聚烯烃八大维度结果绘图
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lib/tools.py
412
lib/tools.py
@ -1,3 +1,4 @@
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import datetime
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from decimal import Decimal
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from langchain_core.documents import Document
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from langchain_openai import ChatOpenAI
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@ -838,228 +839,297 @@ def convert_df_to_pydantic_pp(df_predict, model_id_name_dict, global_config):
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return results
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# 查找上一交易日各维度的最佳模型
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def find_best_models(date='', global_config=None):
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best_models = {}
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model_id_name_dict = get_model_id_name_dict(global_config=global_config)
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import datetime
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if date == '':
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# 处理日期输入
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if not date:
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date = datetime.datetime.now().strftime('%Y-%m-%d')
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else:
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date = datetime.datetime.strptime(
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date, '%Y-%m-%d').strftime('%Y-%m-%d')
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try:
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date = datetime.datetime.strptime(
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date, '%Y-%m-%d').strftime('%Y-%m-%d')
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except ValueError:
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global_config['logger'].error(
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f"日期格式错误，期望格式为 '%Y-%m-%d'，实际输入: {date}")
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return best_models
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# 获取真实价格的八个维度价格
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true_price = pd.read_csv(os.path.join(
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global_config['dataset'], '指标数据.csv'))
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true_price = true_price[['ds', 'y']]
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current_date = datetime.datetime.strptime(date, '%Y-%m-%d')
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# 计算date对应月的一日
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first_day_of_month = current_date.replace(day=1)
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# 计算date对应周的周一
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date_monday = current_date - \
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datetime.timedelta(days=current_date.weekday())
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year = int(date.split('-')[0])
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month = int(date.split('-')[1])
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day = int(date.split('-')[2])
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# 获取真实价格数据
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try:
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true_price = pd.read_csv(os.path.join(
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global_config['dataset'], '指标数据.csv'))[['ds', 'y']]
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except FileNotFoundError:
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global_config['logger'].error(
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f"未找到文件: {os.path.join(global_config['dataset'], '指标数据.csv')}")
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return best_models
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# 计算六月前的年月
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year, month = map(int, date.split('-')[:2])
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if month <= 6:
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year = int(year) - 1
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year -= 1
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month = 12
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else:
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month = month - 6
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month -= 6
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tb = 'v_tbl_predict_pp_prediction_results'
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sql = f'select * from {tb} where data_date >= \'{year}-{month}-01\''
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# 数据库查询对应日期的预测值
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predictresult = global_config['db_mysql'].execute_query(sql)
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if len(predictresult) == 0:
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print('没有预测结果')
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return
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df = pd.DataFrame(predictresult)
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df = df[['data_date', 'model_id']+global_config['price_columns']]
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print('预测结果数量：', df.shape)
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print('预测结果日期范围：', df['data_date'].min(), '到', df['data_date'].max())
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if not predictresult:
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global_config['logger'].info('没有预测结果')
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return best_models
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df = pd.DataFrame(predictresult)[
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['data_date', 'model_id'] + global_config['price_columns']]
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global_config['logger'].info(f'预测结果数量：{df.shape}')
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global_config['logger'].info(
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f'预测结果日期范围：{df["data_date"].min()} 到 {df["data_date"].max()}')
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def query_predict_result(date, model_id, global_config, wd):
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tb = 'v_tbl_predict_pp_prediction_results'
