425 lines
17 KiB
Python
425 lines
17 KiB
Python
"""个股分析 API"""
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import json
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import logging
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from datetime import datetime, timedelta
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from fastapi import APIRouter
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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("/{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("/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}/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": 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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return []
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@router.post("/{ts_code}/diagnose")
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async def diagnose_stock(ts_code: str):
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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 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},
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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 ""
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# MA 信息
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if "ma5" in latest and "ma20" in latest:
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ma5 = latest.get("ma5", 0)
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ma10 = latest.get("ma10", 0)
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ma20 = latest.get("ma20", 0)
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ma60 = latest.get("ma60", 0)
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price = latest["close"]
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ma_info = (
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f"价格与均线关系: 现价{price:.2f}, "
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f"MA5={ma5:.2f}, MA10={ma10:.2f}, MA20={ma20:.2f}, MA60={ma60:.2f}, "
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f"{'价格在MA5上方' if price > ma5 else '价格在MA5下方'}, "
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f"{'价格在MA20上方' if price > ma20 else '价格在MA20下方'}, "
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f"{'均线多头排列' if ma5 > ma10 > ma20 else '均线未多头排列'}"
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)
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flow_str = ""
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if not df_flow.empty:
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df_flow = df_flow.sort_values("trade_date")
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latest_flow_date = str(df_flow.iloc[-1]["trade_date"])
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recent_3 = df_flow.tail(3)
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total_main = 0
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for _, r in recent_3.iterrows():
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main_net = (
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(r.get("buy_elg_amount", 0) or 0) - (r.get("sell_elg_amount", 0) or 0) +
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(r.get("buy_lg_amount", 0) or 0) - (r.get("sell_lg_amount", 0) or 0)
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)
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total_main += main_net
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flow_str = f"近3日主力净流入: {total_main:.0f}万"
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# 资金流向数据新鲜度标注
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today_str = datetime.now().strftime("%Y%m%d")
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if latest_flow_date != today_str:
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flow_str += f"（数据截至{latest_flow_date}，盘中可能滞后一日）"
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# 基本信息
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basic_info = ""
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stock_name = ""
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industry = ""
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basic_df = tushare_client.get_stock_basic()
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if not basic_df.empty:
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row = basic_df[basic_df["ts_code"] == ts_code]
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if not row.empty:
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r = row.iloc[0]
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stock_name = r["name"]
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industry = r.get("industry", "") or ""
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basic_info = f"名称: {r['name']}, 行业: {industry}"
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# 推荐体系评分（如果该股票在推荐列表中）
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rec_score_str = ""
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try:
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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 score, supply_demand_score, price_action_score, "
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"technical_score, position_score, sector, signal "
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"FROM recommendations "
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"WHERE ts_code = :code AND score >= 60 "
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"ORDER BY created_at 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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if rec_row:
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rm = rec_row._mapping
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rec_score_str = (
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f"\n推荐体系评分: 综合={rm['score']}, "
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f"供需={rm['supply_demand_score']}(50%权重), "
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f"形态={rm['price_action_score']}(40%权重), "
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f"趋势={rm['technical_score']}(10%权重), "
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f"位置安全={rm['position_score']}, "
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f"板块={rm['sector']}, "
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f"信号={rm['signal']}"
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)
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except Exception:
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pass
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# 板块热度（如果有该行业板块数据）
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sector_str = ""
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if industry:
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try:
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async with get_db() as db:
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sector_result = await db.execute(
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text(
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"SELECT sector_name, pct_change, heat_score, stage, "
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"days_continuous, limit_up_count "
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"FROM sector_heat "
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"WHERE sector_name LIKE :industry "
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"ORDER BY created_at DESC LIMIT 1"
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),
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{"industry": f"%{industry}%"},
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)
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s_row = sector_result.fetchone()
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if s_row:
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sm = s_row._mapping
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sector_str = (
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f"板块热度: {sm['sector_name']} 涨幅={sm['pct_change']}%, "
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f"热度={sm['heat_score']}, 阶段={sm['stage']}, "
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f"连续{sm['days_continuous']}天, 涨停数={sm['limit_up_count']}"
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)
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except Exception:
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pass
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user_msg = f"""请对以下A股进行全面诊断分析：
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股票: {ts_code} ({basic_info})
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{quote_str}
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技术面: {signal_str}
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位置安全: {position_str}
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趋势: {trend_str}
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{ma_info}
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资金面: {flow_str}
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{rec_score_str}
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{sector_str}
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重要提示：
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1. 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。
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2. 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。
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3. 如果有推荐体系评分，请作为主要分析依据；趋势评分和信号计数从不同维度描述技术面状态。
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{freshness_note}
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请从以下维度分析（Markdown格式，简洁专业）：
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## 综合评级
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（给出1-5星评级和一句话总结，综合趋势评分、位置安全和供需形态）
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## 技术面分析
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（趋势方向、均线关系、支撑压力、量价配合，优先参考趋势评分而非信号计数）
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## 资金面分析
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（主力资金态度、板块联动效应）
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## 操作建议
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（适合什么类型的投资者、入场时机、风险提示）"""
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# ── SSE 流式返回 ──
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async def _stream_diagnosis():
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full_content = ""
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try:
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client = get_client()
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stream = await client.chat.completions.create(
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model=settings.deepseek_model,
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messages=[
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{"role": "system", "content": "你是一位专业的A股分析师，擅长技术面和资金面分析。回复使用Markdown格式，简洁专业，客观理性。"},
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{"role": "user", "content": user_msg},
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],
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max_tokens=1500,
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temperature=0.5,
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stream=True,
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)
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async for chunk in stream:
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if chunk.choices and chunk.choices[0].delta:
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token = chunk.choices[0].delta.content or ""
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if token:
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full_content += token
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yield f"data: {json.dumps({'token': token}, ensure_ascii=False)}\n\n"
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# 流式完成后，保存到数据库
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full_content = full_content.strip()
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if full_content:
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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 or ts_code,
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diagnosis=full_content,
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)
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)
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await db.commit()
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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}")
|
||
|
||
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}")
|
||
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") |