知微
260 lines
8.9 KiB
Python
260 lines
8.9 KiB
Python
#!/usr/bin/env python3
|
||
"""xiaoguo_news_processor.py — 小果新闻情报处理
|
||
|
||
配合 trend_detector（每30分）运行，处理未处理的 sector_signals。
|
||
|
||
流程：
|
||
1. 读未 processed 的 signals（每次1条）
|
||
2. akshare 搜新闻（板块相关个股 + 持仓 + 自选）
|
||
3. 调小果 LLM 逐批分析（每批3-5篇，给摘要+情感）
|
||
4. 写入 signal_news
|
||
5. 标记 signal.processed = true
|
||
"""
|
||
|
||
import json
|
||
import os
|
||
import urllib.request
|
||
import re
|
||
from pathlib import Path
|
||
|
||
try:
|
||
import akshare as ak
|
||
HAS_AKSHARE = True
|
||
except ImportError:
|
||
HAS_AKSHARE = False
|
||
|
||
DATA_DIR = Path(__file__).parent / "data"
|
||
DB_PATH = DATA_DIR / "mofin.db"
|
||
XIAOGUO_API = "http://192.168.1.122:18003/v1/chat/completions"
|
||
XIAOGUO_MODEL = "Qwen3.6-27B-MTPLX-Optimized-Speed"
|
||
MAX_ARTICLES = 5 # 每次最多分析篇数（实测5篇12s）
|
||
|
||
|
||
def clean_proxy():
|
||
for k in ['http_proxy', 'https_proxy', 'HTTP_PROXY', 'HTTPS_PROXY']:
|
||
os.environ.pop(k, None)
|
||
|
||
|
||
def get_conn():
|
||
import sqlite3
|
||
conn = sqlite3.connect(str(DB_PATH))
|
||
conn.row_factory = sqlite3.Row
|
||
return conn
|
||
|
||
|
||
def search_akshare_news(code, max_results=3):
|
||
"""用 akshare 搜个股新闻（含全文）"""
|
||
articles = []
|
||
if not HAS_AKSHARE:
|
||
return articles
|
||
try:
|
||
clean_proxy()
|
||
df = ak.stock_news_em(symbol=code)
|
||
for _, r in df.head(max_results).iterrows():
|
||
title = r.get('新闻标题', '')
|
||
content = r.get('新闻内容', '')
|
||
if title and len(title) > 5:
|
||
articles.append({
|
||
"title": title,
|
||
"content": content,
|
||
"url": r.get('新闻链接', '')
|
||
})
|
||
except:
|
||
pass
|
||
return articles
|
||
|
||
|
||
def extract_json(text):
|
||
"""从回复中提取JSON数组或对象"""
|
||
# 先找 ```json ... ``` 代码块
|
||
m = re.search(r'```(?:json)?\s*(\[[\s\S]*?\]|\{[\s\S]*?\})\s*```', text)
|
||
if m:
|
||
try:
|
||
return json.loads(m.group(1))
|
||
except:
|
||
pass
|
||
# 找第一个 [ 或 { 到最后一个 ] 或 }
|
||
for start_ch, end_ch in [('[', ']'), ('{', '}')]:
|
||
s = text.find(start_ch)
|
||
if s >= 0:
|
||
depth = 0
|
||
for i in range(s, len(text)):
|
||
if text[i] == start_ch:
|
||
depth += 1
|
||
elif text[i] == end_ch:
|
||
depth -= 1
|
||
if depth == 0:
|
||
try:
|
||
return json.loads(text[s:i+1])
|
||
except:
|
||
break
|
||
return None
|
||
|
||
|
||
def call_xiaoguo(articles):
|
||
"""调小果LLM：给摘要+情感"""
|
||
lines = []
|
||
for a in articles:
|
||
title = re.sub(r'\b\d{6}\b', '', a['title']).strip()
|
||
title = re.sub(r'\s+', ' ', title)
|
||
content = a.get('content') or ''
|
||
# 给正文加标点分隔（akshare正文无标点，模型推理会卡）
|
||
if content and not any(c in content for c in '。，！？；'):
|
||
content = '。'.join([content[i:i+20] for i in range(0, len(content), 20)])
|
||
if content:
|
||
lines.append(f"{len(lines)+1}. {title}\n {content}")
|
||
else:
|
||
lines.append(f"{len(lines)+1}. {title}")
|
||
prompt = "\n".join(lines) + "\n\n逐篇分析：给摘要（概括核心内容）和情感（positive/negative/neutral）。JSON数组。"
|
||
|
||
payload = json.dumps({
|
||
"model": XIAOGUO_MODEL,
|
||
"messages": [{"role": "user", "content": prompt}],
|
||
"temperature": 0.1,
|
||
"max_tokens": 2048,
|
||
}).encode()
|
||
|
||
clean_proxy()
|
||
opener = urllib.request.build_opener(urllib.request.ProxyHandler({}))
|
||
req = urllib.request.Request(
|
||
XIAOGUO_API, data=payload,
|
||
headers={"Content-Type": "application/json"}, method="POST"
|
||
)
|
||
try:
|
||
resp = opener.open(req, timeout=60)
|
||
data = json.loads(resp.read())
|
||
content = data["choices"][0]["message"]["content"]
|
||
result = extract_json(content)
|
||
if isinstance(result, list):
|
||
return result
|
||
except Exception as e:
|
||
print(f" 小果调用失败: {e}", flush=True)
|
||
return None
|
||
|
||
|
||
def translate_sentiment(s):
|
||
"""将英文情感转中文"""
|
||
m = {"positive": "利好", "negative": "利空", "neutral": "中性"}
|
||
return m.get(s.lower() if isinstance(s, str) else "", s)
|
||
|
||
|
||
def fallback_classify(batch):
|
||
"""关键词降级分类（小果API不可用时）"""
|
||
positive_kw = ['突破', '增长', '利好', '加单', '订单', '放量', '新高', '获批', '量产',
