236 lines
8.8 KiB
Python
236 lines
8.8 KiB
Python
"""个股分析 API"""
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from fastapi import APIRouter
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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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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.post("/{ts_code}/diagnose")
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async def diagnose_stock(ts_code: str):
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"""AI 诊断个股"""
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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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# 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=30)
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df_flow = tushare_client.get_stock_moneyflow(ts_code, days=10)
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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}, 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"技术评分: {signals.score}/100（基于7项技术信号触发计分，触发少不代表一定差，可能处于蓄势阶段）, "
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f"信号数: {signals.signal_count}/7, "
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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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df_daily = df_daily.sort_values("trade_date")
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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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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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# 基本信息
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basic_info = ""
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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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basic_info = f"名称: {r['name']}, 行业: {r.get('industry', '未知')}"
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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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重要提示：技术评分基于7项信号触发计分，分数低不代表股票差，可能处于蓄势阶段。位置安全评分高(>80)表示股价处于相对低位。请综合技术评分和位置安全评分一起判断。
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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，综合技术面和位置安全评分）
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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，注意区分"技术信号未触发"和"技术面恶化"）
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## 资金面分析
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（主力资金态度、筹码集中度推测）
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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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try:
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client = get_client()
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response = 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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)
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content = response.choices[0].message.content.strip()
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return {"status": "ok", "ts_code": ts_code, "diagnosis": content}
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except Exception as e:
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return {"status": "error", "message": str(e)}
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