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sql = f'select {wd} from {tb} where data_date = \'{date}\' and model_id = {model_id}'
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predictresult = global_config['db_mysql'].execute_query(sql)
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if not predictresult:
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global_config['logger'].info('没有预测结果')
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return None
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predictresult = float(predictresult[0][wd])
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return predictresult
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def calculate_best_model(price, trend, weektrueprice=None, monthtrueprice=None):
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"""
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计算最佳模型的辅助函数
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:param price: 包含预测价格的数据框
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:param trend: 价格趋势
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:param weektrueprice: 周真实价格均值
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:param monthtrueprice: 月真实价格均值
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:return: 最佳模型的 ID 和名称
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"""
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price = price.copy() # Explicitly create a copy of the DataFrame
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price[global_config['price_columns'][i]
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] = price[global_config['price_columns'][i]].astype(float)
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price = price.dropna(subset=[global_config['price_columns'][i]])
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if weektrueprice is not None:
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true_price_value = weektrueprice
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elif monthtrueprice is not None:
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true_price_value = monthtrueprice
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else:
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true_price_value = true_price[true_price['ds']
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== date]['y'].values[0]
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if not price.empty:
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price.loc[:, 'trueprice'] = true_price_value
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price.loc[:, 'trend'] = np.where(
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price['trueprice'] - price[global_config['price_columns'][i]] > 0, 1, -1)
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price.loc[:, 'abs'] = (price['trueprice'] -
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price[global_config['price_columns'][i]]).abs()
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if trend is not None:
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price = price[price['trend'] == trend]
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if not price.empty:
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price = price[price['abs'] == price['abs'].min()]
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best_model_id = price.iloc[0]['model_id']
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best_model_name = model_id_name_dict[best_model_id]
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return best_model_id, best_model_name
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# Return None if the DataFrame is empty
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return None, None
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# 遍历全局配置中的价格列
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for i, wd in enumerate(global_config['price_columns']):
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# 为每个价格列初始化一个空字典，用于存储最佳模型信息
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global_config['logger'].info(
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f'*********************************************************************************************************计算预测{date}的{wd}最佳模型')
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best_models[wd] = {}
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# 处理第一个价格列，计算次日的最佳模型
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if i == 0:
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# 计算当前日期的前一天日期
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ciridate = (pd.Timestamp(date) - pd.Timedelta(days=1)
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).strftime('%Y-%m-%d')
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# 记录日志，提示开始计算次日的最佳模型
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# 计算当前日期的前一工作日日期
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ciridate = (pd.Timestamp(date) -
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pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d')
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global_config['logger'].info(f'计算预测{date}的次日{ciridate}最佳模型')
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# 记录日志，输出当前日期的真实价格
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global_config['logger'].info(
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f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}')
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# 从数据框中选取需要的列
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price = df[['data_date', wd, 'model_id']]
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# 筛选出数据日期在 ciridate 到 date 之间的数据
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price = price[(price['data_date'] == ciridate)
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| (price['data_date'] == date)]
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# 将价格列的数据类型转换为 float
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price[wd] = price[wd].astype(float)
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# 删除价格列中包含缺失值的行
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price = price.dropna(subset=[wd])
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# 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
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trend = 1 if true_price[true_price['ds'] == date]['y'].values[0] - \
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true_price[true_price['ds'] == ciridate]['y'].values[0] > 0 else -1