|
||
'超预期', '投产', '融资', '增持', '回购', '降息', '减税', '补贴',
|
||
'国产替代', '自主可控', '准入']
|
||
negative_kw = ['管制', '限制', '制裁', '利空', '减持', '抛售', '下跌', '跌停',
|
||
'风险', '违约', '调查', '暂停', '取消', '下滑', '亏损', '裁员',
|
||
'诉讼', '退市', '做空', '关税', '禁令']
|
||
|
||
for a in batch:
|
||
text = a['title'] + (a.get('content') or '')
|
||
pos = sum(1 for kw in positive_kw if kw in text)
|
||
neg = sum(1 for kw in negative_kw if kw in text)
|
||
if pos > neg:
|
||
a['sentiment'] = '利好'
|
||
elif neg > pos:
|
||
a['sentiment'] = '利空'
|
||
else:
|
||
a['sentiment'] = '中性'
|
||
a['summary'] = a['title'][:80]
|
||
return batch
|
||
|
||
|
||
def main():
|
||
conn = get_conn()
|
||
signals = conn.execute(
|
||
"SELECT * FROM sector_signals WHERE processed = 0 ORDER BY severity DESC, id ASC LIMIT 1"
|
||
).fetchall()
|
||
|
||
if not signals:
|
||
print("无未处理的信号", flush=True)
|
||
conn.close()
|
||
return
|
||
|
||
signal = dict(signals[0])
|
||
sector = signal["sector"]
|
||
related = json.loads(signal["related_stocks"] or "[]")
|
||
holdings = json.loads(signal["holdings_in_sector"] or "[]")
|
||
watchlist = json.loads(signal["watchlist_in_sector"] or "[]")
|
||
|
||
print(f"处理信号: [{signal['severity']}] {signal['signal_type']} {sector}", flush=True)
|
||
|
||
codes = {}
|
||
for item in related + holdings + watchlist:
|
||
if item.get("code"):
|
||
codes[item["code"]] = item.get("name", "")
|
||
|
||
members = conn.execute(
|
||
"SELECT s.code, s.name FROM stocks s JOIN stock_sectors ss ON s.code=ss.code WHERE ss.sector_name=? LIMIT 5",
|
||
(sector,)
|
||
).fetchall()
|
||
for m in members:
|
||
if m["code"] not in codes:
|
||
codes[m["code"]] = m["name"]
|
||
|
||
all_articles = []
|
||
for code, name in codes.items():
|
||
arts = search_akshare_news(code, 3)
|
||
for a in arts:
|
||
if a["title"] not in [x["title"] for x in all_articles]:
|
||
all_articles.append(a)
|
||
print(f" 搜 {name}({code}): {len(arts)} 篇", flush=True)
|
||
|
||
if not all_articles:
|
||
print(" 未搜到新闻", flush=True)
|
||
conn.execute("UPDATE sector_signals SET processed=1 WHERE id=?", (signal["id"],))
|
||
conn.commit()
|
||
conn.close()
|
||
return
|
||
|
||
# 只取前5篇，跳过含有表格数据的脏内容
|
||
filtered = []
|
||
for a in all_articles:
|
||
c = a.get('content', '') or ''
|
||
if any(kw in c for kw in ['主力资金', '资金净流入', '代码', '简称']):
|
||
continue
|
||
filtered.append(a)
|
||
if len(filtered) >= MAX_ARTICLES:
|
||
break
|
||
batch = filtered[:MAX_ARTICLES]
|
||
print(f" 共{len(all_articles)}篇，送小果分析{len(batch)}篇", flush=True)
|
||
|
||
results = call_xiaoguo(batch)
|
||
if not results:
|
||
print(" 小果API不可用，降级到关键词分类", flush=True)
|
||
fallback_classify(batch)
|
||
results = None # batch already has sentiment/summary set
|
||
|
||
if results and isinstance(results, list):
|
||
# 小果LLM返回结果，按索引匹配
|
||
for i, r in enumerate(results):
|
||
if i < len(batch):
|
||
batch[i]["sentiment"] = translate_sentiment(r.get("sentiment", r.get("情感", "")))
|
||
batch[i]["summary"] = r.get("summary", r.get("摘要", ""))
|
||
else:
|
||
break
|
||
|
||
# 汇总情感
|
||
sentiments = [a.get("sentiment", "中性") for a in batch if a.get("sentiment")]
|
||
pos = sentiments.count("利好")
|
||
neg = sentiments.count("利空")
|
||
overall = "利好" if pos > neg * 1.5 else "利空" if neg > pos * 1.5 else "中性"
|
||
summaries = [a.get("summary", "") for a in batch if a.get("summary")]
|
||
combined = f"{sector}板块信号：{'｜'.join(summaries[:3])}。总体{overall}。"
|
||
|
||
searched_names = list(set(codes.values()))
|
||
conn.execute(
|
||
"INSERT INTO signal_news (signal_id, sector, overall_sentiment, summary, key_articles, searched_stocks) VALUES (?, ?, ?, ?, ?, ?)",
|
||
(signal["id"], sector, overall, combined, json.dumps(batch, ensure_ascii=False), json.dumps(searched_names, ensure_ascii=False))
|
||
)
|
||
conn.execute("UPDATE sector_signals SET processed=1 WHERE id=?", (signal["id"],))
|
||
conn.commit()
|
||
|
||
print(f" 完成: {overall} — {combined[:100]}", flush=True)
|
||
conn.close()
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|