✨ feat(news_analyze.py): 支持缓存分析结果并提取重要新闻原文

news_analyze.py

@@ -146,14 +146,34 @@ def get_news_by_date(date_str, force_update=False):
     return None
 
 
-def analyze_news_in_batches(news_df):
+def analyze_news_in_batches(news_df, date_str=None, force_analyze=False):
-    """将新闻按批次分析后汇总"""
+    """将新闻按批次分析后汇总，支持缓存分析结果"""
     if news_df is None or news_df.empty:
         return None
 
+    # 确保temp目录存在
+    temp_dir = ensure_temp_dir()
+
     config = load_config()
     openai_config = config['openai_api']
 
+    # 如果提供了日期，尝试读取缓存的分析结果
+    cache_file = None
+    if date_str:
+        date_file = datetime.datetime.strptime(date_str, '%Y-%m-%d').strftime('%Y%m%d')
+        cache_file = os.path.join(temp_dir, f"analysis_cache_{date_file}.json")
+
+        # 检查是否有缓存，且不强制重新分析
+        if os.path.exists(cache_file) and not force_analyze:
+            try:
+                import json
+                with open(cache_file, 'r', encoding='utf-8') as f:
+                    cached_analysis = json.load(f)
+                print(f"使用缓存的分析结果: {cache_file}")
+                return cached_analysis
+            except Exception as e:
+                print(f"读取缓存文件时出错: {e}")
+
     # 初始化 OpenAI 客户端
     client = OpenAI(
         api_key=openai_config['api_key'],
@@ -164,6 +184,8 @@ def analyze_news_in_batches(news_df):
     batch_size = 200
     total_batches = math.ceil(len(news_df) / batch_size)
     batch_results = []
+    # 保存每批次的重要新闻原文
+    important_news = []
 
     print(f"将{len(news_df)}条新闻分成{total_batches}批进行分析，每批{batch_size}条")
 
@@ -171,12 +193,41 @@ def analyze_news_in_batches(news_df):
         start_idx = i * batch_size
         end_idx = min((i + 1) * batch_size, len(news_df))
         batch_df = news_df.iloc[start_idx:end_idx]
-
         print(f"分析第{i + 1}/{total_batches}批新闻...")
 
+        # 生成缓存文件名，按批次缓存
+        batch_cache_file = None
+        if date_str:
+            date_file = datetime.datetime.strptime(date_str, '%Y-%m-%d').strftime('%Y%m%d')
+            batch_cache_file = os.path.join(temp_dir, f"batch_analysis_cache_{date_file}_{i + 1}.json")
+
+            # 检查是否有批次缓存，且不强制重新分析
+            if os.path.exists(batch_cache_file) and not force_analyze:
+                try:
+                    import json
+                    with open(batch_cache_file, 'r', encoding='utf-8') as f:
+                        cached_batch = json.load(f)
+                    print(f"使用缓存的批次分析结果: {batch_cache_file}")
+                    batch_results.append(cached_batch)
+
+                    # 添加此批次的重要新闻到列表
+                    if "important_news" in cached_batch:
+                        important_news.extend(cached_batch["important_news"])
+
+                    continue
+                except Exception as e:
+                    print(f"读取批次缓存文件时出错: {e}")
+
         # 合并当前批次新闻内容
         batch_content = ""
+        batch_news = []  # 存储此批次的新闻
+
         for _, row in batch_df.iterrows():
+            news_item = {
+                "datetime": row['datetime'],
+                "content": row['content']
+            }
+            batch_news.append(news_item)
             batch_content += f"{row['datetime']}: {row['content']}\n\n"
 
         # 对当前批次进行初步分析
@@ -185,13 +236,25 @@ def analyze_news_in_batches(news_df):
 
 {batch_content}
 
-请总结以下两点:
+请完成以下两项分析:
 1. 这批新闻中提到的重要政策（特别是中国颁布的法令等），如果没有相关政策可以直接说明
 2. 这些政策可能对哪些股市板块带来影响（利好/利空）
 
