662 lines
27 KiB
Python
662 lines
27 KiB
Python
"""个股分析 API"""
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import json
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import logging
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import traceback
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from datetime import datetime, timedelta
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from fastapi import APIRouter, Query
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from starlette.responses import StreamingResponse
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from app.data.tushare_client import tushare_client
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from app.data import tencent_client
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from app.analysis.technical import add_all_indicators
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from app.analysis.signals import generate_signals
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from app.db.database import get_db
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from app.db import tables
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/stocks", tags=["stocks"])
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@router.get("/search")
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async def search_stock(keyword: str):
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"""搜索股票"""
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basic = tushare_client.get_stock_basic()
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if basic.empty:
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return []
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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(20)
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return matches[["ts_code", "name", "industry"]].to_dict(orient="records")
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@router.get("/{ts_code}/thesis")
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async def get_stock_thesis(ts_code: str):
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"""获取个股推荐推演归档（只读缓存，不触发扫描或 LLM）。"""
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from sqlalchemy import text
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async with get_db() as db:
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rec_result = await db.execute(
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text(
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"SELECT * FROM recommendations "
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"WHERE ts_code = :code "
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"ORDER BY created_at DESC, id DESC LIMIT 1"
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),
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{"code": ts_code},
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)
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rec_row = rec_result.fetchone()
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tracking_rows = []
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diagnosis_rows = []
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if rec_row:
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rec_id = rec_row._mapping["id"]
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tracking_result = await db.execute(
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text(
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"SELECT * FROM recommendation_tracking "
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"WHERE recommendation_id = :rid "
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"ORDER BY track_date DESC, id DESC LIMIT 10"
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),
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{"rid": rec_id},
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)
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tracking_rows = tracking_result.fetchall()
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diagnosis_result = await db.execute(
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text(
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"SELECT id, diagnosis, created_at FROM stock_diagnoses "
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"WHERE ts_code = :code "
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"ORDER BY created_at DESC LIMIT 3"
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),
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{"code": ts_code},
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)
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diagnosis_rows = diagnosis_result.fetchall()
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if not rec_row:
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return {
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"ts_code": ts_code,
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"name": ts_code,
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"has_recommendation": False,
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"recommendation": None,
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"latest_tracking": None,
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"tracking_history": [],
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"diagnoses": [
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{
