Quantitative Factory

The Problem

Individual investors often lack the infrastructure to compete with institutional high-frequency or quantitative traders. Manual trading is subject to emotional bias, decision fatigue, and the inability to process high-volume market data in real-time. Without a systematic approach, the "Biological Wall" of stress often leads to inconsistent performance.

The Solution

A modular, event-driven system that automates the entire quantitative trading pipeline—from data ingestion and AI-driven analysis to deterministic execution.

Technical Implementation

• Modular Architecture: Designed as a suite of decoupled services (Scrapers, Analyzers, Executors) to ensure scalability and reliability.
• AI Strategy Analysis: Leverages LLMs (specifically Google Gemini) to perform sentiment analysis and strategic pattern recognition on unstructured market data.
• Deterministic Execution: A robust execution engine that translates AI-generated insights into precise, rule-based trades.
• Telemetry & Observability: Integrated with real-time logging and metrics to monitor system health and performance.

Hard Problems Solved

1. Data Orchestration: Managed high-volume, real-time data ingestion and processing across multiple market sources.
2. Emotional Neutrality: Built a system that operates purely on data and pre-defined logic, eliminating the "Biological Wall" of trading stress.
3. Strategic Synthesis: Successfully integrated high-level AI analysis with low-level deterministic execution.

Technical Stack

Docker, Node.js, Python, Google Cloud Platform (GCP), Gemini API, LibSQL.