-请只提取重要信息，简明扼要地回答。
+对于重要的政策新闻，请在分析后提供一个"重要新闻列表"，包含新闻的原文和发布时间。
-"""
+请只提取重要信息，过滤掉不相关的新闻（如普通的股价波动、ETF变动等）。
 
+回复格式:
+## 政策分析
+[您的分析内容]
+
+## 板块影响
+[您的影响分析]
+
+## 重要新闻列表
+1. [时间] [完整新闻原文]
+2. [时间] [完整新闻原文]
+...
+"""
         try:
             response = client.chat.completions.create(
                 messages=[
@@ -203,14 +266,43 @@ def analyze_news_in_batches(news_df):
                 model=openai_config['model'],
                 temperature=0.2
             )
-
             batch_analysis = response.choices[0].message.content
 
-            batch_results.append({
+            # 提取重要新闻
+            batch_important_news = []
+            if "## 重要新闻列表" in batch_analysis:
+                news_section = batch_analysis.split("## 重要新闻列表", 1)[1].strip()
+                import re
+                # 匹配时间和新闻内容
+                news_matches = re.findall(r'\d+\.\s+\[([^\]]+)\]\s+(.+?)(?=\n\d+\.|$)', news_section, re.DOTALL)
+                for time_str, content in news_matches:
+                    batch_important_news.append({
+                        "datetime": time_str.strip(),
+                        "content": content.strip()
+                    })
+
+            batch_result = {
                 "batch_number": i + 1,
                 "news_count": len(batch_df),
-                "analysis": batch_analysis
+                "analysis": batch_analysis,
-            })
+                "important_news": batch_important_news
+            }
+
+            batch_results.append(batch_result)
+
+            # 添加重要新闻到总列表
+            important_news.extend(batch_important_news)
+
+            # 保存批次缓存
+            if batch_cache_file:
+                try:
+                    import json
+                    with open(batch_cache_file, 'w', encoding='utf-8') as f:
+                        json.dump(batch_result, f, ensure_ascii=False, indent=2)
+                    print(f"已缓存批次分析结果到: {batch_cache_file}")
+                except Exception as e:
+                    print(f"保存批次缓存文件时出错: {e}")
+
         except Exception as e:
             print(f"分析第{i + 1}批新闻时出错: {e}")
 
@@ -236,6 +328,7 @@ def analyze_news_in_batches(news_df):
    - 提取3-5条最重要的国家层面政策（特别是中国的宏观经济政策）
    - 每条政策用一句话概括核心内容
    - 标注政策发布部门/会议名称
+   - 对每条重要政策，提供对应的原始新闻来源
 
 2. 【板块影响分析】
    - 对每条重要政策，分析直接影响的相关行业板块
@@ -251,17 +344,17 @@ def analyze_news_in_batches(news_df):
 
 ### 一、核心政策摘要
 1. [政策部门/会议] 政策核心内容
+   - 来源新闻: [对应的原始新闻]
    - 影响板块：利好：XXX；利空：XXX
    - 影响逻辑：...
-
 2. [政策部门/会议] 政策核心内容
+   - 来源新闻: [对应的原始新闻]
    - 影响板块：利好：XXX；利空：XXX
    - 影响逻辑：...
 
 ### 二、综合市场影响
 [整体分析，包含市场情绪预判和关键时间节点提醒]
 """
-
     try:
         final_response = client.chat.completions.create(
             messages=[
@@ -273,16 +366,29 @@ def analyze_news_in_batches(news_df):
             model=openai_config['model'],
             temperature=0.2
         )
-
         final_analysis = final_response.choices[0].message.content
 
