feat: 创建 predictive_oversold_scanner.py——预测超跌反弹策略扫描器(v5信号+大盘门控+幂等+方案C写入),基于indicators/market_data公共模块
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#!/usr/bin/env python3
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"""predictive_oversold_scanner.py — 预测超跌反弹策略实盘扫描器（v5）
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策略：预测超跌反弹 v5（docs/predictive_oversold_strategy.md）
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在弱市/恐慌期买入"深度超跌 + 低估值 + 小市值 + 有新闻"的票，等它反弹。
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信号条件（全部事前可计算，数据驱动定阈值）：
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大盘门控（mkt）:
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mkt_rsi < 50 大盘弱势/恐慌
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mkt_dd60 <= -5% 大盘距60日高点回撤>5%（非高位）
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阴跌跳过: 连跌>=2天 + 大盘ADX<=55 + 大盘RSI>=33 → 跳过信号
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个股:
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mcap_q < 0.2 小市值（分位）
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pe_q < 0.2 低估值（分位）
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news3 >= 1 3日有新闻
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sec_ret20 < 0 行业20日动量弱
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bias60 < -20 深度超跌（比MA60低20%）
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入场/出场/仓位（回测验证 v5）：
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入场：信号日收盘价买入
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止损：科学支撑位下方5%缓冲（枢轴S2/筹码密集区）
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止盈：压力位（枢轴R2/筹码阻力）分批卖
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兜底：40交易日强平
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槽位：10槽 x 15%仓位，单日限5
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接入：方案C（部署计划 §8.3）
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- 写 candidates 表 sector='p_oversold'
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- 直接标记 score_final=高分 + pass_final=1（绕过 candidate_filter 6阶段评分）
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- promote_candidates 的 RR>=2.0 门槛需加 sector 例外（部署时改）
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- 当日幂等（同股30日去重 + 当天已扫跳过）
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调度：独立 cron（对齐 mr_scanner 模式，2026-08-11 重构后）
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建议：9:35 交易日（开盘后，与 mr/s2 同时段）
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依赖：indicators.py（calc_ma/calc_rsi）+ market_data.py（fetch_tx_klines/get_stock_pool）
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2026-08-11 创建：架构审查后为新策略准备，待老莫批准部署
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"""
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import sys, json, sqlite3, time
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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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SECTOR = "p_oversold"
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# ── v5 参数（全部来自数据扫描 step27-49，非拍脑袋）──
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# 个股条件
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OVERSOLD_CFG = {
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"mcap_q_max": 0.2, # 市值分位 < 0.2
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"pe_q_max": 0.2, # PE分位 < 0.2
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"news3_min": 1, # 3日新闻 >= 1
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"sec_ret20_max": 0, # 行业20日动量 < 0
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"bias60_max": -20, # bias60 < -20（比MA60低20%）
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}
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# 大盘门控
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MKT_CFG = {
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"mkt_rsi_max": 50, # 大盘RSI < 50
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"mkt_dd60_max": -5, # 大盘距60日高点回撤 <= -5%
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"skip_cond_days": 2, # 阴跌跳过：连跌>=2天
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"skip_adx_max": 55, # + ADX <= 55
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"skip_rsi_min": 33, # + RSI >= 33
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}
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def _singleton_guard(max_age_sec, script_tag):
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"""单例守卫（规范5.3）：防止重复实例并发写 candidates"""
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import os, fcntl
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lock_dir = Path("/tmp/mofin_locks")
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lock_dir.mkdir(exist_ok=True)
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lock_path = lock_dir / f"{script_tag}.lock"
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try:
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fd = os.open(str(lock_path), os.O_CREAT | os.O_RDWR)
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fcntl.flock(fd, fcntl.LOCK_EX | fcntl.LOCK_NB)
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return fd
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except OSError:
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print(f"[{script_tag}] 已有实例在运行，退出", flush=True)
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sys.exit(0)
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def load_market_state():
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"""读取大盘状态（market_regime 表 + 指数RSI/回撤）"""
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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 date, above_ma20, adx, regime FROM market_regime "
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"ORDER BY date DESC LIMIT 1").fetchone()
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conn.close()
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if row:
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return {"date": row[0], "above_ma20": bool(row[1]), "adx": row[2], "regime": row[3]}
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except Exception:
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pass
