Finance Trend Studio

A zero-build, single-file business analytics web tool. Upload an Excel spreadsheet of time-series financials and instantly get growth metrics, trend charts, forecasts, and best/base/worst scenario modelling — all client-side, no server, no backend.

Live demo on GitHub Pages →

Features

• Excel upload (.xlsx, .xls, .csv) parsed in-browser with SheetJS.
• Auto-detection of date / period column and numeric metrics — no schema config needed.
• Key metrics — Latest, Mean, MoM Δ, YoY Δ, CAGR, Volatility (CV), Min / Max.
• Forecasting — choose between linear regression (least-squares) or moving average, project 1–60 future periods.
• Scenario modelling — Best / Base / Worst sliders apply compounding annual growth deltas to the base forecast.
• Charts — line or bar visualisation via Chart.js, with solid history and dashed forecast bands.
• Data preview — collapsible table of historical and forecast values.
• 100% client-side — drop the file open it locally, or host it as a static page.

Quick start

Run locally

git clone https://github.com/alfredang/financialtrend.git
cd financialtrend
# just open it
start index.html       # Windows
open  index.html       # macOS
xdg-open index.html    # Linux

No npm, no build step, no dependencies to install. Libraries (SheetJS, Chart.js) are loaded from CDN.

Try without uploading

Open the page with ?demo=1 (or click Load Sample) for a built-in 24-month synthetic revenue/COGS/OpEx/profit dataset.

Expected Excel shape

The first column with date-like values becomes the x-axis; every other numeric column is selectable as a metric.

Period	Revenue	COGS	OpEx	Net Profit
2024-01	12000	4000	3000	5000
2024-02	13500	4200	3100	6200
...	...	...	...	...

Supported period formats: YYYY-MM, YYYY-MM-DD, MM/YYYY, Q1 2024, plain YYYY, and Excel date serials.

How the math works

Metric	Formula
MoM Δ	(v[n] − v[n−1]) / v[n−1]
YoY Δ	(v[n] − v[n−12]) / v[n−12] (or n−4 for quarterly)
CAGR	(v[end] / v[start])^(1 / years) − 1
Volatility (CV)	stdev(v) / mean(v)
Linear forecast	Least-squares fit y = m·x + b, projected forward
Moving avg forecast	Rolling SMA, window = 3
Scenario adjustment	forecast × (1 + Δ_annual)^(t / steps_per_year)

Tech stack

• HTML + CSS Grid + vanilla JavaScript (ES2020)
• SheetJS / xlsx — Excel parsing
• Chart.js 4 — charts

Deployment

Pushing to main triggers a GitHub Actions workflow (.github/workflows/pages.yml) that publishes the site to GitHub Pages. To enable:

1. Settings → Pages → Source: GitHub Actions.
2. Push a commit to main — the workflow does the rest.

Licence

MIT.