Cybersecurity & Risk professional building systematic trading systems.
I work in IS/IT Risk and Compliance by day — designing control frameworks, evaluating enterprise risk, and navigating regulatory complexity across global environments. Outside of that, I apply the same structured, risk-first thinking to quantitative finance: researching, designing, and testing algorithmic trading strategies for futures markets.
This GitHub documents that second track.
The repositories follow a deliberate pipeline — from learning to research to production-ready systems:
| Repository | Purpose |
|---|---|
| easylanguage-learning | Structured learning path for EasyLanguage (TradeStation / MultiCharts): fundamentals, indicators, strategy architecture |
| systematic-trading-research | Research lab: paper replications, experiments, hypothesis testing before promotion to production |
| algorithmic-trading-strategies (in progress) | Validated, production-ready strategies organized by category |
Primary focus: EasyLanguage (strategy development), Python (data analysis, backtesting support, quantitative research)
- Strategy design grounded in robustness testing and out-of-sample validation
- Risk management as a first-class component, not an afterthought
- Incremental development: ideas are documented, tested, and promoted only when they demonstrate statistical validity
- Current focus: equity index futures (ES, NQ) with planned expansion to bonds and commidities (Gold, Silver)