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@@ -1,450 +0,0 @@
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-import pandas as pd
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-import numpy as np
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-import xgboost as xgb
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-from xgboost import XGBRegressor
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-from sklearn.metrics import mean_squared_error, r2_score
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-import matplotlib.pyplot as plt
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-from skopt import BayesSearchCV
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-from sklearn.preprocessing import StandardScaler
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-from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, TimeSeriesSplit
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-
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-from Dtool import fill_missing_values, reverse_column
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-from api import fetch_data_by_indicators
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-
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-
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-# 使用示例
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-indicators = ["RBWTICKMc1", "C2406121350446455",'USGGBE02 Index', "Cinjcjc4 index",'injcjc4 index','C2201059138_241106232710','C2406036178','C22411071623523660','C2312081670','REFOC-T-EIA_241114135248','C2304065621_241024124344','REFOC-T-EIA_241114135248','C22503031424010431']
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-df = fetch_data_by_indicators(indicators)
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-df = fetch_data_by_indicators(indicators, "data_input/RBOB.xlsx")
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-
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-
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-# ------------ 全局配置参数 ------------
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-FILE_PATH = 'data_input/RBOB.xlsx'
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-
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-NUM_BOOST_ROUND = 1000
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-RANDOM_STATE = 42
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-USE_HYPERPARAM_TUNING = False # 若 False 则直接使用 xgb.train
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-TARGET_COL = '美国RBOB汽油裂解'
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-TEST_PERIOD = 20
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-SEARCH_MODE = 'random' # 可选 'grid' / 'bayesian' / 'random'
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-SHOW_PLOTS = False
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-ADJUST_FULL_PREDICTIONS = True
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-
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-DEFAULT_PARAMS = {
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- 'objective': 'reg:squarederror',
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- 'learning_rate': 0.1,
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- 'max_depth': 8,
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- 'min_child_weight': 3,
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- 'gamma': 2,
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- 'subsample': 0.85,
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- 'colsample_bytree': 0.75,
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- 'eval_metric': 'rmse',
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- 'seed': 42,
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- 'reg_alpha': 0.45,
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- 'reg_lambda': 1.29,
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-}
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-
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-# —— 因子预处理相关配置 ——
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-FILL_METHODS = {
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- '美国2年通胀预期': 'rolling_mean_5',
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- '美国首次申领失业金人数/4WMA': 'interpolate',
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- '道琼斯旅游与休闲/工业平均指数': 'interpolate',
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- '美国EIA成品油总库存(预测/供应需求3年季节性)': 'interpolate',
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- '美国成品车用汽油倒推产量（预测/汽油库存维持上年季节性）/8WMA': 'interpolate',
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- '美国成品车用汽油炼厂与调和装置净产量/4WMA(预测/上年季节性)超季节性/5年': 'interpolate',
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- '美国炼厂可用产能（路透）(预测)': 'interpolate',
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- '美国炼厂CDU装置检修量（新）': 'interpolate',
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- '美湾单位辛烷值价格(预测/季节性)': 'interpolate',
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- '美国汽油调和组分RBOB库存(预测/线性外推)超季节性/3年': 'interpolate'
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-}
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-
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-SHIFT_CONFIG = [
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- ('美国2年通胀预期', 56, '美国2年通胀预期_提前56天'),
