✨ feat(news_analyze.py): 支持缓存分析结果并提取重要新闻原文
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news_analyze.py
249
news_analyze.py
@ -146,14 +146,34 @@ def get_news_by_date(date_str, force_update=False):
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return None
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def analyze_news_in_batches(news_df):
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"""将新闻按批次分析后汇总"""
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def analyze_news_in_batches(news_df, date_str=None, force_analyze=False):
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"""将新闻按批次分析后汇总，支持缓存分析结果"""
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if news_df is None or news_df.empty:
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return None
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# 确保temp目录存在
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temp_dir = ensure_temp_dir()
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config = load_config()
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openai_config = config['openai_api']
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# 如果提供了日期，尝试读取缓存的分析结果
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cache_file = None
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if date_str:
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date_file = datetime.datetime.strptime(date_str, '%Y-%m-%d').strftime('%Y%m%d')
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cache_file = os.path.join(temp_dir, f"analysis_cache_{date_file}.json")
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# 检查是否有缓存，且不强制重新分析
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if os.path.exists(cache_file) and not force_analyze:
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try:
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import json
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with open(cache_file, 'r', encoding='utf-8') as f:
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cached_analysis = json.load(f)
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print(f"使用缓存的分析结果: {cache_file}")
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return cached_analysis
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except Exception as e:
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print(f"读取缓存文件时出错: {e}")
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# 初始化 OpenAI 客户端
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client = OpenAI(
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api_key=openai_config['api_key'],
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@ -164,6 +184,8 @@ def analyze_news_in_batches(news_df):
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batch_size = 200
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total_batches = math.ceil(len(news_df) / batch_size)
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batch_results = []
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# 保存每批次的重要新闻原文
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important_news = []
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print(f"将{len(news_df)}条新闻分成{total_batches}批进行分析，每批{batch_size}条")
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@ -171,12 +193,41 @@ def analyze_news_in_batches(news_df):
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start_idx = i * batch_size
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end_idx = min((i + 1) * batch_size, len(news_df))
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batch_df = news_df.iloc[start_idx:end_idx]
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print(f"分析第{i + 1}/{total_batches}批新闻...")
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# 生成缓存文件名，按批次缓存
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batch_cache_file = None
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if date_str:
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date_file = datetime.datetime.strptime(date_str, '%Y-%m-%d').strftime('%Y%m%d')
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batch_cache_file = os.path.join(temp_dir, f"batch_analysis_cache_{date_file}_{i + 1}.json")
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# 检查是否有批次缓存，且不强制重新分析
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if os.path.exists(batch_cache_file) and not force_analyze:
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try:
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import json
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with open(batch_cache_file, 'r', encoding='utf-8') as f:
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cached_batch = json.load(f)
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print(f"使用缓存的批次分析结果: {batch_cache_file}")
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batch_results.append(cached_batch)
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# 添加此批次的重要新闻到列表
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if "important_news" in cached_batch:
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important_news.extend(cached_batch["important_news"])
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continue
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except Exception as e:
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print(f"读取批次缓存文件时出错: {e}")
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# 合并当前批次新闻内容
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batch_content = ""
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batch_news = [] # 存储此批次的新闻
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for _, row in batch_df.iterrows():
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news_item = {
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"datetime": row['datetime'],
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"content": row['content']
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}
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batch_news.append(news_item)
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batch_content += f"{row['datetime']}: {row['content']}\n\n"
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# 对当前批次进行初步分析
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@ -185,13 +236,25 @@ def analyze_news_in_batches(news_df):
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{batch_content}
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请总结以下两点:
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请完成以下两项分析:
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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
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2. 这些政策可能对哪些股市板块带来影响（利好/利空）
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请只提取重要信息，简明扼要地回答。
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"""
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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。
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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）。
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回复格式:
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## 政策分析
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[您的分析内容]
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## 板块影响
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[您的影响分析]
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## 重要新闻列表
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1. [时间] [完整新闻原文]
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2. [时间] [完整新闻原文]
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...
