826 lines
30 KiB
Python
826 lines
30 KiB
Python
"""趋势突破统一筛选器（自上而下方案）
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三阶段管道：
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Step 1: 板块定位 — 找到有资金流入的热门板块 (3-5个)
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Step 2: 板块内选股 — 在热门板块成分股中筛出有资金流入的候选 (30-50只)
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Step 3: 深度分析 — 供需 + 价格行为 + 趋势 (10-15只推荐)
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评分公式：供需关系 40% + 价格行为 35% + 趋势 25%
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板块和资金流作为前置过滤条件，不参与评分。
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数据源：
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- 盘中模式：Tushare 日线 + 腾讯实时行情混合
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- 盘后模式：Tushare 当日完整数据
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"""
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import logging
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import pandas as pd
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from app.analysis.market_temp import calculate_market_temperature
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from app.analysis.sector_scanner import scan_hot_sectors
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from app.analysis.trend_scanner import scan_trend_breakout
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from app.analysis.signals import generate_signals
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from app.analysis.intraday import intraday_market_temperature, intraday_filter_stocks, intraday_sector_scan
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from app.data.models import MarketTemperature, SectorInfo, TechnicalSignal, Recommendation
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from app.config import settings, is_trading_hours, is_market_session
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logger = logging.getLogger(__name__)
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async def run_screening(trade_date: str = None) -> dict:
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"""执行趋势突破筛选流程
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返回: {
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"market_temp": MarketTemperature,
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"hot_sectors": [SectorInfo],
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"recommendations": [Recommendation],
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"scan_mode": "intraday" | "post_market",
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}
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"""
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intraday = is_market_session()
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scan_mode = "intraday" if intraday else "post_market"
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logger.info(f"=== 筛选模式: {'盘中实时' if intraday else '盘后'} ===")
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# ── 市场温度 ──
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logger.info("=== 市场温度计 ===")
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market_temp = calculate_market_temperature(trade_date)
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if intraday:
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market_temp = await intraday_market_temperature(market_temp)
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logger.info(f"盘中市场温度(实时调整): {market_temp.temperature}")
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else:
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logger.info(f"市场温度: {market_temp.temperature}")
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market_temp_score = market_temp.temperature
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# ── Step 1: 板块定位 ──
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logger.info("=== Step 1: 板块定位 ===")
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all_sectors = scan_hot_sectors(trade_date)
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# 前置过滤：只保留有资金流入 + 非末期的板块
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hot_sectors = [
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s for s in all_sectors
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if s.capital_inflow > 0 and s.stage not in ("end",)
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][:settings.top_sector_count]
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if not hot_sectors:
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logger.info("无合格热门板块（需要资金流入+非末期），回退到全部板块")
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hot_sectors = all_sectors[:settings.top_sector_count]
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for s in hot_sectors:
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logger.info(f" 目标板块: {s.sector_name} 涨幅{s.pct_change}% 资金{s.capital_inflow:.0f}万 "