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"id": row._mapping["id"],
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"diagnosis": row._mapping["diagnosis"] or "",
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"created_at": str(row._mapping["created_at"] or ""),
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}
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for row in diagnosis_rows
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],
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"decision_points": [],
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"data_freshness": {
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"recommendation_created_at": "",
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"tracking_date": "",
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"status": "no_recommendation",
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"message": "暂无推荐归档，可从 AI 诊断页生成个股诊断。",
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},
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}
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r = rec_row._mapping
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def _safe_json_list(value: str | None) -> list:
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if not value:
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return []
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try:
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parsed = json.loads(value)
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return parsed if isinstance(parsed, list) else []
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except Exception:
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return []
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tracking_history = []
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for row in tracking_rows:
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t = row._mapping
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tracking_history.append({
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"track_date": t["track_date"],
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"current_price": t["current_price"],
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"pct_from_entry": t["pct_from_entry"],
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"max_return_pct": t["max_return_pct"],
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"max_drawdown_pct": t["max_drawdown_pct"],
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"days_since_recommendation": t["days_since_recommendation"],
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"hit_target": bool(t["hit_target"]),
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"hit_stop_loss": bool(t["hit_stop_loss"]),
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"close_reason": t["close_reason"] or "",
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"review_note": t["review_note"] or "",
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"status": t["status"] or "",
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})
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latest_tracking = tracking_history[0] if tracking_history else None
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decision_points = [
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{"label": "操作计划", "value": r["action_plan"] or "观察"},
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{"label": "召回来源", "value": " / ".join(_safe_json_list(r.get("recall_tags"))) or "未归档"},
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{"label": "AI预筛", "value": r.get("prefilter_decision") or "未执行"},
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{"label": "触发条件", "value": r["trigger_condition"] or "等待触发条件归档"},
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{"label": "失效条件", "value": r["invalidation_condition"] or "等待失效条件归档"},
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{"label": "建议仓位", "value": f"{r['suggested_position_pct']}%" if r["suggested_position_pct"] is not None else "未设置"},
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{"label": "复盘周期", "value": f"{r['review_after_days'] or 3}个交易日"},
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]
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freshness_status = "fresh"
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freshness_message = "推荐归档可用"
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if not latest_tracking:
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freshness_status = "needs_tracking"
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freshness_message = "暂无跟踪记录，建议管理员执行跟踪更新。"
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elif latest_tracking.get("track_date"):
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freshness_message = f"最近跟踪日期 {latest_tracking['track_date']}"
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return {
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"ts_code": r["ts_code"],
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"name": r["name"],
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"has_recommendation": True,
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"recommendation": {
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"id": r["id"],
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"ts_code": r["ts_code"],
