: row['model_name'] for row in modelsname}
+    return model_id_name_dict
+
+
 def get_modelsname(df, global_config):
     '''
     预测结果和模型表求子集得到模型名称
@@ -682,7 +792,7 @@ def get_modelsname(df, global_config):
     tb = 'v_tbl_predict_models'
     sql = f'select model_name,id from {tb} '
     modelsname = global_config['db_mysql'].execute_query(sql)
-    model_id_name_dict = {row['id']: row['model_name'] for row in modelsname}
+    model_id_name_dict = get_model_id_name_dict(global_config=global_config)
     model_name_list = [row['model_name'] for row in modelsname]
     model_name_list = set(columns) & set(model_name_list)
     model_name_list = list(model_name_list)
@@ -709,5 +819,248 @@ def convert_df_to_pydantic(df_predict, model_id_name_dict, global_config):
     return results
 
 
+def convert_df_to_pydantic_pp(df_predict, model_id_name_dict, global_config):
+    reverse_model_id_name_dict = {
+        value: key for key, value in model_id_name_dict.items()}
+    results = []
+    data = global_config['DEFAULT_CONFIG'].copy()
+    data['data_date'] = df_predict['created_dt'].values[0]
+    if isinstance(data['data_date'], np.datetime64):
+        data['data_date'] = pd.Timestamp(
+            data['data_date']).to_pydatetime()
+    for c in df_predict.columns:
+        if c not in ['ds', 'created_dt']:
+            data['model_id'] = reverse_model_id_name_dict[c]
+            data['predicted_price'] = Decimal(
+                round(df_predict[c].values[0], 2))
+            result = PpPredictionResult(**data)
+            results.append(result)
+    return results
+
+
+# 查找上一交易日各维度的最佳模型
+def find_best_models(date='', global_config=None):
+
+    best_models = {}
+    model_id_name_dict = get_model_id_name_dict(global_config=global_config)
+
+    import datetime
+    if date == '':
+        date = datetime.datetime.now().strftime('%Y-%m-%d')
+    else:
+        date = datetime.datetime.strptime(
+            date, '%Y-%m-%d').strftime('%Y-%m-%d')
+
+    # 获取真实价格的八个维度价格
+    true_price = pd.read_csv(os.path.join(
+        global_config['dataset'], '指标数据.csv'))
+    true_price = true_price[['ds', 'y']]
+
+    year = int(date.split('-')[0])
+    month = int(date.split('-')[1])
+    day = int(date.split('-')[2])
+
+    # 计算六月前的年月
+    if month <= 6:
+        year = int(year) - 1
+        month = 12
+    else:
+        month = month - 6
+
+    tb = 'v_tbl_predict_pp_prediction_results'
+    sql = f'select * from {tb} where data_date >= \'{year}-{month}-01\''
+    # 数据库查询对应日期的预测值
+    predictresult = global_config['db_mysql'].execute_query(sql)
+    if len(predictresult) == 0:
+        print('没有预测结果')
+        return
+    df = pd.DataFrame(predictresult)
+    df = df[['data_date', 'model_id']+global_config['price_columns']]
+    print('预测结果数量：', df.shape)
+    print('预测结果日期范围：', df['data_date'].min(), '到', df['data_date'].max())
+
+    # 遍历全局配置中的价格列
+    for i, wd in enumerate(global_config['price_columns']):
+        # 为每个价格列初始化一个空字典，用于存储最佳模型信息
+        best_models[wd] = {}
+        # 处理第一个价格列，计算次日的最佳模型
+        if i == 0:
+            # 计算当前日期的前一天日期
+            ciridate = (pd.Timestamp(date) - pd.Timedelta(days=1)
+                        ).strftime('%Y-%m-%d')
+            # 记录日志，提示开始计算次日的最佳模型
+            global_config['logger'].info(f'计算预测{date}的次日{ciridate}最佳模型')
+            # 记录日志，输出当前日期的真实价格
+            global_config['logger'].info(
+                f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}')
+            # 从数据框中选取需要的列
+            price = df[['data_date', wd, 'model_id']]
+            # 筛选出数据日期在 ciridate 到 date 之间的数据
+            price = price[(price['data_date'] == ciridate)
+                          | (price['data_date'] == date)]
+            # 将价格列的数据类型转换为 float
+            price[wd] = price[wd].astype(float)
