refactor: 重构项目结构

- 将 Python 代码移至 analyzer/ 目录（含 venv）
- 拆分 Go 服务器代码为模块化结构:
  - config/: 配置加载
  - model/: 请求/响应模型
  - service/: 业务逻辑
  - handler/: API处理器
- 添加 .gitignore 文件
- 删除旧的独立脚本文件

analyzer/category.py

@@ -0,0 +1,168 @@
+"""
+交易分类配置和推断逻辑
+
+配置文件: config/category.yaml
+"""
+import yaml
+from pathlib import Path
+
+# =============================================================================
+# 加载 YAML 配置
+# =============================================================================
+
+CONFIG_FILE = Path(__file__).parent / "config" / "category.yaml"
+
+def load_config():
+    """加载分类配置"""
+    with open(CONFIG_FILE, "r", encoding="utf-8") as f:
+        return yaml.safe_load(f)
+
+def reload_config():
+    """重新加载配置（配置文件修改后调用）"""
+    global _config
+    _config = load_config()
+
+# 初始化加载
+_config = load_config()
+
+
+# =============================================================================
+# 配置访问函数
+# =============================================================================
+
+def get_platform_merchants() -> list[str]:
+    """获取需要重新分类的平台型商家列表"""
+    return _config.get("平台商家", [])
+
+
+# =============================================================================
+# 分类推断函数
+# =============================================================================
+
+def infer_category(merchant: str, product: str, income_expense: str) -> tuple[str, bool]:
+    """
+    根据商户名称和商品信息推断交易分类
+    
+    Args:
+        merchant: 交易对方/商户名称
+        product: 商品说明
+        income_expense: 收/支 ("收入" 或 "支出")
+    
+    Returns:
+        (分类名称, 是否确定) - 如果无法确定分类，第二个值为 False
+    """
+    merchant_lower = merchant.lower()
+    product_lower = product.lower()
+    combined = merchant_lower + " " + product_lower
+    
+    # 收入类型处理
+    if income_expense == "收入":
+        for category, keywords in _config["收入分类"].items():
+            for kw in keywords:
+                if kw.lower() in combined:
+                    return category, True
+        return _config["默认分类"]["收入"], False  # 无法确定
+    
+    # 支出类型处理 - 按优先级顺序匹配
+    for category, keywords in _config["支出分类"].items():
+        for kw in keywords:
+            if kw.lower() in combined:
+                return category, True
+    
+    # 转账类特殊处理
+    if "转账" in combined:
+        return _config["默认分类"]["支出"], False  # 转账无法确定具体分类
+    
+    return _config["默认分类"]["支出"], False  # 无法确定
+
+
+def get_all_categories() -> list:
+    """获取所有支出分类列表"""
+    return list(_config["支出分类"].keys()) + [_config["默认分类"]["支出"]]
+
+
+def get_all_income_categories() -> list:
+    """获取所有收入分类列表"""
+    return list(_config["收入分类"].keys())
+
+
+# =============================================================================
+# 分类重推断（用于修正原始分类错误）
+# =============================================================================
+
+def reclassify_if_needed(original_category: str, merchant: str, product: str, 
+                         income_expense: str) -> tuple[str, bool, int]:
+    """
+    检查是否需要重新分类，如果推断出更准确的分类则返回新分类
+    
+    Args:
+        original_category: 原始分类
+        merchant: 交易对方
+        product: 商品说明
+        income_expense: 收/支
+    
+    Returns:
+        (分类名称, 是否已修改, 复核等级)
+        
+        复核等级:
+            0 = 无需复核（分类确定或原分类可信）
+            1 = 低优先级复核（分类被调整，需确认调整是否正确）
+            2 = 高优先级复核（完全无法判断，原分类也是默认分类）
+    """
+    new_category, is_certain = infer_category(merchant, product, income_expense)
+    
+    default_category = _config["默认分类"].get(
+        "支出" if income_expense == "支出" else "收入"
+    )
+    
+    # 判断原分类是否为默认/笼统分类
+    is_original_default = (
+        original_category == default_category or 
+        original_category in ["其他", "其他支出", "其他收入", ""]
+    )
+    
+    # 情况1：推断出具体分类，且与原分类不同 → 调整分类，低优先级复核
+    if new_category != default_category and new_category != original_category:
+        return new_category, True, 1  # 低优先级复核
+    
+    # 情况2：推断失败，但原分类是具体分类 → 信任原分类，无需复核
+    if not is_certain and not is_original_default:
+        return original_category, False, 0  # 信任支付宝原分类
+    
+    # 情况3：推断失败，原分类也是默认分类 → 高优先级复核
+    if not is_certain and is_original_default:
+        return new_category, False, 2  # 高优先级复核
+    
+    # 情况4：分类确定且未调整 → 无需复核
+    return original_category, False, 0
+
+
+# =============================================================================
+# 调试工具
+# =============================================================================
+
+def test_category(merchant: str, product: str = "", income_expense: str = "支出") -> None:
+    """测试分类推断结果"""
+    category, is_certain = infer_category(merchant, product, income_expense)
+    review_level = "无" if is_certain else "高 ⚠️"
+    print(f"商户: {merchant}")
+    print(f"商品: {product}")
+    print(f"收支: {income_expense}")
+    print(f"分类: {category}")
+    print(f"复核: {review_level}")
+    print()
+
+
+if __name__ == "__main__":
+    print("=== 分类测试 ===\n")
+    
+    test_cases = [
+        ("luckin coffee", "订单付款", "支出"),
+        ("美团", "万达影城-美团App", "支出"),
+        ("美团", "茶百道-美团App", "支出"),
+        ("美团", "美宜佳-美团App", "支出"),
+    ]
+    
+    for merchant, product, ie in test_cases:
+        test_category(merchant, product, ie)
+