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f"涨停{s.limit_up_count} 阶段={s.stage}")
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# 盘中用实时行情更新板块涨幅和涨停数
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if intraday:
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hot_sectors = await intraday_sector_scan(hot_sectors)
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# ── Step 2: 板块内选股 ──
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logger.info("=== Step 2: 板块内选股 ===")
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candidates = await _select_from_hot_sectors(hot_sectors, trade_date, intraday)
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if not candidates:
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logger.info("=== Step 2 无候选，回退到全市场扫描 ===")
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candidates = await scan_trend_breakout(
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trade_date=trade_date,
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market_temp=market_temp,
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hot_sectors=hot_sectors,
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intraday=intraday,
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)
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if not candidates:
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logger.info("=== 筛选完成: 0 只股票 ===")
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return {
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"market_temp": market_temp,
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"hot_sectors": hot_sectors,
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"recommendations": [],
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"scan_mode": scan_mode,
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}
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# ── Step 3: 供需 + 价格行为 + 趋势评分 ──
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logger.info("=== Step 3: 深度分析 ===")
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recommendations = _build_recommendations(
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candidates, market_temp, hot_sectors, market_temp_score, intraday,
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)
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# 过滤低质量推荐
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recommendations = [r for r in recommendations if r.score >= 40]
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logger.info(f"=== 筛选完成: {len(recommendations)} 只股票 ({scan_mode}) ===")
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for r in recommendations[:5]:
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signal_map = {"breakout": "突破型", "breakout_confirm": "确认型", "pullback": "回踩型", "launch": "启动型", "reversal": "反转型"}
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signal_label = signal_map.get(r.entry_signal_type, r.entry_signal_type)
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logger.info(f" [{signal_label}] {r.name}({r.ts_code}) {r.level} 评分={r.score} 信号={r.signal}")
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return {
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"market_temp": market_temp,
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"hot_sectors": hot_sectors,
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"recommendations": recommendations,
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"scan_mode": scan_mode,
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}
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async def _select_from_hot_sectors(
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hot_sectors: list[SectorInfo],
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trade_date: str,
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intraday: bool,
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) -> list[dict]:
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"""Step 2: 从热门板块成分股中选出有资金流入的候选
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流程:
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1. 收集所有热门板块的成分股代码
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2. 用 get_daily_all + get_daily_basic 过滤市值/换手率
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3. 用 get_moneyflow_batch 过滤主力净流入 > 0
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4. 对候选做入场信号初筛（只需满足任一信号类型）
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"""
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from app.data.tushare_client import tushare_client
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from datetime import datetime, timedelta
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import pandas as pd
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if not trade_date:
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trade_date = tushare_client.get_latest_trade_date()
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# 收集热门板块成分股代码
