03 — Story
The problem.
Manual trading research is slow, emotional, and difficult to verify. I needed a system that could test ideas against historical data, track assumptions, and keep live automation under control.
What I built.
I built Forge as a modular Python and Next.js system for strategy research, realistic backtesting, dashboards, risk controls, and alerting. It stays private, but it remains strong evidence for complex backend and data-heavy work.
01
Strategy Research
Python modules for testing ideas, reviewing results, and comparing strategy behavior.
02
Backtesting Pipeline
Historical testing flows that account for real execution details instead of relying on toy examples.
03
Risk Controls
Guardrails, stop conditions, and monitoring logic built around protecting the system before adding more features.
04
Operational Dashboard
Next.js dashboard work for monitoring system state, logs, and current activity.
05
Alerts and Handoffs
Telegram-style alerting and durable logs so system events do not disappear into the terminal.
06
Protected Maintenance
Forge is kept stability-first and separate from the public portfolio demo experiments.