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- ('美国首次申领失业金人数/4WMA', 100, '美国首次申领失业金人数/4WMA_提前100天'),
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- ('美国首次申领失业金人数/4WMA', 112, '美国首次申领失业金人数/4WMA_提前112天'),
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- ('道琼斯旅游与休闲/工业平均指数', 14, '道琼斯旅游与休闲/工业平均指数_提前14天'),
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- ('美国EIA成品油总库存(预测/供应需求3年季节性)', 15,
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- '美国EIA成品油总库存(预测/供应需求3年季节性)_提前15天'),
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- ('美国成品车用汽油炼厂与调和装置净产量/4WMA(预测/上年季节性)超季节性/5年',
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- 30,
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- '美国成品车用汽油炼厂与调和装置净产量/4WMA(预测/上年季节性)超季节性/5年_提前30天'),
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- ('美国炼厂CDU装置检修量（新）', 30, '美国炼厂CDU装置检修量（新）_提前30天'),
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- ('美国炼厂可用产能（路透）(预测)', 100,
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- '美国炼厂可用产能（路透）(预测)_提前100天')
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-]
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-
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-REVERSE_CONFIG = [
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- ('美国首次申领失业金人数/4WMA',
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- '美国首次申领失业金人数/4WMA_逆序'),
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- ('美国首次申领失业金人数/4WMA_提前100天',
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- '美国首次申领失业金人数/4WMA_提前100天_逆序'),
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- ('美国首次申领失业金人数/4WMA_提前112天',
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- '美国首次申领失业金人数/4WMA_提前112天_逆序'),
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- ('美国EIA成品油总库存(预测/供应需求3年季节性)',
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- '美国EIA成品油总库存(预测/供应需求3年季节性)_逆序'),
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- ('美国EIA成品油总库存(预测/供应需求3年季节性)_提前15天',
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- '美国EIA成品油总库存(预测/供应需求3年季节性)_提前15天_逆序'),
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- ('美国炼厂可用产能（路透）(预测)_提前100天',
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- '美国炼厂可用产能（路透）(预测)_逆序'),
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- ('美国汽油调和组分RBOB库存(预测/线性外推)超季节性/3年',
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- '美国汽油调和组分RBOB库存(预测/线性外推)超季节性/3年_逆序')
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-]
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-
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-SPECIAL_REVERSE = {
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- '美国汽油调和组分RBOB库存(预测/线性外推)超季节性/3年_逆序_2022-01-01': {
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- 'base_column': '美国汽油调和组分RBOB库存(预测/线性外推)超季节性/3年_逆序',
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- 'condition_date': pd.Timestamp('2022-01-01')
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- }
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-}
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-
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-
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-# ------------ 数据加载与预处理 ------------
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-def load_and_preprocess_data(file_path):
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- df = pd.read_excel(file_path, sheet_name='Sheet1')
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- print(df)
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- df.rename(columns={'DataTime': 'Date'}, inplace=True)
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- df['Date'] = pd.to_datetime(df['Date'], errors='coerce')
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- df.set_index('Date', inplace=True)
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-
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- full_range = pd.date_range(df.index.min(),df.index.max(),freq='D')
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- df_daily = df.reindex(full_range).reset_index()
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- df_daily.rename(columns={'index': 'Date'}, inplace=True)
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- df_daily = fill_missing_values(df_daily,FILL_METHODS,return_only_filled=False)
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- for col, days, new_col in SHIFT_CONFIG:
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- df_daily[new_col] = df_daily[col].shift(days)
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-
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- last_idx = df_daily[TARGET_COL].last_valid_index()
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- last_day = df_daily.loc[last_idx, 'Date']
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-
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- df_daily = df_daily[(df_daily['Date'] >= '2009-08-01') &(df_daily['Date'] <= last_day +pd.Timedelta(days=30))]
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- df_daily = df_daily[df_daily['Date'].dt.weekday < 5]
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-
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- for base, new in REVERSE_CONFIG:
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- df_daily[new] = reverse_column(df_daily, base)
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- for col, cfg in SPECIAL_REVERSE.items():
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- df_daily[col] = np.where(df_daily['Date'] >= cfg['condition_date'],df_daily[cfg['base_column']],np.nan)
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-
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- df_daily = df_daily[(df_daily['Date'] > last_day)|df_daily[TARGET_COL].notna()]
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-
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- return df_daily, last_day
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-
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-
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-# ------------ 划分与特征构建 ------------
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-def split_and_build_features(df_daily, last_day):
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- train = df_daily[df_daily['Date'] <= last_day].copy()
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- test = train.tail(TEST_PERIOD).copy()
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- train = train.iloc[:-TEST_PERIOD].copy()
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- future = df_daily[df_daily['Date'] > last_day].copy()
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-
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- feature_columns = [
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- '美湾单位辛烷值价格(预测/季节性)',
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- '美国炼厂CDU装置检修量（新）_提前30天',
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- '美国EIA成品油总库存(预测/供应需求3年季节性)_提前15天_逆序',
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- '美国首次申领失业金人数/4WMA_提前100天_逆序',
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- '美国成品车用汽油倒推产量（预测/汽油库存维持上年季节性）/8WMA',
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- '美国成品车用汽油炼厂与调和装置净产量/4WMA(预测/上年季节性)超季节性/5年_提前30天',
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- '美国汽油调和组分RBOB库存(预测/线性外推)超季节性/3年_逆序_2022-01-01'
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- ]
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-
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- X_train = train[feature_columns]
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- y_train = train[TARGET_COL]
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- X_test = test[feature_columns]
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- y_test = test[TARGET_COL]
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- X_future = future[feature_columns]
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-
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- return X_train, y_train, X_test, y_test, X_future, train, test, future
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-
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-
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-# ------------ 特征缩放与异常值权重 ------------
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-def scale_and_weight_features(X_train, X_test, X_future):
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- scaler = StandardScaler()
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- X_tr = scaler.fit_transform(X_train)
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- X_te = scaler.transform(X_test)
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- X_fu = scaler.transform(X_future)
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- return scaler, X_tr, X_te, X_fu
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-
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-
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-def detect_outliers_weights(X,weight_normal=1.0,weight_outlier=0.05,threshold=3):
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- z = np.abs((X - X.mean()) / X.std())
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- mask = (z > threshold).any(axis=1)
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- return np.where(mask, weight_outlier, weight_normal)
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-
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-
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-# ------------ 模型训练 ------------
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-def train_model_with_tuning(X_tr, y_tr, X_te, y_te, weights, use_tuning):
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- if use_tuning:
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- param_dist = {
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- 'learning_rate': list(np.arange(0.01, 0.11, 0.01)),
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- 'max_depth': list(range(4, 11)),
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- 'min_child_weight': list(range(1, 6)),
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- 'gamma': list(np.arange(0, 0.6, 0.1)),
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- 'subsample': list(np.arange(0.5, 1.01, 0.05)),
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- 'colsample_bytree': list(np.arange(0.5, 1.01, 0.05)),
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- 'reg_alpha': [0, 0.1, 0.2, 0.3, 0.4, 0.45, 0.5],
