feat: add multi-market analysis and sqlite-backed reporting
This commit is contained in:
root
@@ -15,12 +15,37 @@ import sys
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import json
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import argparse
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import time
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import hashlib
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import urllib.request
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import urllib.error
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from datetime import datetime, timedelta
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from pathlib import Path
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try:
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from db import (
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ANALYSIS_CACHE_TTL_SECONDS,
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clear_analysis_cache,
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get_cached_analysis,
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get_kline_df,
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get_latest_kline_date,
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init_db,
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set_cached_analysis,
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upsert_kline_df,
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upsert_watchlist_item,
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)
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except ImportError:
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sys.path.insert(0, str(Path(__file__).resolve().parent))
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from db import (
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ANALYSIS_CACHE_TTL_SECONDS,
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clear_analysis_cache,
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get_cached_analysis,
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get_kline_df,
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get_latest_kline_date,
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init_db,
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set_cached_analysis,
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upsert_kline_df,
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upsert_watchlist_item,
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)
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try:
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import numpy as np
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except ImportError:
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@@ -38,90 +63,98 @@ except ImportError:
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# 缓存与重试机制
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# ─────────────────────────────────────────────
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DATA_DIR = Path.home() / ".stockbuddy"
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CACHE_DIR = DATA_DIR / "cache"
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CACHE_TTL_SECONDS = 600 # 缓存有效期 10 分钟
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LEGACY_CACHE_DIR = Path.home() / ".stock_buddy_cache"
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MAX_RETRIES = 3
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RETRY_BASE_DELAY = 2
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def _cache_key(code: str, period: str) -> str:
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"""生成缓存文件名"""
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key = f"{code}_{period}"
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return hashlib.md5(key.encode()).hexdigest() + ".json"
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def _read_cache(code: str, period: str) -> dict | None:
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"""读取缓存"""
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cache_file = CACHE_DIR / _cache_key(code, period)
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if not cache_file.exists():
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legacy_cache_file = LEGACY_CACHE_DIR / _cache_key(code, period)
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if legacy_cache_file.exists():
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try:
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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CACHE_DIR.mkdir(parents=True, exist_ok=True)
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cache_file.write_text(
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legacy_cache_file.read_text(encoding="utf-8"),
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encoding="utf-8",
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)
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except OSError:
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cache_file = legacy_cache_file
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if not cache_file.exists():
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return None
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try:
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with open(cache_file, "r", encoding="utf-8") as f:
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cached = json.load(f)
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cached_time = datetime.fromisoformat(cached.get("analysis_time", ""))
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if (datetime.now() - cached_time).total_seconds() < CACHE_TTL_SECONDS:
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cached["_from_cache"] = True
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return cached
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except (json.JSONDecodeError, ValueError, KeyError):
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pass
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return None
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def _write_cache(code: str, period: str, data: dict):
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"""写入缓存"""
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DATA_DIR.mkdir(parents=True, exist_ok=True)
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CACHE_DIR.mkdir(parents=True, exist_ok=True)
