450 lines
17 KiB
Python
450 lines
17 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 httpx
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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.data.eastmoney_client import (
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SECTOR_LIST_URL,
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SECTOR_HEADERS,
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_parse_eastmoney_json,
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get_a_share_realtime_ranking,
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)
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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涨跌数据 + 东方财富统计涨停跌停"""
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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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eastmoney_quotes = await get_a_share_realtime_ranking(page_size=6000)
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if eastmoney_quotes:
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up_count = sum(1 for q in eastmoney_quotes if q.get("pct_chg", 0) > 0)
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down_count = sum(1 for q in eastmoney_quotes if q.get("pct_chg", 0) < 0)
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limit_up_count = sum(1 for q in eastmoney_quotes if q.get("pct_chg", 0) >= _limit_threshold(q.get("ts_code", "")))
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limit_down_count = sum(1 for q in eastmoney_quotes if q.get("pct_chg", 0) <= -_limit_threshold(q.get("ts_code", "")))
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logger.info(
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"东方财富实时市场温度: 上涨=%s 下跌=%s 涨停=%s 跌停=%s (共%s只)",
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up_count,
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down_count,
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limit_up_count,
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limit_down_count,
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len(eastmoney_quotes),
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)
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else:
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logger.warning("东方财富全市场实时行情为空，尝试腾讯批量行情补充涨跌家数")
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try:
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if not eastmoney_quotes:
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stock_basic = tushare_client.get_stock_basic()
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if stock_basic.empty:
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raise ValueError("股票基础列表为空")
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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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logger.info(
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f"盘中实时涨跌统计: 上涨={up_count} 下跌={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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# ── 用东方财富 clist API 统计涨停跌停（比腾讯涨停价字段更可靠） ──
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try:
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if eastmoney_quotes:
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raise RuntimeError("已使用东方财富全市场实时涨跌停统计")
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realtime_limit_up_count = 0
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realtime_limit_down_count = 0
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for fs, threshold in [
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("m:0+t:6,m:0+t:80,m:0+t:81+s:2048", 9.9), # 主板 10%
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("m:1+t:2,m:1+t:23", 19.9), # 创业板/科创板 20%
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]:
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async with httpx.AsyncClient(follow_redirects=True) as client:
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# 涨停：按涨幅降序取 top 200
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params_up = {
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"pn": "1", "pz": "200", "po": "1", "np": "1",
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"ut": "b1f8f8f8", "fltt": "2", "invt": "2",
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"fid": "f3", "fs": fs,
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"fields": "f3,f12,f14",
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}
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resp = await client.get(SECTOR_LIST_URL, params=params_up, headers=SECTOR_HEADERS, timeout=10)
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data_up = _parse_eastmoney_json(resp, "涨停统计")
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items = data_up.get("data", {}).get("diff", []) if data_up.get("data") else []
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for item in items:
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pct = item.get("f3")
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if pct == "-" or pct is None:
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continue
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if float(pct) >= threshold:
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realtime_limit_up_count += 1
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# 跌停：按涨幅升序取 top 200
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params_down = {
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"pn": "1", "pz": "200", "po": "0", "np": "1",
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"ut": "b1f8f8f8", "fltt": "2", "invt": "2",
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"fid": "f3", "fs": fs,
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"fields": "f3,f12,f14",
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}
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resp_down = await client.get(SECTOR_LIST_URL, params=params_down, headers=SECTOR_HEADERS, timeout=10)
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data_down = _parse_eastmoney_json(resp_down, "跌停统计")
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items_down = data_down.get("data", {}).get("diff", []) if data_down.get("data") else []
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neg_threshold = -threshold
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for item in items_down:
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pct = item.get("f3")
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if pct == "-" or pct is None:
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continue
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if float(pct) <= neg_threshold:
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realtime_limit_down_count += 1
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limit_up_count = realtime_limit_up_count
