hmo
0924cf3124
新建 mofin_db.py 共享数据库模块: - get_conn() 统一连接管理 (WAL + Row factory + 外键) - init_all_tables() 幂等建表 (12张表: market/sector/stock/kline/fundamentals/sectors/holdings/strategies/watchlist/candidates/score_history/events/evaluations) - write_market_snapshot() 市场快照双写 - write_klines() K线数据双写 (stocks + daily/weekly/monthly + fundamentals) - write_price_event() 价格事件双写 - migrate_stock_sectors() 一次性迁移 stock_sector_map.json - query_*() 通用查询函数 (sector_trend/top_inflow/consecutive_inflow/market_mood/db_stats) 重构现有脚本: - market_watch.py: 删除内联 DB 代码，改用 mofin_db - multi_timeframe.py: _save_local_history() 加 SQLite 双写 - price_monitor.py: record_event() 加 SQLite 双写 - mofin_query.py: 改用 mofin_db 查询函数 新增: - migrate_sectors.py: 一次性迁移脚本 清理: - get_realtime_prices.py: 死代码 (只读 portfolio.json，不调API)
628 lines
21 KiB
Python
628 lines
21 KiB
Python
#!/usr/bin/env python3
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"""multi_timeframe.py — 多周期技术分析模块
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从腾讯API获取日/周/月K线数据，计算：
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- 多周期支撑压力位（日线/周线/月线）
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- 移动均线（MA5/10/20/60）
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- 趋势方向判断（上升/下降/震荡）
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- 综合策略调整建议
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集成到 strategy_lifecycle.py 中使用。
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"""
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import json
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import os
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import urllib.request
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import urllib.error
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from datetime import datetime, date, timedelta
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from typing import Optional
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DATA_DIR = "/home/hmo/web-dashboard/data"
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HISTORY_PATH = os.path.join(DATA_DIR, "price_history.json")
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MTF_CACHE_PATH = os.path.join(DATA_DIR, "multi_tf_cache.json") # 多周期缓存独立存储
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# 腾讯API K线端点
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KLINE_URL = "http://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={market}{code},{period},,,{count},qfq"
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# 腾讯实时行情端点（用于市场前缀判断）
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QUOTE_URL = "http://qt.gtimg.cn/q={market}{code}"
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def _write_klines_to_db(code: str, daily: list, weekly: list, monthly: list, fundamentals: dict = None):
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"""K线数据双写 SQLite（失败不影响缓存写入）"""
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try:
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from mofin_db import get_conn, init_all_tables, write_klines
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conn = get_conn()
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init_all_tables(conn)
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# 从 stock_profiles.json 获取名称
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name = code
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try:
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import json
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profiles_path = os.path.join(DATA_DIR, "stock_profiles.json")
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if os.path.exists(profiles_path):
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with open(profiles_path, encoding="utf-8") as f:
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profiles = json.load(f)
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for p in profiles.get("profiles", []):
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if p.get("code") == code:
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name = p.get("name", code)
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break
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except Exception:
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pass
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write_klines(conn, code, name, daily, weekly, monthly, fundamentals)
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conn.close()
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except Exception:
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pass # SQLite 写入失败不影响主流程
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def _market_prefix(code: str) -> str:
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"""根据代码确定市场前缀"""
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raw = str(code).split("_")[0]
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if len(raw) == 5 and raw.isdigit():
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return "hk"
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if raw.startswith("6") or raw.startswith("5"):
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return "sh"
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return "sz"
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def _user_agent() -> dict:
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return {
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"User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36"
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}
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# 多周期缓存TTL（秒）：日K线1小时，周/月K线1天
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_KLINE_CACHE_TTL = {"day": 3600, "week": 86400, "month": 86400}
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# 模块级缓存：避免每次fetch_kline都重新读/写大文件
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_MTF_CACHE_DATA = None # {code: {daily:[], weekly:[], monthly:[], updated_at: float, fundamentals:{}}}