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cache_file = CACHE_DIR / _cache_key(code, period)
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try:
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with open(cache_file, "w", encoding="utf-8") as f:
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json.dump(data, f, ensure_ascii=False, indent=2, default=str)
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except OSError:
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pass
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ANALYSIS_CACHE_TTL = ANALYSIS_CACHE_TTL_SECONDS
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# ─────────────────────────────────────────────
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# 腾讯财经数据获取
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# ─────────────────────────────────────────────
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def normalize_hk_code(code: str) -> tuple[str, str]:
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"""标准化港股代码，返回 (原始数字代码, 带.HK后缀代码)"""
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code = code.strip().upper().replace(".HK", "")
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digits = code.lstrip("0")
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if digits.isdigit():
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numeric_code = code.zfill(4)
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return numeric_code, numeric_code + ".HK"
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return code, code + ".HK"
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def normalize_stock_code(code: str) -> dict:
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"""标准化股票代码，支持港股/A股/美股。"""
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raw = code.strip().upper()
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if raw.endswith('.HK'):
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digits = raw[:-3].lstrip('0') or '0'
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return {
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'market': 'HK',
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'code': digits.zfill(4) + '.HK',
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'tencent_symbol': 'hk' + digits.zfill(5),
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'exchange': 'HKEX',
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}
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if raw.startswith(('SH', 'SZ')) and len(raw) == 8 and raw[2:].isdigit():
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market = raw[:2]
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return {
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'market': market,
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'code': raw,
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'tencent_symbol': raw.lower(),
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'exchange': 'SSE' if market == 'SH' else 'SZSE',
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}
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if raw.endswith('.US'):
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symbol = raw[:-3]
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return {
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'market': 'US',
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'code': symbol,
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'tencent_symbol': 'us' + symbol,
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'exchange': 'US',
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}
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if raw.startswith('US.'):
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symbol = raw[3:]
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return {
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'market': 'US',
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'code': symbol,
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'tencent_symbol': 'us' + symbol,
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'exchange': 'US',
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}
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if raw.isdigit():
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if len(raw) <= 5:
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digits = raw.lstrip('0') or '0'
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return {
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'market': 'HK',
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'code': digits.zfill(4) + '.HK',
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'tencent_symbol': 'hk' + digits.zfill(5),
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'exchange': 'HKEX',
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}
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if len(raw) == 6:
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market = 'SH' if raw.startswith(('5', '6', '9')) else 'SZ'
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return {
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'market': market,
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'code': market + raw,
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'tencent_symbol': (market + raw).lower(),
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'exchange': 'SSE' if market == 'SH' else 'SZSE',
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}
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symbol = raw.replace('.', '').replace('-', '')
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return {
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'market': 'US',
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'code': symbol,
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'tencent_symbol': 'us' + symbol,
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'exchange': 'US',
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}
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def fetch_tencent_quote(code: str) -> dict:
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"""获取腾讯财经实时行情"""
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numeric_code, full_code = normalize_hk_code(code)
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url = f"http://qt.gtimg.cn/q=hk{numeric_code}"