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- 'reg_lambda': list(np.arange(1.0, 1.6, 0.1))
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- }
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- xgb_reg = XGBRegressor(objective='reg:squarederror',
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- eval_metric='rmse',
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- n_estimators=NUM_BOOST_ROUND,
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- seed=RANDOM_STATE)
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- tscv = TimeSeriesSplit(n_splits=3)
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- if SEARCH_MODE == 'grid':
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- search = GridSearchCV(xgb_reg,
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- param_grid=param_dist,
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- scoring='neg_mean_squared_error',
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- cv=tscv,
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- verbose=1,
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- n_jobs=-1)
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- elif SEARCH_MODE == 'bayesian':
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- search = BayesSearchCV(xgb_reg,
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- search_spaces=param_dist,
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- n_iter=50,
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- scoring='neg_mean_squared_error',
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- cv=tscv,
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- random_state=RANDOM_STATE,
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- verbose=1,
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- n_jobs=-1)
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- else:
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- search = RandomizedSearchCV(xgb_reg,
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- param_distributions=param_dist,
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- n_iter=50,
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- scoring='neg_mean_squared_error',
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- cv=tscv,
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- random_state=RANDOM_STATE,
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- verbose=1,
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- n_jobs=-1)
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- search.fit(X_tr, y_tr, sample_weight=weights)
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- best_model = search.best_estimator_
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- print("调优后的最佳参数:", search.best_params_)
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- best_model.fit(X_tr, y_tr,
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- eval_set=[(X_te, y_te)],
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- early_stopping_rounds=20,
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- verbose=200)
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- else:
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- dtrain = xgb.DMatrix(X_tr, label=y_tr, weight=weights)
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- dtest = xgb.DMatrix(X_te, label=y_te)
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- best_model = xgb.train(DEFAULT_PARAMS,
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- dtrain,
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- num_boost_round=NUM_BOOST_ROUND,
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- evals=[(dtrain, 'Train'),
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- (dtest, 'Test')],
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- verbose_eval=False)
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- return best_model
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-
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-
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-# ------------ 评估与预测 ------------
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-def evaluate_and_predict(model, scaler, X_tr, y_tr, X_te, y_te, X_fu,
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- use_tuning):
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- X_tr_s = scaler.transform(X_tr)
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- X_te_s = scaler.transform(X_te)
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- X_fu_s = scaler.transform(X_fu)
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-
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- if isinstance(model, xgb.Booster):
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- y_tr_pred = model.predict(xgb.DMatrix(X_tr_s))
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- y_te_pred = model.predict(xgb.DMatrix(X_te_s))
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- y_fu_pred = model.predict(xgb.DMatrix(X_fu_s))
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- else:
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- y_tr_pred = model.predict(X_tr_s)
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- y_te_pred = model.predict(X_te_s)