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sector_member_codes: set[str] = set()
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sector_code_map: dict[str, str] = {} # ts_code -> sector_name
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for s in hot_sectors:
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try:
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members_df = tushare_client.get_ths_members(s.sector_code)
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if not members_df.empty and "con_code" in members_df.columns:
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codes = members_df["con_code"].tolist()
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sector_member_codes.update(codes)
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for c in codes:
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sector_code_map[c] = s.sector_name
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except Exception as e:
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logger.warning(f"获取板块 {s.sector_name} 成分股失败: {e}")
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if not sector_member_codes:
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logger.info("Step 2: 无板块成分股数据")
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return []
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logger.info(f"Step 2: 热门板块共 {len(sector_member_codes)} 只成分股")
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# 过滤市值/换手率/ST/次新
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stock_basic = tushare_client.get_stock_basic()
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exclude_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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exclude_codes.update(st_codes)
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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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exclude_codes.update(new_codes)
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# 行业映射
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industry_map = {}
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if not stock_basic.empty:
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for _, row in stock_basic.iterrows():
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industry_map[row["ts_code"]] = row.get("industry", "")
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# 用 daily_basic 过滤
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basic = tushare_client.get_daily_basic(trade_date)
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if basic.empty:
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logger.info("Step 2: daily_basic 无数据")
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return []
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basic["circ_mv"] = basic["circ_mv"] / 10000 # 万元 → 亿元
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filtered_basic = basic[
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(basic["ts_code"].isin(sector_member_codes)) &
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(~basic["ts_code"].isin(exclude_codes)) &
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(basic["circ_mv"] >= settings.min_circ_mv) &
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(basic["circ_mv"] <= settings.max_circ_mv) &
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(basic["turnover_rate"] >= settings.min_turnover_rate) &
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(basic["turnover_rate"] <= settings.max_turnover_rate)
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].copy()
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logger.info(f"Step 2 基本面过滤: {len(sector_member_codes)} 只 → {len(filtered_basic)} 只")
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if filtered_basic.empty:
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return []
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# 资金流过滤：主力净流入 > 0
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mf = tushare_client.get_moneyflow_batch(trade_date)
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if mf.empty:
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logger.info("Step 2: 资金流数据为空，跳过资金过滤")
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candidate_codes = set(filtered_basic["ts_code"].tolist())
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else:
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mf["main_net_inflow"] = (
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(mf["buy_elg_amount"] - mf["sell_elg_amount"]) +
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(mf["buy_lg_amount"] - mf["sell_lg_amount"])
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)
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total = (
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mf["buy_elg_amount"] + mf["sell_elg_amount"] +
