204 lines
7.6 KiB
Python
204 lines
7.6 KiB
Python
"""市场概览 API"""
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from datetime import datetime
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from fastapi import APIRouter, Depends
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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.engine.recommender import get_latest_recommendations
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from app.config import is_trading_hours, is_market_session, should_prefer_realtime_today
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from app.core.deps import get_current_admin
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router = APIRouter(prefix="/api/market", tags=["market"])
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@router.get("/temperature")
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async def get_temperature():
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"""获取市场温度（只读缓存，不触发扫描）"""
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result = await get_latest_recommendations()
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mt = result.get("market_temp")
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if mt:
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return {
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"trade_date": mt.trade_date,
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"temperature": mt.temperature,
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"up_count": mt.up_count,
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"down_count": mt.down_count,
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"limit_up_count": mt.limit_up_count,
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"limit_down_count": mt.limit_down_count,
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"max_streak": mt.max_streak,
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"broken_rate": mt.broken_rate,
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"index_above_ma20": getattr(mt, "index_above_ma20", False),
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"is_trading": is_trading_hours(),
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}
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return {
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"trade_date": "",
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"temperature": 0,
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"up_count": 0,
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"down_count": 0,
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"limit_up_count": 0,
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"limit_down_count": 0,
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"max_streak": 0,
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"broken_rate": 0,
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"index_above_ma20": False,
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"is_trading": is_trading_hours(),
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}
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@router.get("/overview")
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async def get_overview():
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"""市场概况：上证、深证、创业板指数
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盘中用腾讯实时行情，盘后用 Tushare 日线（有缓存）。
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"""
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latest_trade_date = tushare_client.get_latest_trade_date()
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if should_prefer_realtime_today(latest_trade_date):
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return await _overview_realtime()
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return _overview_daily()
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@router.get("/strategy-board")
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async def get_strategy_board():
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"""获取今日市场作战面板（只读，不触发 LLM）"""
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from app.llm.strategy_board import build_strategy_board
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return await build_strategy_board(include_llm=False)
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@router.get("/strategy-iteration")
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async def get_strategy_iteration(limit: int = 50):
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"""获取策略复盘迭代建议（只读，不触发 LLM）"""
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from app.llm.strategy_iteration import build_strategy_iteration_report
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return await build_strategy_iteration_report(limit=limit, include_llm=False)
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@router.get("/ops-status")
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async def get_ops_status():
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"""管理员任务中心状态与数据新鲜度（只读，不触发扫描或 LLM）。"""
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from sqlalchemy import text
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from app.db.database import get_db
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from app.engine.recommender import _scan_running
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async with get_db() as db:
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rec_row = (await db.execute(
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text(
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"SELECT created_at FROM recommendations "
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"ORDER BY created_at DESC LIMIT 1"
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)
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)).fetchone()
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tracking_row = (await db.execute(
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text(
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"SELECT track_date, created_at FROM recommendation_tracking "
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"ORDER BY track_date DESC, id DESC LIMIT 1"
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)
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)).fetchone()
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market_row = (await db.execute(
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text(
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"SELECT trade_date, created_at FROM market_temperature "
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"ORDER BY REPLACE(trade_date, '-', '') DESC, id DESC LIMIT 1"
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)
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)).fetchone()
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sector_row = (await db.execute(
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text(
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"SELECT trade_date, created_at FROM sector_heat "
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"ORDER BY REPLACE(trade_date, '-', '') DESC, id DESC LIMIT 1"
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)
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)).fetchone()
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def _fmt_dt(value):
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return str(value or "")
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latest_market_date = str(market_row._mapping["trade_date"]) if market_row else ""
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latest_sector_date = str(sector_row._mapping["trade_date"]) if sector_row else ""
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latest_tracking_date = str(tracking_row._mapping["track_date"]) if tracking_row else ""
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return {
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"scan_running": _scan_running,
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"scan_mode": "intraday" if is_trading_hours() else "post_market",
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"is_trading": is_trading_hours(),
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"data_freshness": {
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"market_trade_date": latest_market_date,
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"sector_trade_date": latest_sector_date,
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"tracking_trade_date": latest_tracking_date,
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"last_recommendation_created_at": _fmt_dt(rec_row._mapping["created_at"]) if rec_row else "",
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"last_tracking_created_at": _fmt_dt(tracking_row._mapping["created_at"]) if tracking_row else "",
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"last_market_created_at": _fmt_dt(market_row._mapping["created_at"]) if market_row else "",
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"last_sector_created_at": _fmt_dt(sector_row._mapping["created_at"]) if sector_row else "",
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"status": "fresh" if latest_market_date else "empty",
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"message": (
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f"最新市场日期 {latest_market_date}，最近跟踪 {latest_tracking_date or '暂无'}"
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if latest_market_date else
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"暂无市场缓存数据，请由管理员触发扫描。"
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),
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"generated_at": datetime.now().isoformat(),
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},
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"actions": [
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{"key": "refresh", "label": "立即扫描", "admin_only": True},
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{"key": "update_tracking", "label": "更新跟踪", "admin_only": True},
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{"key": "generate_strategy_board", "label": "生成策略板", "admin_only": True},
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{"key": "generate_strategy_iteration", "label": "生成策略复盘", "admin_only": True},
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],
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}
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@router.post("/generate-strategy-board")
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async def generate_strategy_board(_admin: dict = Depends(get_current_admin)):
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"""管理员手动生成带 LLM 说明的策略看板"""
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from app.llm.strategy_board import build_strategy_board
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return await build_strategy_board(include_llm=True)
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@router.post("/generate-strategy-iteration")
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async def generate_strategy_iteration(limit: int = 50, _admin: dict = Depends(get_current_admin)):
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"""管理员手动生成带 LLM 分析的策略复盘"""
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from app.llm.strategy_iteration import build_strategy_iteration_report
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return await build_strategy_iteration_report(limit=limit, include_llm=True)
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async def _overview_realtime():
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"""盘中：腾讯实时指数行情"""
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index_data = await tencent_client.get_index_realtime()
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result = []
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name_map = {
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"000001.SH": "上证指数",
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"399001.SZ": "深证成指",
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"399006.SZ": "创业板指",
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}
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for code in ["000001.SH", "399001.SZ", "399006.SZ"]:
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data = index_data.get(code)
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if not data:
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continue
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result.append({
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"name": name_map.get(code, data.get("name", code)),
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"code": code,
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"close": round(data["price"], 2),
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"pct_chg": round(data["pct_chg"], 2),
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"volume": round(data["volume"], 2),
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"realtime": True,
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})
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return result
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def _overview_daily():
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"""盘后：Tushare 日线数据"""
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indices = {
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"上证指数": "000001.SH",
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"深证成指": "399001.SZ",
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"创业板指": "399006.SZ",
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}
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result = []
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for name, code in indices.items():
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df = tushare_client.get_index_daily(code, days=5)
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if df.empty:
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continue
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df = df.sort_values("trade_date")
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latest = df.iloc[-1]
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prev = df.iloc[-2] if len(df) > 1 else latest
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pct = (latest["close"] - prev["close"]) / prev["close"] * 100
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result.append({
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"name": name,
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"code": code,
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"close": round(float(latest["close"]), 2),
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"pct_chg": round(pct, 2),
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"volume": round(float(latest["vol"]), 2),
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"realtime": False,
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})
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return result
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