171 lines
5.6 KiB
Python
171 lines
5.6 KiB
Python
"""LLM 逐股深度分析
|
||
|
||
量化筛选完成后，对每只候选股票单独调用 LLM 做深度分析，
|
||
让 AI 独立判断入场时机并给出具体买卖价格。
|
||
"""
|
||
|
||
import asyncio
|
||
import logging
|
||
import re
|
||
|
||
from app.config import settings
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
|
||
async def analyze_single_stock(candidate: dict, market_summary: str) -> dict:
|
||
"""对单只股票做 LLM 深度分析
|
||
|
||
candidate: 包含 ts_code, name, sector, quant_score, position_score,
|
||
kline_summary, capital_flow_summary
|
||
market_summary: 市场环境摘要
|
||
|
||
返回: {
|
||
"signal": "BUY"/"HOLD"/"SKIP",
|
||
"strength": "强"/"中"/"弱",
|
||
"entry_price": float or None,
|
||
"target_price": float or None,
|
||
"stop_loss": float or None,
|
||
"analysis": str,
|
||
}
|
||
"""
|
||
from app.llm.prompts import SINGLE_STOCK_ANALYSIS_PROMPT
|
||
from app.llm.client import get_client
|
||
|
||
# 构建 prompt — 不传 signal_type，让 LLM 独立判断
|
||
stock_text = f"""\
|
||
股票: {candidate['name']}({candidate['ts_code']})
|
||
板块: {candidate.get('sector', '未知')}
|
||
量化评分: {candidate.get('quant_score', 0)}/100
|
||
位置安全: {candidate.get('position_score', 50)}/100
|
||
当前价: {candidate.get('current_price', '未知')}"""
|
||
|
||
if candidate.get("kline_summary"):
|
||
stock_text += f"\n\n## 技术分析结论\n{candidate['kline_summary']}"
|
||
|
||
if candidate.get("capital_flow_summary"):
|
||
stock_text += f"\n\n## 资金流向\n{candidate['capital_flow_summary']}"
|
||
|
||
user_msg = f"{SINGLE_STOCK_ANALYSIS_PROMPT}\n\n## 市场环境\n{market_summary}\n\n{stock_text}\n\n请给出你的分析。"
|
||
|
||
try:
|
||
client = get_client()
|
||
response = await client.chat.completions.create(
|
||
model=settings.deepseek_model,
|
||
messages=[
|
||
{
|
||
"role": "system",
|
||
"content": (
|
||
"你是一位专业的A股趋势交易分析师，专注于中短线（1-5日）交易。"
|
||
"你根据技术分析结论独立判断入场时机，给出具体的买卖价格建议。"
|
||
"不要被量化评分束缚，给出你真实的判断。"
|
||
),
|
||
},
|
||
{"role": "user", "content": user_msg},
|
||
],
|
||
max_tokens=800,
|
||
temperature=0.3,
|
||
)
|
||
content = response.choices[0].message.content.strip()
|
||
return _parse_single_response(content)
|
||
|
||
except Exception as e:
|
||
logger.error(f"LLM 分析 {candidate.get('ts_code')} 失败: {e}")
|
||
return {
|
||
"signal": "HOLD",
|
||
"strength": "弱",
|
||
"entry_price": None,
|
||
"target_price": None,
|
||
"stop_loss": None,
|
||
"analysis": "AI分析暂不可用",
|
||
}
|
||
|
||
|
||
def _parse_single_response(text: str) -> dict:
|
||
"""解析单只股票的 LLM 返回"""
|
||
# 提取信号
|
||
signal = "HOLD"
|
||
signal_match = re.search(r"信号[:\s]*(BUY|HOLD|SKIP)", text)
|
||
if signal_match:
|
||
signal = signal_match.group(1)
|
||
|
||
# 提取信号强度
|
||
strength = "中"
|
||
strength_match = re.search(r"信号强度[:\s]*(强|中|弱)", text)
|
||
if strength_match:
|
||
strength = strength_match.group(1)
|
||
|
||
# 提取买入价
|
||
entry_price = None
|
||
entry_match = re.search(r"买入价[:\s]*(\d+(?:\.\d+)?)", text)
|
||
if entry_match:
|
||
entry_price = float(entry_match.group(1))
|
||
|
||
# 提取止盈价
|
||
target_price = None
|
||
target_match = re.search(r"止盈价[:\s]*(\d+(?:\.\d+)?)", text)
|
||
if target_match:
|
||
target_price = float(target_match.group(1))
|
||
|
||
# 提取止损价
|
||
stop_loss = None
|
||
stop_match = re.search(r"止损价[:\s]*(\d+(?:\.\d+)?)", text)
|
||
if stop_match:
|
||
stop_loss = float(stop_match.group(1))
|
||
|
||
# 提取分析
|
||
analysis = ""
|
||
analysis_match = re.search(r"分析[:\s]*(.+)", text, re.DOTALL)
|
||
if analysis_match:
|
||
analysis = analysis_match.group(1).strip()
|
||
|
||
return {
|
||
"signal": signal,
|
||
"strength": strength,
|
||
"entry_price": entry_price,
|
||
"target_price": target_price,
|
||
"stop_loss": stop_loss,
|
||
"analysis": analysis or "暂无分析",
|
||
}
|
||
|
||
|
||
async def analyze_candidates_individually(
|
||
candidates: list[dict], market_summary: str, max_concurrent: int = 3
|
||
) -> dict[str, dict]:
|
||
"""对候选股票逐个做 LLM 分析（控制并发数）
|
||
|
||
返回: {ts_code: {"signal", "strength", "entry_price", ...}}
|
||
"""
|
||
if not settings.deepseek_api_key or not candidates:
|
||
return {}
|
||
|
||
results = {}
|
||
semaphore = asyncio.Semaphore(max_concurrent)
|
||
|
||
async def _analyze_with_semaphore(c: dict):
|
||
async with semaphore:
|
||
ts_code = c["ts_code"]
|
||
logger.info(f"LLM 分析: {c.get('name', ts_code)}")
|
||
result = await analyze_single_stock(c, market_summary)
|
||
logger.info(
|
||
f"LLM 结果: {c.get('name', ts_code)} → "
|
||
f"信号={result['signal']} 强度={result['strength']} "
|
||
f"买入={result.get('entry_price')} 止盈={result.get('target_price')} "
|
||
f"止损={result.get('stop_loss')}"
|
||
)
|
||
return ts_code, result
|
||
|
||
tasks = [_analyze_with_semaphore(c) for c in candidates]
|
||
|
||
completed = await asyncio.gather(*tasks, return_exceptions=True)
|
||
|
||
for item in completed:
|
||
if isinstance(item, Exception):
|
||
logger.error(f"LLM 分析任务异常: {item}")
|
||
continue
|
||
ts_code, result = item
|
||
results[ts_code] = result
|
||
|
||
logger.info(f"LLM 逐股分析完成: {len(results)}/{len(candidates)} 只")
|
||
return results
|