替换数据源：Yahoo Finance → 腾讯财经

- 解决 Yahoo Finance 限频问题
- 使用腾讯财经实时行情 API (qt.gtimg.cn)
- 使用腾讯财经 K 线 API (web.ifzq.gtimg.cn)
- 支持港股 PE/PB/市值/52周高低点数据
- 移除 yfinance 依赖，仅保留 numpy/pandas

scripts/analyze_stock.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python3
 """
-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。
+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
 
 用法:
     python3 analyze_stock.py <股票代码> [--period <周期>] [--output <输出文件>]
@@ -9,9 +9,6 @@
     python3 analyze_stock.py 0700.HK
     python3 analyze_stock.py 0700.HK --period 6mo --output report.json
     python3 analyze_stock.py 9988.HK --period 1y
-
-股票代码格式: 数字.HK (如 0700.HK 腾讯控股, 9988.HK 阿里巴巴)
-周期选项: 1mo, 3mo, 6mo, 1y, 2y, 5y (默认 6mo)
 """
 
 import sys
@@ -19,15 +16,11 @@ import json
 import argparse
 import time
 import hashlib
+import urllib.request
+import urllib.error
 from datetime import datetime, timedelta
 from pathlib import Path
 
-try:
-    import yfinance as yf
-except ImportError:
-    print("ERROR: yfinance 未安装。请运行: pip3 install yfinance", file=sys.stderr)
-    sys.exit(1)
-
 try:
     import numpy as np
 except ImportError:
@@ -42,13 +35,13 @@ except ImportError:
 
 
 # ─────────────────────────────────────────────
-#  缓存与重试机制（解决 Yahoo Finance 限频问题）
+#  缓存与重试机制
 # ─────────────────────────────────────────────
 
 CACHE_DIR = Path.home() / ".stock_buddy_cache"
-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）
+CACHE_TTL_SECONDS = 600  # 缓存有效期 10 分钟
-MAX_RETRIES = 4          # 最大重试次数
+MAX_RETRIES = 3
-RETRY_BASE_DELAY = 5     # 重试基础延迟（秒），指数退避: 5s, 10s, 20s, 40s
+RETRY_BASE_DELAY = 2
 
 
 def _cache_key(code: str, period: str) -> str:
@@ -58,7 +51,7 @@ def _cache_key(code: str, period: str) -> str:
 
 
 def _read_cache(code: str, period: str) -> dict | None:
-    """读取缓存，若未过期则返回缓存数据"""
+    """读取缓存"""
     cache_file = CACHE_DIR / _cache_key(code, period)
     if not cache_file.exists():
         return None
@@ -82,37 +75,159 @@ def _write_cache(code: str, period: str, data: dict):
         with open(cache_file, "w", encoding="utf-8") as f:
             json.dump(data, f, ensure_ascii=False, indent=2, default=str)
     except OSError:
-        pass  # 缓存写入失败不影响主流程
+        pass
-
-
-def _retry_request(func, *args, max_retries=MAX_RETRIES, **kwargs):
-    """
-    带指数退避的重试包装器。
-    捕获 Yahoo Finance 限频错误并自动重试。
-    """
-    last_error = None
-    for attempt in range(max_retries):
-        try:
-            return func(*args, **kwargs)
-        except Exception as e:
-            last_error = e
-            error_msg = str(e).lower()
-            # 仅对限频/网络类错误重试
-            is_rate_limit = any(kw in error_msg for kw in [
-                "rate limit", "too many requests", "429", "throttl",
-                "connection", "timeout", "timed out",
-            ])
-            if not is_rate_limit:
-                raise  # 非限频错误直接抛出
-            if attempt < max_retries - 1:
-                delay = RETRY_BASE_DELAY * (2 ** attempt)  # 3s, 6s, 12s
-                print(f"⏳ 请求被限频，{delay}秒后第{attempt+2}次重试...", file=sys.stderr)
-                time.sleep(delay)
-    raise last_error
 
