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✨ 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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Qihang Zhang 2025-04-09 19:10:23 +08:00
parent eb776fd3cb
commit 601a940882
1 changed files with 209 additions and 48 deletions
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257
news_analyze.py
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@ -146,14 +146,34 @@ def get_news_by_date(date_str, force_update=False):
return None
def analyze_news_in_batches(news_df):
"""将新闻按批次分析后汇总"""
def analyze_news_in_batches(news_df, date_str=None, force_analyze=False):
"""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:
content = f.read()
yesterday_analysis = {
"final_analysis": content,
"time_range": yesterday
}
print(f"已从文件中读取昨日分析结果")
# 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.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")