288 lines
9.9 KiB
Python
288 lines
9.9 KiB
Python
#!/usr/bin/env python3
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"""accumulation_scanner.py — 主力建仓期股票扫描
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逻辑：
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1. 从所有可获取行情的股票中，检测量价行为异常
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2. 核心指标：
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- 价格在20日区间中下段（还没爆涨）
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- 成交量较20日均值放大>50%
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- 价格小涨或平盘（不是拉高出货）
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- 连续N日增量（建仓特征）
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- 基本面安全（PB<2或PE合理）
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3. 输出候选到 candidates 表
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数据源：腾讯批量行情API（日K线+实时价）
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"""
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import sys, json, os
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from pathlib import Path
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from datetime import datetime, timedelta
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from collections import defaultdict
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# ── 消息通道统一路由(broadcast/xmpp by delivery) ──
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try:
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from messenger import install_stdio_hook as _msh
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_msh()
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except Exception:
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pass
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DB_PATH = Path("/home/hmo/MoFin/data/mofin.db")
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UA = "Mozilla/5.0"
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def fetch_qq_batch(symbols):
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"""批量行情：读 DB live_prices + stock_daily（2026-08-26 分层铁律：消费层不直连腾讯API）"""
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if not symbols: return {}
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import sqlite3
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results = {}
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# 提取纯代码（去掉 sh/sz/hk 前缀，港股5位如 00700）
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codes = []
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for s in symbols:
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c = str(s).strip()
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for pfx in ("sh", "sz", "hk"):
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if c.startswith(pfx):
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c = c[len(pfx):]
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break
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if c and c not in codes:
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codes.append(c)
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if not codes:
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return results
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try:
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conn = sqlite3.connect(str(DB_PATH), timeout=5)
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conn.execute("PRAGMA busy_timeout=30000")
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# 实时价：一次查
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ph = ",".join("?" * len(codes))
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price_rows = conn.execute(
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f"SELECT code, price, change_pct FROM live_prices WHERE code IN ({ph})", codes
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).fetchall()
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prices = {r[0]: (r[1], r[2]) for r in price_rows}
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# 名称
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name_rows = conn.execute(
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f"SELECT code, name FROM stocks WHERE code IN ({ph})", codes
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).fetchall()
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names = {r[0]: r[1] or "" for r in name_rows}
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# 最近日K（昨收/高低/量/额）
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sd = {}
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for c in codes:
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row = conn.execute(
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"SELECT close, high, low, volume, amount FROM stock_daily "
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"WHERE code=? ORDER BY date DESC LIMIT 1", (c,)
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).fetchone()
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if row:
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sd[c] = row
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conn.close()
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for code in codes:
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if code not in prices:
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continue
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price = prices[code][0]
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change_pct = prices[code][1] or 0
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row = sd.get(code)
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if not price or price <= 0:
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continue
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if not row or not row[3]: # 无日K或无量 → 跳过（中性）
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continue
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prev_close = row[0] or 0
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high = row[1] or 0
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low = row[2] or 0
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volume = int(row[3]) if row[3] else 0 # 股数
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amount = row[4] or 0
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# DB 无 PE/流通市值 → 中性 0（detect 中 pe/mcap 条件不贡献分）
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results[code] = {
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"code": code, "name": names.get(code, code), "price": price,
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"prev_close": prev_close, "high": high, "low": low,
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"volume": volume, "amount": amount,
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"change_pct": change_pct, "pe": 0, "mcap": 0,
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}
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except Exception as e:
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print(f" 批量查询错误: {e}", file=sys.stderr)
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return results
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def get_stock_pool():
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"""获取待扫描股票池"""
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import sqlite3
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conn = sqlite3.connect(str(DB_PATH))
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conn.execute("PRAGMA busy_timeout=30000") # 2026-08-18 整点撞锁等待
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# 从holding_strategies拿已有策略股
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existing = set()
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for r in conn.execute("SELECT code FROM holding_strategies WHERE status='active'"):
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existing.add(r[0])
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for r in conn.execute("SELECT code FROM holdings WHERE is_active=1"):
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existing.add(r[0])
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# 从stocks表拿所有代码
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all_stocks = [r[0] for r in conn.execute("SELECT code FROM stocks").fetchall()]
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conn.close()
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return all_stocks, existing
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def detect_accumulation(code, info):
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"""检测主力建仓特征
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返回 (score, reasons) 或 None
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"""
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price = info["price"]
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volume = info["volume"]
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amount = info["amount"]
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change = info["change_pct"]
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high = info["high"]
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low = info["low"]
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prev_close = info["prev_close"]
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pe = info["pe"]
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mcap = info["mcap"]
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if price <= 0 or volume <= 0:
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return None
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# 成交量估算（没有历史数据时用流通市值估算正常日成交）
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est_normal_volume = max(volume * 0.3, 100000) # 保守估计
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vol_ratio = volume / est_normal_volume if est_normal_volume > 0 else 1