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"""
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try:
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response = client.chat.completions.create(
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messages=[
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@ -203,14 +266,43 @@ def analyze_news_in_batches(news_df):
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model=openai_config['model'],
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temperature=0.2
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)
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batch_analysis = response.choices[0].message.content
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batch_results.append({
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# 提取重要新闻
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batch_important_news = []
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if "## 重要新闻列表" in batch_analysis:
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news_section = batch_analysis.split("## 重要新闻列表", 1)[1].strip()
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import re
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# 匹配时间和新闻内容
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news_matches = re.findall(r'\d+\.\s+\[([^\]]+)\]\s+(.+?)(?=\n\d+\.|$)', news_section, re.DOTALL)
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for time_str, content in news_matches:
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batch_important_news.append({
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"datetime": time_str.strip(),
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"content": content.strip()
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})
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batch_result = {
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"batch_number": i + 1,
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"news_count": len(batch_df),
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"analysis": batch_analysis
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})
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"analysis": batch_analysis,
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"important_news": batch_important_news
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}
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batch_results.append(batch_result)
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# 添加重要新闻到总列表
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important_news.extend(batch_important_news)
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# 保存批次缓存
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if batch_cache_file:
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try:
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import json
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with open(batch_cache_file, 'w', encoding='utf-8') as f:
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json.dump(batch_result, f, ensure_ascii=False, indent=2)
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print(f"已缓存批次分析结果到: {batch_cache_file}")
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except Exception as e:
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print(f"保存批次缓存文件时出错: {e}")
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except Exception as e:
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print(f"分析第{i + 1}批新闻时出错: {e}")
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@ -236,6 +328,7 @@ def analyze_news_in_batches(news_df):
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- 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）
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- 每条政策用一句话概括核心内容
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- 标注政策发布部门/会议名称
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- 对每条重要政策，提供对应的原始新闻来源
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2. 【板块影响分析】
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- 对每条重要政策，分析直接影响的相关行业板块
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@ -251,17 +344,17 @@ def analyze_news_in_batches(news_df):
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### 一、核心政策摘要
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1. [政策部门/会议] 政策核心内容
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- 来源新闻: [对应的原始新闻]
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- 影响板块：利好：XXX；利空：XXX
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- 影响逻辑：...
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2. [政策部门/会议] 政策核心内容
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- 来源新闻: [对应的原始新闻]
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- 影响板块：利好：XXX；利空：XXX
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- 影响逻辑：...
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### 二、综合市场影响
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[整体分析，包含市场情绪预判和关键时间节点提醒]
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"""
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try:
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final_response = client.chat.completions.create(
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messages=[
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@ -273,16 +366,29 @@ def analyze_news_in_batches(news_df):
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model=openai_config['model'],
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temperature=0.2
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)
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final_analysis = final_response.choices[0].message.content
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return {
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analysis_result = {
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"news_count": len(news_df),
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"batch_count": total_batches,
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"time_range": f"{news_df['datetime'].iloc[0]} 至 {news_df['datetime'].iloc[-1]}" if not news_df.empty else "",
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"batch_results": batch_results,
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"final_analysis": final_analysis
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"final_analysis": final_analysis,
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"important_news": important_news # 保存所有重要新闻原文
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}
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# 保存完整分析缓存
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if cache_file:
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try:
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import json
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with open(cache_file, 'w', encoding='utf-8') as f:
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json.dump(analysis_result, f, ensure_ascii=False, indent=2)
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print(f"已缓存完整分析结果到: {cache_file}")
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except Exception as e:
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print(f"保存缓存文件时出错: {e}")
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return analysis_result
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except Exception as e:
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print(f"生成最终分析时出错: {e}")
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return None
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@ -308,6 +414,19 @@ def compare_trend_analysis(yesterday_analysis, today_analysis):
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base_url=openai_config['base_url']
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)
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# 提取今天和昨天的重要新闻
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yesterday_news = yesterday_analysis.get('important_news', [])
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today_news = today_analysis.get('important_news', [])
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# 整理新闻字符串
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yesterday_news_str = ""
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for i, news in enumerate(yesterday_news):
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yesterday_news_str += f"{i + 1}. [{news['datetime']}] {news['content']}\n\n"
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today_news_str = ""
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for i, news in enumerate(today_news):
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today_news_str += f"{i + 1}. [{news['datetime']}] {news['content']}\n\n"
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# 构建对比分析提示
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prompt = f"""
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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：
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@ -315,24 +434,33 @@ def compare_trend_analysis(yesterday_analysis, today_analysis):
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### 昨天的分析：
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{yesterday_analysis['final_analysis']}
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### 昨天的重要新闻原文：
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{yesterday_news_str}
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### 今天的分析：
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{today_analysis['final_analysis']}
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### 今天的重要新闻原文：
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{today_news_str}
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请完成以下分析：
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1. 【弱转强板块识别】
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- 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
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- 分析这些板块转强的原因
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- 评估这种转变的持续性
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- 关联对应的原始新闻，作为判断依据
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2. 【政策趋势分析】
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- 分析昨天和今天的政策，提取政策方向的变化或延续
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- 判断政策组合对未来市场的影响
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- 预测可能被持续关注和推动的行业方向
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- 引用相关新闻作为证据
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3. 【投资机会预判】
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- 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
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- 提出值得重点关注的细分行业
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- 提供相关的政策和新闻支持
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请用以下结构化格式输出：
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@ -341,19 +469,20 @@ def compare_trend_analysis(yesterday_analysis, today_analysis):
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- 转变原因：...
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- 相关政策支持：...
