409 lines
16 KiB
Python
409 lines
16 KiB
Python
"""LLM 工具执行器
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根据工具名调用现有数据层，返回 JSON 字符串供 LLM 使用。
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"""
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import json
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import logging
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import math
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from app.db.error_logger import log_error
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logger = logging.getLogger(__name__)
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_chat_user_context: dict | None = None
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def set_chat_user_context(user: dict | None) -> None:
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global _chat_user_context
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_chat_user_context = user
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async def execute_tool(name: str, arguments: dict) -> str:
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"""执行工具调用，返回 JSON 字符串"""
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try:
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if name == "get_strategy_board":
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return await _get_strategy_board()
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elif name == "get_market_temperature":
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return await _get_market_temperature()
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elif name == "get_hot_sectors":
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return await _get_hot_sectors(arguments.get("limit", 10))
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elif name == "get_latest_recommendations":
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return await _get_latest_recommendations()
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elif name == "get_user_watchlist_snapshot":
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return await _get_user_watchlist_snapshot()
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elif name == "get_stock_kline":
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return await _get_stock_kline(
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arguments["ts_code"], arguments.get("days", 60)
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)
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elif name == "get_stock_capital_flow":
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return await _get_stock_capital_flow(
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arguments["ts_code"], arguments.get("days", 10)
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)
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elif name == "search_stock":
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return await _search_stock(arguments["keyword"])
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elif name == "get_stock_technical_signal":
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return await _get_stock_technical_signal(arguments["ts_code"])
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elif name == "diagnose_stock":
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return await _diagnose_stock(arguments["ts_code"], arguments.get("mode", "entry"))
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elif name == "get_sector_performance":
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return await _get_sector_performance(arguments["sector_name"])
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elif name == "get_realtime_indices":
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return await _get_realtime_indices()
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else:
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return json.dumps({"error": f"未知工具: {name}"}, ensure_ascii=False)
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except Exception as e:
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logger.error(f"工具执行失败 {name}: {e}")
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await log_error(
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"llm_tool",
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f"工具执行失败 {name}: {e}",
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detail=f"arguments={json.dumps(arguments, ensure_ascii=False, default=str)}",
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)
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return json.dumps({"error": str(e)}, ensure_ascii=False)
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def _clean_for_json(obj):
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"""清理 float NaN/Inf 为 None"""
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if isinstance(obj, dict):
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return {k: _clean_for_json(v) for k, v in obj.items()}
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if isinstance(obj, list):
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return [_clean_for_json(v) for v in obj]
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if isinstance(obj, float) and (math.isnan(obj) or math.isinf(obj)):
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return None
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return obj
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async def _get_strategy_board() -> str:
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from app.llm.strategy_board import build_strategy_board
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board = await build_strategy_board(include_llm=False)
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payload = {
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"trade_date": board.get("trade_date", ""),
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"market_regime": board.get("market_regime", ""),
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"risk_level": board.get("risk_level", ""),
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"action_bias": board.get("action_bias", ""),
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"position_suggestion": board.get("position_suggestion", ""),
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"summary": board.get("summary", ""),
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"recommended_mode": board.get("recommended_mode", ""),
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"watch_sectors": board.get("watch_sectors", [])[:5],
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"strategy_focus": board.get("strategy_focus", [])[:4],
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"avoid_rules": board.get("avoid_rules", [])[:4],
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"iteration_notes": board.get("iteration_notes", [])[:3],
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"metrics": board.get("metrics", {}),
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"generated_by": board.get("generated_by", "rules"),
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}
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return json.dumps(_clean_for_json(payload), ensure_ascii=False, default=str)
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async def _get_market_temperature() -> str:
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from app.engine.recommender import get_latest_recommendations
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result = await get_latest_recommendations()