analyzer/clean_bill.py

@@ -0,0 +1,104 @@
+#!/usr/bin/env python3
+"""
+账单清理统一入口
+自动识别微信或支付宝账单，调用对应的清理逻辑
+
+用法: python clean_bill.py <输入文件> [输出文件] [日期筛选选项]
+示例:
+  python clean_bill.py 账单.csv --year 2026
+  python clean_bill.py 账单.csv --year 2026 --month 1
+  python clean_bill.py 账单.csv --start 2026-01-01 --end 2026-01-15
+"""
+import sys
+from pathlib import Path
+
+from cleaners.base import create_arg_parser, compute_date_range
+from cleaners import AlipayCleaner, WechatCleaner
+
+
+def detect_bill_type(filepath: str) -> str | None:
+    """
+    检测账单类型
+    
+    Returns:
+        'alipay' | 'wechat' | None
+    """
+    try:
+        with open(filepath, "r", encoding="utf-8") as f:
+            for _ in range(20):
+                line = f.readline()
+                if not line:
+                    break
+                
+                # 支付宝特征：表头包含"交易分类"和"对方账号"
+                if "交易分类" in line and "对方账号" in line:
+                    return "alipay"
+                
+                # 微信特征：表头包含"交易类型"和"金额(元)"
+                if "交易类型" in line and "金额(元)" in line:
+                    return "wechat"
+                
+                # 数据行特征
+                if line.startswith("202"):
+                    if "￥" in line:
+                        return "wechat"
+                    if "@" in line:
+                        return "alipay"
+    
+    except Exception as e:
+        print(f"读取文件失败: {e}", file=sys.stderr)
+        return None
+    
+    return None
+
+
+def main():
+    parser = create_arg_parser("账单清理统一入口 - 自动识别微信/支付宝账单")
+    parser.add_argument(
+        "--type", "-t", 
+        choices=["alipay", "wechat", "auto"], 
+        default="auto",
+        help="手动指定账单类型（默认自动检测）"
+    )
+    
+    args = parser.parse_args()
+    input_file = args.input_file
+    
+    # 检查文件是否存在
+    if not Path(input_file).exists():
+        print(f"❌ 错误：文件不存在 - {input_file}", file=sys.stderr)
+        sys.exit(1)
+    
+    # 检测账单类型
+    if args.type == "auto":
+        bill_type = detect_bill_type(input_file)
+        if bill_type is None:
+            print("❌ 无法识别账单类型，请使用 --type 参数手动指定", file=sys.stderr)
+            print("   支持: --type alipay (支付宝) 或 --type wechat (微信)", file=sys.stderr)
+            sys.exit(1)
+    else:
+        bill_type = args.type
+    
+    # 显示检测结果
+    type_names = {"alipay": "支付宝", "wechat": "微信"}
+    print(f"📋 检测到账单类型: {type_names[bill_type]}")
+    print()
+    
+    # 计算日期范围
+    start_date, end_date = compute_date_range(args)
+    
+    # 获取输出格式
+    output_format = getattr(args, 'format', 'csv')
+    
+    # 创建对应的清理器
+    if bill_type == "alipay":
+        cleaner = AlipayCleaner(args.input_file, args.output_file, output_format)
+    else:
+        cleaner = WechatCleaner(args.input_file, args.output_file, output_format)
+    
+    cleaner.set_date_range(start_date, end_date)
+    cleaner.clean()
+
+
+if __name__ == "__main__":
+    main()

analyzer/cleaners/__init__.py

@@ -0,0 +1,9 @@
+"""
+账单清理模块
+"""
+from .base import BaseCleaner
+from .alipay import AlipayCleaner
+from .wechat import WechatCleaner
+
+__all__ = ['BaseCleaner', 'AlipayCleaner', 'WechatCleaner']
+