 
 # ─────────────────────────────────────────────
-#  技术指标计算
+#  腾讯财经数据获取
+# ─────────────────────────────────────────────
+
+def normalize_hk_code(code: str) -> tuple[str, str]:
+    """标准化港股代码，返回 (原始数字代码, 带.HK后缀代码)"""
+    code = code.strip().upper().replace(".HK", "")
+    digits = code.lstrip("0")
+    if digits.isdigit():
+        numeric_code = code.zfill(4)
+        return numeric_code, numeric_code + ".HK"
+    return code, code + ".HK"
+
+
+def fetch_tencent_quote(code: str) -> dict:
+    """获取腾讯财经实时行情"""
+    numeric_code, full_code = normalize_hk_code(code)
+    url = f"http://qt.gtimg.cn/q=hk{numeric_code}"
+    
+    for attempt in range(MAX_RETRIES):
+        try:
+            req = urllib.request.Request(url, headers={
+                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
+            })
+            with urllib.request.urlopen(req, timeout=10) as response:
+                data = response.read().decode("gb2312", errors="ignore")
+                return _parse_tencent_quote(data, numeric_code)
+        except urllib.error.URLError as e:
+            if attempt < MAX_RETRIES - 1:
+                time.sleep(RETRY_BASE_DELAY * (attempt + 1))
+            else:
+                raise Exception(f"获取实时行情失败: {e}")
+    return {}
+
+
+def _parse_tencent_quote(data: str, code: str) -> dict:
+    """解析腾讯财经实时行情响应"""
+    var_name = f"v_hk{code}"
+    for line in data.strip().split(";"):
+        line = line.strip()
+        if not line or var_name not in line:
+            continue
+        # 提取引号内的内容
+        parts = line.split('"')
+        if len(parts) < 2:
+            continue
+        values = parts[1].split("~")
+        if len(values) < 35:  # 至少需要35个字段
+            continue
+        
+        def safe_float(idx: int, default: float = 0.0) -> float:
+            try:
+                return float(values[idx]) if values[idx] else default
+            except (ValueError, IndexError):
+                return default
+        
+        def safe_str(idx: int, default: str = "") -> str:
+            return values[idx] if idx < len(values) else default
+        
+        # 字段映射 (根据腾讯财经API实际数据)
+        # 0:市场 1:名称 2:代码 3:现价 4:昨收 5:今开 6:成交量
+        # 30:时间戳 31:涨跌额 32:涨跌幅 33:最高 34:最低
+        # 39:市盈率 47:市净率 37:总市值 48:52周高 49:52周低
+        return {
+            "name": values[1],
+            "code": values[2],
+            "price": safe_float(3),
+            "prev_close": safe_float(4),
+            "open": safe_float(5),
+            "volume": safe_float(6),
+            "high": safe_float(33),
+            "low": safe_float(34),
+            "change_amount": safe_float(31),
+            "change_pct": safe_float(32),
+            "timestamp": safe_str(30),
+            "pe": safe_float(39) if len(values) > 39 else None,
+            "pb": safe_float(47) if len(values) > 47 else None,
+            "market_cap": safe_str(37),
+            "52w_high": safe_float(48) if len(values) > 48 else None,
+            "52w_low": safe_float(49) if len(values) > 49 else None,
+        }
+    return {}
+
+
+def fetch_tencent_kline(code: str, days: int = 120) -> pd.DataFrame:
+    """获取腾讯财经K线数据"""
+    numeric_code, full_code = normalize_hk_code(code)
+    url = f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param=hk{numeric_code},day,,,{days},qfq"
+    
+    for attempt in range(MAX_RETRIES):
+        try:
+            req = urllib.request.Request(url, headers={
+                "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
+            })
+            with urllib.request.urlopen(req, timeout=15) as response:
+                data = json.loads(response.read().decode("utf-8"))
+                return _parse_tencent_kline(data, numeric_code)
+        except (urllib.error.URLError, json.JSONDecodeError) as e:
+            if attempt < MAX_RETRIES - 1:
+                time.sleep(RETRY_BASE_DELAY * (attempt + 1))
+            else:
+                raise Exception(f"获取K线数据失败: {e}")
+    return pd.DataFrame()
+
+
+def _parse_tencent_kline(data: dict, code: str) -> pd.DataFrame:
+    """解析腾讯财经K线数据"""
+    key = f"hk{code}"
+    if data.get("code") != 0 or not data.get("data") or key not in data["data"]:
+        return pd.DataFrame()
+    
+    day_data = data["data"][key].get("day", [])
+    if not day_data:
+        return pd.DataFrame()
+    
+    # 格式: [日期, 开盘价, 收盘价, 最低价, 最高价, 成交量]
+    records = []
+    for item in day_data:
+        if len(item) >= 6:
+            records.append({
+                "Date": item[0],
+                "Open": float(item[1]),
+                "Close": float(item[2]),
+                "Low": float(item[3]),
+                "High": float(item[4]),
+                "Volume": float(item[5]),
+            })
+    
+    df = pd.DataFrame(records)
+    if not df.empty:
+        df["Date"] = pd.to_datetime(df["Date"])
+        df.set_index("Date", inplace=True)
+    return df
+
+
+def period_to_days(period: str) -> int:
+    """将周期字符串转换为天数"""
+    mapping = {
+        "1mo": 30,
+        "3mo": 90,
+        "6mo": 180,
+        "1y": 250,
+        "2y": 500,
+        "5y": 1250,
+    }
+    return mapping.get(period, 180)
+
+
+# ─────────────────────────────────────────────
+#  技术指标计算 (保持不变)
 # ─────────────────────────────────────────────
 