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mt = result.get("market_temp")
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if not mt:
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return json.dumps({"error": "暂无市场温度数据"}, ensure_ascii=False)
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return json.dumps(mt.model_dump(), ensure_ascii=False, default=str)
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async def _get_hot_sectors(limit: int) -> str:
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from app.engine.recommender import get_latest_sectors
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sectors = await get_latest_sectors()
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data = [s.model_dump() for s in sectors[:limit]]
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return json.dumps(data, ensure_ascii=False, default=str)
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async def _get_latest_recommendations() -> str:
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from app.engine.recommender import get_latest_recommendations
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result = await get_latest_recommendations()
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recs = result.get("recommendations", [])
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data = []
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for rec in recs:
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item = rec.model_dump(exclude={"created_at"})
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item["llm_analysis"] = ""
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data.append(item)
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return json.dumps(data, ensure_ascii=False, default=str)
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async def _get_user_watchlist_snapshot() -> str:
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from sqlalchemy import text
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from app.db.database import get_db
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user_id = (_chat_user_context or {}).get("id")
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if not user_id:
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return json.dumps({"error": "当前会话缺少用户上下文"}, ensure_ascii=False)
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async with get_db() as db:
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rows = (await db.execute(
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text(
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"SELECT w.id, w.ts_code, w.name, w.note, w.watch_group, w.cost_price, w.updated_at, "
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"a.conclusion, a.advice, a.trigger_condition, a.risk_note, a.summary, "
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"a.analysis_mode, a.created_at AS analysis_created_at "
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"FROM user_watchlists w "
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"LEFT JOIN watchlist_analyses a ON a.id = ("
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" SELECT id FROM watchlist_analyses "
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" WHERE watchlist_id = w.id ORDER BY created_at DESC, id DESC LIMIT 1"
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") "
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"WHERE w.user_id = :uid AND COALESCE(w.is_active, 1) = 1 "
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"ORDER BY CASE w.watch_group "
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" WHEN 'focus' THEN 1 "
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" WHEN 'candidate' THEN 2 "
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" WHEN 'holding' THEN 3 "
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" ELSE 4 END, w.updated_at DESC, w.id DESC"
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),
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{"uid": user_id},
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)).fetchall()
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items = [dict(row._mapping) for row in rows]
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grouped = {"focus": 0, "candidate": 0, "holding": 0, "observe": 0}
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for item in items:
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key = item.get("watch_group") or "observe"
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if key in grouped:
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grouped[key] += 1
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actionable = [
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item for item in items
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if item.get("conclusion") in {"可操作", "重点关注"}
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][:8]
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payload = {
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"count": len(items),
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"group_counts": grouped,
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"high_priority": actionable,
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"items": items[:20],
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}
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return json.dumps(_clean_for_json(payload), ensure_ascii=False, default=str)
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async def _get_stock_kline(ts_code: str, days: int) -> str:
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from app.data.tushare_client import tushare_client
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from app.analysis.technical import add_all_indicators
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df = tushare_client.get_stock_daily(ts_code, days=days)
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if df.empty:
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return json.dumps({"error": f"未找到 {ts_code} 的K线数据"}, ensure_ascii=False)
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df = df.sort_values("trade_date").reset_index(drop=True)
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df = add_all_indicators(df)
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# 只返回最近 20 条以节省 token
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records = df.tail(20).to_dict(orient="records")
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records = _clean_for_json(records)
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return json.dumps(records, ensure_ascii=False, default=str)
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async def _get_stock_capital_flow(ts_code: str, days: int) -> str:
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from app.data.tushare_client import tushare_client
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df = tushare_client.get_stock_moneyflow(ts_code, days=days)
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if df.empty:
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return json.dumps({"error": f"未找到 {ts_code} 的资金流向数据"}, ensure_ascii=False)
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df = df.sort_values("trade_date")
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records = []
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for _, row in df.iterrows():
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main_net = (
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(row.get("buy_elg_amount", 0) or 0) - (row.get("sell_elg_amount", 0) or 0) +