analyzer/cleaners/alipay.py

@@ -0,0 +1,231 @@
+"""
+支付宝账单清理模块
+"""
+import csv
+from decimal import Decimal
+
+from .base import (
+    BaseCleaner, parse_amount, format_amount, 
+    is_in_date_range, create_arg_parser
+)
+from category import reclassify_if_needed, get_platform_merchants
+
+
+class AlipayCleaner(BaseCleaner):
+    """支付宝账单清理器"""
+    
+    def clean(self) -> None:
+        """执行清理"""
+        self.print_header()
+        
+        # 读取数据
+        with open(self.input_file, "r", encoding="utf-8") as f:
+            reader = csv.reader(f)
+            header = next(reader)
+            rows = list(reader)
+        
+        self.stats["original_count"] = len(rows)
+        print(f"原始数据行数: {len(rows)}")
+        
+        # 第一步：按日期范围筛选
+        rows_filtered = [
+            row for row in rows 
+            if row and is_in_date_range(row[0], self.start_date, self.end_date)
+        ]
+        self.stats["filtered_count"] = len(rows_filtered)
+        
+        date_desc = f"{self.start_date} ~ {self.end_date}" if self.start_date or self.end_date else "全部"
+        print(f"筛选后数据行数: {len(rows_filtered)} ({date_desc})")
+        
+        # 第二步：分离退款和非退款条目
+        refund_rows = []
+        expense_rows = []
+        
+        for row in rows_filtered:
+            if len(row) > 1 and row[1] == "退款":
+                refund_rows.append(row)
+            else:
+                expense_rows.append(row)
+        
+        print(f"退款条目数: {len(refund_rows)}")
+        print(f"非退款条目数: {len(expense_rows)}")
+        
+        # 第三步：处理退款
+        order_refunds = self._aggregate_refunds(refund_rows)
+        print(f"有退款的订单数: {len(order_refunds)}")
+        
+        # 第四步：处理每笔支出
+        final_rows = self._process_expenses(expense_rows, order_refunds)
+        
+        print(f"\n处理结果:")
+        print(f"  全额退款删除: {self.stats['fully_refunded']} 条")
+        print(f"  部分退款调整: {self.stats['partially_refunded']} 条")
+        print(f"  最终保留行数: {len(final_rows)}")
+        
+        # 第五步：重新分类并添加"需复核"标注
+        final_rows = self.reclassify(final_rows, header)
+        
+        if self.stats["category_adjusted"] > 0:
+            print(f"  分类调整: {self.stats['category_adjusted']} 条")
+        
+        self.stats["final_count"] = len(final_rows)
+        
+        # 写入文件
+        self.write_output(header, final_rows)
+        
+        print(f"\n清理后的数据已保存到: {self.output_file}")
+    
+    def _aggregate_refunds(self, refund_rows: list) -> dict:
+        """聚合退款金额"""
+        order_refunds = {}
+        
+        for row in refund_rows:
+            if len(row) >= 11:
+                refund_order_no = row[9].strip()
+                refund_merchant_no = row[10].strip()
+                refund_amount = parse_amount(row[6])
+                
+                original_order = refund_order_no.split("_")[0] if "_" in refund_order_no else refund_order_no
+                key = original_order if original_order else refund_merchant_no
+                
+                if key:
+                    if key not in order_refunds:
+                        order_refunds[key] = Decimal("0")
+                    order_refunds[key] += refund_amount
+                    print(f"  退款记录: {row[0]} | {row[2]} | {refund_amount}元")
+        
+        return order_refunds
+    
+    def _process_expenses(self, expense_rows: list, order_refunds: dict) -> list:
+        """处理支出记录"""
+        final_rows = []
+        
+        for row in expense_rows:
+            if len(row) >= 12:
+                order_no = row[9].strip()
+                merchant_no = row[10].strip()
+                expense_amount = parse_amount(row[6])
+                
+                # 查找对应的退款
+                refund_amount = Decimal("0")
+                matched_key = None
+                
+                for key, amount in order_refunds.items():
+                    if key and (order_no == key or merchant_no == key or order_no.startswith(key)):
+                        refund_amount = amount
+                        matched_key = key
+                        break
