ARES is a personal, always-on finance homelab designed to analyze corporate earnings events through a structured, institutional-style workflow.
Built at the intersection of quantitative finance, event-driven market research, and infrastructure engineering, ARES aims to replicate—at a small, educational scale—the analytical discipline used by professional buy-side desks during earnings season.
ARES is not a trading bot and does not execute orders.
Its purpose is to deliver clear, explainable, and statistically grounded earnings intelligence.
ARES was developed with three main objectives:
-
Understand earnings-driven price dynamics
By studying EPS surprises and historical post-earnings price reactions. -
Build a production-inspired research system
Running continuously on a personal server, with disciplined data handling and reproducible logic. -
Develop institutional market intuition
By transforming raw earnings data into concise, desk-style briefings supported by historical evidence.
This project is part of a long-term academic and professional trajectory toward quantitative trading and systematic research.
ARES runs continuously on a lightweight personal homelab:
- Raspberry Pi 5 (Pironman 5 case)
- Headless Linux environment
- Python-based services
- JSON for structured storage
- Event-driven execution aligned with earnings calendars
- Local LLM-8850 accelerator (TBD)
| Module | Status |
|---|---|
| Raspberry Pi construction | ✅ Done |
Event-driven orchestration (main.py) |
✅ Done |
| Persistent state & idempotency | ✅ Done |
Earnings ingestion (FMP API) |
✅ Done |
| Event study engine (post-earnings returns) | ✅ Done |
| EPS surprise segmentation & hit-rate statistics | ✅ Done |
| Interpretative earnings summary | ✅ Done |
| Automated HTML email reporting | ✅ Done |
Inline visualizations (heatmap) |
✅ Done |
| Raspberry Pi 24/7 deployment | ✅ Done |
Local LLM-assisted synthesis (LLM 8850 accelerator + Pytorch) |
🚧 Planned |
ARES is a personal research and learning project intended to:
- explore event-driven equity behavior,
- practice building production-style financial systems,
- document institutional-grade analytical reasoning.
It makes no claim of predictive certainty and is not intended for live trading.
Axel Juan
BBA Third-Year Student — ESSEC Business School
CFA Level I Candidate
Aspiring Quantitative Trader
ARES is part of a broader personal initiative combining finance, programming, and systems engineering.
🚧 Actively developed — scope and features will evolve.