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mf["buy_lg_amount"] + mf["sell_lg_amount"] +
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mf["buy_md_amount"] + mf["sell_md_amount"] +
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mf["buy_sm_amount"] + mf["sell_sm_amount"]
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)
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mf["inflow_ratio"] = (mf["main_net_inflow"] / total.replace(0, float("nan")) * 100).fillna(0)
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mf_positive = mf[
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(mf["ts_code"].isin(set(filtered_basic["ts_code"]))) &
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(mf["main_net_inflow"] > 0)
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].sort_values("main_net_inflow", ascending=False)
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candidate_codes = set(mf_positive["ts_code"].tolist())
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# 构建资金流查找表
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mf_lookup = {}
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for _, row in mf_positive.iterrows():
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mf_lookup[row["ts_code"]] = {
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"main_net_inflow": float(row["main_net_inflow"]),
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"inflow_ratio": float(row.get("inflow_ratio", 0)),
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}
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logger.info(f"Step 2 资金流过滤: → {len(candidate_codes)} 只主力净流入 > 0")
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if not candidate_codes:
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return []
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# 构建候选列表
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import numpy as np
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candidates = []
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for ts_code in candidate_codes:
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name = ""
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if not stock_basic.empty:
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row = stock_basic[stock_basic["ts_code"] == ts_code]
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if not row.empty:
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name = row.iloc[0]["name"]
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sector_name = sector_code_map.get(ts_code, industry_map.get(ts_code, ""))
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b_row = filtered_basic[filtered_basic["ts_code"] == ts_code]
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turnover_rate = float(b_row.iloc[0]["turnover_rate"]) if not b_row.empty else 0
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circ_mv = float(b_row.iloc[0]["circ_mv"]) if not b_row.empty else 0
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pe = float(b_row.iloc[0]["pe"]) if not b_row.empty and pd.notna(b_row.iloc[0].get("pe")) else None
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pb = float(b_row.iloc[0]["pb"]) if not b_row.empty and pd.notna(b_row.iloc[0].get("pb")) else None
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volume_ratio = float(b_row.iloc[0]["volume_ratio"]) if not b_row.empty and pd.notna(b_row.iloc[0].get("volume_ratio")) else None
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try:
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mf_info = mf_lookup.get(ts_code, {})
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except NameError:
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mf_info = {}
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candidates.append({
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"ts_code": ts_code,
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"name": name,
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"sector": sector_name,
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"turnover_rate": turnover_rate,
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"circ_mv": circ_mv,
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"pe": pe,
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"pb": pb,
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"volume_ratio": volume_ratio,
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"main_net_inflow": mf_info.get("main_net_inflow", 0),
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"inflow_ratio": mf_info.get("inflow_ratio", 0),
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})
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logger.info(f"Step 2 候选: {len(candidates)} 只")
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return candidates
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def _build_recommendations(
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candidates: list[dict],