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_MTF_CACHE_MTIME = 0 # 文件最后修改时间
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def _load_mtf_cache():
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"""加载多周期缓存（带模块级缓存，避免频繁读盘）"""
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global _MTF_CACHE_DATA, _MTF_CACHE_MTIME
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import time
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try:
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current_mtime = os.path.getmtime(MTF_CACHE_PATH)
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if _MTF_CACHE_DATA is not None and current_mtime == _MTF_CACHE_MTIME:
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return _MTF_CACHE_DATA
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with open(MTF_CACHE_PATH) as f:
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_MTF_CACHE_DATA = json.load(f)
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_MTF_CACHE_MTIME = current_mtime
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return _MTF_CACHE_DATA
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except (FileNotFoundError, json.JSONDecodeError, OSError):
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_MTF_CACHE_DATA = {}
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_MTF_CACHE_MTIME = 0
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return {}
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def _save_mtf_cache():
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"""将模块级缓存写回磁盘"""
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global _MTF_CACHE_DATA, _MTF_CACHE_MTIME
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if _MTF_CACHE_DATA is None:
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return
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try:
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os.makedirs(os.path.dirname(MTF_CACHE_PATH), exist_ok=True)
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with open(MTF_CACHE_PATH, "w") as f:
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json.dump(_MTF_CACHE_DATA, f, ensure_ascii=False, indent=2)
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import time
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_MTF_CACHE_MTIME = os.path.getmtime(MTF_CACHE_PATH) if os.path.exists(MTF_CACHE_PATH) else time.time()
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except Exception:
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pass
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def fetch_kline(code: str, period: str = "day", count: int = 120) -> list:
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"""从腾讯API获取K线数据，优先使用本地缓存
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Args:
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code: 股票代码 (如 "300548")
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period: "day" / "week" / "month"
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count: 需要多少条
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Returns:
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list of dict: [{"date":str, "open":float, "close":float,
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"high":float, "low":float, "volume":float}, ...]
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"""
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import time
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now = time.time()
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# 优先检查本地缓存（模块级，避免重复读盘）
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# 注意：缓存中存储的key是'daily'/'weekly'/'monthly'，参数period是'day'/'week'/'month'
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_PERIOD_MAP = {"day": "daily", "week": "weekly", "month": "monthly"}
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cache_data = _load_mtf_cache()
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cached = cache_data.get(code, {})
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cache_key = _PERIOD_MAP.get(period, period)
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cached_klines = cached.get(cache_key, cached.get(period, []))
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updated_at = cached.get("updated_at", 0)
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if cached_klines and updated_at and (now - updated_at) < _KLINE_CACHE_TTL.get(period, 3600):
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return cached_klines
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market = _market_prefix(code)
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url = KLINE_URL.format(market=market, code=code,
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period=period, count=count)
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try:
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req = urllib.request.Request(url, headers=_user_agent())
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with urllib.request.urlopen(req, timeout=10) as resp:
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raw = json.loads(resp.read().decode("utf-8"))
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except Exception as e:
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return {"error": str(e), "code": code, "period": period}
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if not isinstance(raw, dict):
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return {"error": f"API returned {type(raw).__name__}", "raw": str(raw)[:200]}
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api_data = raw.get("data", {})
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if not isinstance(api_data, dict):
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return {"error": f"data field is {type(api_data).__name__}", "raw": str(api_data)[:200]}
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stock_key = f"{market}{code}"
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stock_data = api_data.get(stock_key, {})