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(row.get("buy_lg_amount", 0) or 0) - (row.get("sell_lg_amount", 0) or 0)
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)
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records.append({
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"trade_date": row["trade_date"],
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"main_net_inflow": round(main_net, 2),
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"net_mf_amount": round(float(row.get("net_mf_amount", 0) or 0), 2),
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})
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return json.dumps(records, ensure_ascii=False, default=str)
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async def _search_stock(keyword: str) -> str:
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from app.data.tushare_client import tushare_client
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basic = tushare_client.get_stock_basic()
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if basic.empty:
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return json.dumps([], ensure_ascii=False)
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matches = basic[
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basic["name"].str.contains(keyword, na=False) |
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basic["ts_code"].str.contains(keyword, na=False) |
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basic["symbol"].str.contains(keyword, na=False)
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].head(10)
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data = matches[["ts_code", "name", "industry"]].to_dict(orient="records")
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return json.dumps(data, ensure_ascii=False, default=str)
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async def _get_stock_technical_signal(ts_code: str) -> str:
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"""获取个股技术面信号详情"""
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from app.analysis.signals import generate_signals
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signal = generate_signals(ts_code)
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data = _clean_for_json(signal.model_dump())
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return json.dumps(data, ensure_ascii=False, default=str)
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async def _diagnose_stock(ts_code: str, mode: str = "entry") -> str:
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"""生成系统化个股会诊，供作战问答智能体调用。"""
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from sqlalchemy import text
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from app.db.database import get_db
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from app.db import tables
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from app.llm.client import chat_completion
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mode_map = {
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"entry": "建仓前诊断",
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"holding": "持仓复核",
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"review": "回撤复盘",
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"tracking": "继续跟踪",
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}
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mode = mode if mode in mode_map else "entry"
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strategy_board = await _get_strategy_board()
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latest_recommendations = await _get_latest_recommendations()
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hot_sectors = await _get_hot_sectors(8)
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kline = await _get_stock_kline(ts_code, 80)
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capital_flow = await _get_stock_capital_flow(ts_code, 15)
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technical_signal = await _get_stock_technical_signal(ts_code)
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latest_rec = None
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stock_name = ts_code
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try:
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recs = json.loads(latest_recommendations)
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latest_rec = next((item for item in recs if item.get("ts_code") == ts_code), None)
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except Exception:
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latest_rec = None
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if latest_rec and latest_rec.get("name"):
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stock_name = latest_rec["name"]
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recent_diagnoses = []
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try:
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async with get_db() as db:
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rows = (await db.execute(
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text(
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"SELECT name, diagnosis_mode, diagnosis, created_at "
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"FROM stock_diagnoses WHERE ts_code = :ts_code "
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"ORDER BY created_at DESC, id DESC LIMIT 3"
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),
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{"ts_code": ts_code},
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)).fetchall()
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recent_diagnoses = [dict(row._mapping) for row in rows]
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if recent_diagnoses and recent_diagnoses[0].get("name"):
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stock_name = recent_diagnoses[0]["name"]
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except Exception:
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recent_diagnoses = []
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prompt = f"""请在 A 股作战台语境下，对 {ts_code} 做一次系统化个股会诊。
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诊断模式: {mode_map[mode]}
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今日作战结论:
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{strategy_board}
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最新推荐池中该股记录:
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{json.dumps(latest_rec, ensure_ascii=False, default=str) if latest_rec else "不在最新推荐池"}
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热门板块:
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{hot_sectors}
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资金流:
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{capital_flow}
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K线与价格行为:
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{kline}
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技术信号:
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{technical_signal}
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最近诊断:
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{json.dumps(recent_diagnoses, ensure_ascii=False, default=str)}
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输出要求:
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- 先给明确结论，只能是「可操作 / 重点关注 / 观察 / 回避」
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- 明确当前动作、触发条件、失效条件、仓位边界、下一步观察点