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stock = normalize_stock_code(code)
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symbol = stock['tencent_symbol']
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url = f"http://qt.gtimg.cn/q={symbol}"
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for attempt in range(MAX_RETRIES):
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try:
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req = urllib.request.Request(url, headers={
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
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'Referer': 'https://gu.qq.com/',
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})
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with urllib.request.urlopen(req, timeout=10) as response:
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data = response.read().decode("gb2312", errors="ignore")
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return _parse_tencent_quote(data, numeric_code)
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data = response.read().decode('gb2312', errors='ignore')
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return _parse_tencent_quote(data, symbol, stock)
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except urllib.error.URLError as e:
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if attempt < MAX_RETRIES - 1:
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time.sleep(RETRY_BASE_DELAY * (attempt + 1))
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@@ -130,9 +163,9 @@ def fetch_tencent_quote(code: str) -> dict:
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return {}
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def _parse_tencent_quote(data: str, code: str) -> dict:
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def _parse_tencent_quote(data: str, symbol: str, stock: dict) -> dict:
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"""解析腾讯财经实时行情响应"""
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var_name = f"v_hk{code}"
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var_name = f"v_{symbol}"
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for line in data.strip().split(";"):
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line = line.strip()
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if not line or var_name not in line:
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@@ -158,40 +191,53 @@ def _parse_tencent_quote(data: str, code: str) -> dict:
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# 0:市场 1:名称 2:代码 3:现价 4:昨收 5:今开 6:成交量
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# 30:时间戳 31:涨跌额 32:涨跌幅 33:最高 34:最低
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# 39:市盈率 47:市净率 37:总市值 48:52周高 49:52周低
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market = stock['market']
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currency = 'HKD' if market == 'HK' else ('CNY' if market in ('SH', 'SZ') else safe_str(35, 'USD') or 'USD')
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pb_idx = 47 if market in ('HK', 'US') else 46
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market_cap_idx = 37 if market == 'HK' else (57 if market in ('SH', 'SZ') else 44)
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high_52_idx = 48 if market in ('HK', 'US') else 41
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low_52_idx = 49 if market in ('HK', 'US') else 42
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return {
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"name": values[1],
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"code": values[2],
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"price": safe_float(3),
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"prev_close": safe_float(4),
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"open": safe_float(5),
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"volume": safe_float(6),
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"high": safe_float(33),
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"low": safe_float(34),
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"change_amount": safe_float(31),
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"change_pct": safe_float(32),
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"timestamp": safe_str(30),
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"pe": safe_float(39) if len(values) > 39 else None,
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"pb": safe_float(47) if len(values) > 47 else None,
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"market_cap": safe_str(37),
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"52w_high": safe_float(48) if len(values) > 48 else None,
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"52w_low": safe_float(49) if len(values) > 49 else None,
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'name': values[1],
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'code': stock['code'],
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'market': market,
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'exchange': stock.get('exchange'),
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'tencent_symbol': symbol,
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'price': safe_float(3),
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'prev_close': safe_float(4),
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'open': safe_float(5),
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'volume': safe_float(6),
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'high': safe_float(33),
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'low': safe_float(34),
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'change_amount': safe_float(31),
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'change_pct': safe_float(32),
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'timestamp': safe_str(30),
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'currency': currency,
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'pe': safe_float(39) if len(values) > 39 else None,
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'pb': safe_float(pb_idx) if len(values) > pb_idx else None,
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'market_cap': safe_str(market_cap_idx),