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score = 0
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reasons = []
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# 1. 价格位置：20日高低点（用当日高低估算）
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day_range = (high - low) / prev_close * 100 if prev_close > 0 else 0
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position_in_day = (price - low) / (high - low) if high > low else 0.5
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# 价格没有爆涨（在日内中下段=还没到顶）
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if position_in_day < 0.7:
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score += 1
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else:
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return None # 已经到日内高位，可能是拉高出货
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# 2. 涨跌幅适中（不是暴跌也不是暴涨出货）
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if -1 <= change <= 4:
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score += 1
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else:
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return None # 跌太多或涨太多
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# 3. 成交量放大（有资金活动）
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if vol_ratio > 1.5:
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score += 1
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reasons.append(f"量增{vol_ratio:.0f}倍")
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else:
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return None # 没量没意义
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# 4. 换手率估算（通过成交额/流通市值）
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if mcap > 0 and amount > 0:
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turnover = amount / (mcap * 1e8) * 100 if mcap < 1e6 else amount / mcap * 100
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if 0.5 <= turnover <= 10:
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score += 1
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elif turnover > 10:
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return None # 换手太高可能是出货
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# 5. PE合理（基本面安全）
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if 0 < pe < 100:
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score += 1
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# 6. 日内振幅合理（不是一字板）
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if 1 <= day_range <= 8:
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score += 1
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# 综合评分（2026-07-24 老爸：入门闸 4→5，减少陪跑噪音灌入 candidates）
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if score >= 5:
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entry_low = round(price * 0.95, 2)
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entry_high = round(price * 1.02, 2)
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stop_loss = round(price * 0.92, 2)
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take_profit = round(price * 1.15, 2)
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# 2026-08-17: max_hold 补定义（此前无持有期）——分温区扫描 trend_down 20d 最优
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return {
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"score": score,
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"reasons": "; ".join(reasons),
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"entry_low": entry_low,
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"entry_high": entry_high,
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"stop_loss": stop_loss,
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"take_profit": take_profit,
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"vol_ratio": vol_ratio,
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}
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return None
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def main():
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import sqlite3
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print(f"[ACCUM] {datetime.now().strftime('%H:%M')} 开始主力建仓扫描", flush=True)
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# 获取股票池
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all_stocks, existing = get_stock_pool()
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print(f" 股票池: {len(all_stocks)}只, 已有策略: {len(existing)}只", flush=True)
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if not all_stocks:
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print(" ⚠️ stocks表为空，需先导入股票列表", flush=True)
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return
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# 分批查行情
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symbols = []
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for code in all_stocks:
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if len(str(code)) == 6:
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if str(code).startswith(("5", "6", "9")):
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symbols.append(f"sh{code}")
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else:
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symbols.append(f"sz{code}")
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else:
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symbols.append(f"hk{code}")
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prices = fetch_qq_batch(symbols)
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print(f" 行情返回: {len(prices)}只", flush=True)
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# 逐只检测
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candidates = []
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for code, info in sorted(prices.items()):
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# 跳过已有策略的
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if code in existing:
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continue
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result = detect_accumulation(code, info)
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if result:
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candidates.append((result["score"], code, info, result))
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# 按评分排序
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candidates.sort(reverse=True)
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print(f" 发现建仓特征: {len(candidates)}只", flush=True)
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# 写入DB
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conn = sqlite3.connect(str(DB_PATH))
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conn.execute("PRAGMA busy_timeout=30000") # 2026-08-18 整点撞锁等待
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inserted = 0
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for score, code, info, detail in candidates[:10]: # 最多10只
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name = info["name"]
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price = info["price"]
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entry_low = detail["entry_low"]
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entry_high = detail["entry_high"]
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sl = detail["stop_loss"]
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tp = detail["take_profit"]
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reasons = detail["reasons"]
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vol_ratio = detail["vol_ratio"]
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# 检查是否已在candidates
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exists = conn.execute(
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"SELECT code FROM candidates WHERE code=? AND (promoted IS NULL OR promoted=0)",
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(code,)
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).fetchone()
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if exists:
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continue
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# UPSERT：只更新扫描器自有列，保留 score_*/pass_*/promoted/log/zhiwei_* 等计算列
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# （原 INSERT OR REPLACE 会把 promoted=1 的行整行替换，计算列全部清零——2026-07-23 审计发现）
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_mid_v = (entry_low + entry_high) / 2
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_rr_v = round((tp - _mid_v) / (_mid_v - sl), 2) if _mid_v > sl > 0 else 0
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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, rr, source_strategy, 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, rr=excluded.rr, source_strategy=excluded.source_strategy",
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(code, name, "accumulation",
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f"主力建仓特征({reasons}) 评分{score}/7",
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f"{entry_low}~{entry_high}", sl, tp, _rr_v, "accumulation")
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)
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inserted += 1
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print(f" 🟢 {code} {name} 价{price} 评分{score}/7 {reasons}", flush=True)
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conn.commit()
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conn.close()
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print(f" ✅ 新增{inserted}只候选", flush=True)
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if __name__ == "__main__":
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main()
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