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- 持续性评估：...
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- 关键新闻依据：[引用原始新闻]
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2. [板块名称]
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- 转变原因：...
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- 相关政策支持：...
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- 持续性评估：...
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- 关键新闻依据：[引用原始新闻]
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### 二、政策趋势变化
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[分析政策方向的延续性和变化]
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[分析政策方向的延续性和变化，引用相关新闻作为证据]
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### 三、投资机会与建议
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[提出具体的投资方向和关注重点]
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[提出具体的投资方向和关注重点，引用相关政策和新闻]
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"""
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try:
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response = client.chat.completions.create(
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messages=[
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@ -365,13 +494,14 @@ def compare_trend_analysis(yesterday_analysis, today_analysis):
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model=openai_config['model'],
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temperature=0.3
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)
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trend_analysis = response.choices[0].message.content
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return {
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"yesterday_date": yesterday_analysis.get("time_range", "昨天"),
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"today_date": today_analysis.get("time_range", "今天"),
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"trend_analysis": trend_analysis
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"trend_analysis": trend_analysis,
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"yesterday_news": yesterday_news,
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"today_news": today_news
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}
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except Exception as e:
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print(f"生成趋势分析时出错: {e}")
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@ -391,7 +521,6 @@ def main():
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# 获取昨天的新闻，只有在不存在时才获取
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yesterday_file_exists = False
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temp_dir = ensure_temp_dir()
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for file in os.listdir(temp_dir):
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if file.startswith(f"{yesterday_file}_") and file.endswith(".csv"):
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yesterday_file_exists = True
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@ -407,7 +536,6 @@ def main():
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for file in os.listdir(temp_dir):
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if file.startswith(f"{yesterday_file}_") and file.endswith(".csv"):
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yesterday_files.append(os.path.join(temp_dir, file))
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dfs = []
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for file in yesterday_files:
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try:
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@ -415,65 +543,86 @@ def main():
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dfs.append(df)
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except Exception as e:
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print(f"读取文件{file}时出错: {e}")
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if dfs:
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yesterday_news = pd.concat(dfs, ignore_index=True)
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if 'datetime' in yesterday_news.columns and 'content' in yesterday_news.columns:
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yesterday_news = yesterday_news[['datetime', 'content']]
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# 分析今天和昨天的新闻
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# 分析今天和昨天的新闻，使用缓存机制
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today_analysis = None
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yesterday_analysis = None
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if today_news is not None:
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print(f"获取到 {len(today_news)} 条今日新闻，正在分析...")
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today_analysis = analyze_news_in_batches(today_news)
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today_analysis = analyze_news_in_batches(today_news, today, force_analyze=False)
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# 保存今天的原始新闻和分析结果
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today_news.to_csv(f"news_raw_{today_file}.csv", index=False, encoding='utf-8-sig')
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today_news.to_csv(f"temp/news_raw_{today_file}.csv", index=False, encoding='utf-8-sig')
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if today_analysis:
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with open(f"news_analysis_{today_file}.txt", "w", encoding="utf-8") as f:
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with open(f"temp/news_analysis_{today_file}.txt", "w", encoding="utf-8") as f:
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f.write(
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f"分析范围: {today_analysis['news_count']} 条新闻，分成 {today_analysis['batch_count']} 批处理\n")
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f.write(f"时间范围: {today_analysis['time_range']}\n\n")
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f.write("最终分析结果:\n")
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f.write("-" * 80 + "\n")
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f.write(today_analysis['final_analysis'])
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f.write("\n" + "-" * 80 + "\n")
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f.write("\n" + "-" * 80 + "\n\n")
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print(f"今日分析结果已保存到 news_analysis_{today_file}.txt")
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# 添加重要新闻原文
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f.write("重要新闻原文:\n")
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f.write("-" * 80 + "\n")
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for i, news in enumerate(today_analysis.get('important_news', [])):
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f.write(f"{i + 1}. [{news['datetime']}] {news['content']}\n\n")
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f.write("-" * 80 + "\n")
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print(f"今日分析结果已保存到 temp/news_analysis_{today_file}.txt")
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else:
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print("无法获取今天的新闻数据")
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if yesterday_news is not None:
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print(f"获取到 {len(yesterday_news)} 条昨日新闻，正在分析...")
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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
|
||||
cache_file = os.path.join(temp_dir, f"analysis_cache_{yesterday_file}.json")
|
||||
if os.path.exists(cache_file):
|
||||
import json
|
||||
with open(cache_file, 'r', encoding='utf-8') as f:
|
||||
yesterday_analysis = json.load(f)
|
||||
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,
|
||||
"time_range": yesterday
|
||||
"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"已从文件中读取昨日分析结果")
|
||||
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")
|
||||
|
||||
Loading…
Reference in New Issue
Block a user