+                
+                if matched_key:
+                    if refund_amount >= expense_amount:
+                        # 全额退款，删除
+                        self.stats["fully_refunded"] += 1
+                        print(f"  全额退款删除: {row[0]} | {row[2]} | {row[4][:25]}... | 原{expense_amount}元")
+                    else:
+                        # 部分退款，保留差额
+                        remaining = expense_amount - refund_amount
+                        new_row = row.copy()
+                        new_row[6] = format_amount(remaining)
+                        
+                        original_remark = new_row[11] if len(new_row) > 11 else ""
+                        new_row[11] = f"原金额{expense_amount}元,退款{refund_amount}元{';' + original_remark if original_remark else ''}"
+                        
+                        final_rows.append(new_row)
+                        self.stats["partially_refunded"] += 1
+                        print(f"  部分退款: {row[0]} | {row[2]} | 原{expense_amount}元 -> {format_amount(remaining)}元")
+                else:
+                    final_rows.append(row)
+            else:
+                final_rows.append(row)
+        
+        return final_rows
+    
+    def _is_platform_merchant(self, merchant: str) -> bool:
+        """判断是否为平台型商家（从配置文件读取）"""
+        platform_merchants = get_platform_merchants()
+        return any(platform in merchant for platform in platform_merchants)
+    
+    def reclassify(self, rows: list, header: list) -> list:
+        """
+        重新分类支付宝账单，并添加"复核等级"标注字段
+        
+        只对平台型商家（美团、京东、抖音等）进行分类调整，
+        其他商家直接信任支付宝原分类。
+        
+        复核等级:
+            空 = 无需复核
+            低 = 分类被调整，需确认调整是否正确
+            高 = 完全无法判断，需人工分类
+        
+        字段索引:
+            0: 交易时间
+            1: 交易分类
+            2: 交易对方
+            4: 商品说明
+            5: 收/支
+        """
+        # 添加"复核等级"字段到表头
+        if "复核等级" not in header:
+            header.append("复核等级")
+        
+        review_low_count = 0
+        review_high_count = 0
+        
+        for row in rows:
+            if len(row) >= 6:
+                original_category = row[1]
+                merchant = row[2]
+                product = row[4]
+                income_expense = row[5]
+                
+                review_mark = ""
+                
+                # 只对平台型商家进行重新分类
+                if self._is_platform_merchant(merchant):
+                    new_category, changed, review_level = reclassify_if_needed(
+                        original_category, merchant, product, income_expense
+                    )
+                    
+                    if changed:
+                        row[1] = new_category
+                        self.stats["category_adjusted"] += 1
+                        print(f"  分类调整: {merchant[:15]}... | {original_category} -> {new_category}")
+                    
+                    # 添加复核等级标注
+                    if review_level == 1:
+                        review_mark = "LOW"
+                        review_low_count += 1
+                    elif review_level == 2:
+                        review_mark = "HIGH"
+                        review_high_count += 1
+                # 非平台商家：直接信任支付宝原分类，无需复核
+                
+                # 确保行长度足够
+                while len(row) < len(header) - 1:
+                    row.append("")
+                row.append(review_mark)
+        
+        if review_high_count > 0:
+            print(f"  高优先级复核: {review_high_count} 条（无法判断）")
+        if review_low_count > 0:
+            print(f"  低优先级复核: {review_low_count} 条（分类已调整）")
+        
+        return rows
+
+
+def main():
+    """命令行入口"""
+    parser = create_arg_parser("清理支付宝交易明细数据")
+    args = parser.parse_args()
+    
+    from .base import get_output_file, compute_date_range
+    
+    cleaner = AlipayCleaner(args.input_file, args.output_file)
+    start_date, end_date = compute_date_range(args)
+    cleaner.set_date_range(start_date, end_date)
+    cleaner.clean()
+
+
+if __name__ == "__main__":
+    main()
+

