982 lines
35 KiB
Python
Executable File
982 lines
35 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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港股分析脚本 - 使用腾讯财经数据源进行技术面+基本面分析
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用法:
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python3 analyze_stock.py <股票代码> [--period <周期>] [--output <输出文件>]
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示例:
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python3 analyze_stock.py 0700.HK
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python3 analyze_stock.py 0700.HK --period 6mo --output report.json
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python3 analyze_stock.py 9988.HK --period 1y
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"""
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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 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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print("ERROR: numpy 未安装。请运行: pip3 install numpy", file=sys.stderr)
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sys.exit(1)
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try:
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import pandas as pd
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except ImportError:
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print("ERROR: pandas 未安装。请运行: pip3 install pandas", file=sys.stderr)
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sys.exit(1)
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# ─────────────────────────────────────────────
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# 缓存与重试机制
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# ─────────────────────────────────────────────
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MAX_RETRIES = 3
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RETRY_BASE_DELAY = 2
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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_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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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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'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, 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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else:
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raise Exception(f"获取实时行情失败: {e}")
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return {}
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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_{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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continue
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# 提取引号内的内容
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parts = line.split('"')
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if len(parts) < 2:
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continue
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values = parts[1].split("~")
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if len(values) < 35: # 至少需要35个字段
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continue
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def safe_float(idx: int, default: float = 0.0) -> float:
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try:
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return float(values[idx]) if values[idx] else default
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except (ValueError, IndexError):
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return default
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def safe_str(idx: int, default: str = "") -> str:
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return values[idx] if idx < len(values) else default
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# 字段映射 (根据腾讯财经API实际数据)
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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': 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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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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'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, 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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else:
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raise Exception(f"获取K线数据失败: {e}")
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return 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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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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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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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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})
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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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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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def period_to_days(period: str) -> int:
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"""将周期字符串转换为天数"""