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- 分析顺序必须是：资金与主线 > 量价与价格行为 > 位置与边界 > 技术指标备注
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- A 股优先看资金顺势、主线板块、量价承接、价格行为和位置；技术指标只做节奏与风控确认
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- RSI、MACD、KDJ 的超买超卖不能单独决定买卖，也不能放在核心依据第一位
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- 输出必须包含以下小节：当前结论、资金与主线、量价与价格行为、位置与边界、执行动作、风险清单、技术指标备注
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- 不写传统研报，不堆原始数据，不承诺收益
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- 用 Markdown 输出，保持简洁"""
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resp = await chat_completion([
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{
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"role": "system",
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"content": (
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"你是 A 股投研作战台的个股会诊智能体。"
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"你必须优先分析资金流向、主线板块、量价关系、价格行为和位置边界，"
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"技术指标只能作为最后的节奏与风控备注。"
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"输出可执行但带风险边界的会诊结论。"
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),
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},
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{"role": "user", "content": prompt},
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])
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if not resp or not resp.content:
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return json.dumps({"error": "个股会诊生成失败，LLM 未返回内容"}, ensure_ascii=False)
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diagnosis = resp.content.strip()
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try:
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async with get_db() as db:
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await db.execute(
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tables.stock_diagnoses_table.insert().values(
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ts_code=ts_code,
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name=stock_name,
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diagnosis_mode=mode,
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diagnosis=diagnosis,
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)
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)
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await db.commit()
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except Exception as e:
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logger.warning(f"保存聊天会诊结果失败 {ts_code}: {e}")
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return json.dumps({
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"ts_code": ts_code,
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"name": stock_name,
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"mode": mode,
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"diagnosis": diagnosis,
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"saved": True,
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}, ensure_ascii=False, default=str)
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async def _get_sector_performance(sector_name: str) -> str:
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"""获取板块表现数据"""
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from app.engine.recommender import get_latest_sectors
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sectors = await get_latest_sectors()
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# 模糊匹配板块名称
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matched = [s for s in sectors if sector_name in s.sector_name or s.sector_name in sector_name]
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if not matched:
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# 返回所有热门板块概览
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data = [{"sector_name": s.sector_name, "pct_change": s.pct_change,
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"capital_inflow": s.capital_inflow, "limit_up_count": s.limit_up_count,
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"heat_score": s.heat_score, "stage": s.stage}
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for s in sectors[:10]]
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return json.dumps({"matched": False, "available_sectors": data}, ensure_ascii=False, default=str)
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data = _clean_for_json([s.model_dump() for s in matched])
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return json.dumps({"matched": True, "sectors": data}, ensure_ascii=False, default=str)
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async def _get_realtime_indices() -> str:
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"""获取指数实时行情数据（盘中用腾讯实时数据）"""
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from app.data.tencent_client import get_index_realtime
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from app.config import is_market_session
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is_trading = is_market_session()
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try:
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index_data = await get_index_realtime()
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if not index_data:
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return json.dumps({"error": "获取指数数据失败"}, ensure_ascii=False)
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# 格式化数据
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results = []
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for ts_code, data in index_data.items():
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results.append({
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"ts_code": ts_code,
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"name": data.get("name", ts_code),
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"price": round(data.get("price", 0), 2),
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"pct_chg": round(data.get("pct_chg", 0), 2),
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"volume": data.get("volume", 0),
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"amount": data.get("amount", 0),
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"high": round(data.get("high", 0), 2),
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"low": round(data.get("low", 0), 2),
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"open": round(data.get("open", 0), 2),
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"pre_close": round(data.get("pre_close", 0), 2),
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})
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return json.dumps({
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"is_realtime": is_trading,
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"mode": "盘中实时" if is_trading else "盘后收盘",
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"indices": results
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}, ensure_ascii=False, default=str)
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except Exception as e:
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logger.error(f"获取实时指数失败: {e}")
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await log_error("llm_tool", f"获取实时指数失败: {e}")
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return json.dumps({"error": f"获取指数数据失败: {e}"}, ensure_ascii=False)
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