fix: refresh_mtf_cache 循环后补 flush_mtf_cache 写回 mtf_cache 表（修复缓存no-op）
This commit is contained in:
xxm
@@ -1,103 +1,113 @@
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#!/usr/bin/env python3
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"""多周期缓存刷新脚本 — 在开盘前预填充K线数据
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为所有持仓+自选股预先拉取日/周/月K线，写入 multi_tf_cache.json，
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这样收盘后全量重评(regenerate_all)运行时K线数据已有缓存，无需逐个拉取。
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运行时间：每天9:00（开盘前），no_agent模式。
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无输出 = 成功（避免每天收到无意义消息）。
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"""
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import sys
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import os
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import json
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from datetime import datetime
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# 确保能找到 web-dashboard 模块
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sys.path.insert(0, "/home/hmo/web-dashboard")
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# 控制台UTC日志
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def log(msg):
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ts = datetime.utcnow().strftime("%H:%M:%S")
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print(f"[{ts}] {msg}", file=sys.stderr)
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def main():
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from mofin_db import get_conn
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# 收集所有股票代码
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codes = []
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seen = set()
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conn = get_conn()
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try:
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rows = conn.execute("SELECT code FROM holdings WHERE is_active=1").fetchall()
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for r in rows:
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code = r["code"]
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if code:
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codes.append(("portfolio", code))
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seen.add(code)
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rows = conn.execute("SELECT code FROM watchlist_stocks WHERE is_active=1").fetchall()
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for r in rows:
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code = r["code"]
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if code and code not in seen:
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codes.append(("watchlist", code))
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seen.add(code)
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finally:
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conn.close()
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# 加入指数代码（用于多周期趋势研判）
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INDEXES = {
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"sh000001": "上证指数", "sz399001": "深证成指",
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"sz399006": "创业板指", "sh000688": "科创50",
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"hkHSI": "恒生指数", "hkHSTECH": "恒生科技",
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}
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for idx_code in INDEXES:
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if idx_code not in seen:
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codes.append(("index", idx_code))
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seen.add(idx_code)
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log(f"Pre-populating multi-timeframe cache for {len(codes)} stocks...")
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# 从 DB 读取现有缓存（替代 multi_tf_cache.json）
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from multi_timeframe import _load_mtf_cache, _save_mtf_cache
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existing = _load_mtf_cache()
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import time
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from multi_timeframe import full_multi_tf_analysis
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cached = 0
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fetched = 0
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errors = 0
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for source, code in codes:
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cached_entry = existing.get(code, {})
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updated_at = cached_entry.get("updated_at", 0)
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now = time.time()
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# 检查缓存是否新鲜：日K 1小时内，周/月K 1天内
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has_daily = bool(cached_entry.get("daily"))
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has_weekly = bool(cached_entry.get("weekly"))
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has_monthly = bool(cached_entry.get("monthly"))
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cache_fresh = (updated_at > 0 and (now - updated_at) < 3600)
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if has_daily and has_weekly and has_monthly and cache_fresh:
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cached += 1
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continue
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try:
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r = full_multi_tf_analysis(code)
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if any(k in r for k in ["daily", "weekly", "monthly"]):
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fetched += 1
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log(f" OK {code} ({source})")
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else:
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errors += 1
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log(f" EMPTY {code} ({source})")
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except Exception as e:
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errors += 1
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log(f" FAIL {code} ({source}): {e}")
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log(f"Done: {cached} cached, {fetched} fetched, {errors} errors")
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if __name__ == "__main__":
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main()
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#!/usr/bin/env python3
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"""多周期缓存刷新脚本 — 在开盘前预填充K线数据
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为所有持仓+自选股预先拉取日/周/月K线，写入 multi_tf_cache.json，
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这样收盘后全量重评(regenerate_all)运行时K线数据已有缓存，无需逐个拉取。
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运行时间：每天9:00（开盘前），no_agent模式。
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无输出 = 成功（避免每天收到无意义消息）。
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"""
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import sys
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import os
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import json
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from datetime import datetime
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# 确保能找到 web-dashboard 模块
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sys.path.insert(0, "/home/hmo/web-dashboard")
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# 控制台UTC日志
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def log(msg):
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ts = datetime.utcnow().strftime("%H:%M:%S")
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print(f"[{ts}] {msg}", file=sys.stderr)
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def main():
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from mofin_db import get_conn
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# 收集所有股票代码
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codes = []
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seen = set()
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conn = get_conn()
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try:
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rows = conn.execute("SELECT code FROM holdings WHERE is_active=1").fetchall()
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for r in rows:
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code = r["code"]
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if code:
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codes.append(("portfolio", code))
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seen.add(code)
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rows = conn.execute("SELECT code FROM watchlist_stocks WHERE is_active=1").fetchall()
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for r in rows:
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code = r["code"]
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if code and code not in seen:
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codes.append(("watchlist", code))
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seen.add(code)
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finally:
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conn.close()
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# 加入指数代码（用于多周期趋势研判）
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INDEXES = {
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"sh000001": "上证指数", "sz399001": "深证成指",
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"sz399006": "创业板指", "sh000688": "科创50",
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"hkHSI": "恒生指数", "hkHSTECH": "恒生科技",
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}
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for idx_code in INDEXES:
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if idx_code not in seen:
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codes.append(("index", idx_code))
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seen.add(idx_code)
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log(f"Pre-populating multi-timeframe cache for {len(codes)} stocks...")
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# 从 DB 读取现有缓存（替代 multi_tf_cache.json）
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from multi_timeframe import _load_mtf_cache, _save_mtf_cache
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existing = _load_mtf_cache()
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import time
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from multi_timeframe import full_multi_tf_analysis
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cached = 0
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fetched = 0
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errors = 0
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for source, code in codes:
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cached_entry = existing.get(code, {})
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updated_at = cached_entry.get("updated_at", 0)
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now = time.time()
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# 检查缓存是否新鲜：日K 1小时内，周/月K 1天内
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has_daily = bool(cached_entry.get("daily"))
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has_weekly = bool(cached_entry.get("weekly"))
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has_monthly = bool(cached_entry.get("monthly"))
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cache_fresh = (updated_at > 0 and (now - updated_at) < 3600)
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if has_daily and has_weekly and has_monthly and cache_fresh:
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cached += 1
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continue
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try:
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r = full_multi_tf_analysis(code)
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if any(k in r for k in ["daily", "weekly", "monthly"]):
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fetched += 1
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log(f" OK {code} ({source})")
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else:
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errors += 1
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log(f" EMPTY {code} ({source})")
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except Exception as e:
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errors += 1
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log(f" FAIL {code} ({source}): {e}")
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# 2026-08-11 修复: 循环结束后把模块级缓存刷回 mtf_cache 表
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# （此前 full_multi_tf_analysis 只写 stocks/stock_daily 等表，
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# mtf_cache 表从未被更新 -> 健康检查报 stale。flush 后写入。）
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try:
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from multi_timeframe import flush_mtf_cache
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flush_mtf_cache()
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log("mtf_cache flushed to DB")
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except Exception as e:
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log(f"flush_mtf_cache failed: {e}")
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log(f"Done: {cached} cached, {fetched} fetched, {errors} errors")
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if __name__ == "__main__":
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main()
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