Product
What this is
QuantTrade is a trading research terminal: market data, geopolitical context, and tooling in one place. It is maintained as a product, not a demo — what you see depends on which APIs and database you have wired up.
Operator
Yash Joshi
Engineer; built the stack end-to-end from data ingestion to UI. Motivation was straightforward: retail tooling rarely matches the density of a proper desk, and spreadsheets are a poor substitute for map-backed context.
Capabilities
Research workspace
Symbol pages with prices, charts, indicators, and filing-linked search. Outputs cite sources where the pipeline supports it.
Global Monitor
A single screen for geographic risk: globe, feeds, market-facing panels, and correlation hints. Data freshness depends on upstream APIs and your backend configuration.
Backtesting
Rule-based strategy runs over historical bars. Results are illustrative; they are not a promise of live performance.
Markets overview
Indices, movers, and sector views updated on a schedule appropriate to the data tier you run.
Ideas Lab
Curated setups and narratives derived from internal scoring. Treat them as starting points, not orders.
Data sources
13 named integrations; availability and rate limits vary by key and plan.
- Finnhub
- Quotes and fundamentals
- Alpha Vantage
- Historical OHLCV
- SEC EDGAR
- Filings (via sec-api.io where configured)
- GDELT
- Global event corpus
- ACLED
- Conflict and protest locations
- FRED
- U.S. macro series
- NASA FIRMS
- Fire hotspots
- USGS
- Earthquakes
- OpenSky
- Aviation ADS-B
- AIS Stream
- Maritime positions
- EIA
- U.S. energy statistics
- Polymarket
- Prediction markets
- Guardian
- News by region
Stack
- FrontendNext.js (App Router), React, Tailwind, Lightweight Charts, Three.js where needed.
- BackendFastAPI, SQLAlchemy, background jobs with Celery when enabled.
- DataPostgreSQL (e.g. Neon), optional Redis; external APIs as listed above.
Risk
Securities trading involves loss of principal. This site provides software and information, not personalized investment, tax, or legal advice. Models and automations can be wrong, late, or biased by training data. Past backtests do not predict future results. You are responsible for decisions and for confirming any fact that matters to your thesis.