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  1. Bert_predict.py +216 -0
  2. Best-complete-model.h5 +3 -0
  3. bert_training.csv +728 -0
  4. news_2025-09-12.csv +18 -0
Bert_predict.py ADDED
@@ -0,0 +1,216 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import tensorflow as tf
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+ import transformers
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+ from tensorflow import keras
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+ from transformers import BertTokenizer, TFBertModel
6
+ import pandas as pd
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+ from datetime import date, timedelta
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+ import requests
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+ import time
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+ from typing import List, Optional, Dict, Any
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+
12
+ class BertPredictor:
13
+ """
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+ 用於加載 BERT 模型、獲取新聞並對其進行股市影響預測的類別。
15
+ """
16
+ def __init__(self, tokenizer_name: str = 'hfl/rbt3', max_news_per_keyword: int = 5):
17
+ """
18
+ 初始化預測器,載入分詞器、預訓練模型並獲取新聞。
19
+
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+ Args:
21
+ tokenizer_name (str): BERT 分詞器的名稱。
22
+ max_news_per_keyword (int): 每個關鍵字要抓取的新聞最大數量。
23
+ """
24
+ # --- 路徑和檔案名稱設置 ---
25
+ self.current_dir = os.path.dirname(os.path.abspath(__file__))
26
+ self.model_path = os.path.join(self.current_dir, 'Best-complete-model.h5')
27
+
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+ # 檔案名稱用今天的日期,但內容是昨天的
29
+ today_date_str = date.today().strftime('%Y-%m-%d')
30
+ self.news_csv_path = os.path.join(self.current_dir, f'news_{today_date_str}.csv')
31
+
32
+ # 用於API查詢的日期仍然是昨天
33
+ self.target_date = date.today() - timedelta(days=1)
34
+ self.target_date_str = self.target_date.strftime('%Y-%m-%d')
35
+
36
+ # --- GNews API 設定 ---
37
+ self.api_key = "fd12e84a158c7d9eaf31627aaae0927a" # 請替換成您的 API Key
38
+ self.base_url = "https://gnews.io/api/v4/search"
39
+ self.keywords = ["Fed", "Interest Rates", "Inflation", "Tariffs", "ADR", "Treasury Yields"]
40
+ self.max_news_per_keyword = max_news_per_keyword
41
+
42
+ # --- 模型相關設置 ---
43
+ self.text_max_length = 256
44
+ self.tokenizer = BertTokenizer.from_pretrained(tokenizer_name)
45
+
46
+ # 載入最佳模型
47
+ print("正在加載模型...")
48
+ self.model = keras.models.load_model(
49
+ self.model_path,
50
+ custom_objects={'TFBertModel': TFBertModel}
51
+ )
52
+ print("模型加載完成。")
53
+
54
+ # --- 初始化流程 ---
55
+ self._check_file_and_get_news_if_needed()
56
+
57
+
58
+ # --- 內部使用方法 ---
59
+
60
+ def _encode_texts(self, texts: list):
61
+ """將文本轉換為 BERT 輸入格式 (input_ids, attention_mask)"""
62
+ return self.tokenizer(
63
+ texts,
64
+ max_length=self.text_max_length,
65
+ padding='max_length',
66
+ truncation=True,
67
+ return_tensors='tf'
68
+ )
69
+
70
+ def _predict(self, new_text: str) -> float:
71
+ """
72
+ 對單一新聞文本進行預測。
73
+
74
+ Args:
75
+ new_text (str): 待預測的新聞文本。
76
+
77
+ Returns:
78
+ float: 預測的股市影響分數。
79
+ """
80
+ new_encoding = self._encode_texts([new_text])
81
+ predicted_score = self.model.predict(dict(new_encoding), verbose=0)[0][0]
82
+ return float(predicted_score)
83
+
84
+ def _check_file_and_get_news_if_needed(self):
85
+ """
86
+ 檢查今天的 news csv 是否存在。如果不存在,則呼叫 _get_news() 進行抓取。
87
+ """
88
+ if not os.path.exists(self.news_csv_path):
89
+ print(f"找不到今天的檔案 '{os.path.basename(self.news_csv_path)}'。")
90
+ self._get_news()
91
+ else:
92
+ print(f"已找到今天的檔案 '{os.path.basename(self.news_csv_path)}',將跳過新聞抓取步驟。")
93
+
94
+ def _get_news(self):
95
+ """
96
+ 使用 GNews API 抓取目標日期(昨天)的新聞,即時預測分數並儲存。
97
+ """
98
+ print("開始執行新聞抓取與即時預測...")