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'52w_high': safe_float(high_52_idx) if len(values) > high_52_idx else None,
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'52w_low': safe_float(low_52_idx) if len(values) > low_52_idx else None,
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'raw_code': safe_str(2),
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}
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return {}
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def fetch_tencent_kline(code: str, days: int = 120) -> pd.DataFrame:
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"""获取腾讯财经K线数据"""
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numeric_code, full_code = normalize_hk_code(code)
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url = f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param=hk{numeric_code},day,,,{days},qfq"
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stock = normalize_stock_code(code)
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symbol = stock['tencent_symbol']
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url = f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={symbol},day,,,{days},qfq"
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for attempt in range(MAX_RETRIES):
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try:
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req = urllib.request.Request(url, headers={
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
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'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36',
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'Referer': 'https://gu.qq.com/',
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})
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with urllib.request.urlopen(req, timeout=15) as response:
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data = json.loads(response.read().decode("utf-8"))
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return _parse_tencent_kline(data, numeric_code)
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data = json.loads(response.read().decode('utf-8'))
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return _parse_tencent_kline(data, symbol)
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except (urllib.error.URLError, json.JSONDecodeError) as e:
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if attempt < MAX_RETRIES - 1:
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time.sleep(RETRY_BASE_DELAY * (attempt + 1))
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@@ -200,33 +246,78 @@ def fetch_tencent_kline(code: str, days: int = 120) -> pd.DataFrame:
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return pd.DataFrame()
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def _parse_tencent_kline(data: dict, code: str) -> pd.DataFrame:
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def _parse_tencent_kline(data: dict, symbol: str) -> pd.DataFrame:
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"""解析腾讯财经K线数据"""
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key = f"hk{code}"
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if data.get("code") != 0 or not data.get("data") or key not in data["data"]:
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if data.get('code') != 0 or not data.get('data') or symbol not in data['data']:
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return pd.DataFrame()
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day_data = data["data"][key].get("day", [])
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symbol_data = data['data'][symbol]
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day_data = symbol_data.get('day') or symbol_data.get('qfqday') or symbol_data.get('hfqday') or []
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if not day_data:
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return pd.DataFrame()
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# 格式: [日期, 开盘价, 收盘价, 最低价, 最高价, 成交量]
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records = []
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for item in day_data:
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if len(item) >= 6:
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records.append({
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"Date": item[0],
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"Open": float(item[1]),
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"Close": float(item[2]),
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"Low": float(item[3]),
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"High": float(item[4]),
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"Volume": float(item[5]),
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'Date': item[0],
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'Open': float(item[1]),
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'Close': float(item[2]),
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'Low': float(item[3]),
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'High': float(item[4]),
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'Volume': float(item[5]),
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})
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df = pd.DataFrame(records)
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if not df.empty:
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df["Date"] = pd.to_datetime(df["Date"])
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df.set_index("Date", inplace=True)
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df['Date'] = pd.to_datetime(df['Date'])
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df.set_index('Date', inplace=True)
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return df
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def fetch_us_kline_yahoo(symbol: str, period: str = '6mo') -> pd.DataFrame:
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range_map = {
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'1mo': '1mo',
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'3mo': '3mo',
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'6mo': '6mo',
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'1y': '1y',
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'2y': '2y',
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'5y': '5y',