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"name": r["name"],
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"sector": r["sector"] or "",
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"score": r["score"] or 0,
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"market_temp_score": r["market_temp_score"] or 0,
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"sector_score": r["sector_score"] or 0,
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"capital_score": r["capital_score"] or 0,
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"technical_score": r["technical_score"] or 0,
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"supply_demand_score": r["supply_demand_score"] or 0,
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"price_action_score": r["price_action_score"] or 0,
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"position_score": r["position_score"] or 50,
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"valuation_score": r["valuation_score"] or 50,
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"entry_price": r["entry_price"],
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"target_price": r["target_price"],
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"stop_loss": r["stop_loss"],
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"reasons": _safe_json_list(r["reasons"]),
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"risk_note": r["risk_note"] or "",
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"action_plan": r["action_plan"] or "观察",
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"trigger_condition": r["trigger_condition"] or "",
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"invalidation_condition": r["invalidation_condition"] or "",
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"suggested_position_pct": r["suggested_position_pct"] or 0,
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"review_after_days": r["review_after_days"] or 3,
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"lifecycle_status": r["lifecycle_status"] or "candidate",
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"data_freshness": r["data_freshness"] or "",
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"llm_analysis": r["llm_analysis"] or "",
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"llm_score": r["llm_score"],
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"recall_tags": _safe_json_list(r.get("recall_tags")),
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"prefilter_decision": r.get("prefilter_decision") or "",
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"prefilter_reason": r.get("prefilter_reason") or "",
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"focus_points": _safe_json_list(r.get("focus_points")),
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"strategy": r["strategy"] or "trend_breakout",
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"entry_signal_type": r["entry_signal_type"] or "none",
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"entry_timing": r["entry_timing"] or "",
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"scan_session": r["scan_session"] or "",
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"created_at": str(r["created_at"] or ""),
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},
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"latest_tracking": latest_tracking,
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"tracking_history": tracking_history,
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"diagnoses": [
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{
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"id": row._mapping["id"],
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"diagnosis": row._mapping["diagnosis"] or "",
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"created_at": str(row._mapping["created_at"] or ""),
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}
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for row in diagnosis_rows
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],
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"decision_points": decision_points,
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"data_freshness": {
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"recommendation_created_at": str(r["created_at"] or ""),
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"tracking_date": latest_tracking["track_date"] if latest_tracking else "",
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"status": freshness_status,
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"message": freshness_message,
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},
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}
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@router.get("/{ts_code}/quote")
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async def get_quote(ts_code: str):
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"""获取个股实时行情"""
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quote = await tencent_client.get_realtime_quote(ts_code)
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if not quote:
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return {"error": "获取行情失败"}
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return quote.model_dump()
