Turning complex market data into a focused trader-facing product
We built a membership-based financial analytics and trading intelligence portal for retail day traders and swing traders.
The product ingests market and derivatives data, processes proprietary calculations and presents the output through charts, snapshots, watchlists and market dashboards.
The challenge
The application needed to combine frequent market-data updates, specialized quantitative calculations, historical time-series storage, chart overlays, user accounts, personalized watchlists and administrative controls.
Data freshness and resilience were especially important because the product is used around rapidly changing market information.
What we built
- Financial and market-data API integrations
- Historical time-series storage
- Python quantitative calculation services
- Expected Move calculations
- Gamma Flip and Net Gamma Exposure
- Max Pain and volatility metrics
- Interactive TradingView chart overlays
- Ticker snapshot dashboards
- Market sector visualization
- Personalized watchlists
- Member authentication
- Admin and user management
- Scheduled market-data refresh
How the system works
External market data enters the backend, is normalized and stored, then specialized Python services calculate the proprietary trading metrics.
The processed levels are returned to the web application and overlaid directly on interactive charts so users can understand the calculations in market context.
Architecture & technology
Laravel provides the backend application layer, Next.js the frontend, PostgreSQL with TimescaleDB the time-series data layer, and Python specialized quantitative processing.
The separation between application logic and calculation services makes it easier to evolve the product and introduce future intelligence features.
- Laravel
- Next.js
- Python
- PostgreSQL
- TimescaleDB
- TradingView Lightweight Charts
- Financial APIs
- Scheduled processing
Outcome & impact
The platform centralizes market data, derived metrics, historical context and trader-specific workflows inside one membership product.
Instead of requiring users to collect information from multiple sources and calculate levels manually, the system turns the workflow into a repeatable visual product.
Relevant experience
- Fintech SaaS
- Financial APIs
- Python services
- Time-series data
- Quantitative processing
- Interactive charts
- Membership platforms
- Scheduled jobs
Planning something similar?
Tell us what the users, workflows, integrations and commercial model need to do. We can help shape the product around the real operation.