 def calc_ma(close: pd.Series, windows: list[int] = None) -> dict:
@@ -152,18 +267,14 @@ 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"
-    # 金叉：DIF上穿DEA
     if dif.iloc[-1] > dea.iloc[-1] and dif.iloc[-2] <= dea.iloc[-2]:
         return "金叉-买入信号"
-    # 死叉：DIF下穿DEA
     if dif.iloc[-1] < dea.iloc[-1] and dif.iloc[-2] >= dea.iloc[-2]:
         return "死叉-卖出信号"
-    # 零轴上方
     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"
-    # 零轴下方
     if dif.iloc[-1] < 0 and dea.iloc[-1] < 0:
         if macd_hist.iloc[-1] < macd_hist.iloc[-2]:
             return "空头增强"
@@ -186,7 +297,6 @@ def calc_rsi(close: pd.Series, periods: list[int] = None) -> dict:
         rsi = 100 - (100 / (1 + rs))
         val = round(rsi.iloc[-1], 2)
         result[f"RSI{p}"] = val
-    # 综合信号
     rsi_main = result.get("RSI12", result.get("RSI6", 50))
     if rsi_main > 80:
         result["signal"] = "严重超买-卖出信号"
@@ -321,135 +431,64 @@ def calc_ma_trend(close: pd.Series) -> dict:
 
 
 # ─────────────────────────────────────────────
-#  基本面分析
+#  基本面分析 (基于腾讯数据)
 # ─────────────────────────────────────────────
 