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# 腾讯API的K线字段名: qfqday, qfqweek, qfqmonth
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period_key = f"qfq{period}"
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klines = stock_data.get(period_key, [])
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if not klines:
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# 尝试其他字段名
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for k in stock_data:
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if isinstance(stock_data[k], list) and len(stock_data[k]) > 0:
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if isinstance(stock_data[k][0], list) and len(stock_data[k][0]) >= 6:
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klines = stock_data[k]
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break
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result = []
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for k in klines:
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if len(k) >= 6:
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try:
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result.append({
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"date": str(k[0]),
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"open": float(k[1]),
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"close": float(k[2]),
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"high": float(k[3]),
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"low": float(k[4]),
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"volume": float(k[5]),
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})
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except (ValueError, IndexError):
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continue
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return result
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def calc_moving_averages(klines: list, windows: list = [5, 10, 20, 60]) -> dict:
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"""计算移动均线
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Args:
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klines: K线数据（按时间正序或倒序均可，自动处理）
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windows: 均线周期列表
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Returns:
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dict: {ma5: float|None, ma10: float|None, ...}
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"""
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if not klines:
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return {f"ma{w}": None for w in windows}
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# 确保按时间正序（旧的在前）
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closes = [k["close"] for k in klines]
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# 检查是否倒序（最新的在前）
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if len(closes) >= 2 and closes[0] > closes[-1]:
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closes = list(reversed(closes))
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result = {}
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for w in windows:
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if len(closes) >= w:
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result[f"ma{w}"] = round(sum(closes[-w:]) / w, 2)
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else:
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result[f"ma{w}"] = None
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return result
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def calc_multi_tf_support_resistance(klines: list, lookback: int = 0) -> dict:
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"""基于K线数据计算多周期支撑压力位
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使用近期高点和低点作为关键位：
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- 强阻力 = 近期最高（或倒数第二高）
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- 弱阻力 = 近期中枢上沿
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- 弱支撑 = 近期中枢下沿
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- 强支撑 = 近期最低（或倒数第二低）
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Args:
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klines: K线数据
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lookback: 取最近多少条（0=全部）
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Returns:
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dict: {strong_resist, weak_resist, weak_support, strong_support,
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high_52w, low_52w, range_pct}
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"""
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if not klines or len(klines) < 3:
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return {}
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# 取最近N条（日线看近期，周线/月线看全部）
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if lookback <= 0:
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lookback = min(len(klines), 20) # 日线默认20天
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n = min(len(klines), lookback)
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recent = klines[-n:]
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# 全量数据（用于52周高低）
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all_highs = [k["high"] for k in klines]
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all_lows = [k["low"] for k in klines]
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highs = [k["high"] for k in recent]
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lows = [k["low"] for k in recent]
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max_h = max(highs)
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min_l = min(lows)
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mid = (max_h + min_l) / 2
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# 找第二高和第二低作为更稳健的边界
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sorted_h = sorted(set(highs), reverse=True)
