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Overview

The Reports feature transforms your transaction data into actionable insights through interactive charts, spending breakdowns, and exportable reports. Analyze your financial habits across different time periods and wallets.

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Report Types

Pie Chart

Category breakdown showing spending distribution

Bar Chart

Income vs Expenses vs Transfers comparison

Line Chart

Spending trends over time (daily or monthly)

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Time Range Filters

Reports support flexible time range selection: 
# From app/routes/report.py:26-43
def get_date_range(time_range):
    today = date.today()
    if time_range == 'this_month':
        start_date = date(today.year, today.month, 1)
        last_day = calendar.monthrange(today.year, today.month)[1]
        end_date = date(today.year, today.month, last_day)
    elif time_range == 'last_month':
        first_of_this_month = date(today.year, today.month, 1)
        end_date = first_of_this_month - timedelta(days=1)
        start_date = date(end_date.year, end_date.month, 1)
    elif time_range == 'year':
        start_date = date(today.year, 1, 1)
        end_date = date(today.year, 12, 31)
Time ranges are calculated dynamically based on the current date, ensuring accurate month and year boundaries.

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Generating Report Data

1

Request Report Data

Call the report API with filters:
GET /api/reports/data?time_range=this_month&wallet_id=all&type=expense
Parameters:
  • time_range: this_month, last_month, or year
  • wallet_id: Specific wallet ID or all
  • type: expense or income
2

System Builds Base Query

The backend constructs a filtered query (app/routes/report.py:64-70):
query = db.session.query(Transaction).filter(
    Transaction.user_id == user_id, 
    Transaction.date >= start_date, 
    Transaction.date <= end_date
)
if wallet_id != 'all' and wallet_id: 
    query = query.filter(Transaction.wallet_id == wallet_id)
3

Generate Chart Data

The system creates three chart datasets and a summary in a single response
4

Receive Comprehensive Report

Get all visualizations and statistics in one API call

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Chart Implementations

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Pie Chart: Category Breakdown

Shows spending distribution across categories: 
# From app/routes/report.py:72-76
cat_query = query.filter(Transaction.type == db_type)
category_stats = cat_query.with_entities(
    Category.name, func.sum(Transaction.amount)
).outerjoin(Category, Transaction.category_id == Category.id)
 .group_by(Category.name).all()

pie_labels = [item[0] if item[0] else "Chưa phân loại" for item in category_stats]
pie_data = [float(item[1]) for item in category_stats]
Example response: 
{
  "pie_chart": {
    "labels": ["Ăn uống", "Xăng xe", "Giải trí"],
    "data": [3500000, 1200000, 800000]
  }
}

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Bar Chart: Transaction Type Summary

Compares total income, expenses, and transfers: 
# From app/routes/report.py:78-88
type_stats = query.with_entities(
    Transaction.type, func.sum(Transaction.amount)
).group_by(Transaction.type).all()

totals = {'thu': 0, 'chi': 0, 'chuyen': 0}
for t_type, amount in type_stats:
    if t_type in totals:
        totals[t_type] = float(amount)
        
bar_chart_data = {
    "labels": ["Thu nhập", "Chi tiêu", "Chuyển khoản"], 
    "data": [totals['thu'], totals['chi'], totals['chuyen']]
}
Example response: 
{
  "bar_chart": {
    "labels": ["Thu nhập", "Chi tiêu", "Chuyển khoản"],
    "data": [15000000, 5500000, 2000000]
  }
}
The line chart adapts based on time range:

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Monthly View (for ‘this_month’ or ‘last_month’)

# From app/routes/report.py:102-108
# Group by day
daily_data = {}
for t in transactions_for_line:
    day_str = t.date.strftime('%d/%m')
    daily_data[day_str] = daily_data.get(day_str, 0) + float(t.amount)
line_chart_data["labels"] = list(daily_data.keys())
line_chart_data["data"] = list(daily_data.values())
Example response: 
{
  "line_chart": {
    "labels": ["01/03", "02/03", "03/03", "04/03", "05/03"],
    "data": [250000, 180000, 320000, 150000, 420000]
  }
}

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Yearly View (for ‘year’)