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- y_fu_pred = model.predict(X_fu_s)
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-
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- print("Train MSE:", mean_squared_error(y_tr, y_tr_pred),
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- "Test MSE:", mean_squared_error(y_te, y_te_pred))
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- if len(y_te) >= 2:
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- print("Train R2:", r2_score(y_tr, y_tr_pred),
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- "Test R2:", r2_score(y_te, y_te_pred))
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- else:
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- print("Test 样本不足，跳过 R² 计算")
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-
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- return y_tr_pred, y_te_pred, y_fu_pred
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-
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-
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-# ------------ 结果后处理（生成日度 & 月度 DataFrame） ------------
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-def merge_and_prepare_df(train, test, future, y_te_pred, y_fu_pred):
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- # 合并历史与未来预测
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- test = test.copy()
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- future = future.copy()
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- test['预测值'] = y_te_pred
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- future['预测值'] = y_fu_pred
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-
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- hist_actual = pd.concat([
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- train[train['Date'].dt.year >= 2023][['Date', TARGET_COL]],
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- test[['Date', TARGET_COL]]
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- ])
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- hist_actual.columns = ['Date', '实际值']
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-
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- future_pred = future[future['Date'] >= '2022-08-01'][['Date', '预测值']].rename(columns={'预测值': TARGET_COL}).copy()
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-
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- last_val = float(hist_actual.iloc[-1]['实际值'])
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- future_pred.iloc[0, 1] = last_val
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-
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- merged = pd.merge(hist_actual, future_pred,on='Date', how='outer').sort_values('Date', ascending=False)
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- daily_df = merged.copy()
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-
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- # 月度重采样
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- monthly_df = daily_df.copy()
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- monthly_df['Date'] = pd.to_datetime(monthly_df['Date'])
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- monthly_df.set_index('Date', inplace=True)
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- monthly_df = monthly_df.resample('ME').mean().reset_index()
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-
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- # 方向准确率：仅在实际值和预测值都非空时计算
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- pred_dir = np.sign(monthly_df[TARGET_COL].diff())
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- true_dir = np.sign(monthly_df['实际值'].diff())
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- valid = monthly_df[TARGET_COL].notna() & monthly_df['实际值'].notna()
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- monthly_df['方向准确率'] = np.where(valid & (pred_dir == true_dir), '正确',
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- np.where(valid & (pred_dir != true_dir), '错误', np.nan))
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- # 绝对偏差
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- monthly_df['绝对偏差'] = (monthly_df[TARGET_COL] - monthly_df['实际值']).abs()
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- # 降序 & 打印
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|
- monthly_df = monthly_df.sort_values('Date', ascending=False).reset_index(drop=True)
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|
-
|
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|
- return daily_df, monthly_df
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|
-
|
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|
-
|
|
|
-
|
|
|
-def generate_and_fill_excel(
|
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- daily_df,
|
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|
- monthly_df,
|
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|
- target_name, # 写入的“预测标的”显示名
|
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|
- classification, # 列表页-分类
|
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|
- model_framework, # 列表页-模型框架
|
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|
- creator, # 列表页-创建人
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- pred_date, # 列表页-预测日期