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sorted_l = sorted(set(lows))
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strong_resist = sorted_h[0] if sorted_h else max_h
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strong_support = sorted_l[0] if sorted_l else min_l
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weak_resist = sorted_h[1] if len(sorted_h) > 1 else (max_h + mid) / 2
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weak_support = sorted_l[1] if len(sorted_l) > 1 else (min_l + mid) / 2
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# 最近20日的振幅比例（判断波动率）
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if len(closes := [k["close"] for k in recent]) >= 2:
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recent_range = (max_h - min_l) / min_l * 100 if min_l > 0 else 0
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else:
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recent_range = 0
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return {
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"strong_resist": round(strong_resist, 2),
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"weak_resist": round(weak_resist, 2),
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"weak_support": round(weak_support, 2),
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"strong_support": round(strong_support, 2),
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"high_52w": round(max(all_highs), 2),
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"low_52w": round(min(all_lows), 2),
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"range_pct": round(recent_range, 1),
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}
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def assess_trend(klines: list) -> dict:
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"""判断趋势方向
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Args:
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klines: K线数据
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Returns:
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dict: {trend (up/down/sideways), strength (0~1),
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description, ma_trend}
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"""
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if not klines or len(klines) < 10:
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return {"trend": "unknown", "strength": 0, "description": "数据不足"}
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closes = [k["close"] for k in klines]
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# 确保正序
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if len(closes) >= 2 and closes[0] > closes[-1]:
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closes = list(reversed(closes))
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n = len(closes)
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ma20 = sum(closes[-20:]) / 20 if n >= 20 else sum(closes) / n
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ma60 = sum(closes[-60:]) / 60 if n >= 60 else None
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current = closes[-1]
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# 均线多头/空头排列判断
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ma5 = sum(closes[-5:]) / 5 if n >= 5 else None
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ma10 = sum(closes[-10:]) / 10 if n >= 10 else None
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# 趋势判断
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up_count = sum(1 for i in range(1, len(closes)) if closes[i] > closes[i-1])
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up_ratio = up_count / (len(closes) - 1)
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# 价格相对均线位置
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above_ma20 = current > ma20 if ma20 else True
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if up_ratio > 0.6 and above_ma20:
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if ma60 and current > ma60 * 1.2:
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trend = "strong_up"
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strength = min(1.0, up_ratio + 0.2)
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desc = "强势上升"
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else:
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trend = "up"
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strength = up_ratio
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desc = "震荡上升"
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elif up_ratio < 0.4 and not above_ma20:
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if ma60 and current < ma60 * 0.8:
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trend = "strong_down"
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strength = min(1.0, (1 - up_ratio) + 0.2)
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desc = "强势下跌"
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else:
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trend = "down"
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strength = 1 - up_ratio
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desc = "震荡下跌"
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else:
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trend = "sideways"
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strength = 0.3
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desc = "横盘震荡"
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# 均线排列
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ma_trend = "unknown"
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if ma5 and ma10 and ma20:
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if ma5 > ma10 > ma20:
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ma_trend = "多头排列"
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elif ma5 < ma10 < ma20:
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ma_trend = "空头排列"