# From app/routes/report.py:94-100
# Group by month
monthly_data = {m: 0 for m in range(1, 13)}
for t in transactions_for_line:
    monthly_data[t.date.month] += float(t.amount)
line_chart_data["labels"] = [f"Tháng {m}" for m in range(1, 13)]
line_chart_data["data"] = [monthly_data[m] for m in range(1, 13)]
Example response: 
{
  "line_chart": {
    "labels": ["Tháng 1", "Tháng 2", "Tháng 3"],
    "data": [4500000, 5200000, 5500000]
  }
}
The system automatically chooses daily or monthly grouping based on the selected time range, optimizing readability.

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Top Spending Categories

The report includes a ranked list of categories: 
# From app/routes/report.py:110-121
top_cat_query = query.filter(Transaction.type == 'chi')
    .with_entities(Category.name, func.sum(Transaction.amount))
    .outerjoin(Category, Transaction.category_id == Category.id)
    .group_by(Category.name)
    .order_by(func.sum(Transaction.amount).desc()).all()

total_expense_period = sum([item[1] for item in top_cat_query])
top_spending_list = [
    {
        "category": name if name else "Chưa phân loại", 
        "amount": float(amount), 
        "amount_formatted": "{:,.0f} đ".format(amount).replace(",", "."), 
        "percent": round((amount / total_expense_period * 100), 1)
    } 
    for name, amount in top_cat_query
]
Example response: 
{
  "top_spending": [
    {
      "category": "Ăn uống",
      "amount": 3500000,
      "amount_formatted": "3.500.000 đ",
      "percent": 63.6
    },
    {
      "category": "Xăng xe",
      "amount": 1200000,
      "amount_formatted": "1.200.000 đ",
      "percent": 21.8
    }
  ]
}

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Financial Summary

Each report includes a summary section: 
{
  "summary": {
    "total_income": 15000000,
    "total_expense": 5500000,
    "balance": 9500000
  }
}
The balance represents net cash flow for the selected period.

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Export to Excel

Export your transaction data to Excel format: 
GET /api/reports/export/excel?time_range=this_month
The system uses pandas to generate Excel files: 
# From app/routes/report.py:138-163
transactions = query.all()

data_list = []
for t in transactions:
    loai = "Chi tiêu" if t.type == 'chi' else ("Thu nhập" if t.type == 'thu' else "Chuyển khoản")
    data_list.append({
        "Ngày": t.date.strftime('%d/%m/%Y'), 
        "Danh mục": t.category.name if t.category else "Chưa phân loại", 
        "Nội dung": t.description, 
        "Số tiền": t.amount, 
        "Loại": loai, 
        "Ví": t.wallet.name if t.wallet else "Không xác định"
    })

df = pd.DataFrame(data_list)
output = BytesIO()
with pd.ExcelWriter(output, engine='openpyxl') as writer:
    df.to_excel(writer, index=False, sheet_name='Báo cáo')
output.seek(0)
return send_file(output, download_name=f"Bao_cao_{time_range}_{date.today()}.xlsx", as_attachment=True)
The Excel file includes all transactions in the selected time range with proper Vietnamese formatting.

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Export to PDF

Generate a printable PDF report: 
GET /api/reports/export/pdf?time_range=this_month
The PDF export renders an HTML template with transaction data: 
# From app/routes/report.py:165-192
transactions = db.session.query(Transaction).filter(
    Transaction.user_id == user_id, 
    Transaction.date >= start_date, 
    Transaction.date <= end_date
).outerjoin(Category, Transaction.category_id == Category.id)
 .outerjoin(Wallet, Transaction.wallet_id == Wallet.id)
 .order_by(Transaction.date.desc()).all()
     
total_income = sum(t.amount for t in transactions if t.type == 'thu')
total_expense = sum(t.amount for t in transactions if t.type == 'chi')

return render_template('user/pdf_report.html', 
    transactions=transactions, 
    start_date=start_date.strftime('%d/%m/%Y'), 
    end_date=end_date.strftime('%d/%m/%Y'), 
    total_income=total_income, 
    total_expense=total_expense, 
    user_name=session.get('user_name', 'Người dùng'), 
    today=date.today().strftime('%d/%m/%Y')
)
The template (user/pdf_report.html) formats the data for printing.

