337 lines
13 KiB
Python
337 lines
13 KiB
Python
"""板块分析 API"""
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import json
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from fastapi import APIRouter
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from sqlalchemy import text
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from app.data.tushare_client import tushare_client
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from app.data.cache import cache
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from app.engine.recommender import get_latest_sectors
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from app.db.database import get_db
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router = APIRouter(prefix="/api/sectors", tags=["sectors"])
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@router.get("/hot")
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async def get_hot_sectors(limit: int = 10):
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"""获取最新主线主题排名。
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页面访问只读数据库里的扫描结论，不在 GET 请求中拉取外部实时行情。
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"""
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sectors = await get_latest_sectors()
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trade_date = sectors[0].trade_date if sectors else ""
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sectors_data = [
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{
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"sector_code": s.sector_code,
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"sector_name": s.sector_name,
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"board_type": s.board_type,
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"theme_id": s.theme_id,
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"theme_name": s.theme_name,
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"theme_aliases": s.theme_aliases,
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"pct_change": s.pct_change,
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"capital_inflow": s.capital_inflow,
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"limit_up_count": s.limit_up_count,
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"days_continuous": s.days_continuous,
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"heat_score": s.heat_score,
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"stage": s.stage,
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# 增强分析字段
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"member_count": s.member_count,
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"leading_stocks": s.leading_stocks,
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"pct_trend": s.pct_trend,
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"turnover_avg": s.turnover_avg,
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"main_force_ratio": s.main_force_ratio,
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"trade_date": trade_date,
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"realtime_pct_change": s.realtime_pct_change,
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"realtime_limit_up_count": s.realtime_limit_up_count,
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"realtime_amount": s.realtime_amount,
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"realtime_turnover_rate": s.realtime_turnover_rate,
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"realtime_up_count": s.realtime_up_count,
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"realtime_down_count": s.realtime_down_count,
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"leading_stocks_realtime": s.leading_stocks_realtime,
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"is_realtime": s.is_realtime,
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"data_mode": s.data_mode,
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"source": s.source,
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"data_status": s.data_status,
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"source_detail": s.source_detail,
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"catalyst_score": s.catalyst_score,
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"catalyst_count": s.catalyst_count,
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"catalyst_reasons": s.catalyst_reasons,
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}
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for s in sectors[:limit]
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]
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realtime_enabled = any(s.get("is_realtime") for s in sectors_data)
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mode = sectors[0].data_mode if realtime_enabled and sectors else "daily_snapshot"
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status = _derive_status(sectors_data)
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for s in sectors_data:
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s["data_mode"] = mode
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s["data_status"] = status
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s["structure_trade_date"] = trade_date
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return sectors_data
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def _derive_status(sectors: list[dict]) -> str:
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statuses = {str(s.get("data_status") or "fresh") for s in sectors}
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if not statuses:
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return "snapshot"
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if "fresh" in statuses:
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return "fresh" if len(statuses) == 1 else "mixed"
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if "stale" in statuses:
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return "stale"
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if "fallback" in statuses:
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return "fallback"
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return next(iter(statuses))
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def _safe_json(raw: str | None):
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if not raw:
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return {}
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try:
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return json.loads(raw)
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except Exception:
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return {}
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def _member_theme_matches(value: str, target: str, aliases: list[str]) -> bool:
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value_norm = (value or "").strip().lower()
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target_norm = (target or "").strip().lower()
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alias_norms = {(alias or "").strip().lower() for alias in aliases if alias}
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if not value_norm or not target_norm:
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return False
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return value_norm == target_norm or value_norm in alias_norms or target_norm in value_norm or value_norm in target_norm
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def _sector_identity_matches(value: str, target: str, aliases: list[str]) -> bool:
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value_norm = (value or "").strip().lower()
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target_norm = (target or "").strip().lower()
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alias_norms = {(alias or "").strip().lower() for alias in aliases if alias}
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if not value_norm or not target_norm:
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return False
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return value_norm == target_norm or target_norm in alias_norms or target_norm in value_norm or value_norm in target_norm
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@router.get("/{sector_name}/detail")
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async def get_sector_detail(sector_name: str):
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"""获取板块详情与候选股投研观察。
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只读取最近一次扫描沉淀的数据，不在页面访问时拉外部行情。
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"""
