TradingAnalyzer.ai is an AI-powered Forex analysis platform that consolidates market intelligence into actionable daily trade setups, reducing hours of manual research to ~15 minutes per day. I designed and built the entire backend from scratch as the sole backend developer.

Architecture overview

The system is built as a set of services running on Kubernetes, composed of two distinct layers: a data ingestion layer in Python and a scoring, API, and real-time delivery layer in Kotlin/Spring Boot.

Data ingestion

The ingestion layer is built primarily in Kotlin, with select scrapers written in Python where ecosystem fit made more sense (e.g. data parsing libraries). A set of scheduled pipelines continuously fetch, parse, and normalise data from multiple sources:

• COT reports — CFTC Commitment of Traders data, parsed and transformed into structured signals
• News & RSS feeds — market news ingested and preprocessed for relevance scoring
• Broker / market data APIs — live price feeds and OHLCV data for currency pairs
• Banking & institutional reports — parsed from structured sources into actionable signals

All ingested data lands in PostgreSQL, with Typesense powering fast full-text search across reports and trade setups.

Scoring engine & API

The core of the platform is a custom multi-factor scoring algorithm that aggregates signals from all data sources and produces a ranked score per currency pair. The engine runs on a scheduled cycle and exposes results through a REST API consumed by the frontend.

Real-time score updates and new trade setups are pushed to clients via WebSockets, also served from Spring Boot — eliminating the need for polling on the frontend side.

Subscriptions & billing

Integrated a payment and subscription system managing the platform's 2,000+ subscriber base, including plan lifecycle, webhooks, and access control gating.