99
+ print(f"搜尋日期設定為:{self.target_date_str} (將存檔至檔名含今日日期的檔案)")
100
+
101
+ results = []
102
+ for kw in self.keywords:
103
+ params = {
104
+ "q": kw, "lang": "en", "country": "us", "max": self.max_news_per_keyword,
105
+ "in": "title,description", "apikey": self.api_key,
106
+ "from": f"{self.target_date_str}T00:00:00Z",
107
+ "to": f"{self.target_date_str}T23:59:59Z"
108
+ }
109
+ try:
110
+ response = requests.get(self.base_url, params=params)
111
+ response.raise_for_status()
112
+ data = response.json()
113
+ print(f"關鍵字 '{kw}' 成功抓取到: {data.get('totalArticles', 0)} 則新聞")
114
+ if "articles" in data:
115
+ for article in data["articles"]:
116
+ published_date = pd.to_datetime(article['publishedAt']).strftime('%Y-%m-%d')
117
+ news_content = f"{article['title']} - {article.get('description', '')}"
118
+ score = self._predict(news_content)
119
+ results.append({
120
+ "時間": published_date,
121
+ "分數": score,
122
+ "內容": news_content
123
+ })
124
+ except requests.exceptions.RequestException as e:
125
+ print(f"錯誤:API 請求失敗 - {e}")
126
+ continue
127
+ finally:
128
+ time.sleep(0.5)
129
+
130
+ if not results:
131
+ print("抓取完成。未找到任何相關新聞。")
132
+ df_to_save = pd.DataFrame(columns=['時間', '分數', '內容'])
133
+ else:
134
+ print(f"成功抓取並預測 {len(results)} 筆新聞。")
135
+ df_to_save = pd.DataFrame(results)
136
+
137
+ try:
138
+ print(f"正在將結果寫入檔案 '{self.news_csv_path}'...")
139
+ df_to_save.to_csv(self.news_csv_path, index=False, encoding='utf-8-sig')
140
+ print(f"成功!檔案已儲存至 '{self.news_csv_path}'。")
141
+ except IOError as e:
142
+ print(f"錯誤:寫入檔案失敗 - {e}")
143
+
144
+ # --- 公開方法 ---
145
+
146
+ def get_news_index(self) -> Optional[float]:
147
+ """
148
+ 從今天的 news csv 檔案中讀取所有新聞分數並回傳其平均值。
149
+
150
+ Returns:
151
+ float or None: 所有新聞的平均分數,如果檔案不存在或為空則回傳 None。
152
+ """
153
+ try:
154
+ df = pd.read_csv(self.news_csv_path)
155
+ if df.empty or '分數' not in df.columns:
156
+ print(f"'{self.news_csv_path}' 為空或缺少 '分數' 欄位。")
157
+ return None
158
+
159
+ average_score = pd.to_numeric(df['分數'], errors='coerce').mean()
160
+ return average_score if pd.notna(average_score) else None
161
+
162
+ except FileNotFoundError:
163
+ print(f"錯誤:找不到檔案 '{self.news_csv_path}'。")
164
+ return None
165
+ except Exception as e:
166
+ print(f"讀取或計算 CSV 檔案時發生錯誤:{e}")
167
+ return None
168
+
169
+ def get_news(self) -> Optional[List[str]]:
170
+ """
171
+ 讀取今天的 news csv 檔案,並以 list 格式回傳分數絕對值最高的三則新聞內容。
172
+ """
173
+ try:
174
+ df = pd.read_csv(self.news_csv_path)
175
+ df['分數'] = pd.to_numeric(df['分數'], errors='coerce')
176
+ df.dropna(subset=['分數'], inplace=True)
177
+ if df.empty:
178
+ return []
179
+
180
+ df['abs_score'] = df['分數'].abs()
181
+ top_3_news_df = df.sort_values(by='abs_score', ascending=False).head(3)
182
+
183
+ # 將 '內容' 欄位轉換為 list of strings
184
+ return top_3_news_df['內容'].tolist()
185
+
186
+ except FileNotFoundError:
187
+ print(f"錯誤:找不到檔案 '{self.news_csv_path}'。")