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sectors = await get_latest_sectors()
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matched = next(
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(
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s for s in sectors
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if _sector_identity_matches(s.sector_name, sector_name, [s.theme_name, *(s.theme_aliases or [])])
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),
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None,
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)
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if not matched and sectors:
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matched = next((s for s in sectors if _sector_identity_matches(sector_name, s.sector_name, [])), None)
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aliases = [matched.theme_name, *(matched.theme_aliases or [])] if matched else []
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target_name = matched.sector_name if matched else sector_name
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async with get_db() as db:
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latest_session = (
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await db.execute(
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text("SELECT scan_session FROM research_observations ORDER BY created_at DESC LIMIT 1")
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)
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).scalar()
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rows = []
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if latest_session:
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result = await db.execute(
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text(
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"SELECT id, scan_session, scan_mode, ts_code, name, theme_name, stock_role, "
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"action_plan, final_score, catalyst_score, theme_money_score, stock_money_score, "
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"emotion_role_score, timing_score, entry_signal_type, elimination_reason, detail_json, created_at "
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"FROM research_observations "
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"WHERE scan_session = :session "
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"ORDER BY final_score DESC LIMIT 300"
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),
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{"session": latest_session},
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)
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rows = result.fetchall()
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members = []
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for row in rows:
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r = row._mapping
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theme = r["theme_name"] or ""
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if not _member_theme_matches(theme, target_name, aliases):
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continue
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detail = _safe_json(r["detail_json"])
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stock_detail = detail.get("stock", {}) if isinstance(detail, dict) else {}
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decision_detail = detail.get("decision", {}) if isinstance(detail, dict) else {}
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members.append({
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"ts_code": r["ts_code"],
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"name": r["name"],
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"theme_name": theme,
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"stock_role": r["stock_role"] or "",
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"action_plan": r["action_plan"] or "观察",
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"final_score": r["final_score"] or 0,
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"catalyst_score": r["catalyst_score"] or 0,
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"theme_money_score": r["theme_money_score"] or 0,
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"stock_money_score": r["stock_money_score"] or 0,
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"emotion_role_score": r["emotion_role_score"] or 0,
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"timing_score": r["timing_score"] or 0,
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"entry_signal_type": r["entry_signal_type"] or "none",
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"elimination_reason": r["elimination_reason"] or "",
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"recall_tags": stock_detail.get("recall_tags", []),
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"main_net_inflow": stock_detail.get("main_net_inflow", 0),
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"inflow_ratio": stock_detail.get("inflow_ratio", 0),
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"turnover_rate": stock_detail.get("turnover_rate", 0),
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"volume_ratio": stock_detail.get("volume_ratio"),
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"trigger_condition": decision_detail.get("trigger_condition", ""),
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"invalidation_condition": decision_detail.get("invalidation_condition", ""),
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"created_at": str(r["created_at"] or ""),
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})
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leader_codes = {
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str(item.get("ts_code") or "")
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for item in ((matched.leading_stocks_realtime or matched.leading_stocks) if matched else [])
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}
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for item in members:
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if item["ts_code"] in leader_codes and "龙头" not in item["stock_role"]:
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item["stock_role"] = f"代表股/{item['stock_role'] or '候选'}"
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action_counts = {"可操作": 0, "重点关注": 0, "观察": 0}
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for item in members:
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action_counts[item["action_plan"]] = action_counts.get(item["action_plan"], 0) + 1
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sector_payload = None
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if matched:
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sector_payload = {
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"sector_code": matched.sector_code,
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"sector_name": matched.sector_name,
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"board_type": matched.board_type,
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"theme_id": matched.theme_id,
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"theme_name": matched.theme_name,
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"theme_aliases": matched.theme_aliases,
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"pct_change": matched.pct_change,
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"capital_inflow": matched.capital_inflow,
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"limit_up_count": matched.limit_up_count,
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"days_continuous": matched.days_continuous,
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"heat_score": matched.heat_score,
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"stage": matched.stage,
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"member_count": matched.member_count,
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"leading_stocks": matched.leading_stocks,
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"leading_stocks_realtime": matched.leading_stocks_realtime,
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"turnover_avg": matched.turnover_avg,
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"main_force_ratio": matched.main_force_ratio,
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"trade_date": matched.trade_date,
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"realtime_pct_change": matched.realtime_pct_change,