-        return {
+        analysis_result = {
             "news_count": len(news_df),
             "batch_count": total_batches,
             "time_range": f"{news_df['datetime'].iloc[0]} 至 {news_df['datetime'].iloc[-1]}" if not news_df.empty else "",
             "batch_results": batch_results,
-            "final_analysis": final_analysis
+            "final_analysis": final_analysis,
+            "important_news": important_news  # 保存所有重要新闻原文
         }
+
+        # 保存完整分析缓存
+        if cache_file:
+            try:
+                import json
+                with open(cache_file, 'w', encoding='utf-8') as f:
+                    json.dump(analysis_result, f, ensure_ascii=False, indent=2)
+                print(f"已缓存完整分析结果到: {cache_file}")
+            except Exception as e:
+                print(f"保存缓存文件时出错: {e}")
+
+        return analysis_result
+
     except Exception as e:
         print(f"生成最终分析时出错: {e}")
         return None
@@ -308,31 +414,53 @@ def compare_trend_analysis(yesterday_analysis, today_analysis):
         base_url=openai_config['base_url']
     )
 
+    # 提取今天和昨天的重要新闻
+    yesterday_news = yesterday_analysis.get('important_news', [])
+    today_news = today_analysis.get('important_news', [])
+
+    # 整理新闻字符串
+    yesterday_news_str = ""
+    for i, news in enumerate(yesterday_news):
+        yesterday_news_str += f"{i + 1}. [{news['datetime']}] {news['content']}\n\n"
+
+    today_news_str = ""
+    for i, news in enumerate(today_news):
+        today_news_str += f"{i + 1}. [{news['datetime']}] {news['content']}\n\n"
+
     # 构建对比分析提示
-    prompt = f"""
+    prompt = f""" 
 你是一位资深财经分析师，请对比昨天和今天的政策分析结果，找出从弱势转为强势的板块，分析未来市场趋势：
 
 ### 昨天的分析：
 {yesterday_analysis['final_analysis']}
 
+### 昨天的重要新闻原文：
+{yesterday_news_str}
+
 ### 今天的分析：
 {today_analysis['final_analysis']}
 
+### 今天的重要新闻原文：
+{today_news_str}
+
 请完成以下分析：
 
 1. 【弱转强板块识别】
    - 对比昨天的弱势板块和今天的强势板块，找出明显从弱势转为强势的板块
    - 分析这些板块转强的原因
    - 评估这种转变的持续性
+   - 关联对应的原始新闻，作为判断依据
 
 2. 【政策趋势分析】
    - 分析昨天和今天的政策，提取政策方向的变化或延续
    - 判断政策组合对未来市场的影响
    - 预测可能被持续关注和推动的行业方向
+   - 引用相关新闻作为证据
 
 3. 【投资机会预判】
    - 基于弱转强的板块和政策趋势，预判未来1-2周可能出现的投资机会
    - 提出值得重点关注的细分行业
+   - 提供相关的政策和新闻支持
 
 请用以下结构化格式输出：
 
@@ -341,19 +469,20 @@ def compare_trend_analysis(yesterday_analysis, today_analysis):
    - 转变原因：...
    - 相关政策支持：...
    - 持续性评估：...
+   - 关键新闻依据：[引用原始新闻]
 
 2. [板块名称]
    - 转变原因：...
    - 相关政策支持：...
    - 持续性评估：...
+   - 关键新闻依据：[引用原始新闻]
 
 ### 二、政策趋势变化
-[分析政策方向的延续性和变化]
+[分析政策方向的延续性和变化，引用相关新闻作为证据]
 
 ### 三、投资机会与建议
-[提出具体的投资方向和关注重点]
+[提出具体的投资方向和关注重点，引用相关政策和新闻]
 """
-
     try:
         response = client.chat.completions.create(
             messages=[
@@ -365,13 +494,14 @@ def compare_trend_analysis(yesterday_analysis, today_analysis):
             model=openai_config['model'],
             temperature=0.3
         )
-
         trend_analysis = response.choices[0].message.content
 