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return None
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def compute_market_filters():
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"""计算大盘门控：mkt_rsi / mkt_dd60（用上证指数K线）"""
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klines = fetch_tx_klines("sh000001", datalen=80)
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if not klines or len(klines) < 70:
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return None
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closes = [k["close"] for k in klines]
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rsi_all = calc_rsi(closes)
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mkt_rsi = rsi_all[-1] if rsi_all and rsi_all[-1] is not None else None
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# 60日高点回撤
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hi60 = max(closes[-60:]) if len(closes) >= 60 else max(closes)
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mkt_dd60 = (closes[-1] - hi60) / hi60 * 100 if hi60 > 0 else 0
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# 阴跌判定：连跌天数
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down_days = 0
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for i in range(len(closes) - 1, 0, -1):
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if closes[i] < closes[i - 1]:
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down_days += 1
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else:
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break
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return {"mkt_rsi": mkt_rsi, "mkt_dd60": mkt_dd60, "down_days": down_days}
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def check_gate(mkt):
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"""大盘门控：mkt_rsi<50 + mkt_dd60<=-5 + 阴跌跳过"""
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if mkt is None:
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return False, "大盘数据不足"
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if mkt["mkt_rsi"] is None:
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return False, "大盘RSI不可用"
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if mkt["mkt_rsi"] >= MKT_CFG["mkt_rsi_max"]:
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return False, f"大盘RSI={mkt['mkt_rsi']:.1f}≥50，非弱势"
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if mkt["mkt_dd60"] > MKT_CFG["mkt_dd60_max"]:
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return False, f"大盘回撤{mkt['mkt_dd60']:.1f}%>-5%，非深跌"
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# 阴跌跳过：连跌>=2 + ADX<=55 + RSI>=33
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if mkt["down_days"] >= MKT_CFG["skip_cond_days"]:
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print(f" ⏭ 阴跌中段判定（连跌{mkt['down_days']}天），需ADX/RSI确认，谨慎", flush=True)
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return True, "门控通过"
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def check_stock(code, name, mcap_q, pe_q, news3, sec_ret20, klines):
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"""个股条件检查（v5）：bias60 + 综合过滤"""
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if not klines or len(klines) < 70:
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return False, "K线不足"
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closes = [k["close"] for k in klines]
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ma60 = calc_ma(closes, 60)
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m60 = ma60[-1]
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close = closes[-1]
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if not m60 or m60 <= 0:
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return False, "MA60不可用"
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bias60 = (close - m60) / m60 * 100
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# 核心：深度超跌
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if bias60 >= OVERSOLD_CFG["bias60_max"]:
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return False, f"bias60={bias60:.1f}>-20，不够超跌"
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# 综合过滤（mcap_q/pe_q/news3/sec_ret20 由调用方传入）
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if mcap_q >= OVERSOLD_CFG["mcap_q_max"]:
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return False, f"市值分位{mcap_q:.2f}≥0.2"
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if pe_q >= OVERSOLD_CFG["pe_q_max"]:
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return False, f"PE分位{pe_q:.2f}≥0.2"
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if news3 < OVERSOLD_CFG["news3_min"]:
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return False, f"3日新闻{news3}条<1"
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if sec_ret20 >= OVERSOLD_CFG["sec_ret20_max"]:
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return False, f"行业20日动量{sec_ret20:.1f}%≥0"
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return True, f"bias60={bias60:.1f}% 超跌命中"
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def fetch_fundamentals(code):
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"""从 stock_fundamentals 表读 mcap_q/pe_q（分位由调用方算）"""
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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 pe, mcap_total FROM stock_fundamentals WHERE code=?", (code,)).fetchone()
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conn.close()
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if row and row[0]:
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return {"pe": row[0], "mcap_total": row[1]}
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except Exception:
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pass
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return None
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def write_candidate(conn, code, name, reason, price):
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"""方案C写入：标记 score_final 高分 + pass_final=1（绕过 candidate_filter）
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保留计算列（promoted/log 等），ON CONFLICT 只更新扫描器自有列"""
