196 lines
7.6 KiB
Python
196 lines
7.6 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""b_td1_v3_scanner.py — B组超跌·原池优选 实盘扫描器（2026-08-17 择优激活落地）
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由果及因（老莫指正）：b_td1 原池(5000信号)信号太密集,用 score 每日top5截断 → 信号572/成交316/比1.8
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实盘对齐回测：
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池（b_td1 原池）：dist_lo20 > 5（距20日低点>5%）——news3/mcap_q/pe_q 为回测外部因子,
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实盘用可获取近似：mcap_q<0.3（市值分位,从 stock_daily 市值算）pe_q<0.3 暂缺则放宽
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score（池内超跌评分）：bias60深度 + rsi + sec_ret20 + ret5
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每日 top5（score 降序）
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出场建议：tp15% / sl8% / max35日（原版模拟验证参数）
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数据源：腾讯前复权日K（与 mr_scanner 同源,零偏差）+ 市值分位从 stock_daily
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输出：candidates 表（sector='b_td1_v3'）
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用法：
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python3 b_td1_v3_scanner.py # 完整扫描
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python3 b_td1_v3_scanner.py --force # 忽略门控
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python3 b_td1_v3_scanner.py --top N # 输出前 N 只（默认 5）
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"""
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import sys, json, sqlite3
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from pathlib import Path
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from datetime import datetime
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sys.path.insert(0, str(Path(__file__).parent))
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from indicators import calc_ma, calc_rsi
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from market_data import fetch_tx_klines, get_stock_pool
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DB_PATH = Path("/home/hmo/MoFin/data/mofin.db")
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TOP_N = 5
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EXIT_CFG = {"tp_pct": 0.15, "sl_pct": 0.08, "max_hold_days": 35}
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def load_regime():
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"""当前温区（平滑优先）"""
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try:
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from regime_gate import get_current_regime
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rg = get_current_regime()
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if rg and rg.get("regime") != "unknown":
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return rg.get("regime")
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except Exception:
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pass
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try:
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conn = sqlite3.connect(str(DB_PATH), timeout=5)
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r = conn.execute("SELECT regime FROM market_regime WHERE market='a' ORDER BY date DESC LIMIT 1").fetchone()
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conn.close()
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return r[0] if r else "unknown"
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except Exception:
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return "unknown"
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def mcap_quantile(code):
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"""市值分位（mcap_q）：用 stock_fundamentals.mcap_total（总市值，亿元）
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2026-08-17 修复：原用 stock_daily.amount 成交额不可靠（amount=None 导致全返回0.3缺省，
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而池条件 mcap_q<0.3 会挡掉所有——扫描0候选的根因）"""
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try:
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conn = sqlite3.connect(str(DB_PATH), timeout=5)
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row = conn.execute(
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"SELECT mcap_total FROM stock_fundamentals WHERE code=? ORDER BY updated_at DESC LIMIT 1",
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(code,)).fetchone()
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conn.close()
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if not row or not row[0]:
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return 0.3 # 缺省中值
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# 全市场市值分位（用基本面最新 mcap_total 全量）
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conn = sqlite3.connect(str(DB_PATH), timeout=5)
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rows = conn.execute(
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"SELECT code, mcap_total FROM stock_fundamentals f WHERE updated_at = "
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"(SELECT MAX(updated_at) FROM stock_fundamentals f2 WHERE f2.code=f.code)"
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).fetchall()
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conn.close()
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mcaps = sorted([r[1] for r in rows if r[1] and r[1] > 0])
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if not mcaps:
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return 0.3
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import bisect
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pos = bisect.bisect_left(mcaps, row[0])
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return round(pos / max(len(mcaps), 1), 2)
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except Exception:
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return 0.3
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def score_of(bias60, rsi, sec_ret20, ret5):
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"""池内超跌评分（与回测 b_td1_v3_gen 一致）"""
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sc = 0
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if bias60 is not None:
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sc += 40 if bias60 < -30 else 32 if bias60 < -20 else 20 if bias60 < -10 else 8
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if rsi is not None:
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sc += 30 if rsi < 30 else 24 if rsi < 40 else 14 if rsi < 50 else 6
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if sec_ret20 is not None:
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sc += 20 if sec_ret20 < -20 else 14 if sec_ret20 < -10 else 8 if sec_ret20 < 0 else 3
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if ret5 is not None:
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sc += 10 if ret5 < -25 else 7 if ret5 < -15 else 4 if ret5 < -8 else 1
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return sc
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def sec_ret20_approx(code):
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"""行业20日涨幅近似：用该股所在板块指数或简化为大盘对照"""
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# 实盘简化：返回 None（score 该分项给0），避免复杂行业数据依赖
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return None
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def check_b_td1(klines, code):
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"""b_td1_v3 筛选：池条件 + score"""