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|
- frequency, # 列表页-预测频度
|
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|
- output_path='update.xlsx'
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|
-):
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|
- with pd.ExcelWriter(output_path, engine='xlsxwriter') as writer:
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|
|
- workbook = writer.book
|
|
|
-
|
|
|
- # —— 计算三个汇总值 ——
|
|
|
- # 1) 测试值：最新月度的预测值
|
|
|
- test_value = monthly_df[TARGET_COL].iloc[0]
|
|
|
- # 2) 方向准确率：正确数 / 有效数
|
|
|
- total = monthly_df['方向准确率'].notna().sum()
|
|
|
- correct = (monthly_df['方向准确率'] == '正确').sum()
|
|
|
- direction_accuracy = f"{correct/total:.2%}" if total > 0 else ""
|
|
|
- # 3) 平均绝对偏差
|
|
|
- absolute_deviation = monthly_df['绝对偏差'].mean()
|
|
|
-
|
|
|
- # ========= 列表页 =========
|
|
|
- ws_list = workbook.add_worksheet('列表页')
|
|
|
- writer.sheets['列表页'] = ws_list
|
|
|
-
|
|
|
- headers = ['预测标的','分类','模型框架','创建人','预测日期','测试值','预测频度','方向准确率','绝对偏差']
|
|
|
-
|
|
|
- ws_list.write_row(0, 0, headers)
|
|
|
- ws_list.write_row(1, 0, [
|
|
|
- target_name,
|
|
|
- classification,
|
|
|
- model_framework,
|
|
|
- creator,
|
|
|
- pred_date,
|
|
|
- test_value,
|
|
|
- frequency,
|
|
|
- direction_accuracy,
|
|
|
- absolute_deviation
|
|
|
- ])
|
|
|
-
|
|
|
- # ========= 详情页 =========
|
|
|
- detail_df = monthly_df[['Date', '实际值', TARGET_COL, '方向准确率', '绝对偏差']].copy()
|
|
|
- detail_df.columns = ['指标日期','实际值','预测值','方向','偏差率']
|
|
|
-
|
|
|
- detail_df.to_excel(writer,sheet_name='详情页',index=False,header=False,startrow=2)
|
|
|
-
|
|
|
- ws_detail = writer.sheets['详情页']
|
|
|
- ws_detail.write(0, 0, target_name)
|
|
|
- ws_detail.write_row(1, 0, ['指标日期','实际值','预测值','方向','偏差率'])
|
|
|
-
|
|
|
- # ========= 日度数据表 =========
|
|
|
- daily_out = daily_df[['Date', '实际值', TARGET_COL]].copy()
|
|
|
- daily_out.columns = ['指标日期','实际值','预测值']
|
|
|
-
|
|
|
- daily_out.to_excel(writer,sheet_name='日度数据表',index=False,header=False,startrow=2)
|
|
|
-
|
|
|
- ws_daily = writer.sheets['日度数据表']
|
|
|
- ws_daily.write(0, 0, target_name)
|
|
|
- ws_daily.write_row(1, 0, ['指标日期','实际值','预测值'])
|
|
|
-
|
|
|
- print(f"已生成并填充 {output_path}")
|
|
|
-
|
|
|
-
|
|
|
-# ------------ 全量训练与预测 ------------
|
|
|
-def train_full_model_and_predict(X_train, y_train, X_test, y_test, X_future):
|
|
|
- X_all = pd.concat([X_train, X_test])
|
|
|
- y_all = pd.concat([y_train, y_test])
|
|
|
- scaler_all = StandardScaler().fit(X_all)
|
|
|
- X_all_s = scaler_all.transform(X_all)
|
|
|
- X_fu_s = scaler_all.transform(X_future)
|
|
|
-
|
|
|
- model = XGBRegressor(**DEFAULT_PARAMS, n_estimators=NUM_BOOST_ROUND)
|
|
|
- model.fit(X_all_s, y_all)
|
|
|
- y_fu_full = model.predict(X_fu_s)
|
|
|
-
|
|
|
- return model, y_fu_full, scaler_all
|
|
|
-
|
|
|
-
|
|
|
-# ------------ 可视化 ------------
|
|
|
-def plot_final_predictions(train, y_tr, y_tr_pred, test, y_te, y_te_pred,
|
|
|
- future, last_day):
|
|
|
- plt.figure(figsize=(15, 6))
|
|
|
- plt.plot(train['Date'], y_tr, label='Train True')
|
|
|
- plt.plot(train['Date'], y_tr_pred, label='Train Pred')
|
|
|
- plt.plot(test['Date'], y_te, label='Test True', alpha=0.7)
|
|
|
- plt.plot(test['Date'], y_te_pred, label='Test Pred')
|
|
|
- plt.plot(future['Date'], future['预测值'], label='Future Pred')
|
|
|
- plt.axvline(test['Date'].iloc[0], color='gray', linestyle='--')
|
|
|
- plt.axvline(last_day, color='black', linestyle='--')
|
|
|
- plt.legend()
|
|
|
- plt.xlabel('Date')
|
|
|
- plt.ylabel(TARGET_COL)
|
|
|
- plt.title('Prediction Visualization')
|
|
|
- plt.grid(True)
|
|
|
- plt.show()
|
|
|
-
|
|
|
-
|
|
|
-# ------------ 主函数 ------------
|
|
|
-def main():
|
|
|
- df_daily, last_day = load_and_preprocess_data(FILE_PATH)
|
|
|
-
|
|
|
- X_tr, y_tr, X_te, y_te, X_fu, train, test, future = split_and_build_features(df_daily, last_day)
|
|
|
-
|
|
|
- scaler, X_tr_s, X_te_s, X_fu_s = scale_and_weight_features(X_tr, X_te, X_fu)
|
|
|
-
|
|
|
- weights = detect_outliers_weights(X_tr_s)
|
|
|
-
|
|
|
- model = train_model_with_tuning(X_tr_s, y_tr, X_te_s, y_te, weights,USE_HYPERPARAM_TUNING)
|
|
|
-
|
|
|
- y_tr_pred, y_te_pred, y_fu_pred = evaluate_and_predict(model, scaler, X_tr, y_tr, X_te, y_te, X_fu,USE_HYPERPARAM_TUNING)
|
|
|
-
|
|
|
- daily_df, monthly_df = merge_and_prepare_df(train, test, future,y_te_pred, y_fu_pred)
|
|
|
-
|
|
|
- print(monthly_df)
|
|
|
- print(daily_df)
|
|
|
-
|
|
|
- generate_and_fill_excel(
|
|
|
- daily_df,
|
|
|
- monthly_df,
|
|
|
- target_name='美国RBOB汽油裂解',
|
|
|
- classification='原油',
|
|
|
- model_framework='XGBoost',
|
|
|
- creator='张立舟',
|
|
|
- pred_date='2024/11/11',
|
|
|
- frequency='月度',
|
|
|
- output_path='update.xlsx'
|
|
|
- )
|
|
|
-
|
|
|
- full_model, y_fu_full, scaler_full = train_full_model_and_predict(X_tr, y_tr, X_te, y_te, X_fu)
|
|
|
-
|
|
|
- if ADJUST_FULL_PREDICTIONS:
|
|
|
- offset = y_te.iloc[-1] - y_fu_full[0]
|
|
|
- y_fu_full += offset
|
|
|
-
|
|
|
- if SHOW_PLOTS:
|
|
|
- plot_final_predictions(
|
|
|
- train, y_tr, y_tr_pred, test, y_te, y_te_pred,
|
|
|
- future.assign(预测值=y_fu_full), last_day)
|
|
|
-
|
|
|
- return daily_df, monthly_df
|
|
|
-
|
|
|
-if __name__ == '__main__':
|
|
|
- daily_df, monthly_df = main()
|
ziqidai11