analyzer/cleaners/base.py

@@ -0,0 +1,240 @@
+"""
+账单清理基类和公共工具函数
+"""
+import csv
+import json
+import argparse
+from abc import ABC, abstractmethod
+from datetime import datetime, date, timedelta
+from decimal import Decimal, ROUND_HALF_UP
+from pathlib import Path
+
+
+# =============================================================================
+# 公共工具函数
+# =============================================================================
+
+def parse_date(date_str: str) -> date:
+    """解析日期字符串，支持 YYYY-MM-DD 或 YYYY/MM/DD 格式"""
+    for fmt in ("%Y-%m-%d", "%Y/%m/%d"):
+        try:
+            return datetime.strptime(date_str, fmt).date()
+        except ValueError:
+            continue
+    raise ValueError(f"无法解析日期: {date_str}，请使用 YYYY-MM-DD 格式")
+
+
+def parse_amount(amount_str: str) -> Decimal:
+    """解析金额字符串为Decimal（去掉￥符号）"""
+    try:
+        clean = amount_str.replace("￥", "").replace(" ", "").strip()
+        return Decimal(clean)
+    except:
+        return Decimal("0")
+
+
+def format_amount(amount: Decimal) -> str:
+    """格式化金额为字符串（保留两位小数）"""
+    return str(amount.quantize(Decimal("0.01"), rounding=ROUND_HALF_UP))
+
+
+def compute_date_range(args) -> tuple[date | None, date | None]:
+    """
+    根据参数计算最终的日期范围
+    多重指定时取交集（最小范围）
+    
+    Returns:
+        (start_date, end_date) 或 (None, None) 表示不筛选
+    """
+    start_date = None
+    end_date = None
+    
+    # 1. 根据年份设置范围
+    if args.year:
+        year = int(args.year)
+        start_date = date(year, 1, 1)
+        end_date = date(year, 12, 31)
+    
+    # 2. 根据月份进一步收窄
+    if args.month:
+        month = int(args.month)
+        year = int(args.year) if args.year else datetime.now().year
+        
+        if not start_date:
+            start_date = date(year, 1, 1)
+            end_date = date(year, 12, 31)
+        
+        month_start = date(year, month, 1)
+        if month == 12:
+            month_end = date(year, 12, 31)
+        else:
+            month_end = date(year, month + 1, 1) - timedelta(days=1)
+        
+        start_date = max(start_date, month_start) if start_date else month_start
+        end_date = min(end_date, month_end) if end_date else month_end
+    
+    # 3. 根据 start/end 参数进一步收窄
+    if args.start:
+        custom_start = parse_date(args.start)
+        start_date = max(start_date, custom_start) if start_date else custom_start
+    
+    if args.end:
+        custom_end = parse_date(args.end)
+        end_date = min(end_date, custom_end) if end_date else custom_end
+    
+    return start_date, end_date
+
+
+def is_in_date_range(date_str: str, start_date: date | None, end_date: date | None) -> bool:
+    """检查日期字符串是否在指定范围内"""
+    if start_date is None and end_date is None:
+        return True
+    
+    try:
+        row_date = datetime.strptime(date_str[:10], "%Y-%m-%d").date()
+    except ValueError:
+        return False
+    
+    if start_date and row_date < start_date:
+        return False
+    if end_date and row_date > end_date:
+        return False
+    return True
+
+
+def create_arg_parser(description: str) -> argparse.ArgumentParser:
+    """创建通用的命令行参数解析器"""
+    parser = argparse.ArgumentParser(
+        description=description,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+日期筛选说明:
+  --year      指定年份（如 2026）
+  --month     指定月份（1-12）
+  --start     起始日期（YYYY-MM-DD）
+  --end       结束日期（YYYY-MM-DD）
+  
+  多个条件同时指定时，取交集（最小日期范围）
+
+输出格式:
+  --format    输出格式：csv（默认）或 json
+        """
+    )
+    parser.add_argument("input_file", help="输入的账单CSV文件")
+    parser.add_argument("output_file", nargs="?", default=None, 
+                        help="输出文件（默认为 输入文件名_cleaned.csv/json）")
+    parser.add_argument("--year", "-y", type=str, default=None, 
+                        help="保留的年份（如 2026）")
+    parser.add_argument("--month", "-m", type=int, choices=range(1, 13), 
+                        metavar="1-12", help="保留的月份（1-12）")
+    parser.add_argument("--start", "-s", type=str, help="起始日期（YYYY-MM-DD）")
+    parser.add_argument("--end", "-e", type=str, help="结束日期（YYYY-MM-DD）")
+    parser.add_argument("--format", "-f", choices=["csv", "json"], default="csv",
+                        help="输出格式：csv（默认）或 json")
+    return parser
+
+
+def get_output_file(input_file: str, output_file: str | None, output_format: str = "csv") -> str:
+    """获取输出文件路径"""
+    if output_file:
+        return output_file
+    import os
+    base_name = os.path.splitext(input_file)[0]
+    ext = "json" if output_format == "json" else "csv"
+    return f"{base_name}_cleaned.{ext}"
+
+
+# =============================================================================
+# 账单清理基类
+# =============================================================================
+
+class BaseCleaner(ABC):
+    """账单清理基类"""
+    
+    def __init__(self, input_file: str, output_file: str | None = None, output_format: str = "csv"):
+        self.input_file = input_file
+        self.output_format = output_format
+        self.output_file = get_output_file(input_file, output_file, output_format)
+        self.start_date: date | None = None
+        self.end_date: date | None = None
+        
+        # 统计信息
+        self.stats = {
+            "original_count": 0,
+            "filtered_count": 0,
+            "fully_refunded": 0,
+            "partially_refunded": 0,
+            "category_adjusted": 0,
+            "final_count": 0,
+        }
+    
+    def set_date_range(self, start_date: date | None, end_date: date | None):
+        """设置日期筛选范围"""
+        self.start_date = start_date
+        self.end_date = end_date
+    
+    def print_header(self):
+        """打印处理头信息"""
+        print(f"输入文件: {self.input_file}")
+        print(f"输出文件: {self.output_file}")
+        print(f"输出格式: {self.output_format.upper()}")
+        if self.start_date or self.end_date:
+            print(f"日期范围: {self.start_date or '不限'} ~ {self.end_date or '不限'}")
+        else:
+            print("日期范围: 全部")
+        print()
+    
+    def write_output(self, header: list, rows: list):
+        """
+        写入输出文件（支持 CSV 和 JSON 格式）
+        
+        Args:
+            header: 表头列表
+            rows: 数据行列表
+        """
+        if self.output_format == "json":
+            self._write_json(header, rows)
+        else:
+            self._write_csv(header, rows)
+    
+    def _write_csv(self, header: list, rows: list):
+        """写入 CSV 格式"""
+        with open(self.output_file, "w", encoding="utf-8", newline="") as f:
+            writer = csv.writer(f)
+            writer.writerow(header)
+            writer.writerows(rows)
+    
+    def _write_json(self, header: list, rows: list):
+        """写入 JSON 格式"""
+        # 将每行转换为字典
+        data = []
+        for row in rows:
+            record = {}
+            for i, col in enumerate(header):
+                if i < len(row):
+                    record[col] = row[i]
+                else:
+                    record[col] = ""
+            data.append(record)
+        
+        with open(self.output_file, "w", encoding="utf-8") as f:
+            json.dump(data, f, ensure_ascii=False, indent=2)
+    
+    @abstractmethod
+    def clean(self) -> None:
+        """执行清理，子类实现"""
+        pass
+    
+    @abstractmethod
+    def reclassify(self, rows: list) -> list:
+        """
+        重新分类（子类实现）
+        
+        Args:
+            rows: 待处理的数据行
+        
+        Returns:
+            处理后的数据行
+        """
+        pass
+

