330 lines
11 KiB
Python
330 lines
11 KiB
Python
"""盘中实时扫描
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盘中 Tushare 的 daily / moneyflow / limit_list_d 等接口尚无当日数据，
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因此盘中扫描采用混合策略：
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- 板块热度：使用前一日 Tushare 数据作为基线（哪些板块是近期热点）
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- 个股筛选：用腾讯实时行情替代 Tushare 的当日 daily_basic / moneyflow
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- 技术面：历史 K 线（Tushare） + 当日实时价格（腾讯）拼接后计算
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盘中重点捕捉：
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1. 热门板块成分股中，盘中放量拉升的个股
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2. 量比 > 1.5、换手率适中、涨幅在 2%-7% 的活跃股
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3. 用实时价格更新支撑/压力位和技术信号
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"""
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import logging
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import pandas as pd
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from datetime import datetime
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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.data.models import SectorInfo, Recommendation, MarketTemperature, StockQuote
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from app.analysis.sector_scanner import scan_hot_sectors
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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.analysis.capital_flow import _score_valuation
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from app.config import settings
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logger = logging.getLogger(__name__)
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async def intraday_market_temperature(prev_temp: MarketTemperature) -> MarketTemperature:
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"""盘中市场温度：用腾讯实时行情计算真实涨跌数据"""
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index_data = await tencent_client.get_index_realtime()
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sh_index = index_data.get("000001.SH")
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if not sh_index:
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return prev_temp
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# ── 用腾讯实时行情计算真实涨跌数量 ──
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up_count = prev_temp.up_count
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down_count = prev_temp.down_count
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limit_up_count = prev_temp.limit_up_count
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limit_down_count = prev_temp.limit_down_count
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try:
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stock_basic = tushare_client.get_stock_basic()
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if not stock_basic.empty:
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# 过滤 ST 股票
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all_codes = stock_basic[~stock_basic["name"].str.contains("ST", na=False)]["ts_code"].tolist()
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if all_codes:
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quotes = await tencent_client.get_realtime_quotes_batch(all_codes)
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up_count = sum(1 for q in quotes.values() if q.pct_chg > 0)
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down_count = sum(1 for q in quotes.values() if q.pct_chg < 0)
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limit_up_count = sum(
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1 for q in quotes.values()
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if q.limit_up and q.price >= q.limit_up * 0.995
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)
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limit_down_count = sum(
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1 for q in quotes.values()
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if q.limit_down and q.price <= q.limit_down * 1.005
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)
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logger.info(
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f"盘中实时涨跌统计: 上涨={up_count} 下跌={down_count} "
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f"涨停={limit_up_count} 跌停={limit_down_count} (共{len(quotes)}只)"
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)
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except Exception as e:
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logger.warning(f"获取盘中实时涨跌统计失败，使用上一日数据: {e}")
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# ── 温度分数：基于实时涨跌比重新计算 ──
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ratio = up_count / max(down_count, 1)
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temp_from_ratio = min(ratio / 3.0 * 25, 25) # 涨跌比维度 (0-25)
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temp_from_limit_up = min(limit_up_count / 2, 25) # 涨停数维度 (0-25)
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# 指数方向调整 (0-25)
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pct = sh_index.get("pct_chg", 0)
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temp_from_index = min(max(pct * 8 + 12.5, 0), 25)
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new_temp = round(temp_from_ratio + temp_from_limit_up + temp_from_index + 25, 1)
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new_temp = min(max(new_temp, 0), 100)
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return MarketTemperature(
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trade_date=datetime.now().strftime("%Y%m%d"),
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up_count=up_count,
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down_count=down_count,
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limit_up_count=limit_up_count,
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limit_down_count=limit_down_count,
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max_streak=prev_temp.max_streak,
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broken_rate=prev_temp.broken_rate,
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index_above_ma20=pct > 0 if sh_index else prev_temp.index_above_ma20,
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temperature=new_temp,
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)
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async def intraday_filter_stocks(
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hot_sectors: list[SectorInfo],
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) -> list[dict]:
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"""盘中个股筛选：从热门板块成分股中用腾讯实时行情筛选
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替代 capital_flow.py 中依赖 Tushare 日级数据的逻辑。
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盘中无法获取资金流向，改用 量比 + 换手率 + 涨幅 + 市值 筛选。
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"""
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# 收集热门板块成分股
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stock_sectors: dict[str, list[str]] = {}
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for sector in hot_sectors:
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members = tushare_client.get_ths_members(sector.sector_code)
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if members.empty or "con_code" not in members.columns:
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continue
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for _, m in members.iterrows():
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code = m.get("con_code", "")
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if not code or "." not in str(code):
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continue
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if code not in stock_sectors:
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stock_sectors[code] = []
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stock_sectors[code].append(sector.sector_name)
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if not stock_sectors:
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logger.warning("盘中筛选: 热门板块成分股为空")
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return []
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all_codes = list(stock_sectors.keys())
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logger.info(f"盘中筛选: {len(all_codes)} 只成分股，开始获取实时行情")
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# 过滤 ST 和次新
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stock_basic = tushare_client.get_stock_basic()
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st_codes = set()
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new_codes = set()
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if not stock_basic.empty:
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st_codes = set(stock_basic[stock_basic["name"].str.contains("ST", na=False)]["ts_code"])
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from datetime import timedelta
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cutoff = (datetime.now() - timedelta(days=settings.min_list_days)).strftime("%Y%m%d")
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new_codes = set(stock_basic[stock_basic["list_date"] > cutoff]["ts_code"])
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eligible = [c for c in all_codes if c not in st_codes and c not in new_codes]
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# 获取前一日 daily_basic（PE/PB 估值数据）
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prev_date = tushare_client.get_latest_trade_date()
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basic_df = tushare_client.get_daily_basic(prev_date)
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# 批量获取腾讯实时行情
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quotes = await tencent_client.get_realtime_quotes_batch(eligible)
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results = []
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for ts_code, quote in quotes.items():
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# 硬性条件
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if quote.pct_chg <= 0:
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continue # 盘中只关注上涨股
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if quote.pct_chg > 9.8:
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continue # 已涨停，无买入空间
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if quote.turnover_rate < settings.min_turnover_rate:
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continue
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if quote.turnover_rate > settings.max_turnover_rate:
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continue
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if quote.circ_mv is not None:
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if quote.circ_mv < settings.min_circ_mv or quote.circ_mv > settings.max_circ_mv:
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continue
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# 评分
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score = _score_intraday(quote)
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# 估值评分（用前一日 PE/PB）
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pe = None
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pb = None
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if not basic_df.empty:
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b_row = basic_df[basic_df["ts_code"] == ts_code]
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if not b_row.empty:
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b = b_row.iloc[0]
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pe = float(b["pe_ttm"]) if pd.notna(b.get("pe_ttm")) else None
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pb = float(b["pb"]) if pd.notna(b.get("pb")) else None
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valuation_score = _score_valuation(pe, pb)
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# 名称
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name = quote.name or ts_code
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results.append({
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"ts_code": ts_code,
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"name": name,
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"sector": stock_sectors[ts_code][0],
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"sectors": stock_sectors[ts_code],
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"price": quote.price,
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"pct_chg": quote.pct_chg,
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"turnover_rate": quote.turnover_rate,
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"volume_ratio": quote.volume_ratio,
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"circ_mv": quote.circ_mv,
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"capital_score": round(score, 1),
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"valuation_score": round(valuation_score, 1),
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# 盘中无资金流向数据
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"main_net_inflow": 0,
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"inflow_ratio": 0,
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})
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results.sort(key=lambda x: x["capital_score"], reverse=True)
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top = results[:settings.top_stock_count]
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for r in top:
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logger.info(
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f"盘中筛选: {r['name']}({r['ts_code']}) 板块={r['sector']} "
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f"涨幅={r['pct_chg']}% 量比={r['volume_ratio']} "
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f"换手率={r['turnover_rate']}% 评分={r['capital_score']}"
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)
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return top
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def _score_intraday(quote: StockQuote) -> float:
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"""盘中评分逻辑（替代资金流向评分）
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盘中无法获取大单/特大单数据，改用以下指标：
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- 涨幅区间 (25%): 2%-7% 为活跃区间
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- 量比 (30%): > 1.5 说明资金活跃
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- 换手率 (20%): 5%-10% 最佳
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- 振幅 (10%): 适度振幅说明活跃
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- 市值适配 (15%): 100-300亿最佳