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# 为数据框添加真实价格列
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price['trueprice'] = true_price[true_price['ds']
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== date]['y'].values[0]
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# 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
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price['trend'] = np.where(
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price['trueprice'] - price[wd] > 0, 1, -1)
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# 计算预测价格与真实价格差值的绝对值
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price['abs'] = (price['trueprice'] - price[wd]).abs()
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# 筛选出趋势与整体趋势一致的数据
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price = price[price['trend'] ==
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trend]
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# 筛选出预测价格与真实价格差值绝对值最小的数据
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price = price[price['abs'] == price['abs'].min()]
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# 记录日志，输出筛选后的价格数据
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global_config['logger'].info(price)
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# 获取最佳模型的 ID
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best_model_id = price.iloc[0]['model_id']
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# 记录日志，输出次日预测最准确的模型 ID
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global_config['logger'].info(f'{ciridate}预测最准确的模型：{best_model_id}')
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# 将最佳模型的 ID 存入字典
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best_model_id, best_model_name = calculate_best_model(price, trend)
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best_models[wd]['model_id'] = best_model_id
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# 根据模型 ID 获取模型名称并存入字典
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best_models[wd]['model_name'] = model_id_name_dict[best_model_id]
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# 记录日志，输出次日预测最准确的模型名称
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global_config['logger'].info(f'{ciridate}预测最准确的模型名称：{best_models}')
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best_models[wd]['model_name'] = best_model_name
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global_config['logger'].info(f'{ciridate}预测最准确的模型：{best_model_id}')
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global_config['logger'].info(
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f'{ciridate}预测最准确的模型名称：{best_models[wd]}')
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predictresult = query_predict_result(
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date, best_model_id, global_config, wd)
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if predictresult:
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global_config['logger'].info(
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f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
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best_models[wd]['predictresult'] = predictresult
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# best_models 添加日期，次日为date的下一个工作日
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best_models[wd]['date'] = (pd.Timestamp(date) +
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pd.tseries.offsets.BusinessDay(1)).strftime('%Y-%m-%d')
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if i == 1:
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elif i == 1:
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# 计算五个工作日之前的日期
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benzhoudate = (pd.Timestamp(date) - pd.Timedelta(days=7)
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).strftime('%Y-%m-%d')
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# 记录日志，提示开始计算五天前的最佳模型
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benzhoudate = (pd.Timestamp(date) -
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pd.Timedelta(days=7)).strftime('%Y-%m-%d')
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global_config['logger'].info(f'计算预测{date}的五天前{benzhoudate}最佳模型')
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# 记录日志，输出当前日期的真实价格
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global_config['logger'].info(
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f'{date}真实价格：{true_price[true_price["ds"] == date]["y"].values[0]}')
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# 从数据框中选取需要的列
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price = df[['data_date', wd, 'model_id']]
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# 筛选出数据日期在 benzhoudate 到 date 之间的数据
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price = price[(price['data_date'] == benzhoudate)
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| (price['data_date'] == date)]
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# 将价格列的数据类型转换为 float
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price[wd] = price[wd].astype(float)
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# 删除价格列中包含缺失值的行
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price = price.dropna(subset=[wd])
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# 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
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trend = 1 if true_price[true_price['ds'] == date]['y'].values[0] - \
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true_price[true_price['ds'] == benzhoudate]['y'].values[0] > 0 else -1
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# 记录日志，输出五天前预测最准确的模型名称
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global_config['logger'].info(f'实际趋势是：{trend}')
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# 为数据框添加真实价格列
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price['trueprice'] = true_price[true_price['ds']
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== date]['y'].values[0]
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# 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
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price['trend'] = np.where(
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price['trueprice'] - price[wd] > 0, 1, -1)
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# 计算预测价格与真实价格差值的绝对值
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price['abs'] = (price['trueprice'] - price[wd]).abs()