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@router.get("/{ts_code}/kline")
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async def get_kline(ts_code: str, days: int = 120):
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"""获取个股K线数据（含技术指标）"""
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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 []
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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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# 替换 NaN 为 None（JSON 兼容）
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import math
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records = df.to_dict(orient="records")
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for rec in records:
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for k, v in rec.items():
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if isinstance(v, float) and (math.isnan(v) or math.isinf(v)):
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rec[k] = None
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return records
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@router.get("/{ts_code}/signals")
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async def get_signals(ts_code: str):
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"""获取个股技术面买卖信号"""
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signal = generate_signals(ts_code)
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return signal.model_dump()
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@router.get("/{ts_code}/capital_flow")
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async def get_capital_flow(ts_code: str, days: int = 10):
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"""获取个股资金流向（含大/中/小单分拆）"""
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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 []
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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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"elg_net": round(
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(row.get("buy_elg_amount", 0) or 0) - (row.get("sell_elg_amount", 0) or 0), 2
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),
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"lg_net": round(
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(row.get("buy_lg_amount", 0) or 0) - (row.get("sell_lg_amount", 0) or 0), 2
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),
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"md_net": round(
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(row.get("buy_md_amount", 0) or 0) - (row.get("sell_md_amount", 0) or 0), 2
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),
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"sm_net": round(
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(row.get("buy_sm_amount", 0) or 0) - (row.get("sell_sm_amount", 0) or 0), 2
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),
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})
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return records
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@router.get("/{ts_code}/diagnose/history")
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async def get_diagnose_history(ts_code: str):
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"""获取个股最近5次诊断历史"""
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try:
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from sqlalchemy import text
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async with get_db() as db:
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result = await db.execute(
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text(
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"SELECT id, ts_code, name, diagnosis, created_at "
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"FROM stock_diagnoses "
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"WHERE ts_code = :code "
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"ORDER BY created_at DESC LIMIT 5"
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),
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{"code": ts_code},
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)
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rows = result.fetchall()
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history = []
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for row in rows:
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r = row._mapping
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history.append({
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"id": r["id"],
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"ts_code": r["ts_code"],
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"name": r["name"],
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"diagnosis_mode": r.get("diagnosis_mode", "entry"),
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"diagnosis": r["diagnosis"],
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"created_at": str(r["created_at"]),
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})
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return history
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except Exception as e:
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logger.error(f"获取诊断历史失败: {e}")
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await log_error("stocks", f"获取诊断历史失败: {e}", detail=traceback.format_exc())
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return []
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@router.post("/{ts_code}/diagnose")
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async def diagnose_stock(ts_code: str, mode: str = Query("entry")):
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"""AI 诊断个股（SSE 流式返回）"""
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from app.config import settings
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if not settings.deepseek_api_key:
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return {"status": "error", "message": "未配置 LLM API Key"}
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from app.llm.client import get_client
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from sqlalchemy import text
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# ── 检查是否有最近30分钟内的诊断记录，若有则直接返回 ──
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try:
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async with get_db() as db:
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result = await db.execute(
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text(
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"SELECT id, ts_code, name, diagnosis, created_at "
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"FROM stock_diagnoses "
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"WHERE ts_code = :code "
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"AND diagnosis_mode = :mode "
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"AND created_at >= datetime('now', '-30 minutes', 'localtime') "
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"ORDER BY created_at DESC LIMIT 1"
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),
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{"code": ts_code, "mode": mode},
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)
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recent_row = result.fetchone()
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if recent_row:
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r = recent_row._mapping
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# 直接返回缓存结果
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async def _cached_stream():
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yield f"data: {json.dumps({'cached': True, 'diagnosis': r['diagnosis']}, ensure_ascii=False)}\n\n"
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yield f"data: {json.dumps({'done': True, 'ts_code': ts_code})}\n\n"
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return StreamingResponse(_cached_stream(), media_type="text/event-stream")
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except Exception as e:
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logger.warning(f"检查诊断缓存失败: {e}")
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# ── 收集数据 ──
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quote = await tencent_client.get_realtime_quote(ts_code)
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signals = generate_signals(ts_code)
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df_daily = tushare_client.get_stock_daily(ts_code, days=120)
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df_flow = tushare_client.get_stock_moneyflow(ts_code, days=10)
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# ── 数据新鲜度检查 ──
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freshness_note = ""
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data_stale = False
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current_price_source = ""
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if not df_daily.empty:
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df_daily = df_daily.sort_values("trade_date")
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latest_kline_date = str(df_daily.iloc[-1]["trade_date"])
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# 检查 K 线数据是否超过 10 天未更新
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cutoff_date = (datetime.now() - timedelta(days=10)).strftime("%Y%m%d")
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if latest_kline_date < cutoff_date:
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logger.warning(f"K线数据过时 {ts_code}: 最新={latest_kline_date}, 10天前阈值={cutoff_date}")
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data_stale = True
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# 如果最新 K 线日期不是今天，添加新鲜度提示
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today_str = datetime.now().strftime("%Y%m%d")
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if latest_kline_date != today_str:
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freshness_note = f"\n\n注意：K线数据最新日期为{latest_kline_date}，非当日数据，部分分析可能滞后。"
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# 数据过时时，使用实时报价价格作为"当前价"替代
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if data_stale and quote and quote.price > 0:
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current_price_source = f"（实时报价价 {quote.price}，K线收盘价 {df_daily.iloc[-1]['close']} 可能滞后）"
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# 构建数据摘要