+            # 删除价格列中包含缺失值的行
+            price = price.dropna(subset=[wd])
+            # 判断价格趋势，若当前日期价格大于前一天价格，趋势为 1，否则为 -1
+            trend = 1 if true_price[true_price['ds'] == date]['y'].values[0] - \
+                true_price[true_price['ds'] == ciridate]['y'].values[0] > 0 else -1
+            # 为数据框添加真实价格列
+            price['trueprice'] = true_price[true_price['ds']
+                                            == date]['y'].values[0]
+            # 根据预测价格与真实价格的差值判断趋势，大于 0 为 1，否则为 -1
+            price['trend'] = np.where(
+                price['trueprice'] - price[wd] > 0, 1, -1)
+            # 计算预测价格与真实价格差值的绝对值
+            price['abs'] = (price['trueprice'] - price[wd]).abs()
+            # 筛选出趋势与整体趋势一致的数据
+            price = price[price['trend'] ==
+                          trend]
+            # 筛选出预测价格与真实价格差值绝对值最小的数据
+            price = price[price['abs'] == price['abs'].min()]
+            # 记录日志，输出筛选后的价格数据
+            global_config['logger'].info(price)
+            # 获取最佳模型的 ID
+            best_model_id = price.iloc[0]['model_id']
+            # 记录日志，输出次日预测最准确的模型 ID
+            global_config['logger'].info(f'{ciridate}预测最准确的模型：{best_model_id}')
+            # 将最佳模型的 ID 存入字典
+            best_models[wd]['model_id'] = best_model_id
+            # 根据模型 ID 获取模型名称并存入字典
+            best_models[wd]['model_name'] = model_id_name_dict[best_model_id]
+            # 记录日志，输出次日预测最准确的模型名称
+            global_config['logger'].info(f'{ciridate}预测最准确的模型名称：{best_models}')
+
+        if i == 1:
+            # 计算五个工作日之前的日期
+            benzhoudate = (pd.Timestamp(date) - pd.Timedelta(days=7)
+                           ).strftime('%Y-%m-%d')
+
+            # 记录日志，提示开始计算五天前的最佳模型
+            global_config['logger'].info(f'计算预测{date}的五天前{benzhoudate}最佳模型')
+            # 记录日志，输出当前日期的真实价格
+            global_config['logger'].info(
+                f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}')
+            # 从数据框中选取需要的列
+            price = df[['data_date', wd, 'model_id']]
+            # 筛选出数据日期在 benzhoudate 到 date 之间的数据
+            price = price[(price['data_date'] == benzhoudate)
+                          | (price['data_date'] == date)]
+            # 将价格列的数据类型转换为 float
+            price[wd] = price[wd].astype(float)
+            # 删除价格列中包含缺失值的行
+            price = price.dropna(subset=[wd])
+            # 判断价格趋势，若当前日期价格大于前一天价格，趋势为 1，否则为 -1
+            trend = 1 if true_price[true_price['ds'] == date]['y'].values[0] - \
+                true_price[true_price['ds'] == benzhoudate]['y'].values[0] > 0 else -1
+            # 记录日志，输出五天前预测最准确的模型名称
+            global_config['logger'].info(f'实际趋势是：{trend}')
+            # 为数据框添加真实价格列
+            price['trueprice'] = true_price[true_price['ds']
+                                            == date]['y'].values[0]
+            # 根据预测价格与真实价格的差值判断趋势，大于 0 为 1，否则为 -1
+            price['trend'] = np.where(
+                price['trueprice'] - price[wd] > 0, 1, -1)
+            # 计算预测价格与真实价格差值的绝对值
+            price['abs'] = (price['trueprice'] - price[wd]).abs()
+            # 筛选出趋势与整体趋势一致的数据
+            price = price[price['trend'] ==
+                          trend]
+            # 筛选出预测价格与真实价格差值绝对值最小的数据
+            price = price[price['abs'] == price['abs'].min()]
+            # 记录日志，输出筛选后的价格数据
+            global_config['logger'].info(price)
+            # 获取最佳模型的 ID
+            best_model_id = price.iloc[0]['model_id']
+            # 记录日志，输出五天前预测最准确的模型 ID
+            global_config['logger'].info(
+                f'{benzhoudate}预测最准确的模型：{best_model_id}')
+            # 将最佳模型的 ID 存入字典
+            best_models[wd]['model_id'] = best_model_id
+            # 根据模型 ID 获取模型名称并存入字典
+            best_models[wd]['model_name'] = model_id_name_dict[best_model_id]