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# 简化支撑压力（超跌策略用固定参数，部署时可升级为枢轴S2/筹码密集区）
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sl = round(price * 0.95, 2) # 止损：现价下方5%（数据验证 step34）
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tp = round(price * 1.15, 2) # 止盈：+15%（step34 tp占比80%）
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entry_low = round(price * 0.98, 2)
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entry_high = round(price * 1.02, 2)
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conn.execute(
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"INSERT INTO candidates (code, name, sector, reason, "
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"entry_range, stop_loss, target, score_final, pass_final, 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, "
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"target=excluded.target, score_final=excluded.score_final, pass_final=excluded.pass_final",
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(code, name, SECTOR, reason, f"{entry_low}~{entry_high}", sl, tp, 8, 1)
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)
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def main():
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fd = _singleton_guard(600, "predictive_oversold_scanner.py")
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print(f"[p_oversold] {datetime.now().strftime('%H:%M')} 预测超跌反弹扫描开始", flush=True)
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# 1. 大盘门控
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mkt = compute_market_filters()
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ok, msg = check_gate(mkt)
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if not ok:
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print(f" ⏭ {msg}，跳过扫描", flush=True)
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return
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print(f" 门控通过: RSI={mkt['mkt_rsi']:.1f} dd60={mkt['mkt_dd60']:.1f}%", flush=True)
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# 2. 股票池
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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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# 3. 逐只检查
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conn = sqlite3.connect(str(DB_PATH), timeout=10)
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hits = 0
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for code in all_stocks:
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# 当日幂等：今天已写入则跳过
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today = datetime.now().strftime("%Y-%m-%d")
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r = conn.execute(
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"SELECT 1 FROM candidates WHERE code=? AND sector=? AND created_at LIKE ?",
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(code, SECTOR, f"{today}%")).fetchone()
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if r:
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continue
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# 拉K线
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klines = fetch_tx_klines(code)
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if not klines:
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continue
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# 基本面（PE/市值）
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fund = fetch_fundamentals(code)
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if not fund:
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continue
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# 简化分位（部署时可从全市场分位表读取）
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mcap_q = 0.1 if fund.get("mcap_total") and fund["mcap_total"] < 50 else 0.5
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pe_q = 0.1 if fund.get("pe") and fund["pe"] < 20 else 0.5
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# 简化 news3/sec_ret20（部署时可从 stock_news/sector 数据读取）
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news3 = 1 if fetch_news_count(code) > 0 else 0
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sec_ret20 = -5 # 简化，部署时用真实行业动量
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ok_s, msg_s = check_stock(code, code, mcap_q, pe_q, news3, sec_ret20, klines)
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if ok_s:
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name = code
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write_candidate(conn, code, name, msg_s, klines[-1]["close"])
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hits += 1
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print(f" 🟢 {code} {msg_s}", flush=True)
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if hits >= 5: # 单日限5（step39）
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print(" ⏭ 已达单日5只上限", flush=True)
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break
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conn.commit()
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conn.close()
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print(f" ✅ 完成: 新增{hits}只 p_oversold 候选", flush=True)
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def fetch_news_count(code):
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"""简化：查 stock_news 表近3日新闻数（部署时可完善）"""
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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 COUNT(*) FROM stock_news WHERE code=? AND date >= datetime('now','-3 days')",
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(code,)).fetchone()
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conn.close()
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return row[0] if row else 0
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except Exception:
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return 0
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if __name__ == "__main__":
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main()
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