fix: 添加日K/周K/月K聚合功能

- 新增 aggregate_klines() 函数，与前端 filterAndProcessKLineData 一致
- 周K：将同一周的日K聚合成一根K线
- 月K：将同一月的日K聚合成一根K线

测试结果：
- 日K：724 条
- 周K：154 条
- 月K：37 条

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

validate.py

@@ -72,6 +72,94 @@ def get_window_start_date(window: str) -> str:
     return start_date.strftime('%Y%m%d')
 
 
+# ── K 线数据聚合（与前端 filterAndProcessKLineData 一致）───────────────────────
+def aggregate_klines(klines: dict, freq: str) -> dict:
+    """
+    将日K数据聚合成周K或月K（与前端 filterAndProcessKLineData 一致）
+
+    Args:
+        klines: 日K数据 { dates: [...], open: [...], high: [...], low: [...], close: [...] }
+        freq: 频率 '日' / '周' / '月'
+
+    Returns:
+        聚合后的 klines
+    """
+    if freq == '日':
+        return klines
+
+    from datetime import datetime
+
+    dates = klines['dates']
+    open_prices = klines['open']
+    high_prices = klines['high']
+    low_prices = klines['low']
+    close_prices = klines['close']
+
+    # 构建日数据列表
+    daily_data = []
+    for i in range(len(dates)):
+        daily_data.append({
+            'date': dates[i],
+            'open': open_prices[i],
+            'high': high_prices[i],
+            'low': low_prices[i],
+            'close': close_prices[i],
+        })
+
+    # 按周/月分组
+    grouped_data = {}
+
+    for d in daily_data:
+        date_str = str(d['date'])  # YYYYMMDD
+        year = int(date_str[:4])
+        month = int(date_str[4:6])
+        day = int(date_str[6:8])
+
+        date_obj = datetime(year, month, day)
+
+        if freq == '周':
+            # 计算该日期所在周的周一
+            day_of_week = date_obj.weekday()  # 0 是周一
+            from datetime import timedelta
+            monday = date_obj - timedelta(days=day_of_week)
+            key = monday.strftime('%Y%m%d')
+        else:  # 月
+            key = date_str[:6]  # YYYYMM
+
+        if key not in grouped_data:
+            grouped_data[key] = []
+        grouped_data[key].append(d)
+
+    # 聚合每组数据
+    result_dates = []
+    result_open = []
+    result_high = []
+    result_low = []
+    result_close = []
+
+    for key in sorted(grouped_data.keys()):
+        group = grouped_data[key]
+        # 按日期排序
+        group.sort(key=lambda x: x['date'])
+
+        result_dates.append(group[-1]['date'])  # 取最后一天作为标签
+        result_open.append(group[0]['open'])    # 开盘取第一天
+        result_high.append(max(d['high'] for d in group))  # 最高取最高
+        result_low.append(min(d['low'] for d in group))    # 最低取最低
+        result_close.append(group[-1]['close'])  # 收盘取最后一天
+
+    print(f'[INFO] K 线聚合: {len(dates)} 条日K → {len(result_dates)} 条{freq}K')
+
+    return {
+        'dates': result_dates,
+        'open': result_open,
+        'high': result_high,
+        'low': result_low,
+        'close': result_close,
+        'volume': [0] * len(result_dates),
+    }
+
+
 # ── CLI 解析 ──────────────────────────────────────────────────────────────────
 def parse_args():
     parser = argparse.ArgumentParser(
@@ -335,6 +423,9 @@ def run(args):
             print('[ERROR] 没有 K 线数据，跳过绘图。')
             return
 
+    # 根据频率聚合 K 线数据（与前端 filterAndProcessKLineData 一致）
+    klines = aggregate_klines(klines, args.freq)
+
     cd['klines'] = klines
 
     # 5. 绘图