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Complete API Response

A full report API response combines all components: 
{
  "pie_chart": {
    "labels": ["Ăn uống", "Xăng xe", "Giải trí"],
    "data": [3500000, 1200000, 800000]
  },
  "bar_chart": {
    "labels": ["Thu nhập", "Chi tiêu", "Chuyển khoản"],
    "data": [15000000, 5500000, 2000000]
  },
  "line_chart": {
    "labels": ["01/03", "02/03", "03/03", "04/03", "05/03"],
    "data": [250000, 180000, 320000, 150000, 420000]
  },
  "top_spending": [
    {
      "category": "Ăn uống",
      "amount": 3500000,
      "amount_formatted": "3.500.000 đ",
      "percent": 63.6
    }
  ],
  "summary": {
    "total_income": 15000000,
    "total_expense": 5500000,
    "balance": 9500000
  }
}

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AI-Powered Dashboard Insights

The dashboard includes AI-generated financial insights: 
GET /api/dashboard-insights
This feature uses the chatbot engine to generate personalized advice: 
# From app/routes/ai.py:168-225
transactions = Transaction.query.filter(
    Transaction.user_id == user_id,
    Transaction.date >= first_day
).all()

total_income = sum(t.amount for t in transactions if t.type == 'thu')
total_expense = sum(t.amount for t in transactions if t.type == 'chi')

context_text = f"Thống kê tháng này - Thu nhập: {int(total_income):,} VND. Chi tiêu: {int(total_expense):,} VND."

question = (
    "Dựa vào số liệu trên, hãy đưa ra 3 lời khuyên tài chính. "
    "YÊU CẦU NGHIÊM NGẶT ĐỂ ĐỊNH DẠNG: "
    "1. TUYỆT ĐỐI KHÔNG chào hỏi, KHÔNG xưng hô. "
    "2. TUYỆT ĐỐI KHÔNG có câu dẫn dắt. "
    "3. Trả về ĐÚNG 3 dòng, mỗi dòng là một lời khuyên trực tiếp. "
    "4. Độ dài tối đa: Dưới 15 chữ cho MỖI dòng. "
    "5. Không dùng markdown, không dùng icon."
)
Response: 
{
  "status": "success",
  "data": [
    "Chi tiêu đang vượt 36% so với thu nhập",
    "Nên cắt giảm ăn uống xuống 2.5 triệu",
    "Cố gắng tiết kiệm ít nhất 20% thu nhập"
  ]
}
The AI prompt is heavily constrained to return exactly 3 short, actionable tips in a consistent format for dashboard display.

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Disabling AI Insights

Users can disable AI suggestions in settings: 
# From app/routes/ai.py:171-177
user_setting = UserSetting.query.get(user_id)
if user_setting and user_setting.ai_suggestions == 0:
    return jsonify({
        'status': 'disabled',
        'message': 'Tính năng AI đang bị tắt.'
    })

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Performance Optimizations

The reports system uses several optimizations:

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1. Single Query for Type Statistics

# From app/routes/report.py:78-83
# Groups all transaction types in ONE query instead of 3 separate queries
type_stats = query.with_entities(
    Transaction.type, func.sum(Transaction.amount)
).group_by(Transaction.type).all()

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2. Explicit JOIN Operations

# From app/routes/report.py:173-178
# Explicitly joins related tables to prevent N+1 query problems
.outerjoin(Category, Transaction.category_id == Category.id)
.outerjoin(Wallet, Transaction.wallet_id == Wallet.id)

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3. Aggregation at Database Level

All summation and grouping happens in SQL rather than Python, leveraging database performance.

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User Interface Flow

1

Access Reports Page

Navigate to /reports in the web interface
2

Select Filters

Choose time range (this month, last month, year) and wallet
3

View Interactive Charts

Charts render dynamically using the report data API
4

Analyze Top Spending

Review the ranked category list with percentages
5

Export if Needed

Download Excel or PDF for external analysis or record-keeping

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