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market_temp: MarketTemperature,
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hot_sectors: list[SectorInfo],
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market_temp_score: float = 0,
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intraday: bool = False,
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) -> list[Recommendation]:
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"""Step 3: 对候选做供需 + 价格行为 + 趋势深度分析
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评分公式：供需关系 40% + 价格行为 35% + 趋势 25%
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板块和资金流已在前置过滤中处理。
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"""
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from app.data.tushare_client import tushare_client
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from app.analysis.technical import add_all_indicators
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from app.analysis.breakout_signals import (
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classify_entry_signal,
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score_supply_demand,
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analyze_volume_pattern,
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EntrySignal,
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)
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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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# 名称和行业映射
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stock_basic = tushare_client.get_stock_basic()
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name_map = {}
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industry_map = {}
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if not stock_basic.empty:
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for _, row in stock_basic.iterrows():
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name_map[row["ts_code"]] = row["name"]
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industry_map[row["ts_code"]] = row.get("industry", "")
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recommendations = []
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total = len(candidates)
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signal_counts = {"breakout": 0, "breakout_confirm": 0, "pullback": 0, "launch": 0, "reversal": 0, "none": 0}
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for idx, stock in enumerate(candidates):
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ts_code = stock.get("ts_code", "")
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if not ts_code:
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continue
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name = stock.get("name") or name_map.get(ts_code, ts_code)
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sector = stock.get("sector") or industry_map.get(ts_code, "")
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try:
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# 获取 120 日 K 线
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df = tushare_client.get_stock_daily(ts_code, 120)
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if df.empty or len(df) < 30:
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continue
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# 添加技术指标
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df = add_all_indicators(df)
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# ── 入场信号分类 ──
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entry_signal = classify_entry_signal(df)
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signal_type = entry_signal["signal_type"]
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if signal_type == EntrySignal.NONE:
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signal_counts["none"] += 1
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continue
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signal_counts[signal_type.value] += 1
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# ── 三维度评分 ──
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# 1. 供需关系评分 (40%)
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supply_demand_score = score_supply_demand(df)
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# 2. 价格行为评分 (35%)
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price_action_score = _score_price_action(df, entry_signal)
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# 3. 趋势评分 (25%)
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trend_score = _score_trend(df)
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# 综合评分
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final_score = (
|
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supply_demand_score * 0.40 +
