The bet
"Develop consistency through process, not outcome."
Most retail traders fail because they lack structured processes and risk management. I built TraderEdge to centralize PnL calculations, enforce strict daily and weekly loss limits, and provide performance analytics that force disciplined behavior.
What it does
TraderEdge is essentially an institutional-grade risk desk for a discretionary retail trader.
- Trade Journaling: Automatically logs trades, entry/exit points, and emotional state.
- Server-verified PnL Limits: Enforces robust, database-driven daily and weekly loss limits. If the limit is hit, the system acts as a defensive gatekeeper and blocks further execution.
- Automated Data Sync: Integrates directly with exchange APIs to keep the UI accurately synced after every trade activity.
- Vencotrade Integration: Interfaces with a separate algorithmic trading system written in Python.
Architecture
- Frontend: Next.js
- Database: Supabase Postgres, managed via Prisma ORM
- Integrations: Binance API for execution and market data
- AI: Google Gemini integration for post-trade analysis and performance coaching
Why build this?
Trading is the ultimate test of systems thinking. Building TraderEdge was an exercise in creating a rigid technological boundary against human emotional failure.
It also served as the perfect testbed for building highly responsive, state-managed applications where a 500ms delay in syncing state can mean the difference between a profitable execution and a substantial loss.