spending-review

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Spending Review

支出回顾

Overview

概述

Produces a categorized breakdown of your spending for a given period, compares it against prior months to show trends, and highlights categories where spending increased or decreased significantly.

生成指定时间段内的支出分类明细，与前几个月的数据进行对比以展示趋势，并突出支出大幅增加或减少的类别。

Wilson Tools Used

使用Wilson的工具

```
spending_summary
```
— get total spending grouped by category for any date range
```
transaction_search
```
— drill into specific categories for transaction-level detail

```
spending_summary
```
— 获取任意日期范围内按类别分组的总支出
```
transaction_search
```
— 深入查看特定类别的交易级详情

Workflow

工作流程

Run
```
spending_summary
```
for the current month to get category-level totals.
Run
```
spending_summary
```
for the previous month to establish a comparison baseline.
Calculate the dollar change and percentage change for each category between the two months.
Flag any category where spending increased by more than 20% or more than $100.
For each flagged category, run
```
transaction_search
```
with
```
category: "<flagged_category>"
```
and
```
months: 1
```
to pull the individual transactions driving the increase.
Present results as a summary table with columns: Category, This Month, Last Month, Change ($), Change (%), Trend Arrow.
Below the table, list the top 3 categories by absolute spending and the top 3 by percentage increase.
Provide a one-paragraph narrative summary describing the overall spending pattern and notable changes.

针对当月运行
```
spending_summary
```
，获取类别级别的总支出。
针对上月运行
```
spending_summary
```
，建立对比基准。
计算每个类别在两个月之间的金额变化和百分比变化。
标记支出增加超过20%或超过100美元的类别。
对于每个标记的类别，运行
```
transaction_search
```
并设置
```
category: "<flagged_category>"
```
和
```
months: 1
```
，提取推动支出增长的单独交易记录。
将结果整理为汇总表格，列包括：类别、本月、上月、变化金额（美元）、变化百分比、趋势箭头。
在表格下方，列出绝对支出最高的3个类别和百分比增长最高的3个类别。
提供一段叙述性总结，描述整体支出模式和显著变化。

Without Wilson

不使用Wilson的方法

Export your last two months of transactions as CSV files from your bank (most banks: Account > Statements > Download > CSV format).
Open both CSVs in Google Sheets. Combine them into one sheet with a "Month" column added.
If your bank doesn't categorize transactions, manually add a "Category" column. Common categories: Groceries, Dining, Transportation, Utilities, Entertainment, Shopping, Health, Subscriptions.
Create a pivot table: Rows = Category, Columns = Month, Values = SUM of Amount.
Add a calculated column for change:
```
=B2-C2
```
(this month minus last month).
Add a percentage change column:
```
=IF(C2<>0, (B2-C2)/ABS(C2)*100, "New")
```
.
Conditional format the change column: red for increases, green for decreases (since spending is negative, reverse the logic or use absolute values).
Sort by absolute change descending to see your biggest movers.
For a quick visual, insert a bar chart from the pivot table showing this month vs. last month by category.

从银行导出最近两个月的交易记录为CSV文件（大多数银行操作：账户 > 对账单 > 下载 > CSV格式）。
在Google Sheets中打开两个CSV文件，将它们合并到一个工作表中，并添加"月份"列。
如果银行未对交易进行分类，手动添加"类别"列。常见类别：食品杂货、餐饮、交通、公用事业、娱乐、购物、健康、订阅。
创建数据透视表：行 = 类别，列 = 月份，值 = 金额总和。
添加计算列以显示变化：
```
=B2-C2
```
（本月减上月）。

添加百分比变化列：

=IF(C2<>0, (B2-C2)/ABS(C2)*100, "新增")

。

为变化列设置条件格式：增长显示红色，减少显示绿色（由于支出为负值，需反转逻辑或使用绝对值）。
按绝对变化降序排序，查看变化最大的类别。
为快速可视化，从数据透视表插入柱状图，展示各类别本月与上月的对比。

Important Notes

重要说明

Category accuracy depends on your bank's auto-categorization or Wilson's categorization rules. Run the
```
categorize
```
tool first if many transactions are uncategorized.
One-time large purchases (appliances, travel, medical) can skew month-over-month comparisons. Consider whether a spike is a true trend or an outlier.
For a more meaningful view, compare against a 3-month rolling average rather than just the prior month.

分类的准确性取决于银行的自动分类或Wilson的分类规则。如果大量交易未分类，请先运行
```
categorize
```
工具。
一次性大额采购（家电、旅行、医疗）可能会影响月度环比对比。请考虑支出激增是真实趋势还是异常值。
为获得更有意义的视图，建议与3个月滚动平均值对比，而非仅与上月对比。