+            # 记录日志，输出五天前预测最准确的模型名称
+            global_config['logger'].info(
+                f'{benzhoudate}预测最准确的模型名称：{best_models}')
+
+        if i == 2:
+            # 计算当前周的前两周的周一和周日的日期
+            current_date = datetime.datetime.strptime(date, '%Y-%m-%d')
+            # 计算前两一周周一
+            one_weeks_ago_monday = current_date - \
+                datetime.timedelta(days=current_date.weekday() + 7)
+            # 计算前一周周日
+            one_weeks_ago_sunday = one_weeks_ago_monday + \
+                datetime.timedelta(days=6)
+            cizhoudate = f"{one_weeks_ago_monday.strftime('%Y-%m-%d')} - {one_weeks_ago_sunday.strftime('%Y-%m-%d')}"
+            print(f'计算预测{date}次周最佳模型，前一周日期区间: {cizhoudate}')
+        if i == 3:
+            # 计算当前周的前两周的周一和周日的日期
+            current_date = datetime.datetime.strptime(date, '%Y-%m-%d')
+            # 计算前两周周一
+            two_weeks_ago_monday = current_date - \
+                datetime.timedelta(days=current_date.weekday() + 14)
+            # 计算前两周周日
+            two_weeks_ago_sunday = two_weeks_ago_monday + \
+                datetime.timedelta(days=6)
+            gezhoudate = f"{two_weeks_ago_monday.strftime('%Y-%m-%d')} - {two_weeks_ago_sunday.strftime('%Y-%m-%d')}"
+            print(f'计算预测{date}隔周最佳模型，前两周日期区间: {gezhoudate}')
+        if i == 4:
+            # 计算当上月的1日及最后一日
+            current_date = pd.Timestamp(date)
+            # 获取上月第一天
+            last_month_first_day = (
+                current_date - pd.offsets.MonthBegin(2)).strftime('%Y-%m-%d')
+            # 获取上月最后一天
+            last_month_last_day = (pd.Timestamp(
+                last_month_first_day) + pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d')
+            print(
+                f'计算预测{date}次月最佳模型，上月日期区间: {last_month_first_day} - {last_month_last_day}')
+        if i == 5:
+            # 计算两月前的1日及最后一日
+            current_date = pd.Timestamp(date)
+            # 获取上上月第一天
+            last_month_first_day = (
+                current_date - pd.offsets.MonthBegin(3)).strftime('%Y-%m-%d')
+            # 获取上上月最后一天
+            last_month_last_day = (pd.Timestamp(
+                last_month_first_day) + pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d')
+            print(
+                f'计算预测{date}次二月最佳模型，两月前日期区间: {last_month_first_day} - {last_month_last_day}')
+        if i == 6:
+            # 计算三月前的1日及最后一日
+            current_date = pd.Timestamp(date)
+            # 获取前三月第一天
+            last_month_first_day = (
+                current_date - pd.offsets.MonthBegin(4)).strftime('%Y-%m-%d')
+            # 获取前三月最后一天
+            last_month_last_day = (pd.Timestamp(
+                last_month_first_day) + pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d')
+            print(
+                f'计算预测{date}次三月最佳模型，三月前日期区间: {last_month_first_day} - {last_month_last_day}')
+        if i == 7:
+            # 计算四月前的1日及最后一日
+            current_date = pd.Timestamp(date)
+            # 获取前四月第一天
+            last_month_first_day = (
+                current_date - pd.offsets.MonthBegin(5)).strftime('%Y-%m-%d')
+            # 获取前四月最后一天
+            last_month_last_day = (pd.Timestamp(
+                last_month_first_day) + pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d')
+            print(
+                f'计算预测{date}次四月最佳模型，四月前日期区间: {last_month_first_day} - {last_month_last_day}')
+
+    # # 获取真实价格的八个维度价格
+    # true_price = pd.read_csv(os.path.join(
+    #     global_config['dataset'], '指标数据.csv'))
+    # true_price = true_price[['ds', 'y']]
+    # print(true_price.head())
+
+    # # 根据当前日期date,计算对应八个维度的价格
+    # bdwd_price = get_bdwd_price(date, true_price)
+
+    return predictresult
+
+
 if __name__ == '__main__':
     print('This is a tool, not a script.')