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quote_str = ""
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if quote:
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quote_str = (
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f"当前价: {quote.price}{current_price_source}, 涨跌幅: {quote.pct_chg}%, "
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f"换手率: {quote.turnover_rate}%, 量比: {quote.volume_ratio}, "
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f"PE: {quote.pe}, PB: {quote.pb}, "
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f"总市值: {quote.total_mv}亿, 流通市值: {quote.circ_mv}亿"
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)
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signal_str = (
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f"推荐体系评分: 趋势评分={signals.trend_score}/100（均线排列+高低点结构+MA20方向，主评分10%权重), "
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f"辅助信号计数={signals.signal_count}/7（触发计分，仅供参考不参与主评分), "
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f"均线多头: {signals.ma_bullish}, "
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f"放量突破: {signals.volume_breakout}, "
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f"MACD金叉: {signals.macd_golden}, "
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f"RSI健康: {signals.rsi_healthy}, "
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f"缩量回踩: {signals.pullback_support}, "
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f"放量长阳: {signals.big_yang}, "
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f"布林支撑: {signals.boll_support}, "
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f"支撑位: {signals.support_price}, "
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f"压力位: {signals.resist_price}, "
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f"止损位: {signals.stop_loss_price}"
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)
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position_str = (
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f"位置安全评分: {signals.position_score}/100（越高表示位置越低越安全，96分以上表示处于相对低位）, "
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f"近5日涨幅: {signals.rally_pct_5d}%, "
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f"近10日涨幅: {signals.rally_pct_10d}%, "
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f"距60日高点: {signals.distance_from_high}%"
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)
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trend_str = ""
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ma_info = ""
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if not df_daily.empty:
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latest = df_daily.iloc[-1]
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if len(df_daily) >= 5:
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pct_5d = (latest["close"] - df_daily.iloc[-5]["close"]) / df_daily.iloc[-5]["close"] * 100
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trend_str += f"5日涨幅: {pct_5d:.2f}%, "
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if len(df_daily) >= 20:
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pct_20d = (latest["close"] - df_daily.iloc[-20]["close"]) / df_daily.iloc[-20]["close"] * 100
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trend_str += f"20日涨幅: {pct_20d:.2f}%, "
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vol_avg_5 = df_daily.tail(5)["vol"].mean()
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vol_latest = latest["vol"]
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trend_str += f"量比(5日均): {vol_latest / vol_avg_5:.2f}" if vol_avg_5 > 0 else ""
|
||
# MA 信息
|
||
if "ma5" in latest and "ma20" in latest:
|
||
ma5 = latest.get("ma5", 0)
|
||
ma10 = latest.get("ma10", 0)
|
||
ma20 = latest.get("ma20", 0)
|
||
ma60 = latest.get("ma60", 0)
|
||
price = latest["close"]
|
||
ma_info = (
|
||
f"价格与均线关系: 现价{price:.2f}, "
|
||
f"MA5={ma5:.2f}, MA10={ma10:.2f}, MA20={ma20:.2f}, MA60={ma60:.2f}, "
|
||
f"{'价格在MA5上方' if price > ma5 else '价格在MA5下方'}, "
|
||
f"{'价格在MA20上方' if price > ma20 else '价格在MA20下方'}, "
|
||
f"{'均线多头排列' if ma5 > ma10 > ma20 else '均线未多头排列'}"
|
||
)
|
||
|
||
flow_str = ""
|
||
if not df_flow.empty:
|
||
df_flow = df_flow.sort_values("trade_date")
|
||
latest_flow_date = str(df_flow.iloc[-1]["trade_date"])
|
||
recent_3 = df_flow.tail(3)
|
||
total_main = 0
|
||
for _, r in recent_3.iterrows():
|
||
main_net = (
|
||
(r.get("buy_elg_amount", 0) or 0) - (r.get("sell_elg_amount", 0) or 0) +
|
||
(r.get("buy_lg_amount", 0) or 0) - (r.get("sell_lg_amount", 0) or 0)
|
||
)
|
||
total_main += main_net
|
||
flow_str = f"近3日主力净流入: {total_main:.0f}万"
|
||
# 资金流向数据新鲜度标注
|
||
today_str = datetime.now().strftime("%Y%m%d")
|
||
if latest_flow_date != today_str:
|
||
flow_str += f"（数据截至{latest_flow_date}，盘中可能滞后一日）"
|
||
|
||
# 基本信息
|
||
basic_info = ""
|
||
stock_name = ""
|
||
industry = ""
|
||
basic_df = tushare_client.get_stock_basic()
|
||
if not basic_df.empty:
|
||
row = basic_df[basic_df["ts_code"] == ts_code]
|
||
if not row.empty:
|
||
r = row.iloc[0]
|
||
stock_name = r["name"]
|
||
industry = r.get("industry", "") or ""
|
||
basic_info = f"名称: {r['name']}, 行业: {industry}"
|
||
|
||
# 推荐体系评分（如果该股票在推荐列表中）
|
||
rec_score_str = ""
|
||
try:
|
||
async with get_db() as db:
|
||
rec_result = await db.execute(
|
||
text(
|
||
"SELECT score, supply_demand_score, price_action_score, "
|
||