analyzer/cleaners/wechat.py

@@ -0,0 +1,288 @@
+"""
+微信账单清理模块
+"""
+import csv
+import re
+from decimal import Decimal
+
+from .base import (
+    BaseCleaner, parse_amount, format_amount,
+    is_in_date_range, create_arg_parser
+)
+from category import infer_category
+
+
+# 与支付宝对齐的表头（包含"复核等级"字段）
+ALIGNED_HEADER = [
+    "交易时间", "交易分类", "交易对方", "对方账号", "商品说明",
+    "收/支", "金额", "收/付款方式", "交易状态", "交易订单号", "商家订单号", "备注", "复核等级"
+]
+
+
+class WechatCleaner(BaseCleaner):
+    """微信账单清理器"""
+    
+    def clean(self) -> None:
+        """执行清理"""
+        self.print_header()
+        
+        # 读取数据
+        with open(self.input_file, "r", encoding="utf-8") as f:
+            reader = csv.reader(f)
+            header = next(reader)
+            rows = list(reader)
+        
+        self.stats["original_count"] = len(rows)
+        print(f"原始数据行数: {len(rows)}")
+        
+        # 第一步：按日期范围筛选
+        rows_filtered = [
+            row for row in rows
+            if row and is_in_date_range(row[0], self.start_date, self.end_date)
+        ]
+        self.stats["filtered_count"] = len(rows_filtered)
+        
+        date_desc = f"{self.start_date} ~ {self.end_date}" if self.start_date or self.end_date else "全部"
+        print(f"筛选后数据行数: {len(rows_filtered)} ({date_desc})")
+        
+        # 第二步：分离退款、支出、收入
+        refund_rows, expense_rows, income_rows = self._separate_rows(rows_filtered)
+        
+        print(f"退款条目数: {len(refund_rows)}")
+        print(f"支出条目数: {len(expense_rows)}")
+        print(f"其他收入条目数: {len(income_rows)}")
+        
+        # 第三步：处理退款（包括转账退款）
+        final_expense_rows, income_rows = self._process_refunds(expense_rows, income_rows)
+        
+        print(f"\n处理结果:")
+        print(f"  全额退款删除: {self.stats['fully_refunded']} 条")
+        print(f"  部分退款调整: {self.stats['partially_refunded']} 条")
+        print(f"  保留支出条目: {len(final_expense_rows)} 条")
+        print(f"  保留收入条目: {len(income_rows)} 条")
+        
+        # 第四步：转换为对齐格式并重新分类
+        aligned_expense = [self._convert_and_reclassify(r, remark) for r, remark in final_expense_rows]
+        aligned_income = [self._convert_and_reclassify((r, None), None) for r in income_rows]
+        
+        # 合并并排序
+        final_rows = aligned_expense + aligned_income
+        final_rows.sort(key=lambda x: x[0], reverse=True)
+        
+        # 统计复核数量
+        review_high_count = sum(1 for row in final_rows if row[-1] == "HIGH")
+        
+        self.stats["final_count"] = len(final_rows)
+        print(f"  最终保留行数: {len(final_rows)}")
+        if review_high_count > 0:
+            print(f"  高优先级复核: {review_high_count} 条（无法判断）")
+        
+        # 写入文件
+        self.write_output(ALIGNED_HEADER, final_rows)
+        
+        print(f"\n清理后的数据已保存到: {self.output_file}")
+        
+        # 统计支出
+        self._print_expense_summary(aligned_expense)
+    
+    def _separate_rows(self, rows: list) -> tuple[list, list, list]:
+        """分离退款、支出、收入记录"""
+        refund_rows = []
+        expense_rows = []
+        income_rows = []
+        
+        for row in rows:
+            if len(row) < 6:
+                continue
+            
+            transaction_type = row[1]
+            income_expense = row[4]
+            
+            if "-退款" in transaction_type:
+                refund_rows.append(row)
+            elif income_expense == "支出":
+                expense_rows.append(row)
+            elif income_expense == "收入" and "-退款" not in transaction_type:
+                income_rows.append(row)
+        
+        return refund_rows, expense_rows, income_rows
+    
+    def _process_refunds(self, expense_rows: list, income_rows: list) -> tuple[list, list]:
+        """
+        处理退款（包括转账退款）
+        
+        微信的退款有两种形式：
+        1. 状态标注：支出记录的"当前状态"列标注"已退款"
+        2. 转账退款：同一交易对方有收入记录（转账退回）
+        """
+        # 3.1 识别转账退款
+        transfer_refunds = {}
+        transfer_refund_rows = []
+        
+        for row in income_rows:
+            merchant = row[2].strip()
+            amount = parse_amount(row[5])
+            
+            # 检查是否有对应的支出记录
+            has_matching_expense = any(exp[2].strip() == merchant for exp in expense_rows)
+            
+            if has_matching_expense:
+                if merchant not in transfer_refunds:
+                    transfer_refunds[merchant] = Decimal("0")
+                transfer_refunds[merchant] += amount
+                transfer_refund_rows.append(row)
+        
+        # 从收入中移除已识别的转账退款
+        for row in transfer_refund_rows:
+            income_rows.remove(row)
+        
+        if transfer_refunds:
+            print(f"  识别到转账退款: {len(transfer_refunds)} 笔")
+        
+        # 3.2 处理支出记录
+        final_expense_rows = []
+        
+        for row in expense_rows:
+            status = row[7]