main_juxiting.py

@@ -2,7 +2,7 @@
 
 from lib.dataread import *
 from config_juxiting import *
-from lib.tools import SendMail, exception_logger, convert_df_to_pydantic, exception_logger, get_modelsname
+from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, get_modelsname
 from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf
 import datetime
 import torch
@@ -24,6 +24,7 @@ global_config.update({
     'settings': settings,
 
     'bdwdname': bdwdname,
+    'price_columns': price_columns,
 
 
     # 模型参数
@@ -169,10 +170,7 @@ def sql_inset_predict(global_config):
     wd = ['day_price', 'week_price']
     model_name_list, model_id_name_dict = get_modelsname(df, global_config)
 
-    PRICE_COLUMNS = [
+    PRICE_COLUMNS = global_config['price_columns']
-        'day_price', 'week_price', 'second_week_price', 'next_week_price',
-        'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
-    ]
 
     params_list = []
     for df, price_type in zip([next_day_df, this_week_df], wd):
@@ -208,7 +206,7 @@ def sql_inset_predict(global_config):
 
         next_day_df = df[['ds', 'created_dt'] + model_name_list]
 
-        pydantic_results = convert_df_to_pydantic(
+        pydantic_results = convert_df_to_pydantic_pp(
             next_day_df, model_id_name_dict, global_config)
         if pydantic_results:
 
@@ -556,7 +554,9 @@ if __name__ == '__main__':
     #         logger.info(f'预测失败：{e}')
     #         continue
 
-    predict_main()
+    # predict_main()
 
     # push_market_value()
     # sql_inset_predict(global_config)
+    from lib.tools import find_best_models
+    find_best_models(date='2025-07-18', global_config=global_config)

main_juxiting_yuedu.py

@@ -2,7 +2,7 @@
 
 from lib.dataread import *
 from config_juxiting_yuedu import *
-from lib.tools import SendMail, convert_df_to_pydantic, exception_logger, get_modelsname
+from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, get_modelsname
 from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_export_pdf
 import datetime
 import torch
@@ -218,7 +218,7 @@ def sql_inset_predict(global_config):
 
         next_day_df = df[['ds', 'created_dt'] + model_name_list]
 
-        pydantic_results = convert_df_to_pydantic(
+        pydantic_results = convert_df_to_pydantic_pp(
             next_day_df, model_id_name_dict, global_config)
         if pydantic_results:
 
@@ -252,7 +252,6 @@ def sql_inset_predict(global_config):
     config.db_mysql.close()
 
 
-
 def predict_main():
     """
     主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。

main_juxiting_zhoudu.py

@@ -2,7 +2,7 @@
 
 from lib.dataread import *
 from config_juxiting_zhoudu import *
-from lib.tools import SendMail, exception_logger, convert_df_to_pydantic, exception_logger, get_modelsname
+from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, get_modelsname
 from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf
 import datetime
 import torch
@@ -205,7 +205,7 @@ def sql_inset_predict(global_config):
 
         next_day_df = df[['ds', 'created_dt'] + model_name_list]
 