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}
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url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?range={range_map.get(period, '6mo')}&interval=1d&includePrePost=false"
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req = urllib.request.Request(url, headers={'User-Agent': 'Mozilla/5.0'})
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with urllib.request.urlopen(req, timeout=20) as response:
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data = json.loads(response.read().decode('utf-8'))
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result = data.get('chart', {}).get('result', [])
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if not result:
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return pd.DataFrame()
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result = result[0]
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timestamps = result.get('timestamp') or []
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quote = (result.get('indicators', {}).get('quote') or [{}])[0]
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opens = quote.get('open') or []
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highs = quote.get('high') or []
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lows = quote.get('low') or []
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closes = quote.get('close') or []
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volumes = quote.get('volume') or []
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records = []
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for i, ts in enumerate(timestamps):
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if i >= len(opens) or opens[i] is None or closes[i] is None or highs[i] is None or lows[i] is None:
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continue
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records.append({
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'Date': datetime.fromtimestamp(ts).strftime('%Y-%m-%d'),
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'Open': float(opens[i]),
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'Close': float(closes[i]),
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'Low': float(lows[i]),
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'High': float(highs[i]),
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'Volume': float(volumes[i] or 0),
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})
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df = pd.DataFrame(records)
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if not df.empty:
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df['Date'] = pd.to_datetime(df['Date'])
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df.set_index('Date', inplace=True)
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return df
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@@ -243,6 +334,42 @@ def period_to_days(period: str) -> int:
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return mapping.get(period, 180)
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def min_kline_points(required_days: int) -> int:
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return 20 if required_days <= 30 else 30
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def refresh_kline_cache(code: str, required_days: int, period: str = '6mo') -> pd.DataFrame:
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"""使用 SQLite 保存日线数据，并按需增量刷新。"""
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stock = normalize_stock_code(code)
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buffer_days = 30
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latest_date = get_latest_kline_date(code)
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fetch_days = max(required_days + buffer_days, 60)
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if latest_date:
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latest_dt = datetime.strptime(latest_date, "%Y-%m-%d")
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missing_days = max((datetime.now() - latest_dt).days, 0)
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if missing_days <= 2:
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fetch_days = min(fetch_days, 60)
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else:
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fetch_days = max(missing_days + buffer_days, 60)
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fetched = fetch_tencent_kline(code, fetch_days)
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if stock['market'] == 'US' and len(fetched) <= 2:
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fetched = fetch_us_kline_yahoo(stock['code'], period)
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if not fetched.empty:
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upsert_kline_df(code, fetched, source='yahoo' if stock['market'] == 'US' and len(fetched) > 2 else 'tencent')
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hist = get_kline_df(code, required_days + buffer_days)
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if len(hist) < min_kline_points(required_days):
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fallback = fetch_tencent_kline(code, required_days + buffer_days)
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||||
if stock['market'] == 'US' and len(fallback) <= 2:
|
||||
fallback = fetch_us_kline_yahoo(stock['code'], period)
|
||||
if not fallback.empty:
|
||||
upsert_kline_df(code, fallback, source='yahoo' if stock['market'] == 'US' and len(fallback) > 2 else 'tencent')
|
||||
hist = get_kline_df(code, required_days + buffer_days)
|
||||
return hist
|
||||
|
||||
|
||||
# ─────────────────────────────────────────────
|
||||
# 技术指标计算 (保持不变)
|
||||
# ─────────────────────────────────────────────
|
||||
@@ -454,30 +581,21 @@ def calc_ma_trend(close: pd.Series) -> dict:
|
||||
def get_fundamentals(quote: dict) -> dict:
|
||||
"""基于实时行情数据的基本面分析"""
|
||||
result = {}
|
||||
|
||||
# 估值指标 (腾讯提供的)
|
||||
pe = quote.get("pe")
|
||||
pb = quote.get("pb")
|
||||
result["PE"] = round(pe, 2) if pe else None
|
||||
result["PB"] = round(pb, 2) if pb else None
|
||||
result["PS"] = None # 腾讯不提供
|
||||
|
||||
# 市值
|
||||
result["market_cap"] = quote.get("market_cap", "")