-def get_fundamentals(ticker: yf.Ticker) -> dict:
+def get_fundamentals(quote: dict) -> dict:
-    """获取基本面数据"""
+    """基于实时行情数据的基本面分析"""
-    info = ticker.info
     result = {}
-
+    
-    # 估值指标
+    # 估值指标 (腾讯提供的)
-    pe = info.get("trailingPE") or info.get("forwardPE")
+    pe = quote.get("pe")
-    pb = info.get("priceToBook")
+    pb = quote.get("pb")
-    ps = info.get("priceToSalesTrailing12Months")
     result["PE"] = round(pe, 2) if pe else None
     result["PB"] = round(pb, 2) if pb else None
-    result["PS"] = round(ps, 2) if ps else None
+    result["PS"] = None  # 腾讯不提供
-
+    
-    # 股息 (yfinance 有时返回异常值，限制在合理范围)
-    div_yield = info.get("dividendYield")
-    if div_yield is not None and 0 < div_yield < 1:
-        result["dividend_yield_pct"] = round(div_yield * 100, 2)
-    elif div_yield is not None and div_yield >= 1:
-        # 可能已经是百分比形式
-        result["dividend_yield_pct"] = round(div_yield, 2) if div_yield < 30 else None
-    else:
-        result["dividend_yield_pct"] = None
-
     # 市值
-    market_cap = info.get("marketCap")
+    result["market_cap"] = quote.get("market_cap", "")
-    if market_cap:
+    
-        if market_cap >= 1e12:
-            result["market_cap"] = f"{market_cap/1e12:.2f} 万亿"
-        elif market_cap >= 1e8:
-            result["market_cap"] = f"{market_cap/1e8:.2f} 亿"
-        else:
-            result["market_cap"] = f"{market_cap:,.0f}"
-    else:
-        result["market_cap"] = None
-
-    # 盈利能力
-    result["profit_margin_pct"] = round(info.get("profitMargins", 0) * 100, 2) if info.get("profitMargins") else None
-    result["roe_pct"] = round(info.get("returnOnEquity", 0) * 100, 2) if info.get("returnOnEquity") else None
-    result["roa_pct"] = round(info.get("returnOnAssets", 0) * 100, 2) if info.get("returnOnAssets") else None
-
-    # 增长指标
-    result["revenue_growth_pct"] = round(info.get("revenueGrowth", 0) * 100, 2) if info.get("revenueGrowth") else None
-    result["earnings_growth_pct"] = round(info.get("earningsGrowth", 0) * 100, 2) if info.get("earningsGrowth") else None
-
-    # 负债
-    result["debt_to_equity"] = round(info.get("debtToEquity", 0), 2) if info.get("debtToEquity") else None
-
     # 52周价格区间
-    result["52w_high"] = info.get("fiftyTwoWeekHigh")
+    result["52w_high"] = quote.get("52w_high")
-    result["52w_low"] = info.get("fiftyTwoWeekLow")
+    result["52w_low"] = quote.get("52w_low")
-    result["50d_avg"] = info.get("fiftyDayAverage")
+    
-    result["200d_avg"] = info.get("twoHundredDayAverage")
-
     # 公司信息
-    result["company_name"] = info.get("longName") or info.get("shortName", "未知")
+    result["company_name"] = quote.get("name", "未知")
-    result["sector"] = info.get("sector", "未知")
+    result["sector"] = "港股"
-    result["industry"] = info.get("industry", "未知")
+    result["industry"] = "港股"
-    result["currency"] = info.get("currency", "HKD")
+    result["currency"] = "HKD"
-
+    
-    # 基本面评分
+    # 基本面信号
     result["fundamental_signal"] = _fundamental_signal(result)
-
+    
     return result
 
 
 def _fundamental_signal(data: dict) -> str:
-    """基本面信号判断"""
+    """基本面信号判断 (简化版)"""
     score = 0
     reasons = []
 
-    # PE 评估
     pe = data.get("PE")
-    if pe is not None:
+    if pe is not None and pe > 0:
-        if pe < 0:
+        if pe < 15:
-            score -= 1
-            reasons.append("PE为负(亏损)")
-        elif pe < 15:
             score += 2
-            reasons.append("PE低估值")
+            reasons.append(f"PE低估值({pe})")
         elif pe < 25:
             score += 1
-            reasons.append("PE合理")
+            reasons.append(f"PE合理({pe})")
         elif pe > 40:
             score -= 1
-            reasons.append("PE高估")
+            reasons.append(f"PE偏高({pe})")
 