188
+ return None
189
+ except Exception as e:
190
+ print(f"讀取或處理 CSV 檔案時發生錯誤:{e}")
191
+ return None
192
+
193
+ # --- 主程式區塊:只有當腳本直接執行時才運行 ---
194
+ if __name__ == "__main__":
195
+ if not os.path.exists(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'Best-complete-model.h5')):
196
+ print("錯誤:找不到模型文件 'Best-complete-model.h5'。請先訓練模型並確保它已保存。")
197
+ else:
198
+ predictor = BertPredictor(max_news_per_keyword=3)
199
+ print("\n" + "="*30)
200
+ avg_score = predictor.get_news_index()
201
+ if avg_score is not None:
202
+ print(f"從新聞檔案中計算出的平均分數為:{avg_score:.4f}")
203
+ else:
204
+ print("無法計算新聞檔案中的平均分數。")
205
+
206
+ print("\n" + "="*30)
207
+ top_news_content = predictor.get_news()
208
+ if top_news_content:
209
+ print("\n分數絕對值最高的三則新聞內容:")
210
+ for i, content in enumerate(top_news_content):
211
+ print(f" {i+1}. {content}")
212
+ elif top_news_content == []:
213
+ print("新聞檔案中無有效內容可顯示。")
214
+ else:
215
+ print("無法獲取最高分新聞。")
216
+
Best-complete-model.h5 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:29f23bf897642801e113b143bcdb9c82bbe04f72dd2ab9b6937d3300fe2ec919
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+ size 461982212
bert_training.csv ADDED
@@ -0,0 +1,728 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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news_2025-09-12.csv ADDED
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1
+ 時間,分數,內容
2
+ 2025-09-11,0.41315504908561707,"Sticky Inflation Data Complicates Fed’s Next Move - Market Analysis by covering: Walmart Inc, JM Smucker Company, Best Buy Co Inc, Target Corporation. Read 's Market Analysis on Investing.com"
3
+ 2025-09-11,0.27886176109313965,"Inflation And Unemployment Are Soaring-The Fed Is Now Trapped - U.S. inflation accelerated in August while jobless claims hit a four-year high, leaving the Federal Reserve trapped."
4
+ 2025-09-11,0.07986071705818176,"Inflation ticked up in August; September Fed rate cut in play - U.S. inflation picked up moderately in August, with prices up 2.9% from a year earlier. The Federal Reserve next meets Sept. 16-17."
5
+ 2025-09-11,0.6202773451805115,"ECB Holds Its Fire Despite Political Uncertainty and Tariffs - Welcome to the Brussels Edition. I’m Suzanne Lynch, Bloomberg’s Brussels bureau chief, bringing you the latest from the EU each weekday. Make sure you're signed up.The European Central Bank opted to leave interest rates steady, a sign it believes the euro zone economy can weather the impact of Donald Trump’s tariffs and continuing political uncertainty in France.Within the last hour, the bank announced that the deposit rate will remain unchanged at 2% – widely in line with economists’ expectatio"
6
+ 2025-09-11,0.33216044306755066,"Lower After Inflation Data - The headline news is sending markets, bitcoin included, lower, but isn't likely to derail the Fed from trimming interest rates next week."