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# 筛选出趋势与整体趋势一致的数据
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price = price[price['trend'] ==
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trend]
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# 筛选出预测价格与真实价格差值绝对值最小的数据
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price = price[price['abs'] == price['abs'].min()]
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# 记录日志，输出筛选后的价格数据
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global_config['logger'].info(price)
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# 获取最佳模型的 ID
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best_model_id = price.iloc[0]['model_id']
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# 记录日志，输出五天前预测最准确的模型 ID
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global_config['logger'].info(
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f'{benzhoudate}预测最准确的模型：{best_model_id}')
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# 将最佳模型的 ID 存入字典
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best_model_id, best_model_name = calculate_best_model(price, trend)
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best_models[wd]['model_id'] = best_model_id
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# 根据模型 ID 获取模型名称并存入字典
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best_models[wd]['model_name'] = model_id_name_dict[best_model_id]
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# 记录日志，输出五天前预测最准确的模型名称
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best_models[wd]['model_name'] = best_model_name
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global_config['logger'].info(
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f'{benzhoudate}预测最准确的模型名称：{best_models}')
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f'{benzhoudate}预测最准确的模型名称：{best_models[wd]}')
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predictresult = query_predict_result(
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date, best_model_id, global_config, wd)
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if predictresult:
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global_config['logger'].info(
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f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
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best_models[wd]['predictresult'] = predictresult
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else:
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best_models[wd]['predictresult'] = None
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best_models[wd]['date'] = (pd.Timestamp(date) +
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pd.tseries.offsets.BusinessDay(5)).strftime('%Y-%m-%d')
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if i == 2:
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# 计算当前周的前两周的周一和周日的日期
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current_date = datetime.datetime.strptime(date, '%Y-%m-%d')
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# 计算前两一周周一
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one_weeks_ago_monday = current_date - \
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datetime.timedelta(days=current_date.weekday() + 7)
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# 计算前一周周日
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one_weeks_ago_sunday = one_weeks_ago_monday + \
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datetime.timedelta(days=6)
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cizhoudate = f"{one_weeks_ago_monday.strftime('%Y-%m-%d')} - {one_weeks_ago_sunday.strftime('%Y-%m-%d')}"
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print(f'计算预测{date}次周最佳模型，前一周日期区间: {cizhoudate}')
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if i == 3:
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# 计算当前周的前两周的周一和周日的日期
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current_date = datetime.datetime.strptime(date, '%Y-%m-%d')
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# 计算前两周周一
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two_weeks_ago_monday = current_date - \
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datetime.timedelta(days=current_date.weekday() + 14)
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# 计算前两周周日
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two_weeks_ago_sunday = two_weeks_ago_monday + \
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datetime.timedelta(days=6)
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gezhoudate = f"{two_weeks_ago_monday.strftime('%Y-%m-%d')} - {two_weeks_ago_sunday.strftime('%Y-%m-%d')}"
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print(f'计算预测{date}隔周最佳模型，前两周日期区间: {gezhoudate}')
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if i == 4:
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# 计算当上月的1日及最后一日
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current_date = pd.Timestamp(date)
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# 获取上月第一天
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elif i in [2, 3]:
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weeks_ago = 1 if i == 2 else 2
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ago_monday = current_date - \
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datetime.timedelta(days=current_date.weekday() + 7 * weeks_ago)
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ago_sunday = ago_monday + datetime.timedelta(days=6)
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ago_date_str = f"{ago_monday.strftime('%Y-%m-%d')} - {ago_sunday.strftime('%Y-%m-%d')}"
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global_config['logger'].info(
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f'计算预测{date}的前{weeks_ago}周{ago_date_str}最佳模型')
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weektrueprice = true_price[(true_price['ds'] >= date_monday.strftime(
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'%Y-%m-%d')) & (true_price['ds'] <= date)]['y'].mean()
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global_config['logger'].info(
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f'当周{date_monday.strftime("%Y-%m-%d")}---{date}真实价格的周均价：{weektrueprice}')
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price = df[['data_date', wd, 'model_id']]
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price = price[(price['data_date'] >= ago_monday) &