+            merchant = row[2].strip()
+            original_amount = parse_amount(row[5])
+            
+            # 计算总退款金额
+            status_refund = Decimal("0")
+            transfer_refund = transfer_refunds.get(merchant, Decimal("0"))
+            
+            if "已全额退款" in status:
+                self.stats["fully_refunded"] += 1
+                print(f"  全额退款删除: {row[0]} | {row[2]} | {row[3][:25]}... | {row[5]}")
+                continue
+            elif "已退款" in status:
+                status_refund = self._extract_refund_amount(status) or Decimal("0")
+            
+            total_refund = status_refund + transfer_refund
+            
+            if total_refund > 0:
+                if total_refund >= original_amount:
+                    self.stats["fully_refunded"] += 1
+                    print(f"  全额退款删除: {row[0]} | {row[2]} | {row[3][:25]}... | {row[5]}")
+                else:
+                    remaining = original_amount - total_refund
+                    new_row = row.copy()
+                    new_row[5] = f"￥{format_amount(remaining)}"
+                    remark = f"原金额{row[5]},退款￥{total_refund}"
+                    
+                    final_expense_rows.append((new_row, remark))
+                    self.stats["partially_refunded"] += 1
+                    print(f"  部分退款: {row[0]} | {row[2]} | 原{row[5]} -> ￥{format_amount(remaining)}")
+                    
+                    if merchant in transfer_refunds:
+                        del transfer_refunds[merchant]
+            else:
+                final_expense_rows.append((row, None))
+        
+        return final_expense_rows, income_rows
+    
+    def _extract_refund_amount(self, status: str) -> Decimal | None:
+        """从状态中提取已退款金额"""
+        match = re.search(r'已退款[（(]?￥?([\d.]+)[）)]?', status)
+        if match:
+            return Decimal(match.group(1))
+        if "已全额退款" in status:
+            return None
+        return Decimal("0")
+    
+    def _convert_and_reclassify(self, row_tuple: tuple, remark_override: str | None) -> list:
+        """
+        转换为对齐格式并重新分类
+        
+        微信原始字段:
+            0: 交易时间, 1: 交易类型, 2: 交易对方, 3: 商品,
+            4: 收/支, 5: 金额(元), 6: 支付方式, 7: 当前状态,
+            8: 交易单号, 9: 商户单号, 10: 备注
+        
+        对齐后字段:
+            交易时间, 交易分类, 交易对方, 对方账号, 商品说明,
+            收/支, 金额, 收/付款方式, 交易状态, 交易订单号, 商家订单号, 备注, 需复核
+        """
+        if isinstance(row_tuple, tuple):
+            row, remark = row_tuple
+        else:
+            row, remark = row_tuple, None
+        
+        remark = remark_override if remark_override else remark
+        
+        transaction_time = row[0]
+        merchant = row[2]
+        product = row[3]
+        income_expense = row[4]
+        amount = parse_amount(row[5])
+        payment_method = row[6]
+        status = row[7]
+        order_no = row[8]
+        merchant_order_no = row[9] if len(row) > 9 else ""
+        final_remark = remark if remark else (row[10] if len(row) > 10 else "/")
+        
+        # 重新分类（微信原始的"交易类型"太笼统）
+        category, is_certain = infer_category(merchant, product, income_expense)
+        
+        # 复核等级: 空=无需复核, HIGH=无法判断
+        review_mark = "" if is_certain else "HIGH"
+        
+        return [
+            transaction_time,
+            category,
+            merchant,
+            "/",  # 对方账号（微信无此字段）
+            product,
+            income_expense,
+            format_amount(amount),
+            payment_method,
+            status,
+            order_no,
+            merchant_order_no,
+            final_remark,
+            review_mark
+        ]
+    
+    def reclassify(self, rows: list) -> list:
+        """
+        重新分类微信账单
+        
+        微信账单在 _convert_and_reclassify 中已完成分类
+        此方法为接口兼容保留
+        """
+        return rows
+    
+    def _print_expense_summary(self, expense_rows: list):
+        """打印支出统计"""
+        total = Decimal("0")
+        categories = {}
+        
+        for row in expense_rows:
+            if row[5] == "支出":
+                amt = Decimal(row[6])
+                total += amt
+                cat = row[1]
+                categories[cat] = categories.get(cat, Decimal("0")) + amt
+        
+        print(f"清理后支出总额: ￥{total}")
+        print("\n=== 按分类统计 ===")
+        for cat, amt in sorted(categories.items(), key=lambda x: -x[1]):
+            print(f"  {cat}: ￥{amt}")
+
+
+def main():
+    """命令行入口"""
+    parser = create_arg_parser("清理微信支付账单数据")
+    args = parser.parse_args()
+    
+    from .base import compute_date_range
+    
+    cleaner = WechatCleaner(args.input_file, args.output_file)
+    start_date, end_date = compute_date_range(args)
+    cleaner.set_date_range(start_date, end_date)
+    cleaner.clean()
+
+
+if __name__ == "__main__":
+    main()
+