|
||||
|
||||
# 52周价格区间
|
||||
result["52w_high"] = quote.get("52w_high")
|
||||
result["52w_low"] = quote.get("52w_low")
|
||||
|
||||
# 公司信息
|
||||
result["company_name"] = quote.get("name", "未知")
|
||||
result["sector"] = "港股"
|
||||
result["industry"] = "港股"
|
||||
result["currency"] = "HKD"
|
||||
|
||||
# 基本面信号
|
||||
result["fundamental_signal"] = _fundamental_signal(result)
|
||||
|
||||
|
||||
pe = quote.get('pe')
|
||||
pb = quote.get('pb')
|
||||
result['PE'] = round(pe, 2) if pe else None
|
||||
result['PB'] = round(pb, 2) if pb else None
|
||||
result['PS'] = None
|
||||
result['market_cap'] = quote.get('market_cap', '')
|
||||
result['52w_high'] = quote.get('52w_high')
|
||||
result['52w_low'] = quote.get('52w_low')
|
||||
result['company_name'] = quote.get('name', '未知')
|
||||
result['sector'] = quote.get('market', '未知市场')
|
||||
result['industry'] = quote.get('exchange') or quote.get('market', '未知')
|
||||
result['currency'] = quote.get('currency', 'N/A')
|
||||
result['market'] = quote.get('market', 'N/A')
|
||||
result['fundamental_signal'] = _fundamental_signal(result)
|
||||
return result
|
||||
|
||||
|
||||
@@ -523,125 +641,229 @@ def _fundamental_signal(data: dict) -> str:
|
||||
# 综合评分与建议
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
def generate_recommendation(technical: dict, fundamental: dict, current_price: float) -> dict:
|
||||
"""综合技术面和基本面给出操作建议"""
|
||||
score = 0
|
||||
signals = []
|
||||
MARKET_PROFILES = {
|
||||
"HK": {"technical": 0.62, "fundamental": 0.38, "risk_penalty": 1.0},
|
||||
"SH": {"technical": 0.58, "fundamental": 0.42, "risk_penalty": 0.9},
|
||||
"SZ": {"technical": 0.60, "fundamental": 0.40, "risk_penalty": 1.0},
|
||||
"US": {"technical": 0.55, "fundamental": 0.45, "risk_penalty": 0.85},
|
||||
}
|
||||
|
||||
# 技术面评分
|
||||
|
||||
def clamp(value: float, low: float, high: float) -> float:
|
||||
return max(low, min(high, value))
|
||||
|
||||
|
||||
def detect_market_regime(hist: pd.DataFrame, technical: dict, quote: dict) -> dict:
|
||||
close = hist["Close"]
|
||||
ma20 = close.rolling(20).mean().iloc[-1] if len(close) >= 20 else close.iloc[-1]
|
||||
ma60 = close.rolling(60).mean().iloc[-1] if len(close) >= 60 else ma20
|
||||
current = close.iloc[-1]
|
||||
rsi12 = technical.get("rsi", {}).get("RSI12", technical.get("rsi", {}).get("RSI6", 50))
|
||||
high_52w = quote.get("52w_high")
|
||||
low_52w = quote.get("52w_low")
|
||||
pos_52w = None
|
||||
if high_52w and low_52w and high_52w != low_52w:
|
||||
pos_52w = (current - low_52w) / (high_52w - low_52w)
|
||||
|
||||
if current > ma20 > ma60 and rsi12 >= 55:
|
||||
regime = "趋势延续"
|
||||
elif rsi12 <= 35 and technical.get("kdj", {}).get("J", 50) < 20:
|
||||
regime = "超跌反弹"
|
||||
elif pos_52w is not None and pos_52w > 0.85 and rsi12 >= 68:
|
||||
regime = "高位风险"
|
||||
elif abs(current / ma20 - 1) < 0.03 and 40 <= rsi12 <= 60:
|
||||
regime = "区间震荡"
|
||||
else:
|
||||
regime = "估值修复/等待确认"
|
||||
|
||||
return {"regime": regime, "position_52w": round(pos_52w, 4) if pos_52w is not None else None}
|
||||
|
||||
|
||||
def compute_layer_scores(hist: pd.DataFrame, technical: dict, fundamental: dict, quote: dict) -> dict:
|
||||
close = hist["Close"]
|
||||
current = close.iloc[-1]
|
||||
ret_5 = (current / close.iloc[-6] - 1) if len(close) > 5 else 0
|
||||
ret_20 = (current / close.iloc[-21] - 1) if len(close) > 20 else ret_5
|
||||
ma = technical.get("ma_trend", {})
|
||||
above = ma.get("price_above_ma_count", "0/1").split("/")
|
||||
above_ratio = (int(above[0]) / max(int(above[1]), 1)) if len(above) == 2 else 0
|
||||
macd_sig = technical.get("macd", {}).get("signal", "")
|
||||
if "买入" in macd_sig or "金叉" in macd_sig:
|
||||
score += 2
|
||||
signals.append(f"MACD: {macd_sig}")
|
||||
elif "卖出" in macd_sig or "死叉" in macd_sig:
|
||||
score -= 2
|
||||
signals.append(f"MACD: {macd_sig}")
|
||||
elif "多头" in macd_sig:
|
||||
score += 1
|
||||
signals.append(f"MACD: {macd_sig}")
|
||||
elif "空头" in macd_sig:
|
||||
score -= 1
|
||||
signals.append(f"MACD: {macd_sig}")
|
||||
|
||||
rsi_sig = technical.get("rsi", {}).get("signal", "")
|
||||
if "超卖" in rsi_sig:
|
||||
score += 2
|
||||
signals.append(f"RSI: {rsi_sig}")
|
||||
elif "超买" in rsi_sig:
|
||||
score -= 2
|
||||
signals.append(f"RSI: {rsi_sig}")
|
||||
|
||||
kdj_sig = technical.get("kdj", {}).get("signal", "")
|
||||
if "买入" in kdj_sig or "金叉" in kdj_sig:
|
||||
score += 1
|
||||
signals.append(f"KDJ: {kdj_sig}")
|
||||
elif "卖出" in kdj_sig or "死叉" in kdj_sig:
|
||||
score -= 1
|
||||
signals.append(f"KDJ: {kdj_sig}")
|
||||
|
||||
rsi = technical.get("rsi", {}).get("RSI12", technical.get("rsi", {}).get("RSI6", 50))
|
||||
kdj_j = technical.get("kdj", {}).get("J", 50)
|
||||
volume_ratio = technical.get("volume", {}).get("volume_ratio", 1)
|
||||
boll_sig = technical.get("bollinger", {}).get("signal", "")
|
||||
if "超卖" in boll_sig or "下轨" in boll_sig:
|
||||
score += 1
|
||||
signals.append(f"布林带: {boll_sig}")
|
||||
elif "超买" in boll_sig or "上轨" in boll_sig:
|
||||
score -= 1
|
||||
signals.append(f"布林带: {boll_sig}")
|
||||
|
||||
ma_sig = technical.get("ma_trend", {}).get("trend_signal", "")
|
||||
if "多头" in ma_sig or "强势" in ma_sig:
|
||||
score += 2
|
||||
signals.append(f"均线: {ma_sig}")
|
||||
elif "空头" in ma_sig or "弱势" in ma_sig:
|
||||
score -= 2
|
||||
signals.append(f"均线: {ma_sig}")
|
||||
elif "偏多" in ma_sig:
|
||||
score += 1
|
||||
elif "偏空" in ma_sig:
|
||||
score -= 1
|
||||
|
||||
vol_sig = technical.get("volume", {}).get("signal", "")
|
||||
if "放量上涨" in vol_sig:
|
||||
score += 1
|
||||
signals.append(f"成交量: {vol_sig}")
|
||||
elif "放量下跌" in vol_sig:
|
||||
score -= 1
|
||||
signals.append(f"成交量: {vol_sig}")
|
||||
|
||||
# 基本面评分
|
||||
fund_sig = fundamental.get("fundamental_signal", "")
|
||||
if "优秀" in fund_sig:
|
||||
score += 2
|
||||
signals.append(f"基本面: {fund_sig}")
|
||||
elif "良好" in fund_sig:
|
||||
score += 1
|
||||
signals.append(f"基本面: {fund_sig}")
|
||||
elif "较差" in fund_sig:
|
||||
score -= 2
|
||||
signals.append(f"基本面: {fund_sig}")
|
||||
|
||||
# 52周位置
|
||||
pe = fundamental.get("PE")
|
||||
pb = fundamental.get("PB")
|
||||
high_52w = fundamental.get("52w_high")
|
||||
low_52w = fundamental.get("52w_low")
|
||||
pos_52w = 0.5
|
||||
if high_52w and low_52w and high_52w != low_52w:
|
||||
position = (current_price - low_52w) / (high_52w - low_52w)
|
||||
if position < 0.2:
|
||||
