218 lines
7.3 KiB
Python
218 lines
7.3 KiB
Python
"""个股分析 API — 关键价位 + AI 四维分析 + 报告持久化。
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路由前缀: /api/stock-analysis
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端点:
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GET /levels?symbol= 11 类关键价位(图表 markLine 数据源)
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POST /analyze AI 流式四维分析(NDJSON)
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GET /reports 历史报告列表
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POST /reports 保存一条报告
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DELETE /reports/{report_id} 删除一条报告
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"""
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from __future__ import annotations
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import logging
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import math
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from datetime import date, timedelta
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import polars as pl
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from fastapi import APIRouter, HTTPException, Query, Request
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from fastapi.responses import StreamingResponse
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from pydantic import BaseModel
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from app.indicators.levels import compute_levels, summarize_levels
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from app.services import stock_reports
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from app.services.stock_analyzer import analyze_stock_stream
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/stock-analysis", tags=["stock-analysis"])
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def _to_float_list(series: pl.Series) -> list:
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"""polars Series → JSON 安全的 float 列表(null/NaN → None)。"""
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out: list = []
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for v in series.to_list():
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if v is None:
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out.append(None)
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continue
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try:
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f = float(v)
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out.append(round(f, 2) if math.isfinite(f) else None)
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except (TypeError, ValueError):
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out.append(None)
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return out
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def _build_series(df: pl.DataFrame) -> dict:
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"""提取带状指标(布林带 / Keltner通道 / ATR止损)的每日时间序列。
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这些指标的本质是"每日一条线",随 MA/ATR/σ 漂移,画成曲线才能体现通道形态。
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其余固定价位(枢轴/前高前低等)不在此,仍用水平 markLine。
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返回结构(每个 value 都是按日期对齐的数组):
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{
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"boll": {"upper": [...], "lower": [...]},
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"keltner_s": {"upper": [...], "lower": [...]}, # 短期 MA20±2ATR
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"keltner_m": {"upper": [...], "lower": [...]}, # 中期 MA60±2.5ATR
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"keltner_l": {"upper": [...], "lower": [...]}, # 长期 MA120±3ATR
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"atr": {"stop_loss": [...], "take_profit": [...]}, # close∓2ATR
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}
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"""
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if df.is_empty() or "close" not in df.columns:
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return {}
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out: dict[str, dict] = {}
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close = df["close"]
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has_atr = "atr_14" in df.columns
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# 布林带(上/下/中轨;中轨 = MA20,数据层已预计算)
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if "boll_upper" in df.columns and "boll_lower" in df.columns:
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out["boll"] = {
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"upper": _to_float_list(df["boll_upper"]),
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"lower": _to_float_list(df["boll_lower"]),
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"mid": _to_float_list(df["ma20"]) if "ma20" in df.columns else None,
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}
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# Keltner 通道三档(需要 ATR)
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if has_atr:
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atr = df["atr_14"]
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# MA120 现场算(不在预计算列中)
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ma120 = df.select(pl.col("close").rolling_mean(120))["close"] if df.height >= 120 else None
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def _channel(ma: pl.Series, n: float) -> dict:
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return {
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"upper": _to_float_list(ma + n * atr),
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"lower": _to_float_list(ma - n * atr),
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}
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if "ma20" in df.columns:
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out["keltner_s"] = _channel(df["ma20"], 2.0)
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if "ma60" in df.columns:
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out["keltner_m"] = _channel(df["ma60"], 2.5)
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if ma120 is not None:
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out["keltner_l"] = _channel(ma120, 3.0)
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# ATR 止损/止盈: close ± 2×ATR(跟随行情漂移的动态止损线)
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out["atr"] = {
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"stop_loss": _to_float_list(close - 2 * atr),
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"take_profit": _to_float_list(close + 2 * atr),
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}
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return out
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@router.get("/levels")
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def get_levels(
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request: Request,
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symbol: str = Query(..., description="标的代码,如 000001.SZ"),
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days: int = Query(120, ge=30, le=500, description="计算样本天数"),
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):
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"""计算 11 类关键价位(成交密集区压力支撑 / 枢轴点 / 前高前低 /
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布林带 / Keltner短中长 / ATR止损 / 缺口 / 斐波那契 / 整数关口)。
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返回 {levels: {sr, pivot, extreme, boll, keltner_s, keltner_m, keltner_l,
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atr_stop, gap, fib, round}, close, summary, dates, series}。
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前端按 levels 的 key 渲染开关按钮,逐组显隐 markLine / 曲线。
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"""
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if not symbol:
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raise HTTPException(400, "symbol 不能为空")
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repo = request.app.state.repo
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end = date.today()
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start = end - timedelta(days=days * 2)
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df = repo.get_daily(symbol, start, end)
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if df.is_empty():
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return {"levels": {"sr": [], "pivot": [], "extreme": [],
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"boll": [], "keltner_s": [], "keltner_m": [], "keltner_l": [],
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"atr_stop": [], "gap": [], "fib": [], "round": []},
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"close": None, "summary": "无数据", "symbol": symbol,
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"dates": [], "series": {}}
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levels = compute_levels(df)
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close = float(df.tail(1)["close"][0]) if "close" in df.columns else None
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# 日期 + 带状曲线序列(供前端画 Keltner/ATR/布林带曲线)
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dates = df["date"].to_list()
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series = _build_series(df)
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return {
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"levels": levels,
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"close": close,
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"summary": summarize_levels(levels, close),
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"symbol": symbol,
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"dates": [str(d) for d in dates],
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"series": series,
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}
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class AnalyzeRequest(BaseModel):
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"""AI 个股分析请求。"""
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symbol: str
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focus: str = "" # 可选:用户追加的分析关注点
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@router.post("/analyze")
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async def analyze_stock(request: Request, req: AnalyzeRequest):
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"""AI 个股四维分析 — NDJSON 流式返回。
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组合 K 线(技术指标)+ 财务表 + 关键价位 → 实战派提示词 →
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流式调用 LLM → 逐 chunk 以 NDJSON 推给前端(每行一个 JSON)。
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"""
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if not req.symbol:
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raise HTTPException(400, "symbol 不能为空")
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repo = request.app.state.repo
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data_dir = repo.store.data_dir
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async def stream_gen():
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async for chunk in analyze_stock_stream(repo, data_dir, req.symbol, req.focus):
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yield chunk + "\n"
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return StreamingResponse(
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stream_gen(),
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media_type="application/x-ndjson",
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headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
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)
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# ================================================================
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# 报告 CRUD(历史报告持久化)
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# ================================================================
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class SaveReportRequest(BaseModel):
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"""保存一条 AI 个股分析报告。"""
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symbol: str
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name: str = ""
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focus: str = ""
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content: str
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summary: str = ""
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close: float | None = None
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levels: dict | None = None
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@router.get("/reports")
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def list_reports(request: Request):
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"""获取全部历史报告(按时间降序,后端已裁剪到上限)。"""
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return {"reports": stock_reports.list_reports()}
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@router.post("/reports")
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def save_report(request: Request, req: SaveReportRequest):
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"""保存一条报告。"""
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report = stock_reports.save_report({
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"symbol": req.symbol,
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"name": req.name,
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"focus": req.focus,
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"content": req.content,
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"summary": req.summary,
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"close": req.close,
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"levels": req.levels,
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})
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return {"ok": True, "report": report}
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@router.delete("/reports/{report_id}")
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def delete_report(request: Request, report_id: str):
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"""删除一条报告。"""
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ok = stock_reports.delete_report(report_id)
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return {"ok": ok}
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