-        pydantic_results = convert_df_to_pydantic(
+        pydantic_results = convert_df_to_pydantic_pp(
             next_day_df, model_id_name_dict, global_config)
         if pydantic_results:
 
@@ -238,6 +238,7 @@ def sql_inset_predict(global_config):
         config.logger.info(f"成功插入或更新 {affected_rows} 条记录")
     config.db_mysql.close()
 
+
 def predict_main():
     """
     主预测函数，用于从 ETA 获取数据、处理数据、训练模型并进行预测。
@@ -318,7 +319,7 @@ def predict_main():
 
         # 数据处理
         df = zhoududatachuli(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
-                                end_time=end_time)
+                             end_time=end_time)
 
     else:
         # 读取数据

up_week_dates.csv

@@ -1,17 +0,0 @@
-ds,NHITS,Informer,LSTM,iTransformer,TSMixer,TSMixerx,PatchTST,RNN,GRU,TCN,BiTCN,DilatedRNN,MLP,DLinear,NLinear,TFT,StemGNN,MLPMultivariate,TiDE,DeepNPTS,y,min_within_quantile,max_within_quantile,min_price,max_price,id,CREAT_DATE,序号,LOW_PRICE,HIGH_PRICE,ACCURACY
-2024-12-09,71.63069,72.14122,70.19943,69.644196,71.80898,70.52471,71.53064,67.38501,70.75815,73.546684,71.92033,70.327484,71.95594,71.18323,71.882935,71.90951,73.94965,72.28691,71.25138,73.34018,72.13999938964844,67.38501,73.94965,67.38501,73.94965,51,2024-12-06,5.0,70.92,72.65,1.0
-2024-12-10,71.14343,71.9462,70.405106,69.48242,71.70601,70.66241,71.57484,67.23587,70.46323,73.37324,71.720894,70.45846,71.88132,72.34705,71.75997,72.41326,73.74943,72.31887,71.47953,72.78831,72.19000244140625,67.23587,73.74943,67.23587,73.74943,52,2024-12-06,4.0,70.73,71.77,1.0
-2024-12-11,71.71588,72.31544,70.175125,69.58213,71.59609,70.91783,71.54794,67.433334,70.518196,73.76477,71.84062,70.746284,72.27111,71.85789,70.77939,72.912704,73.91716,72.42111,71.47695,72.61624,73.5199966430664,67.433334,73.91716,67.433334,73.91716,53,2024-12-06,3.0,72.15,73.75,1.0
-2024-12-12,72.46348,71.87648,70.26041,69.922165,71.65103,70.689384,71.72716,67.54506,70.99872,73.52567,71.78495,70.777115,72.34328,72.756325,70.9607,73.391495,73.944244,72.465836,71.445244,71.69109,73.88999938964844,67.54506,73.944244,67.54506,73.944244,54,2024-12-06,2.0,72.42,74.0,0.9647113924050618
-2024-12-13,72.85073,72.36679,70.489136,69.759766,71.78641,70.69935,71.60861,67.47295,70.81146,73.85618,71.966835,70.923485,72.63866,72.29209,71.11011,73.777534,73.82516,72.58803,71.807915,72.6083,,67.47295,73.85618,67.47295,73.85618,55,2024-12-06,1.0,73.3,74.59,0.43114728682170156
-2024-12-10,71.97169,73.11586,71.41545,70.59598,71.81998,71.541016,72.397606,68.78458,71.30409,73.65467,71.78349,71.03456,71.8188,71.85696,72.014534,72.33824,73.77812,72.2061,71.36985,72.46154,72.19000244140625,68.78458,73.77812,68.78458,73.77812,56,2024-12-09,4.0,70.73,71.77,1.0