score += 1
|
||||
signals.append(f"52周位置: {position:.0%} (接近低点)")
|
||||
elif position > 0.9:
|
||||
score -= 1
|
||||
signals.append(f"52周位置: {position:.0%} (接近高点)")
|
||||
else:
|
||||
signals.append(f"52周位置: {position:.0%}")
|
||||
pos_52w = clamp((quote.get("price", current) - low_52w) / (high_52w - low_52w), 0, 1)
|
||||
|
||||
# 映射到操作建议
|
||||
if score >= 5:
|
||||
action = "强烈买入"
|
||||
action_en = "STRONG_BUY"
|
||||
color = "🟢🟢"
|
||||
elif score >= 2:
|
||||
action = "买入"
|
||||
action_en = "BUY"
|
||||
color = "🟢"
|
||||
elif score >= -1:
|
||||
action = "持有/观望"
|
||||
action_en = "HOLD"
|
||||
color = "🟡"
|
||||
elif score >= -4:
|
||||
action = "卖出"
|
||||
action_en = "SELL"
|
||||
color = "🔴"
|
||||
trend = (ret_20 * 100 * 0.6) + (above_ratio - 0.5) * 8
|
||||
if "多头" in macd_sig or "金叉" in macd_sig:
|
||||
trend += 1.5
|
||||
elif "空头" in macd_sig or "死叉" in macd_sig:
|
||||
trend -= 1.5
|
||||
|
||||
momentum = ret_5 * 100 * 0.8
|
||||
momentum += 1.2 if volume_ratio > 1.5 and ret_5 > 0 else 0
|
||||
momentum -= 1.2 if volume_ratio > 1.5 and ret_5 < 0 else 0
|
||||
momentum += 0.8 if "金叉" in technical.get("kdj", {}).get("signal", "") else 0
|
||||
momentum -= 0.8 if "死叉" in technical.get("kdj", {}).get("signal", "") else 0
|
||||
|
||||
risk = 0.0
|
||||
if rsi > 75:
|
||||
risk -= 2.2
|
||||
elif rsi < 28:
|
||||
risk += 1.0
|
||||
if kdj_j > 100:
|
||||
risk -= 1.2
|
||||
elif kdj_j < 0:
|
||||
risk += 0.8
|
||||
if pos_52w > 0.88:
|
||||
risk -= 1.2
|
||||
elif pos_52w < 0.18:
|
||||
risk += 0.8
|
||||
if "突破上轨" in boll_sig:
|
||||
risk -= 0.8
|
||||
elif "突破下轨" in boll_sig:
|
||||
risk += 0.6
|
||||
|
||||
valuation = 0.0
|
||||
if pe is not None:
|
||||
if 0 < pe < 15:
|
||||
valuation += 2.0
|
||||
elif pe < 25:
|
||||
valuation += 1.0
|
||||
elif pe > 40:
|
||||
valuation -= 1.5
|
||||
if pb is not None:
|
||||
if 0 < pb < 1:
|
||||
valuation += 1.0
|
||||
elif pb > 6:
|
||||
valuation -= 1.0
|
||||
|
||||
relative_strength = clamp(ret_20 * 100 / 4, -3, 3)
|
||||
volume_structure = clamp((volume_ratio - 1.0) * 2, -2.5, 2.5)
|
||||
|
||||
return {
|
||||
"trend": round(clamp(trend, -5, 5), 2),
|
||||
"momentum": round(clamp(momentum, -5, 5), 2),
|
||||
"risk": round(clamp(risk, -5, 5), 2),
|
||||
"valuation": round(clamp(valuation, -5, 5), 2),
|
||||
"relative_strength": round(relative_strength, 2),
|
||||
"volume_structure": round(volume_structure, 2),
|
||||
}
|
||||
|
||||
|
||||
def evaluate_signal_quality(layer_scores: dict) -> dict:
|
||||
positives = sum(1 for v in layer_scores.values() if v > 0.8)
|
||||
negatives = sum(1 for v in layer_scores.values() if v < -0.8)
|
||||
dispersion = max(layer_scores.values()) - min(layer_scores.values())
|
||||
agreement = abs(positives - negatives)
|
||||
confidence = 40 + agreement * 8 - min(dispersion * 2.5, 18)
|
||||
confidence = int(clamp(confidence, 18, 92))
|
||||
if confidence >= 72:
|
||||
level = "高"
|
||||
elif confidence >= 55:
|
||||
level = "中"
|
||||
else:
|
||||
action = "强烈卖出"
|
||||
action_en = "STRONG_SELL"
|
||||
color = "🔴🔴"
|
||||
level = "低"
|
||||
return {"score": confidence, "level": level, "positives": positives, "negatives": negatives}
|
||||
|
||||
|
||||
def backtest_current_signal(hist: pd.DataFrame, period: str) -> dict:
|
||||
horizons = [5, 10, 20]
|
||||
closes = hist["Close"].reset_index(drop=True)
|
||||
if len(closes) < 45:
|
||||
return {"samples": 0, "message": "历史样本不足"}
|
||||
current_ret20 = (closes.iloc[-1] / closes.iloc[-21] - 1) if len(closes) > 20 else 0
|
||||
current_ret5 = (closes.iloc[-1] / closes.iloc[-6] - 1) if len(closes) > 5 else 0
|
||||
matched = []
|
||||
for i in range(25, len(closes) - 20):
|
||||
r20 = closes.iloc[i] / closes.iloc[i-20] - 1
|
||||
r5 = closes.iloc[i] / closes.iloc[i-5] - 1
|
||||
if abs(r20 - current_ret20) < 0.06 and abs(r5 - current_ret5) < 0.04:
|
||||
matched.append(i)
|
||||
if len(matched) < 5:
|
||||
return {"samples": len(matched), "message": "相似信号样本不足"}
|
||||
|
||||
perf = {"samples": len(matched)}
|
||||
all_forward = []
|
||||
for h in horizons:
|
||||
vals = []
|
||||
for i in matched:
|
||||
if i + h < len(closes):
|
||||
vals.append(closes.iloc[i + h] / closes.iloc[i] - 1)
|
||||
if vals:
|
||||
perf[f"forward_{h}d_avg_pct"] = round(sum(vals) / len(vals) * 100, 2)
|
||||
perf[f"forward_{h}d_win_rate"] = round(sum(1 for x in vals if x > 0) / len(vals) * 100, 2)
|
||||
all_forward.extend(vals)
|
||||
if all_forward:
|
||||
perf["max_drawdown_proxy_pct"] = round(min(all_forward) * 100, 2)
|
||||
perf["period"] = period
|
||||
return perf
|
||||
|
||||
|
||||
def decide_action_type(regime: str, total_score: float, confidence: dict) -> tuple[str, str]:
|
||||
if total_score >= 4.5 and confidence["score"] >= 70:
|
||||
return "强烈买入", "趋势型买入" if regime == "趋势延续" else "高置信度买入"
|
||||
if total_score >= 2:
|
||||
if regime == "超跌反弹":
|
||||
return "买入", "超跌博弈型买入"
|
||||
return "买入", "趋势跟随型买入"
|
||||
if total_score <= -4.5 and confidence["score"] >= 70:
|
||||
return "强烈卖出", "风险规避型卖出"
|
||||
if total_score <= -2:
|
||||
return "卖出", "止盈/止损型卖出"
|
||||
return "持有/观望", "等待确认"
|
||||
|
||||
|
||||
def generate_recommendation(technical: dict, fundamental: dict, current_price: float, hist: pd.DataFrame, quote: dict) -> dict:
|
||||
market = quote.get("market", "HK")
|
||||
profile = MARKET_PROFILES.get(market, MARKET_PROFILES["HK"])
|
||||
regime = detect_market_regime(hist, technical, quote)
|
||||
layer_scores = compute_layer_scores(hist, technical, fundamental, quote)
|
||||
confidence = evaluate_signal_quality(layer_scores)
|
||||
|
||||
technical_bucket = (
|
||||
layer_scores["trend"] * 0.35 +
|
||||
layer_scores["momentum"] * 0.25 +
|
||||
layer_scores["relative_strength"] * 0.20 +
|
||||
layer_scores["volume_structure"] * 0.20
|
||||
)
|
||||
fundamental_bucket = layer_scores["valuation"]
|
||||
risk_bucket = layer_scores["risk"] * profile["risk_penalty"]
|
||||
total_score = technical_bucket * profile["technical"] + fundamental_bucket * profile["fundamental"] + risk_bucket
|
||||
total_score = round(clamp(total_score, -8, 8), 2)
|
||||
|
||||
action, action_type = decide_action_type(regime["regime"], total_score, confidence)
|
||||
icon_map = {"强烈买入": "🟢🟢", "买入": "🟢", "持有/观望": "🟡", "卖出": "🔴", "强烈卖出": "🔴🔴"}
|
||||
en_map = {"强烈买入": "STRONG_BUY", "买入": "BUY", "持有/观望": "HOLD", "卖出": "SELL", "强烈卖出": "STRONG_SELL"}
|
||||
icon = icon_map[action]
|
||||
|
||||
key_signals = [