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else:
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ma_trend = "粘合/交叉"
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return {
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"trend": trend,
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"strength": round(strength, 2),
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"description": desc,
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"ma_trend": ma_trend,
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"ma5": round(ma5, 2) if ma5 else None,
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"ma10": round(ma10, 2) if ma10 else None,
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"ma20": round(ma20, 2),
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"ma60": round(ma60, 2) if ma60 else None,
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"current_above_ma20": current > ma20 if ma20 else None,
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}
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def full_multi_tf_analysis(code: str) -> dict:
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"""完整多周期分析入口
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同时获取日/周/月K线，计算：
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- 各周期支撑压力位
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- 均线系统
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- 趋势方向
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- 综合策略建议
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Args:
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code: 股票代码 (如 "300548")
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Returns:
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dict: 完整分析结果
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"""
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# 获取三个周期的数据
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daily = fetch_kline(code, "day", 120)
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weekly = fetch_kline(code, "week", 24)
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monthly = fetch_kline(code, "month", 12)
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# 如果API失败，检查是否有本地缓存
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if isinstance(daily, dict) and "error" in daily:
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daily = _load_local_history(code, "daily")
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if isinstance(weekly, dict) and "error" in weekly:
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weekly = _load_local_history(code, "weekly")
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if isinstance(monthly, dict) and "error" in monthly:
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monthly = _load_local_history(code, "monthly")
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result = {
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"code": code,
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"analyzed_at": datetime.now().strftime("%Y-%m-%d %H:%M"),
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}
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# 日线分析
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if daily and not (isinstance(daily, dict) and "error" in daily):
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result["daily"] = {
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"count": len(daily),
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"latest": daily[-1] if daily else None,
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"support_resistance": calc_multi_tf_support_resistance(daily, lookback=20),
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"mas": calc_moving_averages(daily, [5, 10, 20, 60]),
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"trend": assess_trend(daily),
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}
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# 周线分析
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if weekly and not (isinstance(weekly, dict) and "error" in weekly):
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result["weekly"] = {
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"count": len(weekly),
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"latest": weekly[-1] if weekly else None,
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"support_resistance": calc_multi_tf_support_resistance(weekly, lookback=12),
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"mas": calc_moving_averages(weekly, [5, 10]),
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"trend": assess_trend(weekly),
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}
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# 月线分析
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if monthly and not (isinstance(monthly, dict) and "error" in monthly):
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result["monthly"] = {
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"count": len(monthly),
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"latest": monthly[-1] if monthly else None,
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"support_resistance": calc_multi_tf_support_resistance(monthly, lookback=6),
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"mas": calc_moving_averages(monthly, [5]),
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"trend": assess_trend(monthly),
|
||
}
|
||
|
||
# 综合策略建议
|
||
result["strategy_adjustment"] = _generate_strategy_adjustment(result)
|
||
|
||
# 写入本地缓存（供离线使用）
|
||
_save_local_history(code, daily, weekly, monthly)
|
||
|
||
return result
|
||
|
||
|
||
def flush_mtf_cache():
|
||
"""将模块级缓存显式刷回磁盘（供批量处理后调用）"""
|
||
_save_mtf_cache()
|
||
|
||
|
||
def _generate_strategy_adjustment(analysis: dict) -> dict:
|
||
"""基于多周期分析生成策略调整建议"""
|
||
adj = {
|
||
"stop_loss_reference": None,
|
||
"take_profit_reference": None,
|
||
"trend_alignment": "unknown",
|
||
"multi_tf_summary": {},
|
||
"cautions": [],
|
||
}
|
||
|
||