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price_action_score * 0.35 +
|
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trend_score * 0.25
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)
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# 风险乘数
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tech_signal = generate_signals(ts_code, name)
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if tech_signal:
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if tech_signal.rally_pct_5d > 20:
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final_score *= 0.65
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elif tech_signal.rally_pct_5d > 15:
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final_score *= 0.80
|
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|
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# 板块末期惩罚（板块信息来自 hot_sectors）
|
||
sector_stage = _get_sector_stage(sector, hot_sectors)
|
||
if sector_stage == "end":
|
||
final_score *= 0.70
|
||
elif sector_stage == "late":
|
||
final_score *= 0.88
|
||
|
||
# 市场温度风控
|
||
if market_temp_score < 30:
|
||
final_score *= 0.75
|
||
elif market_temp_score < 50:
|
||
final_score *= 0.88
|
||
|
||
# 高置信度入场信号奖励
|
||
if entry_signal.get("signal_score", 0) >= 80:
|
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final_score *= 1.10
|
||
|
||
# 估值评分（辅助参考，不参与主评分）
|
||
pe = stock.get("pe")
|
||
pb = stock.get("pb")
|
||
valuation_score = _score_valuation(pe, pb)
|
||
|
||
# 确定信号和等级
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||
level = _score_to_level(final_score)
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||
signal = "HOLD"
|
||
position_score = tech_signal.position_score if tech_signal else 50
|
||
if (signal_type != EntrySignal.NONE
|
||
and entry_signal.get("signal_score", 0) >= 50
|
||
and position_score >= 30
|
||
and final_score >= 60):
|
||
signal = "BUY"
|
||
|
||
# 价格参考
|
||
entry_price = None
|
||
target_price = None
|
||
stop_loss = None
|
||
if tech_signal:
|
||
entry_price = tech_signal.support_price
|
||
target_price = tech_signal.resist_price
|
||
stop_loss = tech_signal.stop_loss_price
|
||
|
||
details = entry_signal.get("details", {})
|
||
st = signal_type.value
|
||
if st in ("breakout", "breakout_confirm") and details.get("resist_level"):
|
||
entry_price = details["resist_level"]
|
||
target_price = round(entry_price * 1.05, 2)
|
||
elif st == "pullback" and details.get("support_price"):
|
||
entry_price = details["support_price"]
|
||
target_price = round(entry_price * 1.05, 2)
|
||
elif st == "launch" and details.get("resist_level"):
|
||
entry_price = round(details["resist_level"] * 1.01, 2)
|
||
target_price = round(details["resist_level"] * 1.08, 2)
|
||
elif st == "reversal" and tech_signal and tech_signal.support_price:
|
||
entry_price = tech_signal.support_price
|
||
target_price = round(entry_price * 1.08, 2)
|
||
|
||
# 生成推荐理由
|
||
reasons = _generate_reasons(stock, entry_signal, tech_signal, df, intraday)
|
||
risk_note = _generate_risk_note(market_temp, tech_signal, stock)
|
||
|
||
# 量价模式
|
||
vol_pattern = analyze_volume_pattern(df)
|
||
|
||
rec = Recommendation(
|
||
ts_code=ts_code,
|
||
name=name,
|
||
sector=sector,
|
||
score=round(final_score, 1),
|
||
market_temp_score=round(market_temp_score, 1),
|
||
sector_score=round(_get_sector_heat(sector, hot_sectors), 1),
|
||
capital_score=round(_score_capital_simple(stock), 1),
|
||
technical_score=round(trend_score, 1),
|
||
position_score=round(position_score, 1),
|
||
valuation_score=round(valuation_score, 1),
|
||
signal=signal,
|
||
entry_price=entry_price,
|
||
target_price=target_price,
|
||
stop_loss=stop_loss,
|
||
reasons=reasons,
|
||
risk_note=risk_note,
|
||
level=level,
|
||
strategy="trend_breakout",
|
||
entry_signal_type=signal_type.value,
|
||
)
|
||
recommendations.append(rec)
|
||
|
||
if len(recommendations) >= settings.top_stock_count:
|
||
break
|
||
|
||
except Exception as e:
|
||
logger.debug(f"深度分析 {ts_code} 失败: {e}")
|
||
continue
|
||
|
||
# 让出控制权（同步函数中无法 await，跳过）
|
||
# idx % 10 == 0 的让步在 _select_from_hot_sectors 的上层 async 函数中处理
|
||
|
||
logger.info(
|
||
f"Step 3 入场信号分布: "
|
||
f"突破={signal_counts['breakout']} 确认={signal_counts['breakout_confirm']} "
|
||
f"回踩={signal_counts['pullback']} 启动={signal_counts['launch']} "
|
||
f"反转={signal_counts['reversal']} 无信号={signal_counts['none']} "
|
||
f"(共分析{total}只)"
|
||
)
|
||
|
||
return recommendations
|
||
|
||
|
||
# ── 价格行为评分 ──
|
||
|
||
|
||
def _score_price_action(df, entry_signal: dict) -> float:
|
||
"""价格行为学评分 (0-100)
|
||
|
||
纯粹关注 K 线形态和量价配合，不重复评估趋势/均线因素。
|
||
|
||
维度：
|
||
- K线形态强度 (35): 实体占比、收盘位置、下影线
|
||
- 量价配合 (35): 放量/缩量与价格方向的配合度
|
||
- 入场形态质量 (30): 各信号类型的形态完成度
|
||
"""
|
||
import pandas as pd
|
||
score = 0
|
||
last = df.iloc[-1]
|
||
details = entry_signal.get("details", {})
|
||
signal_type = entry_signal.get("signal_type")
|
||
|
||
# K线形态强度 (35)
|
||
day_range = last["high"] - last["low"]