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"""
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score = 0.0
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# 涨幅区间 (25分)
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pct = quote.pct_chg
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if 3 <= pct <= 7:
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score += 25
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elif 2 <= pct < 3:
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score += 18
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elif 7 < pct <= 9:
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score += 15
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elif 0 < pct < 2:
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score += 8
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# 量比 (30分)
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vr = quote.volume_ratio
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if vr is not None:
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if vr > 3.0:
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score += 30
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elif vr > 2.0:
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score += 24
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elif vr > 1.5:
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score += 18
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elif vr > 1.0:
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score += 10
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# 换手率 (20分)
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tr = quote.turnover_rate
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if 5 <= tr <= 10:
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score += 20
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elif 3 <= tr < 5:
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score += 14
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elif 10 < tr <= 15:
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score += 10
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# 振幅 (10分) - 适度振幅
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amp = quote.amplitude or 0
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if 3 <= amp <= 8:
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score += 10
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elif 2 <= amp < 3:
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score += 6
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elif amp > 8:
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score += 3
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# 市值适配 (15分)
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if quote.circ_mv is not None:
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mv = quote.circ_mv
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if 100 <= mv <= 300:
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score += 15
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elif settings.min_circ_mv <= mv <= settings.max_circ_mv:
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score += 10
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elif mv > 0:
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score += 3
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return score
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async def intraday_sector_scan(prev_sectors: list[SectorInfo]) -> list[SectorInfo]:
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"""盘中板块热度更新：用腾讯实时行情刷新板块涨幅和涨停数
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基于前一日的板块列表（来自 Tushare），用成分股的实时行情
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重新计算板块涨跌幅和涨停家数。
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"""
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if not prev_sectors:
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return prev_sectors
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# 收集所有板块的成分股
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sector_members: dict[str, list[str]] = {}
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all_codes = set()
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for sector in prev_sectors:
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members = tushare_client.get_ths_members(sector.sector_code)
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if members.empty or "con_code" not in members.columns:
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continue
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codes = [c for c in members["con_code"].tolist() if "." in str(c)]
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sector_members[sector.sector_code] = codes
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all_codes.update(codes)
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if not all_codes:
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return prev_sectors
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# 批量获取实时行情
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quotes = await tencent_client.get_realtime_quotes_batch(list(all_codes))
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if not quotes:
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return prev_sectors
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# 构建涨停集合
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limit_up_codes = set()
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for code, q in quotes.items():
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if q.limit_up and q.price >= q.limit_up * 0.995:
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limit_up_codes.add(code)
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# 更新每个板块的数据
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for sector in prev_sectors:
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codes = sector_members.get(sector.sector_code, [])
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if not codes:
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continue
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sector_quotes = [quotes[c] for c in codes if c in quotes]
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if not sector_quotes:
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continue
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# 实时涨跌幅（成分股均值）
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pct_changes = [q.pct_chg for q in sector_quotes if q.pct_chg is not None]
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if pct_changes:
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sector.pct_change = round(sum(pct_changes) / len(pct_changes), 2)
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# 实时涨停家数
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sector.limit_up_count = len([c for c in codes if c in limit_up_codes])
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logger.info(
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f"盘中板块实时更新: {len(prev_sectors)} 个板块, "
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f"涨幅最高={max(prev_sectors, key=lambda s: s.pct_change).sector_name} "
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f"({max(s.pct_change for s in prev_sectors):.1f}%)"
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)
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return prev_sectors
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