-2024-12-11,72.35509,73.087166,71.63927,70.430176,71.82658,71.76624,72.57018,68.59579,71.03884,73.45442,71.75791,71.15297,72.066956,72.28318,72.36303,72.75233,73.594864,72.24322,71.16944,72.39418,73.5199966430664,68.59579,73.594864,68.59579,73.594864,57,2024-12-09,3.0,72.15,73.75,0.9030400000000004
-2024-12-12,73.268654,73.31714,71.39498,70.65145,71.76794,71.6176,73.038315,68.85988,71.12751,73.84763,71.82094,71.41621,72.99199,72.42496,71.474464,73.17588,73.74838,72.31574,71.692444,72.48112,73.88999938964844,68.85988,73.84763,68.85988,73.84763,58,2024-12-09,2.0,72.42,74.0,0.9035632911392374
-2024-12-13,73.11824,73.70929,71.442184,70.72518,71.878975,71.82435,73.13541,68.760376,71.51967,73.62109,71.86448,71.44768,72.952484,72.107254,72.57947,73.61362,73.77317,72.3857,71.873566,72.40313,,68.760376,73.77317,68.760376,73.77317,59,2024-12-09,1.0,73.3,74.59,0.36679844961239827
-2024-12-11,72.372795,72.6546,71.37516,71.41169,71.977135,72.13211,72.774284,69.25488,71.62906,73.70365,72.27147,71.489716,72.77891,71.61932,72.090294,72.166916,73.72794,72.78662,71.960884,72.473854,73.5199966430664,69.25488,73.72794,69.25488,73.72794,61,2024-12-10,3.0,72.15,73.75,0.9862125000000024
-2024-12-12,72.91286,72.80857,71.612946,71.193184,72.05397,72.48625,72.85768,69.06833,71.35641,73.5444,72.19988,71.585815,73.47307,72.22734,72.219765,72.48997,73.544106,72.96339,71.66363,72.122116,73.88999938964844,69.06833,73.5444,69.06833,73.5444,62,2024-12-10,2.0,72.42,74.0,0.7116455696202503
-2024-12-13,73.01643,72.78796,71.3701,71.516174,72.21711,72.4036,73.66599,69.338615,71.467476,73.9247,72.2582,71.84913,73.643265,71.70761,72.41268,72.85244,73.69811,72.91225,71.75731,71.76489,,69.338615,73.9247,69.338615,73.9247,63,2024-12-10,1.0,73.3,74.59,0.4842635658914738
-2024-12-12,73.82699,73.58274,72.81162,73.18572,72.8827,72.958374,74.36039,71.09452,72.673904,74.025635,73.17438,72.72577,73.668365,72.48243,73.224655,74.286606,73.70824,73.668076,72.934685,73.16352,73.88999938964844,71.09452,74.36039,71.09452,74.36039,66,2024-12-11,2.0,72.42,74.0,1.0
-2024-12-13,74.025696,73.275696,73.0588,72.85848,72.719444,73.238945,74.05083,71.060295,72.455666,73.90173,72.966385,72.81345,73.77382,72.488365,73.78919,74.5482,73.52537,73.58226,72.8307,73.26384,,71.060295,74.5482,71.060295,74.5482,67,2024-12-11,1.0,73.3,74.59,0.9675968992247993
-2024-12-13,73.624176,73.15132,73.069374,73.517944,73.56706,74.08185,73.880775,71.83479,73.23418,74.28763,73.372154,73.431366,73.51813,73.90266,73.87494,74.43155,73.691505,73.46715,73.56533,73.50562,,71.83479,74.43155,71.83479,74.43155,71,2024-12-12,1.0,73.3,74.59,0.877170542635658
-2024-12-13,73.624176,73.15132,73.069374,73.517944,73.56706,74.08185,73.880775,71.83479,73.23418,74.28763,73.372154,73.431366,73.51813,73.90266,73.87494,74.43155,73.691505,73.46715,73.56533,73.50562,,71.83479,74.43155,71.83479,74.43155,76,2024-12-13,1.0,73.3,74.59,0.877170542635658