Tacit Lab
7032875977
- SKILL.md: skill 定义文件（触发条件、工作流、输出模板） - scripts/analyze_stock.py: 单只股票技术面+基本面分析（含缓存+重试机制） - scripts/portfolio_manager.py: 持仓管理与批量分析 - scripts/install_deps.sh: 依赖自动安装脚本 - references/: 港股代码映射、技术指标说明、输出模板参考 特性: - 综合评分体系（-10~+10）给出买入/卖出/持有建议 - 10分钟本地缓存 + 指数退避重试，解决 Yahoo Finance 限频 - 批量分析时自动间隔请求，避免触发限流
735 lines
25 KiB
Python
Executable File
735 lines
25 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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港股分析脚本 - 获取港股数据并进行技术面+基本面分析，给出操作建议。
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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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股票代码格式: 数字.HK (如 0700.HK 腾讯控股, 9988.HK 阿里巴巴)
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周期选项: 1mo, 3mo, 6mo, 1y, 2y, 5y (默认 6mo)
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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 hashlib
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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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import yfinance as yf
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except ImportError:
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print("ERROR: yfinance 未安装。请运行: pip3 install yfinance", file=sys.stderr)
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sys.exit(1)
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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（解决 Yahoo Finance 限频问题）
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# ─────────────────────────────────────────────
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CACHE_DIR = Path.home() / ".stock_buddy_cache"
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CACHE_TTL_SECONDS = 600 # 缓存有效期 10 分钟（同一股票短时间内不重复请求）
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MAX_RETRIES = 4 # 最大重试次数
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RETRY_BASE_DELAY = 5 # 重试基础延迟（秒），指数退避: 5s, 10s, 20s, 40s
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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，若未过期则返回缓存数据"""
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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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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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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 # 缓存写入失败不影响主流程
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def _retry_request(func, *args, max_retries=MAX_RETRIES, **kwargs):
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"""
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带指数退避的重试包装器。
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捕获 Yahoo Finance 限频错误并自动重试。
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"""
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last_error = None
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for attempt in range(max_retries):
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try:
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return func(*args, **kwargs)
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except Exception as e:
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last_error = e
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error_msg = str(e).lower()
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# 仅对限频/网络类错误重试
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is_rate_limit = any(kw in error_msg for kw in [
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"rate limit", "too many requests", "429", "throttl",
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"connection", "timeout", "timed out",
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])
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if not is_rate_limit:
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raise # 非限频错误直接抛出
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if attempt < max_retries - 1:
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delay = RETRY_BASE_DELAY * (2 ** attempt) # 3s, 6s, 12s
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print(f"⏳ 请求被限频，{delay}秒后第{attempt+2}次重试...", file=sys.stderr)
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time.sleep(delay)
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raise last_error
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# ─────────────────────────────────────────────
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# 技术指标计算
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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"""
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if windows is None:
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windows = [5, 10, 20, 60, 120, 250]
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result = {}
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for w in windows:
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if len(close) >= w:
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ma = close.rolling(window=w).mean()
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result[f"MA{w}"] = round(ma.iloc[-1], 3)
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return result
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def calc_ema(close: pd.Series, span: int) -> pd.Series:
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"""计算指数移动平均线"""
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return close.ewm(span=span, adjust=False).mean()
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def calc_macd(close: pd.Series, fast: int = 12, slow: int = 26, signal: int = 9) -> dict:
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"""计算MACD指标"""
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ema_fast = calc_ema(close, fast)
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ema_slow = calc_ema(close, slow)
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dif = ema_fast - ema_slow
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dea = dif.ewm(span=signal, adjust=False).mean()
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macd_hist = 2 * (dif - dea)
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return {