|
||
if day_range > 0:
|
||
# 实体占比（实体/全振幅）
|
||
body = abs(last["close"] - last["open"])
|
||
body_ratio = body / day_range
|
||
if body_ratio > 0.7:
|
||
score += 20 # 大实体，方向明确
|
||
elif body_ratio > 0.4:
|
||
score += 12
|
||
elif body_ratio > 0.2:
|
||
score += 6
|
||
|
||
# 收盘位置（越接近高点越好）
|
||
close_position = (last["close"] - last["low"]) / day_range
|
||
if close_position > 0.8:
|
||
score += 10 # 收在上部 20%
|
||
elif close_position > 0.6:
|
||
score += 6
|
||
elif close_position > 0.4:
|
||
score += 3
|
||
|
||
# 下影线（回踩型/启动型利好）
|
||
lower_wick = (last["open"] - last["low"]) if last["close"] > last["open"] else (last["close"] - last["low"])
|
||
if lower_wick > 0:
|
||
wick_ratio = lower_wick / day_range
|
||
if signal_type and signal_type.value in ("pullback", "reversal") and wick_ratio > 0.2:
|
||
score += 5 # 回踩型/反转型有下影线支撑
|
||
|
||
# 量价配合 (35)
|
||
vol_ma_col = "vol_ma5" if "vol_ma5" in df.columns else None
|
||
if vol_ma_col and not pd.isna(last[vol_ma_col]) and last[vol_ma_col] > 0:
|
||
vol_ratio = last["vol"] / last[vol_ma_col]
|
||
price_up = last["pct_chg"] > 0 if "pct_chg" in df.columns else last["close"] > last["open"]
|
||
|
||
if price_up and vol_ratio > 2.0:
|
||
score += 35 # 放量大阳
|
||
elif price_up and vol_ratio > 1.5:
|
||
score += 25
|
||
elif price_up and vol_ratio > 1.2:
|
||
score += 18
|
||
elif not price_up and vol_ratio < 0.7:
|
||
score += 25 # 缩量回调（良性）
|
||
elif not price_up and vol_ratio < 0.9:
|
||
score += 15
|
||
elif price_up and vol_ratio > 1.0:
|
||
score += 10
|
||
else:
|
||
score += 10
|
||
|
||
# 入场形态质量 (30) — 只评估形态完成度，不涉及均线/MACD
|
||
if signal_type and signal_type.value == "breakout":
|
||
breakout_pct = details.get("breakout_pct", 0)
|
||
vol_ratio = details.get("volume_ratio", 1)
|
||
if breakout_pct > 2 and vol_ratio > 2:
|
||
score += 30
|
||
elif breakout_pct > 1 and vol_ratio > 1.5:
|
||
score += 20
|
||
elif breakout_pct > 0:
|
||
score += 12
|
||
else:
|
||
score += 6
|
||
|
||
elif signal_type and signal_type.value == "breakout_confirm":
|
||
vol_ratio = details.get("volume_ratio", 1)
|
||
confirm_pct = details.get("confirm_pct", 0)
|
||
if vol_ratio > 2 and confirm_pct > 2:
|
||
score += 30
|
||
elif vol_ratio > 1.5 and confirm_pct > 1:
|
||
score += 22
|
||
elif vol_ratio > 1.0:
|
||
score += 14
|
||
else:
|
||
score += 8
|
||
|
||
elif signal_type and signal_type.value == "pullback":
|
||
support_ma = details.get("support_ma", "")
|
||
shrink = details.get("volume_shrink_ratio", 1)
|
||
if support_ma == "MA20" and shrink < 0.6:
|
||
score += 30
|
||
elif support_ma == "MA20":
|
||
score += 22
|
||
elif support_ma == "MA10" and shrink < 0.6:
|
||
score += 18
|
||
else:
|
||
score += 10
|
||
|
||
elif signal_type and signal_type.value == "launch":
|
||
range_pct = details.get("price_range_pct", 10)
|
||
if range_pct < 3:
|
||
score += 30
|
||
elif range_pct < 5:
|
||
score += 20
|
||
else:
|
||
score += 10
|
||
|
||
elif signal_type and signal_type.value == "reversal":
|
||
reversal_pct = details.get("reversal_pct", 0)
|
||
vol_ratio = details.get("volume_ratio", 1)
|
||
if reversal_pct > 5 and vol_ratio > 2.5:
|
||
score += 30
|
||
elif reversal_pct > 3 and vol_ratio > 2:
|
||
score += 22
|
||
elif reversal_pct > 3:
|
||
score += 14
|
||
else:
|
||
score += 8
|
||
|
||
else:
|
||
score += 10
|
||
|
||
return min(score, 100)
|
||
|
||
|
||
# ── 趋势评分 ──
|
||
|
||
|
||
def _score_trend(df) -> float:
|
||
"""趋势评分 (0-100)
|
||
|
||
维度：
|
||
- 均线排列 (40): MA5>MA10>MA20>MA60
|
||
- 更高高点/更高低点结构 (35): 近 20 日价格结构
|
||
- MA20 方向 (25): MA20 是否持续上行
|
||
"""
|
||
import pandas as pd
|
||
score = 0
|
||
last = df.iloc[-1]
|
||
|
||
# 均线排列 (40)
|
||
ma_cols = [c for c in ["ma5", "ma10", "ma20", "ma60"] if c in df.columns]
|
||
if len(ma_cols) >= 4 and not any(pd.isna(last[c]) for c in ma_cols):
|
||
if last["ma5"] > last["ma10"] > last["ma20"] > last["ma60"]:
|
||
score += 40 # 完美多头
|
||
elif last["ma5"] > last["ma10"] > last["ma20"]:
|
||
score += 28
|
||
elif last["ma5"] > last["ma20"]:
|
||
score += 15
|
||
elif "ma5" in df.columns and "ma20" in df.columns:
|
||
if not pd.isna(last["ma5"]) and not pd.isna(last["ma20"]) and last["ma5"] > last["ma20"]:
|
||
score += 15
|
||
|
||
# 更高高点/更高低点结构 (35)
|
||
if len(df) >= 20:
|
||
recent = df.tail(20)
|
||
# 检查高点抬升
|
||
first_10_high = recent["high"].iloc[:10].max()
|
||
second_10_high = recent["high"].iloc[10:].max()
|
||
# 检查低点抬升
|
||
first_10_low = recent["low"].iloc[:10].min()
|
||
second_10_low = recent["low"].iloc[10:].min()
|
||
|
||
if second_10_high > first_10_high and second_10_low > first_10_low:
|
||
score += 35 # 既抬高点又抬低点，最健康
|
||
elif second_10_high > first_10_high:
|
||
score += 20 # 至少高点抬升
|
||
elif second_10_low > first_10_low:
|
||
score += 12 # 至少低点抬升
|
||
|
||
# MA20 方向 (25)
|
||
if "ma20" in df.columns and len(df) >= 5:
|
||
ma20_now = last["ma20"]
|
||
ma20_5d = df.iloc[-5]["ma20"]
|
||
if not pd.isna(ma20_now) and not pd.isna(ma20_5d) and ma20_5d > 0:
|
||
ma20_pct = (ma20_now - ma20_5d) / ma20_5d * 100
|
||
if ma20_pct > 2:
|
||
score += 25
|
||
elif ma20_pct > 1:
|
||
score += 18
|
||
elif ma20_pct > 0:
|
||
score += 10
|
||
|
||
return min(score, 100)
|
||
|
||
|
||
# ── 辅助函数 ──
|
||
|
||
|
||
def _get_sector_stage(sector_name: str, hot_sectors: list[SectorInfo]) -> str:
|
||
"""获取板块所处阶段"""
|
||
for s in hot_sectors:
|
||
if s.sector_name == sector_name:
|
||
return s.stage
|
||
return "mid"
|
||
|
||
|
||
def _get_sector_heat(sector_name: str, hot_sectors: list[SectorInfo]) -> float:
|
||
"""获取板块热度得分"""
|
||
for s in hot_sectors:
|
||
if s.sector_name == sector_name:
|
||
return s.heat_score
|
||
return 30.0
|
||
|
||
|
||
def _score_capital_simple(stock: dict) -> float:
|
||
"""资金流简单评分（仅基于已有数据，不额外调 API）"""
|
||
main_net = stock.get("main_net_inflow", 0) or 0
|
||
inflow_ratio = stock.get("inflow_ratio", 0) or 0
|
||
|
||
score = 0
|
||
if main_net > 10000:
|
||
score += 60
|
||
elif main_net > 5000:
|
||
score += 45
|
||
elif main_net > 2000:
|
||
score += 30
|
||
elif main_net > 0:
|
||
score += 15
|
||
|
||
if inflow_ratio > 15:
|
||
score += 40
|
||
elif inflow_ratio > 10:
|
||
score += 30
|
||
elif inflow_ratio > 5:
|
||
score += 20
|
||
elif inflow_ratio > 0:
|
||
score += 10
|
||
|
||
return min(score, 100)
|
||
|
||
|
||
def _score_to_level(score: float) -> str:
|
||
if score >= 80:
|
||
return "强烈推荐"
|
||
elif score >= 60:
|
||
return "推荐"
|
||
elif score >= 40:
|
||
return "观望"
|
||
else:
|
||
return "回避"
|
||
|
||
|
||
def _generate_reasons(
|
||
stock: dict, entry_signal: dict, tech: TechnicalSignal | None,
|
||
df, intraday: bool = False,
|
||
) -> list[str]:
|
||
"""生成推荐理由"""
|
||
import pandas as pd
|
||
from app.analysis.breakout_signals import EntrySignal
|
||
reasons = []
|
||
signal_type = entry_signal.get("signal_type")
|
||
details = entry_signal.get("details", {})
|
||
signal_map = {EntrySignal.BREAKOUT: "突破型", EntrySignal.BREAKOUT_CONFIRM: "确认型",
|
||
EntrySignal.PULLBACK: "回踩型", EntrySignal.LAUNCH: "启动型",
|
||
EntrySignal.REVERSAL: "反转型"}
|
||
entry_label = signal_map.get(signal_type, "")
|
||
|
||
# 入场信号
|
||
if entry_label and signal_type:
|
||
st = signal_type.value
|
||
if st == "breakout":
|
||
breakout_pct = details.get("breakout_pct", 0)
|
||
vol_ratio = details.get("volume_ratio", 0)
|
||
reasons.append(f"放量突破20日阻力位（涨幅{breakout_pct:.1f}%，量比{vol_ratio:.1f}倍）")
|
||
elif st == "breakout_confirm":
|
||
vol_ratio = details.get("volume_ratio", 0)
|
||
confirm_pct = details.get("confirm_pct", 0)
|
||
reasons.append(f"突破后放量确认（确认日涨{confirm_pct:.1f}%，量比{vol_ratio:.1f}倍）")
|
||
elif st == "pullback":
|
||
support = details.get("support_ma", "")
|
||
shrink = details.get("volume_shrink_ratio", 0)
|
||
reasons.append(f"缩量回踩{support}支撑（量能收缩至{shrink:.0%}）")
|
||
elif st == "launch":
|
||
range_pct = details.get("price_range_pct", 0)
|
||
reasons.append(f"缩量横盘整理{range_pct:.1f}%后首日放量启动")
|
||
elif st == "reversal":
|
||
reversal_pct = details.get("reversal_pct", 0)
|
||
vol_ratio = details.get("volume_ratio", 0)
|
||
reasons.append(f"连续下跌后放量长阳反转（涨{reversal_pct:.1f}%，量比{vol_ratio:.1f}倍）")
|
||
|
||
# 供需分析
|
||
if len(df) >= 10:
|
||
recent = df.tail(10)
|
||
up_days = recent[recent["pct_chg"] > 0]
|
||
down_days = recent[recent["pct_chg"] <= 0]
|
||
if len(up_days) > 0 and len(down_days) > 0:
|
||
avg_up_vol = up_days["vol"].mean()
|
||
avg_down_vol = down_days["vol"].mean()
|
||
if avg_down_vol > 0:
|
||
ds_ratio = avg_up_vol / avg_down_vol
|
||
if ds_ratio > 1.5:
|
||
reasons.append(f"需求主导（上涨均量/下跌均量={ds_ratio:.1f}）")
|
||
|
||
# 资金流
|
||
main_net = stock.get("main_net_inflow", 0)
|
||
if main_net > 5000:
|
||
reasons.append(f"主力资金大幅流入{main_net:.0f}万元")
|
||
elif main_net > 1000:
|
||
reasons.append(f"主力资金持续流入{main_net:.0f}万元")
|
||
|
||
# 板块
|
||
sector = stock.get("sector", "")
|
||
if sector:
|
||
reasons.append(f"所属热门板块【{sector}】")
|
||
|
||
return reasons[:3]
|
||
|
||
|
||
def _generate_risk_note(
|
||
market: MarketTemperature,
|
||
tech: TechnicalSignal | None,
|
||
stock: dict,
|
||
) -> str:
|
||
"""生成风险提示"""
|
||
notes = []
|
||
entry_type = stock.get("entry_signal_type", "")
|
||
|
||
if entry_type == "breakout":
|
||
notes.append("突破型需警惕假突破，关注量能是否持续")
|
||
elif entry_type == "breakout_confirm":
|
||
notes.append("确认型需观察后续量能是否跟上，防止冲高回落")
|
||
elif entry_type == "pullback":
|
||
notes.append("回踩型可能继续下探支撑，注意止损纪律")
|
||
elif entry_type == "launch":
|
||
notes.append("启动型整理可能延长，注意时间成本")
|
||
elif entry_type == "reversal":
|
||
notes.append("反转型可能二次探底，确认底部后再加仓")
|
||
|
||
if market.temperature < 30:
|
||
notes.append("市场情绪偏冷，系统性风险较高")
|
||
elif market.temperature < 50:
|
||
notes.append("市场情绪一般，注意仓位控制")
|
||
|
||
if tech:
|
||
if tech.position_score < 30:
|
||
notes.append(f"近期涨幅较大(5日{tech.rally_pct_5d}%)，追高风险")
|
||
if tech.rally_pct_10d > 20:
|
||
notes.append(f"10日累涨{tech.rally_pct_10d}%，警惕回调")
|
||
|
||
if not notes:
|
||
return "注意设好止损，控制仓位"
|
||
return "；".join(notes)
|