         return {
             "yesterday_date": yesterday_analysis.get("time_range", "昨天"),
             "today_date": today_analysis.get("time_range", "今天"),
-            "trend_analysis": trend_analysis
+            "trend_analysis": trend_analysis,
+            "yesterday_news": yesterday_news,
+            "today_news": today_news
         }
     except Exception as e:
         print(f"生成趋势分析时出错: {e}")
@@ -391,7 +521,6 @@ def main():
     # 获取昨天的新闻，只有在不存在时才获取
     yesterday_file_exists = False
     temp_dir = ensure_temp_dir()
-
     for file in os.listdir(temp_dir):
         if file.startswith(f"{yesterday_file}_") and file.endswith(".csv"):
             yesterday_file_exists = True
@@ -407,7 +536,6 @@ def main():
         for file in os.listdir(temp_dir):
             if file.startswith(f"{yesterday_file}_") and file.endswith(".csv"):
                 yesterday_files.append(os.path.join(temp_dir, file))
-
         dfs = []
         for file in yesterday_files:
             try:
@@ -415,65 +543,86 @@ def main():
                 dfs.append(df)
             except Exception as e:
                 print(f"读取文件{file}时出错: {e}")
-
         if dfs:
             yesterday_news = pd.concat(dfs, ignore_index=True)
             if 'datetime' in yesterday_news.columns and 'content' in yesterday_news.columns:
                 yesterday_news = yesterday_news[['datetime', 'content']]
 
-    # 分析今天和昨天的新闻
+    # 分析今天和昨天的新闻，使用缓存机制
     today_analysis = None
     yesterday_analysis = None
 
     if today_news is not None:
         print(f"获取到 {len(today_news)} 条今日新闻，正在分析...")
-        today_analysis = analyze_news_in_batches(today_news)
+        today_analysis = analyze_news_in_batches(today_news, today, force_analyze=False)
 
         # 保存今天的原始新闻和分析结果
-        today_news.to_csv(f"news_raw_{today_file}.csv", index=False, encoding='utf-8-sig')
+        today_news.to_csv(f"temp/news_raw_{today_file}.csv", index=False, encoding='utf-8-sig')
-
         if today_analysis:
-            with open(f"news_analysis_{today_file}.txt", "w", encoding="utf-8") as f:
+            with open(f"temp/news_analysis_{today_file}.txt", "w", encoding="utf-8") as f:
                 f.write(
                     f"分析范围: {today_analysis['news_count']} 条新闻，分成 {today_analysis['batch_count']} 批处理\n")
                 f.write(f"时间范围: {today_analysis['time_range']}\n\n")
                 f.write("最终分析结果:\n")
                 f.write("-" * 80 + "\n")
                 f.write(today_analysis['final_analysis'])
-                f.write("\n" + "-" * 80 + "\n")
+                f.write("\n" + "-" * 80 + "\n\n")
 
-            print(f"今日分析结果已保存到 news_analysis_{today_file}.txt")
+                # 添加重要新闻原文
+                f.write("重要新闻原文:\n")
+                f.write("-" * 80 + "\n")
+                for i, news in enumerate(today_analysis.get('important_news', [])):
+                    f.write(f"{i + 1}. [{news['datetime']}] {news['content']}\n\n")
+                f.write("-" * 80 + "\n")
+
+            print(f"今日分析结果已保存到 temp/news_analysis_{today_file}.txt")
     else:
         print("无法获取今天的新闻数据")
 
     if yesterday_news is not None:
         print(f"获取到 {len(yesterday_news)} 条昨日新闻，正在分析...")
-        yesterday_analysis = analyze_news_in_batches(yesterday_news)
+        yesterday_analysis = analyze_news_in_batches(yesterday_news, yesterday, force_analyze=False)
 
         # 保存昨天的原始新闻和分析结果
-        yesterday_news.to_csv(f"news_raw_{yesterday_file}.csv", index=False, encoding='utf-8-sig')
+        yesterday_news.to_csv(f"temp/news_raw_{yesterday_file}.csv", index=False, encoding='utf-8-sig')
-
         if yesterday_analysis:
-            with open(f"news_analysis_{yesterday_file}.txt", "w", encoding="utf-8") as f:
+            with open(f"temp/news_analysis_{yesterday_file}.txt", "w", encoding="utf-8") as f:
                 f.write(
                     f"分析范围: {yesterday_analysis['news_count']} 条新闻，分成 {yesterday_analysis['batch_count']} 批处理\n")
                 f.write(f"时间范围: {yesterday_analysis['time_range']}\n\n")
                 f.write("最终分析结果:\n")
                 f.write("-" * 80 + "\n")
                 f.write(yesterday_analysis['final_analysis'])
-                f.write("\n" + "-" * 80 + "\n")
+                f.write("\n" + "-" * 80 + "\n\n")
 
