Financial Report Automator

Automated quarterly financial report generation with dividend-adjusted total return calculations. Use when generating stock performance reports, quarterly earnings summaries, or return analytics for any ticker with CSV price data.

Generates quarterly stock performance reports with dividend-adjusted total return.

What It Does

• Parses CSV price data (Yahoo Finance export format)
• Computes price return (raw close) and total return (adjusted close with dividends)
• Produces structured quarterly reports: start/end price, returns, high/low, avg volume, dividends paid

Key Fix (v1.0.1)

Previous versions used raw close for return calculations, which understated returns for dividend-paying stocks like AAPL. v1.0.1 introduces:

1. Column name normalization — handles mixed-case CSV headers (Date → date)
2. Adjusted close computation — backward-adjusts prices for dividends so total return reflects reinvested dividends
3. Separate price_return vs total_return — both are reported for transparency
4. Dividend tracking — dividend column is optional; defaults to 0 if absent

Usage

As a Python module

python

from scripts.stock_analyzer import StockAnalyzer

analyzer = StockAnalyzer(data_path="finance/AAPL.csv")

# 30-day total return (dividend-adjusted)
print(analyzer.calculate_returns(30))

# 30-day price return only (no dividends)
print(analyzer.calculate_returns(30, use_adj=False))

# Full quarterly report
report = analyzer.generate_quarterly_report(quarter=2, year=2026)
print(report)

CLI

bash

python3 scripts/stock_analyzer.py

CSV Format Expected

text

Date,Close,Volume,Open,High,Low
01/02/2026,$269.77,21755774,$272.05,$277.82,$269.11

Optional columns:

• dividend — per-share dividend on ex-div date
• adj_close — pre-computed adjusted close (overrides internal calculation)

Report Output

json

{
  "quarter": "Q2 2026",
  "start_date": "2026-04-01",
  "end_date": "2026-06-30",
  "start_price": 223.0,
  "end_price": 235.5,
  "price_return_pct": 5.61,
  "total_return_pct": 6.12,
  "dividends_per_share": 0.25,
  "highest_price": 240.0,
  "lowest_price": 218.5,
  "average_volume": 35000000
}

Requirements

• Python 3.10+
• pandas