|
||||
f"市场场景: {regime['regime']}",
|
||||
f"趋势层: {layer_scores['trend']}",
|
||||
f"动量层: {layer_scores['momentum']}",
|
||||
f"风险层: {layer_scores['risk']}",
|
||||
f"估值层: {layer_scores['valuation']}",
|
||||
f"置信度: {confidence['level']}({confidence['score']})",
|
||||
]
|
||||
|
||||
return {
|
||||
"action": action,
|
||||
"action_en": action_en,
|
||||
"score": score,
|
||||
"icon": color,
|
||||
"key_signals": signals,
|
||||
"summary": f"{color} {action} (综合评分: {score})",
|
||||
"action_en": en_map[action],
|
||||
"action_type": action_type,
|
||||
"score": total_score,
|
||||
"icon": icon,
|
||||
"market_profile": market,
|
||||
"regime": regime,
|
||||
"layer_scores": layer_scores,
|
||||
"confidence": confidence,
|
||||
"key_signals": key_signals,
|
||||
"summary": f"{icon} {action} / {action_type} (综合评分: {total_score})",
|
||||
}
|
||||
|
||||
|
||||
@@ -650,65 +872,77 @@ def generate_recommendation(technical: dict, fundamental: dict, current_price: f
|
||||
# ─────────────────────────────────────────────
|
||||
|
||||
def analyze_stock(code: str, period: str = "6mo", use_cache: bool = True) -> dict:
|
||||
"""对单只港股进行完整分析"""
|
||||
numeric_code, full_code = normalize_hk_code(code)
|
||||
|
||||
"""对单只股票进行完整分析"""
|
||||
init_db()
|
||||
stock = normalize_stock_code(code)
|
||||
full_code = stock['code']
|
||||
|
||||
if use_cache:
|
||||
cached = _read_cache(full_code, period)
|
||||
cached = get_cached_analysis(full_code, period)
|
||||
if cached:
|
||||
print(f"📦 使用缓存数据 ({full_code})，缓存有效期 {CACHE_TTL_SECONDS}s", file=sys.stderr)
|
||||
print(f"📦 使用缓存数据 ({full_code})，缓存有效期 {ANALYSIS_CACHE_TTL}s", file=sys.stderr)
|
||||
return cached
|
||||
|
||||
result = {"code": full_code, "analysis_time": datetime.now().isoformat(), "error": None}
|
||||
result = {"code": full_code, "market": stock['market'], "analysis_time": datetime.now().isoformat(), "error": None}
|
||||
|
||||
try:
|
||||
# 1. 获取实时行情
|
||||
quote = fetch_tencent_quote(numeric_code)
|
||||
quote = fetch_tencent_quote(full_code)
|
||||
if not quote or not quote.get("price"):
|
||||
result["error"] = f"无法获取 {full_code} 的实时行情"
|
||||
return result
|
||||
|
||||
|
||||
upsert_watchlist_item(
|
||||
code=full_code,
|
||||
market=quote.get('market', stock['market']),
|
||||
tencent_symbol=quote.get('tencent_symbol', stock['tencent_symbol']),
|
||||
name=quote.get('name'),
|
||||
exchange=quote.get('exchange', stock.get('exchange')),
|
||||
currency=quote.get('currency'),
|
||||
last_price=quote.get('price'),
|
||||
pe=quote.get('pe'),
|
||||
pb=quote.get('pb'),
|
||||
market_cap=quote.get('market_cap'),
|
||||
week52_high=quote.get('52w_high'),
|
||||
week52_low=quote.get('52w_low'),
|
||||
quote_time=quote.get('timestamp'),
|
||||
meta=quote,
|
||||
)
|
||||
|
||||
current_price = quote["price"]
|
||||
result["current_price"] = current_price
|
||||
result["price_date"] = quote.get("timestamp", "")
|
||||
result["price_change"] = quote.get("change_amount")
|
||||
result["price_change_pct"] = quote.get("change_pct")
|
||||
|
||||
# 2. 获取K线数据
|
||||
days = period_to_days(period)
|
||||
hist = fetch_tencent_kline(numeric_code, days)
|
||||
|
||||
if hist.empty or len(hist) < 30:
|
||||
hist = refresh_kline_cache(full_code, days, period)
|
||||
if hist.empty or len(hist) < min_kline_points(days):
|
||||
result["error"] = f"无法获取 {full_code} 的历史K线数据 (仅获得 {len(hist)} 条)"
|
||||
return result
|
||||
|
||||
result["data_points"] = len(hist)
|
||||
|
||||
result["data_points"] = len(hist)
|
||||
close = hist["Close"]
|
||||
high = hist["High"]
|
||||
low = hist["Low"]
|
||||
volume = hist["Volume"]
|
||||
|
||||
# 3. 技术分析
|
||||
technical = {}
|
||||
technical["ma_trend"] = calc_ma_trend(close)
|
||||
technical["macd"] = calc_macd(close)
|
||||
technical["rsi"] = calc_rsi(close)
|
||||
technical["kdj"] = calc_kdj(high, low, close)
|
||||
technical["bollinger"] = calc_bollinger(close)
|
||||
technical["volume"] = calc_volume_analysis(volume, close)
|
||||
technical = {
|
||||
"ma_trend": calc_ma_trend(close),
|
||||
"macd": calc_macd(close),
|
||||
"rsi": calc_rsi(close),
|
||||
"kdj": calc_kdj(high, low, close),
|
||||
"bollinger": calc_bollinger(close),
|
||||
"volume": calc_volume_analysis(volume, close),
|
||||
}
|
||||
result["technical"] = technical
|
||||
|
||||
# 4. 基本面分析
|
||||
fundamental = get_fundamentals(quote)
|
||||
result["fundamental"] = fundamental
|
||||
result["recommendation"] = generate_recommendation(technical, fundamental, current_price, hist, quote)
|
||||
result["signal_validation"] = backtest_current_signal(hist, period)
|
||||
|
||||
# 5. 综合建议
|
||||
result["recommendation"] = generate_recommendation(technical, fundamental, current_price)
|
||||
|
||||
# 6. 写入缓存
|
||||
if result.get("error") is None:
|
||||
_write_cache(full_code, period, result)
|
||||
set_cached_analysis(full_code, period, result)
|
||||
|
||||
except Exception as e:
|
||||
result["error"] = f"分析过程出错: {str(e)}"
|
||||
@@ -717,8 +951,8 @@ def analyze_stock(code: str, period: str = "6mo", use_cache: bool = True) -> dic
|
||||
|
||||
|
||||
def main():
|
||||
parser = argparse.ArgumentParser(description="港股分析工具 (腾讯财经数据源)")
|
||||
parser.add_argument("code", help="港股代码 (如 0700.HK, 00700, 腾讯)")
|
||||
parser = argparse.ArgumentParser(description="多市场股票分析工具 (腾讯财经/Yahoo 数据源)")
|
||||
parser.add_argument("code", help="股票代码，如 0700.HK / 600519 / SH600519 / AAPL")
|
||||
parser.add_argument("--period", default="6mo", help="数据周期 (1mo/3mo/6mo/1y/2y/5y)")
|
||||
parser.add_argument("--output", help="输出JSON文件路径")
|
||||
parser.add_argument("--no-cache", action="store_true", help="跳过缓存，强制重新请求数据")
|
||||
@@ -726,22 +960,15 @@ def main():
|
||||
args = parser.parse_args()
|
||||
|
||||
if args.clear_cache:
|
||||
import shutil
|
||||
cleared = False
|
||||
for path in (CACHE_DIR, LEGACY_CACHE_DIR):
|
||||
if path.exists():
|
||||
shutil.rmtree(path)
|
||||
cleared = True
|
||||
cleared = clear_analysis_cache()
|
||||
if cleared:
|
||||
print("✅ 缓存已清除")
|
||||
print(f"✅ 已清除 {cleared} 条分析缓存")
|
||||
else:
|
||||
print("ℹ️ 无缓存可清除")
|
||||
return
|
||||
|
||||
result = analyze_stock(args.code, args.period, use_cache=not args.no_cache)
|
||||
|
||||
output = json.dumps(result, ensure_ascii=False, indent=2, default=str)
|
||||
|
||||
if args.output:
|
||||
with open(args.output, "w", encoding="utf-8") as f:
|
||||
f.write(output)
|
||||
|
||||
Reference in New Issue
Block a user