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if not klines or len(klines) < 60:
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return None
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closes = [k["close"] for k in klines]
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lows = [k["low"] for k in klines]
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i = len(klines) - 1
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close = closes[i]
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if close <= 0:
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return None
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ma60 = calc_ma(closes, 60)
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m60 = ma60[i]
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if not m60 or m60 <= 0:
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return None
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bias60 = (close - m60) / m60 * 100
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# 池条件：dist_lo20 > 5（距20日低点>5%）
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lo20 = min(lows[max(0, i - 19):i + 1])
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dist_lo20 = (close - lo20) / lo20 * 100 if lo20 > 0 else 0
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if dist_lo20 <= 5:
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return None
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rsi = calc_rsi(closes)
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rsi_v = rsi[i] if i < len(rsi) else None
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prev5 = closes[i - 5] if i >= 5 else 0
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ret5 = (close - prev5) / prev5 * 100 if prev5 > 0 else 0
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# 市值分位
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mcap_q = mcap_quantile(code)
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if mcap_q >= 0.3:
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return None # 池条件：小市值
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# score
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sec20 = sec_ret20_approx(code)
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sc = score_of(bias60, rsi_v, sec20, ret5)
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return {
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"price": close, "bias60": round(bias60, 2), "rsi": round(rsi_v, 2) if rsi_v else None,
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"ret5": round(ret5, 2), "dist_lo20": round(dist_lo20, 2), "mcap_q": mcap_q,
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"score": sc, "target": round(close * (1 + EXIT_CFG["tp_pct"]), 2),
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"stop_loss": round(close * (1 - EXIT_CFG["sl_pct"]), 2),
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"date": klines[i]["date"],
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}
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def main():
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import argparse
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ap = argparse.ArgumentParser()
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ap.add_argument("--force", action="store_true")
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ap.add_argument("--top", type=int, default=TOP_N)
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args = ap.parse_args()
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regime = load_regime()
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print(f"[b_td1_v3] {datetime.now().strftime('%H:%M')} 扫描开始 温区={regime}", flush=True)
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# 温区门控：trend_down/choppy 才扫（超跌池主战场）
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if not args.force and regime not in ("trend_down", "choppy"):
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print(f" 温区 {regime} 非超跌池主战场，跳过", flush=True)
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return
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all_stocks, existing = get_stock_pool()
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print(f" 股票池 {len(all_stocks)} 只", flush=True)
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hits = []
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for code in all_stocks:
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if code in existing:
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continue
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try:
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klines = fetch_tx_klines(code, datalen=120)
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sig = check_b_td1(klines, code)
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if sig:
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hits.append((code, code, sig)) # name 暂用 code（与 mr_scanner 同源）
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except Exception:
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pass
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# score 降序 top-N
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hits.sort(key=lambda x: -x[2]["score"])
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hits = hits[: args.top]
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print(f" 命中 {len(hits)} 只（score降序前{args.top}）", flush=True)
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conn = sqlite3.connect(str(DB_PATH), timeout=10)
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inserted = 0
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for code, name, sig in hits:
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reasons = (f"dist_lo20={sig['dist_lo20']}% bias60={sig['bias60']}% "
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f"rsi={sig['rsi']} ret5={sig['ret5']}% mcap_q={sig['mcap_q']} score={sig['score']}")
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conn.execute(
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"INSERT INTO candidates (code, name, sector, reason, entry_range, stop_loss, target, created_at) "
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"VALUES (?,?,?,?,?,?,?,datetime('now','localtime')) "
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"ON CONFLICT(code) DO UPDATE SET "
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"name=excluded.name, sector=excluded.sector, reason=excluded.reason, "
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"entry_range=excluded.entry_range, stop_loss=excluded.stop_loss, target=excluded.target",
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(code, code, "b_td1_v3", reasons,
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f"{sig['price']*0.98:.2f}~{sig['price']:.2f}", sig["stop_loss"], sig["target"]))
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inserted += 1
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print(f" 🟢 {code} {name} 价{sig['price']} score={sig['score']} {reasons}", flush=True)
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conn.commit()
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conn.close()
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print(f" ✅ 新增 {inserted} 只 b_td1_v3 候选", flush=True)
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if __name__ == "__main__":
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main()
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