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mapping = {
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"1mo": 30,
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"3mo": 90,
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"6mo": 180,
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"1y": 250,
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"2y": 500,
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"5y": 1250,
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}
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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:
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fallback = fetch_us_kline_yahoo(stock['code'], period)
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||
if not fallback.empty:
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upsert_kline_df(code, fallback, source='yahoo' if stock['market'] == 'US' and len(fallback) > 2 else 'tencent')
|
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hist = get_kline_df(code, required_days + buffer_days)
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||
return hist
|
||
|
||
|
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# ─────────────────────────────────────────────
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# 技术指标计算 (保持不变)
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# ─────────────────────────────────────────────
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|
||
def calc_ma(close: pd.Series, windows: list[int] = None) -> dict:
|
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"""计算多周期移动平均线"""
|
||
if windows is None:
|
||
windows = [5, 10, 20, 60, 120, 250]
|
||
result = {}
|
||
for w in windows:
|
||
if len(close) >= w:
|
||
ma = close.rolling(window=w).mean()
|
||
result[f"MA{w}"] = round(ma.iloc[-1], 3)
|
||
return result
|
||
|
||
|
||
def calc_ema(close: pd.Series, span: int) -> pd.Series:
|
||
"""计算指数移动平均线"""
|
||
return close.ewm(span=span, adjust=False).mean()
|
||
|
||
|
||
def calc_macd(close: pd.Series, fast: int = 12, slow: int = 26, signal: int = 9) -> dict:
|
||
"""计算MACD指标"""
|
||
ema_fast = calc_ema(close, fast)
|
||
ema_slow = calc_ema(close, slow)
|
||
dif = ema_fast - ema_slow
|
||
dea = dif.ewm(span=signal, adjust=False).mean()
|
||
macd_hist = 2 * (dif - dea)
|
||
|
||
return {
|
||
"DIF": round(dif.iloc[-1], 4),
|
||
"DEA": round(dea.iloc[-1], 4),
|
||
"MACD": round(macd_hist.iloc[-1], 4),
|
||
"signal": _macd_signal(dif, dea, macd_hist),
|
||
}
|
||
|
||
|
||
def _macd_signal(dif: pd.Series, dea: pd.Series, macd_hist: pd.Series) -> str:
|
||
"""MACD信号判断"""
|
||
if len(dif) < 3:
|
||
return "中性"
|
||
if dif.iloc[-1] > dea.iloc[-1] and dif.iloc[-2] <= dea.iloc[-2]:
|
||
return "金叉-买入信号"
|
||
if dif.iloc[-1] < dea.iloc[-1] and dif.iloc[-2] >= dea.iloc[-2]:
|
||
return "死叉-卖出信号"
|
||
if dif.iloc[-1] > 0 and dea.iloc[-1] > 0:
|
||
if macd_hist.iloc[-1] > macd_hist.iloc[-2]:
|
||
return "多头增强"
|
||
return "多头区域"
|
||
if dif.iloc[-1] < 0 and dea.iloc[-1] < 0:
|
||
if macd_hist.iloc[-1] < macd_hist.iloc[-2]:
|
||
return "空头增强"
|
||
return "空头区域"
|
||
return "中性"
|
||
|
||
|
||
def calc_rsi(close: pd.Series, periods: list[int] = None) -> dict:
|
||
"""计算RSI指标"""
|
||
if periods is None:
|
||
periods = [6, 12, 24]
|
||
result = {}
|
||
delta = close.diff()
|
||
for p in periods:
|
||
if len(close) < p + 1:
|
||
continue
|
||
gain = delta.clip(lower=0).rolling(window=p).mean()
|
||
loss = (-delta.clip(upper=0)).rolling(window=p).mean()
|
||
rs = gain / loss.replace(0, np.nan)
|
||
rsi = 100 - (100 / (1 + rs))
|
||
val = round(rsi.iloc[-1], 2)
|
||
result[f"RSI{p}"] = val
|
||
rsi_main = result.get("RSI12", result.get("RSI6", 50))
|
||
if rsi_main > 80:
|
||
result["signal"] = "严重超买-卖出信号"
|
||
elif rsi_main > 70:
|
||
result["signal"] = "超买-注意风险"
|
||
elif rsi_main < 20:
|
||
result["signal"] = "严重超卖-买入信号"
|
||
elif rsi_main < 30:
|
||
result["signal"] = "超卖-关注买入"
|
||
else:
|
||
result["signal"] = "中性"
|
||
return result
|
||
|
||
|
||
def calc_kdj(high: pd.Series, low: pd.Series, close: pd.Series, n: int = 9) -> dict:
|
||
"""计算KDJ指标"""
|
||
if len(close) < n:
|
||
return {"K": 50, "D": 50, "J": 50, "signal": "数据不足"}
|
||
lowest_low = low.rolling(window=n).min()
|
||
highest_high = high.rolling(window=n).max()
|
||
rsv = (close - lowest_low) / (highest_high - lowest_low).replace(0, np.nan) * 100
|
||
|
||
k = pd.Series(index=close.index, dtype=float)
|
||
d = pd.Series(index=close.index, dtype=float)
|
||
k.iloc[n - 1] = 50
|
||
d.iloc[n - 1] = 50
|
||
for i in range(n, len(close)):
|
||
k.iloc[i] = 2 / 3 * k.iloc[i - 1] + 1 / 3 * rsv.iloc[i]
|
||
d.iloc[i] = 2 / 3 * d.iloc[i - 1] + 1 / 3 * k.iloc[i]
|
||
j = 3 * k - 2 * d
|
||
|
||
k_val = round(k.iloc[-1], 2)
|
||
d_val = round(d.iloc[-1], 2)
|
||
j_val = round(j.iloc[-1], 2)
|
||
|
||
signal = "中性"
|
||
if k_val > d_val and k.iloc[-2] <= d.iloc[-2]:
|
||
signal = "金叉-买入信号"
|
||
elif k_val < d_val and k.iloc[-2] >= d.iloc[-2]:
|
||
signal = "死叉-卖出信号"
|
||
elif j_val > 100:
|
||
signal = "超买区域"
|
||
elif j_val < 0:
|
||
signal = "超卖区域"
|
||
|
||
return {"K": k_val, "D": d_val, "J": j_val, "signal": signal}
|
||
|
||
|
||
def calc_bollinger(close: pd.Series, window: int = 20, num_std: float = 2) -> dict:
|
||
"""计算布林带"""
|
||
if len(close) < window:
|
||
return {"signal": "数据不足"}
|
||
ma = close.rolling(window=window).mean()
|
||
std = close.rolling(window=window).std()
|
||
upper = ma + num_std * std
|
||
lower = ma - num_std * std