-            print(f"昨日分析结果已保存到 news_analysis_{yesterday_file}.txt")
+                # 添加重要新闻原文
+                f.write("重要新闻原文:\n")
+                f.write("-" * 80 + "\n")
+                for i, news in enumerate(yesterday_analysis.get('important_news', [])):
+                    f.write(f"{i + 1}. [{news['datetime']}] {news['content']}\n\n")
+                f.write("-" * 80 + "\n")
+
+            print(f"昨日分析结果已保存到 temp/news_analysis_{yesterday_file}.txt")
     else:
         # 尝试读取昨天的分析结果文件
         try:
-            with open(f"news_analysis_{yesterday_file}.txt", "r", encoding="utf-8") as f:
+            # 首先尝试读取JSON缓存文件
-                content = f.read()
+            cache_file = os.path.join(temp_dir, f"analysis_cache_{yesterday_file}.json")
-                yesterday_analysis = {
+            if os.path.exists(cache_file):
-                    "final_analysis": content,
+                import json
-                    "time_range": yesterday
+                with open(cache_file, 'r', encoding='utf-8') as f:
-                }
+                    yesterday_analysis = json.load(f)
-            print(f"已从文件中读取昨日分析结果")
+                print(f"已从缓存中读取昨日分析结果")
+            else:
+                # 如果没有缓存，尝试读取文本文件
+                with open(f"temp/news_analysis_{yesterday_file}.txt", "r", encoding="utf-8") as f:
+                    content = f.read()
+                    yesterday_analysis = {
+                        "final_analysis": content.split("-" * 80)[1].strip(),
+                        "time_range": yesterday,
+                        "important_news": []  # 从文本文件中难以结构化提取新闻，置为空列表
+                    }
+                print(f"已从文本文件中读取昨日分析结果")
         except Exception as e:
             print(f"无法读取昨日分析结果: {e}")
 
@@ -481,17 +630,29 @@ def main():
     if today_analysis and yesterday_analysis:
         print("正在进行今日与昨日的趋势对比分析...")
         trend_analysis = compare_trend_analysis(yesterday_analysis, today_analysis)
-
         if trend_analysis:
             # 保存趋势分析结果
-            with open(f"trend_analysis_{today_file}.txt", "w", encoding="utf-8") as f:
+            with open(f"temp/trend_analysis_{today_file}.txt", "w", encoding="utf-8") as f:
                 f.write(f"对比分析范围: 昨日({yesterday}) 与 今日({today})\n\n")
                 f.write("趋势分析结果:\n")
                 f.write("-" * 80 + "\n")
                 f.write(trend_analysis['trend_analysis'])
-                f.write("\n" + "-" * 80 + "\n")
+                f.write("\n" + "-" * 80 + "\n\n")
 
-            print(f"趋势分析结果已保存到 trend_analysis_{today_file}.txt")
+                # 添加弱转强的新闻原文供参考
+                f.write("昨日重要新闻原文:\n")
+                f.write("-" * 80 + "\n")
+                for i, news in enumerate(trend_analysis.get('yesterday_news', [])):
+                    f.write(f"{i + 1}. [{news['datetime']}] {news['content']}\n\n")
+                f.write("-" * 80 + "\n\n")
+
+                f.write("今日重要新闻原文:\n")
+                f.write("-" * 80 + "\n")
+                for i, news in enumerate(trend_analysis.get('today_news', [])):
+                    f.write(f"{i + 1}. [{news['datetime']}] {news['content']}\n\n")
+                f.write("-" * 80 + "\n")
+
+            print(f"趋势分析结果已保存到 temp/trend_analysis_{today_file}.txt")
 
             # 打印趋势分析结果
             print("\n=== 今日与昨日趋势对比分析 ===\n")