7
+ 2025-09-11,0.5494590401649475,What's next for mortgage rates if Fed cuts rates in September? - A Michigan credit union is offering a mortgage rate sale for a limited time. But what's next for interest rates and the overall mortgage market?
8
+ 2025-09-11,0.41315504908561707,"Sticky Inflation Data Complicates Fed’s Next Move - Market Analysis by covering: Walmart Inc, JM Smucker Company, Best Buy Co Inc, Target Corporation. Read 's Market Analysis on Investing.com"
9
+ 2025-09-11,-0.3198307156562805,"Dow Jones Today: Stock Futures Rise as Wall Street Awaits Rate Cuts; AI Enthusiasm Lifts Oracle Stock Higher - U.S. stock futures were higher in premarket trading on Thursday after consumer inflation data came in as expected, adding to Wall Street's conviction that the Federal Reserve will resume rate cuts next week."
10
+ 2025-09-11,-0.04599543288350105,"US inflation rose last month as gas, food costs jumped - Inflation moved higher last month as the price of gas, groceries, hotel rooms and airfare rose, along with the cost of clothes and used cars."
11
+ 2025-09-11,0.6202773451805115,"ECB Holds Its Fire Despite Political Uncertainty and Tariffs - Welcome to the Brussels Edition. I’m Suzanne Lynch, Bloomberg’s Brussels bureau chief, bringing you the latest from the EU each weekday. Make sure you're signed up.The European Central Bank opted to leave interest rates steady, a sign it believes the euro zone economy can weather the impact of Donald Trump’s tariffs and continuing political uncertainty in France.Within the last hour, the bank announced that the deposit rate will remain unchanged at 2% – widely in line with economists’ expectatio"
12
+ 2025-09-11,0.3247354328632355,"Supreme Court to quickly consider if President Donald Trump has power to impose sweeping tariffs - The case was brought by Vernon Hills-based toy makers Learning Resources and Hand2Mind, who say tariffs have caused companies a lot of ""pain."""
13
+ 2025-09-11,0.17200306057929993,"Inflation shoots higher as Trump tariffs ripple through economy - Consumer prices rose 2.9% in August from a year earlier, up from 2.7% the previous month and the biggest increase since January."
14
+ 2025-09-11,-0.4757874608039856,"Royal Bank of Canada Buys 1,705,324 Shares of VNET Group, Inc. - Unsponsored ADR $VNET - Read Royal Bank of Canada Buys 1,705,324 Shares of VNET Group, Inc. – Unsponsored ADR $VNET at ETF Daily News"
15
+ 2025-09-11,-0.10765713453292847,PDD Holdings Inc. Sponsored ADR (NASDAQ:PDD) Given Consensus Rating of “Moderate Buy” by Brokerages - Read PDD Holdings Inc. Sponsored ADR (NASDAQ:PDD) Given Consensus Rating of “Moderate Buy” by Brokerages at ETF Daily News
16
+ 2025-09-11,-0.11705034971237183,ARM Holdings PLC Sponsored ADR (NASDAQ:ARM) Receives $169.67 Average Price Target from Analysts - Read ARM Holdings PLC Sponsored ADR (NASDAQ:ARM) Receives $169.67 Average Price Target from Analysts at ETF Daily News
17
+ 2025-09-11,0.4297889173030853,Bond Traders May Be Too Bullish Going Into Inflation Report - Two-year Treasury yields have dropped to the lowest since April on rate-cut bets.
18
+ 2025-09-11,0.03189132362604141,10-year Treasury yield rises ahead of U.S. inflation print - Treasury yields were broadly higher Thursday morning ahead of the second of two key inflation prints this week.