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(price['data_date'] <= ago_sunday)]
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price = price.groupby('model_id')[wd].mean().reset_index()
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best_model_id, best_model_name = calculate_best_model(
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price, None, weektrueprice=weektrueprice)
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best_models[wd]['model_id'] = best_model_id
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best_models[wd]['model_name'] = best_model_name
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global_config['logger'].info(
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f'{ago_date_str}预测最准确的模型名称：{best_models[wd]}')
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predictresult = query_predict_result(
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date, best_model_id, global_config, wd)
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if predictresult:
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global_config['logger'].info(
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f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
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best_models[wd]['predictresult'] = predictresult
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else:
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best_models[wd]['predictresult'] = None
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# best_models 添加日期，本周日下个周日
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best_models[wd]['date'] = (pd.Timestamp(ago_sunday) +
|
||||
pd.tseries.offsets.Week(weeks_ago*2)).strftime('%Y-%m-%d')
|
||||
|
||||
elif i in [4, 5, 6, 7]:
|
||||
months_ago = i - 3
|
||||
current_date_ts = pd.Timestamp(date)
|
||||
last_month_first_day = (
|
||||
current_date - pd.offsets.MonthBegin(2)).strftime('%Y-%m-%d')
|
||||
# 获取上月最后一天
|
||||
current_date_ts - pd.offsets.MonthBegin(months_ago)).strftime('%Y-%m-%d')
|
||||
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}')
|
||||
global_config['logger'].info(
|
||||
f'计算预测{date}的{months_ago}月前{last_month_first_day}-{last_month_last_day}最佳模型')
|
||||
monthtrueprice = true_price[(true_price['ds'] >= first_day_of_month.strftime(
|
||||
'%Y-%m-%d')) & (true_price['ds'] <= date)]['y'].mean()
|
||||
global_config['logger'].info(
|
||||
f'当月{first_day_of_month.strftime("%Y-%m-%d")}-{date}真实价格的月均价：{monthtrueprice}')
|
||||
price = df[['data_date', wd, 'model_id']]
|
||||
price = price[(price['data_date'] >= last_month_first_day) & (
|
||||
price['data_date'] <= last_month_last_day)]
|
||||
price = price.groupby('model_id')[wd].mean().reset_index()
|
||||
best_model_id, best_model_name = calculate_best_model(
|
||||
price, None, monthtrueprice=monthtrueprice)
|
||||
best_models[wd]['model_id'] = best_model_id
|
||||
best_models[wd]['model_name'] = best_model_name
|
||||
global_config['logger'].info(
|
||||
f'{last_month_first_day}-{last_month_last_day}预测最准确的模型名称：{best_models[wd]}')
|
||||
predictresult = query_predict_result(
|
||||
date, best_model_id, global_config, wd)
|
||||
if predictresult:
|
||||
global_config['logger'].info(
|
||||
f'最佳模型{best_models[wd]}在{date}预测结果：{predictresult}')
|
||||
best_models[wd]['predictresult'] = predictresult
|
||||
else:
|
||||
best_models[wd]['predictresult'] = None
|
||||
best_models[wd]['date'] = (pd.Timestamp(date) +
|
||||
pd.tseries.offsets.MonthEnd(months_ago+1)).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']]
|
||||
# print(true_price.head())
|
||||
return best_models
|
||||
|
||||
# # 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
|
||||
def plot_pp_predict_result(y_hat, global_config):
|
||||
"""
|
||||
绘制PP期货预测结果的图表
|
||||
"""
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
|
||||
# 获取y的真实值
|
||||
y = pd.read_csv(os.path.join(
|
||||
global_config['dataset'], '指标数据.csv'))[['ds', 'y']]
|
||||
y['ds'] = pd.to_datetime(y['ds'])
|
||||
y = y[y['ds'] < y_hat['ds'].iloc[0]][-30:]
|
||||
|
||||
# 创建图表和子图布局，为表格预留空间
|
||||
fig, ax = plt.subplots(figsize=(16, 9))
|
||||
|
||||
# 对日期列进行排序，确保日期大的在右边
|
||||
y_hat = y_hat.sort_values(by='ds')
|
||||
y = y.sort_values(by='ds')
|
||||
|
||||
# 绘制 y_hat 的折线图，颜色为橙色
|
||||
sns.lineplot(x=y_hat['ds'], y=y_hat['predictresult'],
|
||||
color='orange', label='y_hat', ax=ax, linestyle='--')
|
||||
# 绘制 y 的折线图，颜色为蓝色
|
||||
sns.lineplot(x=y['ds'], y=y['y'], color='blue', label='y', ax=ax)
|
||||
|
||||
# date_str = pd.Timestamp(y_hat["ds"].iloc[0]).strftime('%Y-%m-%d')
|
||||
ax.set_title(f'{global_config["end_time"]} PP期货八大维度 预测价格走势')
|
||||
ax.set_xlabel('日期')
|
||||
ax.set_ylabel('预测结果')
|
||||
ax.tick_params(axis='x', rotation=45)
|
||||
|
||||
# 准备表格数据
|
||||
y_hat = y_hat[['predictresult']].T
|
||||
print(y_hat)
|
||||
y_hat.rename(columns={'day_price': '次日', 'week_price': '本周',
|
||||
'second_week_price': '次周', 'next_week_price': '隔周',
|
||||
'next_month_price': '次月', 'next_february_price': '次二月',
|
||||
'next_march_price': '次三月', 'next_april_price': '次四月',
|
||||
}, inplace=True)
|
||||
columns = y_hat.columns.tolist()
|
||||
data = y_hat.values.tolist()
|
||||
|
||||
# 将日期转换为字符串格式
|
||||
for row in data:
|
||||
if isinstance(row[0], pd.Timestamp):
|
||||
row[0] = row[0].strftime('%Y-%m-%d')
|
||||
|
||||
# 在图表下方添加表格
|
||||
table = ax.table(cellText=data, colLabels=columns,
|
||||
loc='bottom', bbox=[0, -0.6, 1, 0.2])
|
||||
table.auto_set_font_size(False)
|
||||
table.set_fontsize(14)
|
||||
|
||||
plt.tight_layout(rect=[0, 0.1, 1, 1]) # 调整布局，为表格留出空间
|
||||
plt.savefig('pp_predict_result.png')
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
@ -2,7 +2,7 @@
|
||||
|
||||
from lib.dataread import *
|
||||
from config_juxiting import *
|
||||
from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, get_modelsname
|
||||
from lib.tools import SendMail, exception_logger, convert_df_to_pydantic_pp, exception_logger, get_modelsname, plot_pp_predict_result
|
||||
from models.nerulforcastmodels import ex_Model_Juxiting, model_losss_juxiting, pp_export_pdf
|
||||
import datetime
|
||||
import torch
|
||||
@ -559,4 +559,9 @@ if __name__ == '__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)
|
||||
best_bdwd_price = find_best_models(
|
||||
date='2025-07-22', global_config=global_config)
|
||||
y_hat = pd.DataFrame(best_bdwd_price).T[['date', 'predictresult']]
|
||||
y_hat['ds'] = pd.to_datetime(y_hat['date'])
|
||||
# 绘制PP期货预测结果的图表
|
||||
plot_pp_predict_result(y_hat, global_config)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user