analyzer/config/category.yaml

@@ -0,0 +1,237 @@
+# =============================================================================
+# 交易分类关键词配置
+# 按优先级从上到下匹配，排在前面的分类优先级更高
+# =============================================================================
+
+# =============================================================================
+# 需要重新分类的平台型商家
+# 这些平台的交易类型多样（外卖、电影、酒店、打车等），支付宝原分类可能不准确
+# 只有匹配到以下商家时才会触发分类调整逻辑
+# =============================================================================
+平台商家:
+  - 美团
+  - 京东
+  - 抖音
+  - 饿了么
+  - 拼多多
+  - 携程
+  - 金山便利店
+
+# =============================================================================
+# 支出分类关键词
+# =============================================================================
+支出分类:
+  # 医疗健康 - 最高优先级
+  医疗健康:
+    - 医院
+    - 药房
+    - 药店
+    - 诊所
+    - 健康
+    - 皮肤
+    - 医疗
+    - 体检
+    - 口腔
+    - 牙科
+    - 眼科
+    - 中医
+    - 西医
+    - 门诊
+    - 挂号
+
+  # 交通出行
+  交通出行:
+    - 出行
+    - 打车
+    - 单车
+    - 骑行
+    - 骑安      # 广州骑安共享单车
+    - 滴滴
+    - 高德
+    - 班车
+    - 通勤
+    - 公交
+    - 地铁
+    - 火车
+    - 机票
+    - 航空
+    - 共享
+    - 京庐      # 京庐出行
+    - 哈啰
+    - 美团单车
+    - 青桔
+    - 摩拜
+    - 加油
+    - 停车
+    - 高速
+    - 过路费
+    - "12306"
+    - 携程
+    - 飞猪
+
+  # 充值缴费
+  充值缴费:
+    - 充值
+    - 缴费
+    - 水费
+    - 电费
+    - 燃气
+    - 话费
+    - 流量
+    - 宽带
+    - 物业
+    - 房租
+    - 供暖
+    - 暖气
+
+  # 运动健身
+  运动健身:
+    - 健身
+    - 健身房
+    - 运动
+    - 滑雪
+    - 冰雪
+    - 游泳
+    - 瑜伽
+    - 跑步
+    - 球场
+    - 篮球
+    - 足球
+    - 羽毛球
+    - 乒乓
+    - 网球
+    - 众雪      # 众雪滑雪用品店
+    - 雪具
+    - 滑板
+    - 攀岩
+    - 骑行装备
+
+  # 文化休闲（优先级高于日用百货，避免万达影城被识别为万达商场）
+  文化休闲:
+    - 影城      # 电影院
+    - 影院
+    - 电影
+    - 游戏
+    - 娱乐
+    - 旅游
+    - 景区
+    - 门票
+    - 会员
+    - 视频
+    - 音乐
+    - KTV
+    - 网吧
+    - 桌游
+    - 剧本杀
+    - 密室
+    - 酒吧
+    - 演出
+    - 演唱会
+    - 酒店      # 美团酒店
+    - 如家
+    - 汉庭
+    - 7天
+    - 民宿
+
+  # 日用百货 - 盒马放在前面，避免被餐饮匹配
+  日用百货:
+    - 京邦达    # 京东物流
+    - 快递
+    - 拼多多
+    - 便利店
+    - 超市
+    - 商场
+    - 购物
+    - 永辉
+    - 华润
+    - 万达
+    - 苏宁
+    - 国美
+    - 得物
+    - 唯品会
+    - 可多      # Codow可多便利店
+    - 全家
+    - "7-11"
+    - 罗森
+    - 美宜佳
+    - 妍丽      # AFIONA妍丽美妆店
+    - 屈臣氏
+    - 丝芙兰
+
+  # 餐饮美食
+  餐饮美食:
+    - coffee
+    - 咖啡
+    - luckin
+    - 瑞幸
+    - 星巴克
+    - starbucks
+    - 食堂
+    - 订餐
+    - 餐厅
+    - 饭店
+    - 饮品
+    - 美食
+    - 烧烤
+    - 火锅
+    - 面馆
+    - 粥
+    - 小吃
+    - 甜品
+    - 蛋糕
+    - 麦当劳
+    - 肯德基
+    - 必胜客
+    - 汉堡王
+    - 德克士
+    - 奶茶
+    - 茶饮
+    - 喜茶
+    - 奈雪
+    - 蜜雪
+    - 茶百道
+    - 书亦
+    - 外卖
+    - 饿了么
+    - 美团外卖
+    - 半秋山    # 西餐厅
+    - 西餐
+    - 中餐
+    - 日料
+    - 韩餐
+    - 川菜
+    - 湘菜
+    - 酸汤
+    - 黔有财    # 贵州酸汤火锅
+    - 烤鸭
+    - 臭豆腐
+    - 炸鸡
+    - 披萨
+    - 寿司
+    - 拉面
+    - 米线
+    - 螺蛳粉
+    - 串串
+    - 冒菜
+    - 麻辣烫
+    - 黄焖鸡
+    - 煲仔饭
+    - 蛙来哒    # 牛蛙餐厅
+    - 粒上皇    # 炒货零食店
+
+  # 转账红包
+  转账红包:
+    - 红包
+    - 转账给
+
+
+收入分类:
+  退款:
+    - 退款
+  其他收入: []
+
+
+默认分类:
+  支出: 其他支出
+  收入: 其他收入
+

analyzer/requirements.txt

@@ -0,0 +1,2 @@
+pyyaml>=6.0
+