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"realtime_limit_up_count": matched.realtime_limit_up_count,
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"realtime_amount": matched.realtime_amount,
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"realtime_turnover_rate": matched.realtime_turnover_rate,
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"realtime_up_count": matched.realtime_up_count,
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"realtime_down_count": matched.realtime_down_count,
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"is_realtime": matched.is_realtime,
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"data_mode": matched.data_mode,
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"source": matched.source,
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"data_status": matched.data_status,
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"source_detail": matched.source_detail,
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"catalyst_score": matched.catalyst_score,
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"catalyst_count": matched.catalyst_count,
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"catalyst_reasons": matched.catalyst_reasons,
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}
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return {
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"sector": sector_payload,
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"scan_session": latest_session or "",
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"members": members,
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"action_counts": action_counts,
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"top_candidates": members[:12],
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"summary": {
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"member_count": len(members),
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"actionable_count": action_counts.get("可操作", 0),
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"watch_count": action_counts.get("重点关注", 0),
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"observe_count": action_counts.get("观察", 0),
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"avg_score": round(sum(item["final_score"] for item in members) / len(members), 1) if members else 0,
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},
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}
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@router.get("/rotation")
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async def get_sector_rotation(days: int = 5):
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"""获取近N日板块轮动数据（用于热力图）"""
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# 检查缓存
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cache_key = f"sector_rotation:{days}"
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cached = cache.get(cache_key)
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if cached is not None:
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return cached
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trade_date = tushare_client.get_latest_trade_date()
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# 获取交易日历
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trade_dates_df = tushare_client.get_trade_dates()
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today = trade_date
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past_dates = [d for d in trade_dates_df if d <= today]
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# 取最近 N 天
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recent_dates = past_dates[-days:] if len(past_dates) >= days else past_dates
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# 获取板块指数列表用于名字映射
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index_list = tushare_client.get_ths_index_list()
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name_map = {}
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if not index_list.empty:
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for _, row in index_list.iterrows():
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name_map[row["ts_code"]] = row["name"]
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all_sectors = []
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for td in recent_dates:
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df = tushare_client.get_sector_moneyflow(td)
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if df.empty:
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continue
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for _, row in df.iterrows():
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code = row.get("ts_code", "")
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# Use industry field from moneyflow data, fallback to name_map
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industry_name = row.get("industry", "") or name_map.get(code, code)
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all_sectors.append({
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"sector_code": code,
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"sector_name": industry_name,
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"trade_date": td,
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"net_amount": round(float(row.get("net_amount", 0) or 0), 2),
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})
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# 获取板块日线来补充涨跌幅
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sector_codes = list(set(s["sector_code"] for s in all_sectors))
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sector_pct_map: dict[str, dict[str, float]] = {}
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for code in sector_codes:
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df_daily = tushare_client.get_ths_daily(code, days=days + 10)
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if not df_daily.empty:
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for _, r in df_daily.iterrows():
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if r["trade_date"] in recent_dates:
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if code not in sector_pct_map:
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sector_pct_map[code] = {}
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sector_pct_map[code][r["trade_date"]] = float(r.get("pct_change", 0) or 0)
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# 按板块分组
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sector_map: dict[str, dict] = {}
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for s in all_sectors:
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code = s["sector_code"]
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if code not in sector_map:
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sector_map[code] = {
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"sector_code": code,
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"sector_name": s["sector_name"],
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"daily_data": [],
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}
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pct = sector_pct_map.get(code, {}).get(s["trade_date"], 0)
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sector_map[code]["daily_data"].append({
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"trade_date": s["trade_date"],
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"pct_change": round(pct, 2),
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"net_amount": s["net_amount"],
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})
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# 按最近一天涨幅排序，取 top 20
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sorted_sectors = sorted(
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sector_map.values(),
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key=lambda x: max((d["pct_change"] for d in x["daily_data"]), default=0),
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reverse=True,
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)[:20]
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result = {"trade_date": trade_date, "dates": recent_dates, "sectors": sorted_sectors}
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# 写入缓存，TTL 300秒（5分钟）
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cache.set(cache_key, result, ttl=300)
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return result
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