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"DIF": round(dif.iloc[-1], 4),
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"DEA": round(dea.iloc[-1], 4),
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"MACD": round(macd_hist.iloc[-1], 4),
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"signal": _macd_signal(dif, dea, macd_hist),
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}
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def _macd_signal(dif: pd.Series, dea: pd.Series, macd_hist: pd.Series) -> str:
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"""MACD信号判断"""
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if len(dif) < 3:
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return "中性"
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# 金叉：DIF上穿DEA
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if dif.iloc[-1] > dea.iloc[-1] and dif.iloc[-2] <= dea.iloc[-2]:
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return "金叉-买入信号"
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# 死叉：DIF下穿DEA
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if dif.iloc[-1] < dea.iloc[-1] and dif.iloc[-2] >= dea.iloc[-2]:
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return "死叉-卖出信号"
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# 零轴上方
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if dif.iloc[-1] > 0 and dea.iloc[-1] > 0:
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if macd_hist.iloc[-1] > macd_hist.iloc[-2]:
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return "多头增强"
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return "多头区域"
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# 零轴下方
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if dif.iloc[-1] < 0 and dea.iloc[-1] < 0:
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if macd_hist.iloc[-1] < macd_hist.iloc[-2]:
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return "空头增强"
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return "空头区域"
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return "中性"
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def calc_rsi(close: pd.Series, periods: list[int] = None) -> dict:
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"""计算RSI指标"""
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if periods is None:
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periods = [6, 12, 24]
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result = {}
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delta = close.diff()
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for p in periods:
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if len(close) < p + 1:
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continue
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gain = delta.clip(lower=0).rolling(window=p).mean()
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loss = (-delta.clip(upper=0)).rolling(window=p).mean()
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rs = gain / loss.replace(0, np.nan)
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rsi = 100 - (100 / (1 + rs))
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val = round(rsi.iloc[-1], 2)
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result[f"RSI{p}"] = val
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# 综合信号
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rsi_main = result.get("RSI12", result.get("RSI6", 50))
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if rsi_main > 80:
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result["signal"] = "严重超买-卖出信号"
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elif rsi_main > 70:
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result["signal"] = "超买-注意风险"
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elif rsi_main < 20:
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result["signal"] = "严重超卖-买入信号"
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elif rsi_main < 30:
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result["signal"] = "超卖-关注买入"
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else:
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result["signal"] = "中性"
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return result
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def calc_kdj(high: pd.Series, low: pd.Series, close: pd.Series, n: int = 9) -> dict:
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"""计算KDJ指标"""
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if len(close) < n:
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return {"K": 50, "D": 50, "J": 50, "signal": "数据不足"}
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lowest_low = low.rolling(window=n).min()
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highest_high = high.rolling(window=n).max()
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rsv = (close - lowest_low) / (highest_high - lowest_low).replace(0, np.nan) * 100
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k = pd.Series(index=close.index, dtype=float)
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d = pd.Series(index=close.index, dtype=float)
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k.iloc[n - 1] = 50
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d.iloc[n - 1] = 50
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for i in range(n, len(close)):
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k.iloc[i] = 2 / 3 * k.iloc[i - 1] + 1 / 3 * rsv.iloc[i]
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d.iloc[i] = 2 / 3 * d.iloc[i - 1] + 1 / 3 * k.iloc[i]
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j = 3 * k - 2 * d
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k_val = round(k.iloc[-1], 2)
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d_val = round(d.iloc[-1], 2)
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j_val = round(j.iloc[-1], 2)
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signal = "中性"
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if k_val > d_val and k.iloc[-2] <= d.iloc[-2]:
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signal = "金叉-买入信号"
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elif k_val < d_val and k.iloc[-2] >= d.iloc[-2]:
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signal = "死叉-卖出信号"
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elif j_val > 100:
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signal = "超买区域"
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elif j_val < 0:
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signal = "超卖区域"
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return {"K": k_val, "D": d_val, "J": j_val, "signal": signal}
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def calc_bollinger(close: pd.Series, window: int = 20, num_std: float = 2) -> dict:
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"""计算布林带"""
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if len(close) < window:
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return {"signal": "数据不足"}
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ma = close.rolling(window=window).mean()
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std = close.rolling(window=window).std()
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upper = ma + num_std * std
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lower = ma - num_std * std
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current = close.iloc[-1]
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upper_val = round(upper.iloc[-1], 3)
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lower_val = round(lower.iloc[-1], 3)
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mid_val = round(ma.iloc[-1], 3)
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bandwidth = round((upper_val - lower_val) / mid_val * 100, 2)
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signal = "中性"
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if current > upper_val:
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signal = "突破上轨-超买"
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elif current < lower_val:
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signal = "突破下轨-超卖"
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elif current > mid_val:
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signal = "中轨上方-偏强"
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else:
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signal = "中轨下方-偏弱"
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return {
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"upper": upper_val,
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"middle": mid_val,
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"lower": lower_val,
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"bandwidth_pct": bandwidth,
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"signal": signal,
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}
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def calc_volume_analysis(volume: pd.Series, close: pd.Series) -> dict:
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"""成交量分析"""
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if len(volume) < 20:
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return {"signal": "数据不足"}
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avg_5 = volume.rolling(5).mean().iloc[-1]
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avg_20 = volume.rolling(20).mean().iloc[-1]
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current = volume.iloc[-1]
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vol_ratio = round(current / avg_5, 2) if avg_5 > 0 else 0
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price_change = close.iloc[-1] - close.iloc[-2]
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signal = "中性"
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if vol_ratio > 2 and price_change > 0:
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signal = "放量上涨-强势"
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elif vol_ratio > 2 and price_change < 0:
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signal = "放量下跌-弱势"
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elif vol_ratio < 0.5 and price_change > 0:
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signal = "缩量上涨-动力不足"
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elif vol_ratio < 0.5 and price_change < 0:
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signal = "缩量下跌-抛压减轻"
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return {
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"current_volume": int(current),
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"avg_5d_volume": int(avg_5),
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"avg_20d_volume": int(avg_20),
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"volume_ratio": vol_ratio,
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"signal": signal,
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}
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def calc_ma_trend(close: pd.Series) -> dict:
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"""均线趋势分析"""
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mas = calc_ma(close, [5, 10, 20, 60])
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current = close.iloc[-1]
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above_count = sum(1 for v in mas.values() if current > v)
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total = len(mas)
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if above_count == total and total > 0:
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signal = "多头排列-强势"
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elif above_count == 0:
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signal = "空头排列-弱势"
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elif above_count >= total * 0.7:
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signal = "偏多"
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elif above_count <= total * 0.3:
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signal = "偏空"
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else:
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signal = "震荡"
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return {**mas, "trend_signal": signal, "price_above_ma_count": f"{above_count}/{total}"}