daily_trend = analysis.get("daily", {}).get("trend", {})
|
||
weekly_trend = analysis.get("weekly", {}).get("trend", {})
|
||
monthly_trend = analysis.get("monthly", {}).get("trend", {})
|
||
|
||
# 均线数据
|
||
daily_mas = analysis.get("daily", {}).get("mas", {})
|
||
daily_sr = analysis.get("daily", {}).get("support_resistance", {})
|
||
weekly_sr = analysis.get("weekly", {}).get("support_resistance", {})
|
||
monthly_sr = analysis.get("monthly", {}).get("support_resistance", {})
|
||
|
||
current = analysis.get("daily", {}).get("latest", {}).get("close", 0)
|
||
|
||
# 多周期趋势一致性
|
||
up_tfs, down_tfs = 0, 0
|
||
tf_details = []
|
||
for tf_name, tf_data in [("daily", daily_trend), ("weekly", weekly_trend),
|
||
("monthly", monthly_trend)]:
|
||
t = tf_data.get("trend", "unknown")
|
||
desc = tf_data.get("description", "")
|
||
ma_t = tf_data.get("ma_trend", "")
|
||
tf_details.append(f"{tf_name}:{desc}({ma_t})")
|
||
if "up" in t or "strong_up" in t:
|
||
up_tfs += 1
|
||
elif "down" in t or "strong_down" in t:
|
||
down_tfs += 1
|
||
|
||
adj["multi_tf_summary"] = {
|
||
"daily_trend": daily_trend.get("description", "未知"),
|
||
"weekly_trend": weekly_trend.get("description", "未知"),
|
||
"monthly_trend": monthly_trend.get("description", "未知"),
|
||
"daily_ma_trend": daily_trend.get("ma_trend", "未知"),
|
||
}
|
||
|
||
if up_tfs >= 2:
|
||
adj["trend_alignment"] = "多周期看多"
|
||
elif down_tfs >= 2:
|
||
adj["trend_alignment"] = "多周期看空"
|
||
elif up_tfs >= 1 and down_tfs >= 1:
|
||
adj["trend_alignment"] = "多周期分化"
|
||
else:
|
||
adj["trend_alignment"] = "震荡/无明显方向"
|
||
|
||
if not current:
|
||
return adj
|
||
|
||
# ===== 参考止损位（三级递进）=====
|
||
# 第一级：MA20（短线交易的生命线）
|
||
ma20 = daily_mas.get("ma20")
|
||
# 第二级：日线弱支撑（近20天次低点）
|
||
daily_ws = daily_sr.get("weak_support")
|
||
# 第三级：日线强支撑 / MA60
|
||
ma60 = daily_mas.get("ma60")
|
||
daily_ss = daily_sr.get("strong_support")
|
||
|
||
stop_candidates = []
|
||
if ma20:
|
||
stop_candidates.append(("MA20", ma20, abs(current - ma20) / current * 100))
|
||
if daily_ws:
|
||
stop_candidates.append(("日弱支撑", daily_ws, abs(current - daily_ws) / current * 100))
|
||
if ma60:
|
||
stop_candidates.append(("MA60", ma60, abs(current - ma60) / current * 100))
|
||
if daily_ss:
|
||
stop_candidates.append(("日强支撑", daily_ss, abs(current - daily_ss) / current * 100))
|
||
|
||
if stop_candidates:
|
||
# 选一个合理的止损参考：MA20优先（如果距现价不太近），否则选日弱支撑
|
||
best_stop = None
|
||
for name, level, dist in stop_candidates:
|
||
if level < current: # 止损必须在现价下方
|
||
if 2 <= dist <= 15: # 距现价2~15%之间比较合理
|
||
best_stop = {"source": name, "level": level,
|
||
"distance_pct": round(dist, 2)}
|
||
break
|
||
if not best_stop:
|
||
# 没有2~15%内的，选最近的一个
|
||
below = [(n, l, d) for n, l, d in stop_candidates if l < current]
|
||
if below:
|
||
nearest = min(below, key=lambda x: x[2])
|
||
best_stop = {"source": nearest[0], "level": nearest[1],
|
||
"distance_pct": round(nearest[2], 2)}
|
||
if best_stop:
|
||
adj["stop_loss_reference"] = best_stop
|
||
|
||
# ===== 参考止盈位 =====
|
||
take_candidates = []
|
||
# 日线阻力
|
||
for name, level in [("日弱阻", daily_sr.get("weak_resist")),
|
||
("日强阻", daily_sr.get("strong_resist")),
|
||
("周强阻", weekly_sr.get("strong_resist")),
|
||
("月强阻", monthly_sr.get("strong_resist"))]:
|
||
if level and level > current:
|
||
dist = (level - current) / current * 100
|
||
take_candidates.append((name, level, dist))
|
||
|
||
if take_candidates:
|
||
# 选距现价5~30%内的最高阻力位
|
||
best_take = None
|
||
for name, level, dist in sorted(take_candidates, key=lambda x: x[1], reverse=True):
|
||
if 3 <= dist <= 40:
|
||
best_take = {"source": name, "level": level,
|
||
"distance_pct": round(dist, 2)}
|
||
break
|
||
if not best_take:
|
||
farthest = max(take_candidates, key=lambda x: x[2])
|
||
best_take = {"source": farthest[0], "level": farthest[1],
|
||
"distance_pct": round(farthest[2], 2)}
|
||
if best_take:
|
||
adj["take_profit_reference"] = best_take
|
||
|
||
# ===== 风险提示 =====
|
||
if ma20 and current < ma20:
|
||
adj["cautions"].append(f"价格{current}<MA20{ma20}，短线转弱")
|
||
if ma60 and current < ma60 * 1.05:
|
||
if current < ma60:
|
||
adj["cautions"].append(f"价格{current}<MA60{ma60}，中期趋势转弱")
|
||
else:
|
||
adj["cautions"].append(f"价格接近MA60{ma60}，中期支撑面临考验")
|
||
|
||
return adj
|
||
|
||
|
||
def _load_local_history(code: str, period: str) -> list:
|
||
"""从本地多周期缓存读取历史数据，不修改 price_history.json"""
|
||
try:
|
||
with open(MTF_CACHE_PATH) as f:
|
||
data = json.load(f)
|
||
except (FileNotFoundError, json.JSONDecodeError):
|
||
return []
|
||
|
||
stock = data.get(code, {})
|
||
return stock.get(period, [])
|
||
|
||
|
||
def _save_local_history(code: str, daily: list, weekly: list, monthly: list):
|
||
"""将多周期数据写入模块级缓存（含时间戳），不直接写磁盘"""
|
||
import time
|
||
global _MTF_CACHE_DATA
|
||
cache_data = _load_mtf_cache()
|
||
stock = cache_data.get(code, {})
|
||
|
||
if daily and not (isinstance(daily, dict) and "error" in daily):
|
||
stock["daily"] = daily
|
||
if weekly and not (isinstance(weekly, dict) and "error" in weekly):
|
||
stock["weekly"] = weekly
|
||
if monthly and not (isinstance(monthly, dict) and "error" in monthly):
|
||
stock["monthly"] = monthly
|
||
stock["updated_at"] = time.time() # 缓存时间戳
|
||
|
||
cache_data[code] = stock
|
||
_MTF_CACHE_DATA = cache_data # 更新模块级缓存
|
||
|
||
# ── SQLite 双写 ──
|
||
_write_klines_to_db(code, daily, weekly, monthly, stock.get("fundamentals"))
|
||
|
||
|
||
def batch_update_all(codes: list):
|
||
"""批量更新多只股票的多周期数据"""
|
||
results = {}
|
||
for code in codes:
|
||
try:
|
||
r = full_multi_tf_analysis(code)
|
||
results[code] = {
|
||
"status": "ok",
|
||
"periods": [k for k in ["daily", "weekly", "monthly"]
|
||
if k in r]
|
||
}
|
||
except Exception as e:
|
||
results[code] = {"status": "error", "error": str(e)}
|
||
return results
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import sys
|
||
codes = sys.argv[1:] or ["300548", "600110"]
|
||
for code in codes:
|
||
r = full_multi_tf_analysis(code)
|
||
print(json.dumps(r, ensure_ascii=False, indent=2))
|
||
print("-" * 60)
|