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limit_down_count = realtime_limit_down_count
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logger.info(f"东方财富盘中涨跌停: 涨停={limit_up_count} 跌停={limit_down_count}")
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except Exception as e:
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if not eastmoney_quotes:
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logger.warning(f"东方财富涨跌停统计失败，使用基线数据: {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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def _limit_threshold(ts_code: str) -> float:
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code = ts_code.split(".")[0] if ts_code else ""
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if code.startswith(("300", "301", "688")):
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return 19.8
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return 9.8
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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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async def intraday_active_market_recall(limit: int = 80) -> list[dict]:
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"""实时全市场活跃股召回，不依赖 Tushare 板块成分映射。"""
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quotes = await get_a_share_realtime_ranking(sort_by="f8", descending=True, page_size=800)
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if not quotes:
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return []
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results = []
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for item in quotes:
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ts_code = item.get("ts_code", "")
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name = item.get("name", "")
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if not ts_code or not name or "ST" in name:
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continue
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pct_chg = float(item.get("pct_chg", 0) or 0)
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turnover_rate = float(item.get("turnover_rate", 0) or 0)
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circ_mv_raw = item.get("circ_mv")
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circ_mv = float(circ_mv_raw or 0) / 100000000 if circ_mv_raw else None
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if pct_chg <= 0 or pct_chg >= _limit_threshold(ts_code):
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continue
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if turnover_rate < max(settings.min_turnover_rate * 0.5, 1.0):
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continue
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if circ_mv is not None and circ_mv > 0:
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if circ_mv < settings.min_circ_mv or circ_mv > settings.max_circ_mv * 1.5:
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continue
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recall_score = 35
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recall_score += min(max(pct_chg, 0), 8) * 4
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recall_score += min(turnover_rate, 12) * 2
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if item.get("main_net_inflow", 0) > 0:
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recall_score += 8
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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": "实时活跃",
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"sector_stage": "intraday",
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"price": item.get("price"),
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"pct_chg": pct_chg,
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"turnover_rate": turnover_rate,
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"circ_mv": circ_mv,
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"pe": item.get("pe"),
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"pb": item.get("pb"),
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"volume_ratio": None,
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"main_net_inflow": float(item.get("main_net_inflow", 0) or 0) / 10000,
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"inflow_ratio": 0,
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"recall_score": round(recall_score, 1),
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"recall_tags": ["realtime_active"],
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"stock_role_hint": "今日全市场活跃股",
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})
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results.sort(key=lambda x: (x["recall_score"], x.get("turnover_rate", 0)), reverse=True)
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return results[:limit]
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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：用东方财富实时板块数据刷新涨幅和涨停数
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一次请求替代之前腾讯批量获取数千只成分股的方式。
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"""
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if not prev_sectors:
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return prev_sectors
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# 从东方财富获取实时板块排名（1次 HTTP 请求）
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try:
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from app.data.eastmoney_client import get_sector_realtime_ranking
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em_sectors = await get_sector_realtime_ranking()
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except Exception as e:
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logger.warning(f"东方财富板块实时数据获取失败: {e}")
|
||
return prev_sectors
|
||
|
||
if not em_sectors:
|
||
return prev_sectors
|
||
|
||
# 按板块名称匹配更新数据
|
||
matched = 0
|
||
for sector in prev_sectors:
|
||
em_data = None
|
||
# 先精确匹配
|
||
for em_s in em_sectors:
|
||
if em_s["sector_name"] == sector.sector_name:
|
||
em_data = em_s
|
||
break
|
||
# 模糊匹配
|
||
if not em_data:
|
||
for em_s in em_sectors:
|
||
em_name = em_s["sector_name"].rstrip("行业").rstrip("板块").strip()
|
||
ts_name = sector.sector_name.rstrip("行业").rstrip("板块").strip()
|
||
if em_name == ts_name or (len(em_name) <= len(ts_name) and em_name in ts_name) or (len(ts_name) <= len(em_name) and ts_name in em_name):
|
||
em_data = em_s
|
||
break
|
||
|
||
if em_data:
|
||
matched += 1
|
||
sector.pct_change = em_data["pct_change"]
|
||
# 涨停数保留 Tushare 数据（东方财富此字段不可用）
|
||
|
||
logger.info(
|
||
f"盘中板块实时更新: {matched}/{len(prev_sectors)} 匹配成功, "
|
||
f"涨幅最高={max(prev_sectors, key=lambda s: s.pct_change).sector_name} "
|
||
f"({max(s.pct_change for s in prev_sectors):.1f}%)"
|
||
)
|
||
|
||
return prev_sectors
|