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# ─────────────────────────────────────────────
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# 基本面分析
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# ─────────────────────────────────────────────
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def get_fundamentals(ticker: yf.Ticker) -> dict:
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"""获取基本面数据"""
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info = ticker.info
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result = {}
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# 估值指标
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pe = info.get("trailingPE") or info.get("forwardPE")
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pb = info.get("priceToBook")
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ps = info.get("priceToSalesTrailing12Months")
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result["PE"] = round(pe, 2) if pe else None
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result["PB"] = round(pb, 2) if pb else None
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result["PS"] = round(ps, 2) if ps else None
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# 股息 (yfinance 有时返回异常值，限制在合理范围)
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div_yield = info.get("dividendYield")
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if div_yield is not None and 0 < div_yield < 1:
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result["dividend_yield_pct"] = round(div_yield * 100, 2)
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elif div_yield is not None and div_yield >= 1:
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# 可能已经是百分比形式
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result["dividend_yield_pct"] = round(div_yield, 2) if div_yield < 30 else None
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else:
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result["dividend_yield_pct"] = None
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# 市值
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market_cap = info.get("marketCap")
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if market_cap:
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if market_cap >= 1e12:
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result["market_cap"] = f"{market_cap/1e12:.2f} 万亿"
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elif market_cap >= 1e8:
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result["market_cap"] = f"{market_cap/1e8:.2f} 亿"
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else:
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result["market_cap"] = f"{market_cap:,.0f}"
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else:
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result["market_cap"] = None
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# 盈利能力
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result["profit_margin_pct"] = round(info.get("profitMargins", 0) * 100, 2) if info.get("profitMargins") else None
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result["roe_pct"] = round(info.get("returnOnEquity", 0) * 100, 2) if info.get("returnOnEquity") else None
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result["roa_pct"] = round(info.get("returnOnAssets", 0) * 100, 2) if info.get("returnOnAssets") else None
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# 增长指标
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result["revenue_growth_pct"] = round(info.get("revenueGrowth", 0) * 100, 2) if info.get("revenueGrowth") else None
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result["earnings_growth_pct"] = round(info.get("earningsGrowth", 0) * 100, 2) if info.get("earningsGrowth") else None
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# 负债
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result["debt_to_equity"] = round(info.get("debtToEquity", 0), 2) if info.get("debtToEquity") else None
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# 52周价格区间
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result["52w_high"] = info.get("fiftyTwoWeekHigh")
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result["52w_low"] = info.get("fiftyTwoWeekLow")
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result["50d_avg"] = info.get("fiftyDayAverage")
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result["200d_avg"] = info.get("twoHundredDayAverage")
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# 公司信息
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result["company_name"] = info.get("longName") or info.get("shortName", "未知")
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result["sector"] = info.get("sector", "未知")
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result["industry"] = info.get("industry", "未知")
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result["currency"] = info.get("currency", "HKD")
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# 基本面评分
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result["fundamental_signal"] = _fundamental_signal(result)
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return result
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def _fundamental_signal(data: dict) -> str:
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"""基本面信号判断"""
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score = 0
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reasons = []
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# PE 评估
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pe = data.get("PE")
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if pe is not None:
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if pe < 0:
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score -= 1
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reasons.append("PE为负(亏损)")
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elif pe < 15:
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score += 2
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reasons.append("PE低估值")
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elif pe < 25:
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score += 1