-    # PB 评估
     pb = data.get("PB")
     if pb is not None:
         if pb < 1:
             score += 1
-            reasons.append("PB破净")
+            reasons.append(f"PB破净({pb})")
         elif pb > 5:
             score -= 1
-
+            reasons.append(f"PB偏高({pb})")
-    # 股息率
-    div = data.get("dividend_yield_pct")
-    if div is not None and div > 3:
-        score += 1
-        reasons.append(f"高股息{div}%")
-
-    # ROE
-    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"
@@ -460,7 +499,7 @@ def _fundamental_signal(data: dict) -> str:
     else:
         signal = "基本面较差"
 
-    return f"{signal} ({'; '.join(reasons[:4])})" if reasons else signal
+    return f"{signal} ({'; '.join(reasons[:3])})" if reasons else signal
 
 
 # ─────────────────────────────────────────────
@@ -469,10 +508,10 @@ def _fundamental_signal(data: dict) -> str:
 
 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
+    score = 0
     signals = []
 
-    # ── 技术面评分 ──
+    # 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
@@ -531,7 +570,7 @@ def generate_recommendation(technical: dict, fundamental: dict, current_price: f
         score -= 1
         signals.append(f"成交量: {vol_sig}")
 
-    # ── 基本面评分 ──
+    # 基本面评分
     fund_sig = fundamental.get("fundamental_signal", "")
     if "优秀" in fund_sig:
         score += 2
@@ -557,7 +596,7 @@ def generate_recommendation(technical: dict, fundamental: dict, current_price: f
         else:
             signals.append(f"52周位置: {position:.0%}")
 
-    # ── 映射到操作建议 ──
+    # 映射到操作建议
     if score >= 5:
         action = "强烈买入"
         action_en = "STRONG_BUY"
@@ -593,82 +632,47 @@ def generate_recommendation(technical: dict, fundamental: dict, current_price: f
 #  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）"""
+    """对单只港股进行完整分析"""
-    code = normalize_hk_code(code)
+    numeric_code, full_code = normalize_hk_code(code)
-
+    
-    # 1. 尝试读取缓存
     if use_cache:
-        cached = _read_cache(code, period)
+        cached = _read_cache(full_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)
+            print(f"📦 使用缓存数据 ({full_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}
+    result = {"code": full_code, "analysis_time": datetime.now().isoformat(), "error": None}
 
     try:
-        ticker = yf.Ticker(code)
+        # 1. 获取实时行情
-
+        quote = fetch_tencent_quote(numeric_code)
-        # 2. 带重试的数据获取（限频时可能返回空数据或抛异常）
+        if not quote or not quote.get("price"):
-        hist = None
+            result["error"] = f"无法获取 {full_code} 的实时行情"
-        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
+        
+        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:
+            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"]
-        current_price = round(close.iloc[-1], 3)
 
-        result["current_price"] = current_price
+        # 3. 技术分析
-        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)
@@ -678,20 +682,16 @@ def analyze_stock(code: str, period: str = "6mo", use_cache: bool = True) -> dic
         technical["volume"] = calc_volume_analysis(volume, close)
         result["technical"] = technical
 
-        # 3. 带重试的基本面数据获取
+        # 4. 基本面分析
-        try:
+        fundamental = get_fundamentals(quote)
-            fundamental = _retry_request(get_fundamentals, ticker)
+        result["fundamental"] = fundamental
-            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
+        # 5. 综合建议
         result["recommendation"] = generate_recommendation(technical, fundamental, current_price)
 
-        # 4. 写入缓存（仅成功分析时）
+        # 6. 写入缓存
         if result.get("error") is None:
-            _write_cache(code, period, result)
+            _write_cache(full_code, period, result)
 
     except Exception as e:
         result["error"] = f"分析过程出错: {str(e)}"
@@ -700,15 +700,14 @@ 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")
+    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)")
+    parser.add_argument("code", help="港股代码 (如 0700.HK, 00700, 腾讯)")
     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():