|
||
|
||
current = close.iloc[-1]
|
||
upper_val = round(upper.iloc[-1], 3)
|
||
lower_val = round(lower.iloc[-1], 3)
|
||
mid_val = round(ma.iloc[-1], 3)
|
||
bandwidth = round((upper_val - lower_val) / mid_val * 100, 2)
|
||
|
||
signal = "中性"
|
||
if current > upper_val:
|
||
signal = "突破上轨-超买"
|
||
elif current < lower_val:
|
||
signal = "突破下轨-超卖"
|
||
elif current > mid_val:
|
||
signal = "中轨上方-偏强"
|
||
else:
|
||
signal = "中轨下方-偏弱"
|
||
|
||
return {
|
||
"upper": upper_val,
|
||
"middle": mid_val,
|
||
"lower": lower_val,
|
||
"bandwidth_pct": bandwidth,
|
||
"signal": signal,
|
||
}
|
||
|
||
|
||
def calc_volume_analysis(volume: pd.Series, close: pd.Series) -> dict:
|
||
"""成交量分析"""
|
||
if len(volume) < 20:
|
||
return {"signal": "数据不足"}
|
||
avg_5 = volume.rolling(5).mean().iloc[-1]
|
||
avg_20 = volume.rolling(20).mean().iloc[-1]
|
||
current = volume.iloc[-1]
|
||
vol_ratio = round(current / avg_5, 2) if avg_5 > 0 else 0
|
||
price_change = close.iloc[-1] - close.iloc[-2]
|
||
|
||
signal = "中性"
|
||
if vol_ratio > 2 and price_change > 0:
|
||
signal = "放量上涨-强势"
|
||
elif vol_ratio > 2 and price_change < 0:
|
||
signal = "放量下跌-弱势"
|
||
elif vol_ratio < 0.5 and price_change > 0:
|
||
signal = "缩量上涨-动力不足"
|
||
elif vol_ratio < 0.5 and price_change < 0:
|
||
signal = "缩量下跌-抛压减轻"
|
||
|
||
return {
|
||
"current_volume": int(current),
|
||
"avg_5d_volume": int(avg_5),
|
||
"avg_20d_volume": int(avg_20),
|
||
"volume_ratio": vol_ratio,
|
||
"signal": signal,
|
||
}
|
||
|
||
|
||
def calc_ma_trend(close: pd.Series) -> dict:
|
||
"""均线趋势分析"""
|
||
mas = calc_ma(close, [5, 10, 20, 60])
|
||
current = close.iloc[-1]
|
||
|
||
above_count = sum(1 for v in mas.values() if current > v)
|
||
total = len(mas)
|
||
|
||
if above_count == total and total > 0:
|
||
signal = "多头排列-强势"
|
||
elif above_count == 0:
|
||
signal = "空头排列-弱势"
|
||
elif above_count >= total * 0.7:
|
||
signal = "偏多"
|
||
elif above_count <= total * 0.3:
|
||
signal = "偏空"
|
||
else:
|
||
signal = "震荡"
|
||
|
||
return {**mas, "trend_signal": signal, "price_above_ma_count": f"{above_count}/{total}"}
|
||
|
||
|
||
# ─────────────────────────────────────────────
|
||
# 基本面分析 (基于腾讯数据)
|
||
# ─────────────────────────────────────────────
|
||
|
||
def get_fundamentals(quote: dict) -> dict:
|
||
"""基于实时行情数据的基本面分析"""
|
||
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
|
||
|
||
|
||
def _fundamental_signal(data: dict) -> str:
|
||
"""基本面信号判断 (简化版)"""
|
||
score = 0
|
||
reasons = []
|
||
|
||
pe = data.get("PE")
|
||
if pe is not None and pe > 0:
|
||
if pe < 15:
|
||
score += 2
|
||
reasons.append(f"PE低估值({pe})")
|
||
elif pe < 25:
|
||
score += 1
|
||
reasons.append(f"PE合理({pe})")
|
||
elif pe > 40:
|
||
score -= 1
|
||
reasons.append(f"PE偏高({pe})")
|
||
|
||
pb = data.get("PB")
|
||
if pb is not None:
|
||
if pb < 1:
|
||
score += 1
|
||
reasons.append(f"PB破净({pb})")
|
||
elif pb > 5:
|
||
score -= 1
|
||
reasons.append(f"PB偏高({pb})")
|
||
|
||
if score >= 3:
|
||
signal = "基本面优秀"
|
||
elif score >= 1:
|
||
signal = "基本面良好"
|
||
elif score >= 0:
|
||
signal = "基本面一般"
|
||
else:
|
||
signal = "基本面较差"
|
||
|
||
return f"{signal} ({'; '.join(reasons[:3])})" if reasons else signal
|
||
|
||
|
||
# ─────────────────────────────────────────────
|
||
# 综合评分与建议
|
||
# ─────────────────────────────────────────────
|
||
|
||
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},
|
||
}
|
||
|
||
|
||
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", "")
|
||
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", "")
|
||
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:
|
||
pos_52w = clamp((quote.get("price", current) - low_52w) / (high_52w - low_52w), 0, 1)
|
||
|
||
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:
|
||
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": 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})",
|
||
}
|
||
|
||
|
||
# ─────────────────────────────────────────────
|
||
# 主流程
|
||
# ─────────────────────────────────────────────
|
||
|
||
def analyze_stock(code: str, period: str = "6mo", use_cache: bool = True) -> dict:
|
||
"""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 = get_cached_analysis(full_code, period)
|
||
if cached:
|
||
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, "market": stock['market'], "analysis_time": datetime.now().isoformat(), "error": None}
|
||
|
||
try:
|
||
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")
|
||
|
||
days = period_to_days(period)
|
||
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)
|
||
close = hist["Close"]
|
||
high = hist["High"]
|
||
low = hist["Low"]
|
||
volume = hist["Volume"]
|
||
|
||
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
|
||
|
||
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)
|
||
|
||
if result.get("error") is None:
|
||
set_cached_analysis(full_code, period, result)
|
||
|
||
except Exception as e:
|
||
result["error"] = f"分析过程出错: {str(e)}"
|
||
|
||
return result
|
||
|
||
|
||
def main():
|
||
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")
|
||
parser.add_argument("--clear-cache", action="store_true", help="清除所有缓存后退出")
|
||
args = parser.parse_args()
|
||
|
||
if args.clear_cache:
|
||
cleared = clear_analysis_cache()
|
||
if cleared:
|
||
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)
|
||
print(f"分析结果已保存至 {args.output}")
|
||
else:
|
||
print(output)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
main()
|