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reasons.append("PE合理")
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elif pe > 40:
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score -= 1
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reasons.append("PE高估")
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# PB 评估
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pb = data.get("PB")
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if pb is not None:
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if pb < 1:
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score += 1
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reasons.append("PB破净")
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elif pb > 5:
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score -= 1
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# 股息率
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div = data.get("dividend_yield_pct")
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if div is not None and div > 3:
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score += 1
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reasons.append(f"高股息{div}%")
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# ROE
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roe = data.get("roe_pct")
|
||
if roe is not None:
|
||
if roe > 15:
|
||
score += 1
|
||
reasons.append("ROE优秀")
|
||
elif roe < 5:
|
||
score -= 1
|
||
|
||
# 增长
|
||
rev_growth = data.get("revenue_growth_pct")
|
||
if rev_growth is not None and rev_growth > 10:
|
||
score += 1
|
||
reasons.append("收入增长良好")
|
||
|
||
earnings_growth = data.get("earnings_growth_pct")
|
||
if earnings_growth is not None and earnings_growth > 15:
|
||
score += 1
|
||
reasons.append("利润增长强劲")
|
||
|
||
# 负债
|
||
de = data.get("debt_to_equity")
|
||
if de is not None and de > 200:
|
||
score -= 1
|
||
reasons.append("负债率偏高")
|
||
|
||
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[:4])})" if reasons else signal
|
||
|
||
|
||
# ─────────────────────────────────────────────
|
||
# 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 # 范围大约 -10 到 +10
|
||
signals = []
|
||
|
||
# ── 技术面评分 ──
|
||
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}")
|
||
|
||
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
|
||
high_52w = fundamental.get("52w_high")
|
||
low_52w = fundamental.get("52w_low")
|
||
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%}")
|
||
|
||
# ── 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 = "🔴"
|
||
else:
|
||
action = "强烈卖出"
|
||
action_en = "STRONG_SELL"
|
||
color = "🔴🔴"
|
||
|
||
return {
|
||
"action": action,
|
||
"action_en": action_en,
|
||
"score": score,
|
||
"icon": color,
|
||
"key_signals": signals,
|
||
"summary": f"{color} {action} (综合评分: {score})",
|
||
}
|
||
|
||
|
||
# ─────────────────────────────────────────────
|
||
# 主流程
|
||
# ─────────────────────────────────────────────
|
||
|
||
def normalize_hk_code(code: str) -> str:
|
||
"""标准化港股代码"""
|
||
code = code.strip().upper()
|
||
if not code.endswith(".HK"):
|
||
# 尝试补全
|
||
digits = code.lstrip("0")
|
||
if digits.isdigit():
|
||
code = code.zfill(4) + ".HK"
|
||
return code
|
||
|
||
|
||
def analyze_stock(code: str, period: str = "6mo", use_cache: bool = True) -> dict:
|
||
"""对单只港股进行完整分析（内置缓存 + 自动重试）"""
|
||
code = normalize_hk_code(code)
|
||
|
||
# 1. 尝试读取缓存
|
||
if use_cache:
|
||
cached = _read_cache(code, period)
|
||
if cached:
|
||
print(f"📦 使用缓存数据 ({code})，缓存有效期 {CACHE_TTL_SECONDS}s", file=sys.stderr)
|
||
return cached
|
||
|
||
result = {"code": code, "analysis_time": datetime.now().isoformat(), "error": None}
|
||
|
||
try:
|
||
ticker = yf.Ticker(code)
|
||
|
||
# 2. 带重试的数据获取（限频时可能返回空数据或抛异常）
|
||
hist = None
|
||
for attempt in range(MAX_RETRIES):
|
||
try:
|
||
hist = ticker.history(period=period)
|
||
if hist is not None and not hist.empty:
|
||
break # 成功获取数据
|
||
# 空数据可能是限频导致，重试
|
||
if attempt < MAX_RETRIES - 1:
|
||
delay = RETRY_BASE_DELAY * (2 ** attempt)
|
||
print(f"⏳ 数据为空（可能限频），{delay}秒后第{attempt+2}次重试...", file=sys.stderr)
|
||
time.sleep(delay)
|
||
ticker = yf.Ticker(code) # 重新创建 ticker 对象
|
||
except Exception as e:
|
||
error_msg = str(e).lower()
|
||
is_retriable = any(kw in error_msg for kw in [
|
||
"rate limit", "too many requests", "429", "throttl",
|
||
"connection", "timeout", "timed out",
|
||
])
|
||
if not is_retriable or attempt >= MAX_RETRIES - 1:
|
||
raise
|
||
delay = RETRY_BASE_DELAY * (2 ** attempt)
|
||
print(f"⏳ 请求被限频，{delay}秒后第{attempt+2}次重试...", file=sys.stderr)
|
||
time.sleep(delay)
|
||
ticker = yf.Ticker(code)
|
||
|
||
if hist is None or hist.empty:
|
||
result["error"] = f"无法获取 {code} 的历史数据，请检查股票代码是否正确"
|
||
return result
|
||
|
||
close = hist["Close"]
|
||
high = hist["High"]
|
||
low = hist["Low"]
|
||
volume = hist["Volume"]
|
||
current_price = round(close.iloc[-1], 3)
|
||
|
||
result["current_price"] = current_price
|
||
result["price_date"] = str(hist.index[-1].date())
|
||
result["data_points"] = len(hist)
|
||
|
||
# 价格变动
|
||
if len(close) > 1:
|
||
prev_close = close.iloc[-2]
|
||
change = current_price - prev_close
|
||
change_pct = change / prev_close * 100
|
||
result["price_change"] = round(change, 3)
|
||
result["price_change_pct"] = round(change_pct, 2)
|
||
|
||
# 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)
|
||
result["technical"] = technical
|
||
|
||
# 3. 带重试的基本面数据获取
|
||
try:
|
||
fundamental = _retry_request(get_fundamentals, ticker)
|
||
result["fundamental"] = fundamental
|
||
except Exception as e:
|
||
result["fundamental"] = {"error": str(e), "fundamental_signal": "数据获取失败"}
|
||
fundamental = result["fundamental"]
|
||
|
||
# 综合建议
|
||
result["recommendation"] = generate_recommendation(technical, fundamental, current_price)
|
||
|
||
# 4. 写入缓存（仅成功分析时）
|
||
if result.get("error") is None:
|
||
_write_cache(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="港股分析工具")
|
||
parser.add_argument("code", help="港股代码 (如 0700.HK)")
|
||
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()
|
||
|
||
# 清除缓存
|
||
if args.clear_cache:
|
||
import shutil
|
||
if CACHE_DIR.exists():
|
||
shutil.rmtree(CACHE_DIR)
|
||
print("✅ 缓存已清除")
|
||
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()
|