"technical_score, position_score, sector, signal "
|
||
"FROM recommendations "
|
||
"WHERE ts_code = :code "
|
||
"AND (action_plan IN ('可操作', '重点关注') OR COALESCE(llm_score, 0) >= 6 OR score >= 56) "
|
||
"ORDER BY created_at DESC LIMIT 1"
|
||
),
|
||
{"code": ts_code},
|
||
)
|
||
rec_row = rec_result.fetchone()
|
||
if rec_row:
|
||
rm = rec_row._mapping
|
||
rec_score_str = (
|
||
f"\n推荐体系评分: 综合={rm['score']}, "
|
||
f"供需={rm['supply_demand_score']}(50%权重), "
|
||
f"形态={rm['price_action_score']}(40%权重), "
|
||
f"趋势={rm['technical_score']}(10%权重), "
|
||
f"位置安全={rm['position_score']}, "
|
||
f"板块={rm['sector']}, "
|
||
f"信号={rm['signal']}"
|
||
)
|
||
except Exception:
|
||
pass
|
||
|
||
# 板块热度（如果有该行业板块数据）
|
||
sector_str = ""
|
||
if industry:
|
||
try:
|
||
async with get_db() as db:
|
||
sector_result = await db.execute(
|
||
text(
|
||
"SELECT sector_name, pct_change, heat_score, stage, "
|
||
"days_continuous, limit_up_count "
|
||
"FROM sector_heat "
|
||
"WHERE sector_name LIKE :industry "
|
||
"ORDER BY created_at DESC LIMIT 1"
|
||
),
|
||
{"industry": f"%{industry}%"},
|
||
)
|
||
s_row = sector_result.fetchone()
|
||
if s_row:
|
||
sm = s_row._mapping
|
||
sector_str = (
|
||
f"板块热度: {sm['sector_name']} 涨幅={sm['pct_change']}%, "
|
||
f"热度={sm['heat_score']}, 阶段={sm['stage']}, "
|
||
f"连续{sm['days_continuous']}天, 涨停数={sm['limit_up_count']}"
|
||
)
|
||
except Exception:
|
||
pass
|
||
|
||
mode_instruction_map = {
|
||
"entry": "这是建仓前诊断。必须明确当前是可操作、重点关注、观察还是回避，并给出触发与失效边界。",
|
||
"holding": "这是持仓复核。必须回答逻辑是否还成立，当前更适合持有、减仓、退出还是继续观察。",
|
||
"review": "这是回撤复盘。重点拆清问题来自市场、板块还是个股执行，并提出下一轮修正动作。",
|
||
"tracking": "这是继续跟踪。必须说明保留理由、升级条件和移除条件，避免空泛表述。",
|
||
}
|
||
mode_label_map = {
|
||
"entry": "建仓前诊断",
|
||
"holding": "持仓复核",
|
||
"review": "回撤复盘",
|
||
"tracking": "继续跟踪",
|
||
}
|
||
mode_instruction = mode_instruction_map.get(mode, mode_instruction_map["entry"])
|
||
mode_label = mode_label_map.get(mode, mode_label_map["entry"])
|
||
|
||
user_msg = f"""请基于当前 AI 推荐体系，对以下A股进行结构化个股会诊：
|
||
|
||
诊断模式: {mode_label}
|
||
模式要求: {mode_instruction}
|
||
|
||
股票: {ts_code} ({basic_info})
|
||
{quote_str}
|
||
|
||
技术面: {signal_str}
|
||
|
||
位置安全: {position_str}
|
||
|
||
趋势: {trend_str}
|
||
{ma_info}
|
||
资金面: {flow_str}
|
||
{rec_score_str}
|
||
{sector_str}
|
||
|
||
重要提示：
|
||
1. 你不是在写传统研报，而是在给交易作战台输出结构化会诊意见。
|
||
2. 如果有推荐体系评分、操作计划、跟踪信息，请优先沿用当前推荐体系，而不是另起一套标准。
|
||
3. 趋势评分是推荐体系的技术面核心分数（均线排列40+高低点结构35+MA20方向25=满分100），辅助信号计数仅供参考不参与主评分。
|
||
4. 位置安全评分高(>80)表示股价处于相对低位，低(<40)表示可能追高。
|
||
5. 板块信息和推荐体系信息优先级高于单一技术指标。
|
||
6. 先给结论和动作，再解释原因；不要先铺陈背景再拖到最后才下结论。
|
||
7. 如果证据不足，也要明确给出“观察”或“回避”，不能写成模糊建议。
|
||
{freshness_note}
|
||
|
||
请严格按以下 Markdown 结构输出，不要写成泛泛长文：
|
||
|
||
## 当前结论
|
||
- 结论: 只能从「可操作 / 重点关注 / 观察 / 回避」中选一个
|
||
- 一句话判断: 用一句话解释为什么
|
||
- 当前动作: 只能从「执行 / 等确认 / 继续跟踪 / 暂不参与」中选一个
|
||
- 适配模式: 说明更适合启动试错、分歧回流、趋势跟随还是只观察
|
||
|
||
## 核心逻辑
|
||
- 市场环境: 当前大盘和风格是否支持这只票
|
||
- 板块位置: 所属板块是主线、次主线还是观察线
|
||
- 个股角色: 龙头 / 跟风 / 独立逻辑 / 非核心
|
||
- 关键证据: 只提最重要的两到三条证据，不要抄原始数据
|
||
|
||
## 执行动作
|
||
- 触发条件: 什么情况下才可以行动
|
||
- 失效条件: 什么情况下放弃
|
||
- 仓位建议: 用低 / 中 / 高 或百分比表达
|
||
- 适合谁: 适合激进试错、低吸等待、还是不适合参与
|
||
- 跟踪重点: 下一交易时段最该盯住什么
|
||
|
||
## 风险清单
|
||
- 风险1:
|
||
- 风险2:
|
||
- 风险3:
|
||
|
||
## 复盘问题
|
||
- 如果后续走势不符合预期，优先检查哪两个问题
|
||
|
||
## 会诊纪要
|
||
- 用两到三句话总结本次会诊，不要写成长文，不要复制前面的条目
|
||
|
||
要求：
|
||
- 结论必须明确，不能模糊两可
|
||
- 少写形容词，多写交易判断
|
||
- 不要重复原始数据
|
||
- 文字保持简洁，避免旧式研报语气
|
||
- 每个条目尽量一句话说清，不要堆砌长段落"""
|
||
|
||
# ── SSE 流式返回 ──
|
||
async def _stream_diagnosis():
|
||
full_content = ""
|
||
try:
|
||
client = get_client()
|
||
stream = await client.chat.completions.create(
|
||
model=settings.deepseek_model,
|
||
messages=[
|
||
{"role": "system", "content": "你是A股AI投研作战台中的个股会诊模块。你输出的是交易会诊单，不是传统研报。必须先给明确结论，再给执行动作、风险边界和跟踪重点。回复必须使用Markdown，结构严格、结论清晰、语言简短，禁止空泛抒情。"},
|
||
{"role": "user", "content": user_msg},
|
||
],
|
||
max_tokens=1500,
|
||
temperature=0.5,
|
||
stream=True,
|
||
)
|
||
async for chunk in stream:
|
||
if chunk.choices and chunk.choices[0].delta:
|
||
token = chunk.choices[0].delta.content or ""
|
||
if token:
|
||
full_content += token
|
||
yield f"data: {json.dumps({'token': token}, ensure_ascii=False)}\n\n"
|
||
|
||
# 流式完成后，保存到数据库
|
||
full_content = full_content.strip()
|
||
if full_content:
|
||
try:
|
||
async with get_db() as db:
|
||
await db.execute(
|
||
tables.stock_diagnoses_table.insert().values(
|
||
ts_code=ts_code,
|
||
name=stock_name or ts_code,
|
||
diagnosis_mode=mode,
|
||
diagnosis=full_content,
|
||
)
|
||
)
|
||
await db.commit()
|
||
logger.info(f"已保存诊断结果到数据库: {ts_code}")
|
||
except Exception as e:
|
||
logger.error(f"保存诊断结果到数据库失败: {e}")
|
||
from app.db.error_logger import log_error
|
||
await log_error("stocks", f"保存诊断结果到数据库失败: {e}", detail=traceback.format_exc())
|
||
|
||
yield f"data: {json.dumps({'done': True, 'ts_code': ts_code}, ensure_ascii=False)}\n\n"
|
||
|
||
except Exception as e:
|
||
error_msg = str(e)
|
||
logger.error(f"诊断流式调用失败: {error_msg}")
|
||
await log_error(
|
||
"stocks",
|
||
f"诊断流式调用失败: {error_msg}",
|
||
detail=traceback.format_exc(),
|
||
context={"method": "POST", "path": f"/api/stocks/{ts_code}/diagnose"},
|
||
)
|
||
yield f"data: {json.dumps({'error': error_msg}, ensure_ascii=False)}\n\n"
|
||
yield f"data: {json.dumps({'done': True, 'ts_code': ts_code}, ensure_ascii=False)}\n\n"
|
||
|
||
return StreamingResponse(_stream_diagnosis(), media_type="text/event-stream")
|