Finertia — Momentum Backtesting SaaS
A momentum-strategy backtesting platform with a pure pandas/numpy engine (no backtesting libraries), a React front end, and a cost-capped Cloud Run backend. Live.
Finertia is a quantitative backtesting SaaS for momentum strategies — configure a strategy, run it against historical data, and see how it would have performed. It is live at finertia.hulage.in.
The problem
Off-the-shelf backtesting libraries hide the mechanics that actually decide whether a result is trustworthy — how signals are ranked, when positions rebalance, how look-ahead bias is avoided. I built the engine myself so every one of those decisions is explicit and auditable.
How it works
- Pure engine — the backtester is written entirely on pandas/numpy with no backtesting frameworks, giving full control over signal ranking, rebalancing, and position sizing.
- Product surface — a React front end for strategy configuration, run history, and result visualization, so a non-programmer can drive it end to end.
- Cost-safe deploy — FastAPI backend on Cloud Run (asia-south1) behind a Vercel same-origin proxy, with hard cost caps so a public demo can’t run up a bill.
Stack
FastAPI + React + Firebase, pandas/numpy for the numerics, Cloud Run + Vercel for hosting. Built and submitted for a BFSI fintech pitch competition.