The Problem
Markets move in milliseconds. Signals like new patents, FDA approvals, insider trades, or geopolitical impulses are scattered across hundreds of channels. A human cannot correlate these "Boom Signals" in time. Whoever connects the information first, wins.
- Information overload causes blindness
- Signals are scattered across too many sources
- Manual analysis is too slow
- Emotional decision errors in trading
The Solution
The current approach is a swarm of specialized AI agents working together like an intelligence agency. Scouts scan patent databases, news feeds, social media, and government contracts. The "Head Team" (ASLAN, WATCHER, BIG MAMA, LISA) evaluates impulses in an automated team meeting. Many parts are still evolving: I continuously optimize the agents, flows, and decision logic.
Agent Swarm
TA4U is intentionally built as a learning project: I am currently testing 21+ specialized agents across four layers to understand and implement swarm agentics in practice. Scouts collect raw signals, analysis agents compress patterns, the head team prioritizes, and guardrail agents secure risk, logging, and execution.
Head Team
ASLAN, WATCHER, BIG MAMA, and LISA form the decision round. They evaluate opportunity, risk, capital, and timing.
Scout Agents
Sector scouts for tech, biotech, defense, macro, patents, insider filings, social, and breaking news.
Analysis Agents
Condense sentiment, price structure, source quality, and signal strength into actionable hypotheses.
Execution & Guardrails
Paper trading, alerting, logging, and risk checks accompany every step up to demo execution.
My Role
Founder & Architect
Features
Alternative Data Mining
Scanning patents, job postings, and insider filings for true alpha.
Multi-Agent Consensus
No decision without a team meeting: Different perspectives (risk, opportunity, capital) are united.
Multi-Markt Abdeckung
Monitoring US markets (NASDAQ, NYSE) and the Turkish market (BIST).
Boom Effect Detector
Correlation of weak signals across multiple channels into a strong buy signal.
AI Risk Management
BIG MAMA protects the portfolio from emotional overreactions and calculates every position precisely.
Fully Automated Pipeline
From raw impulse to analysis to paper-trading order – all without human intervention.
Website Modules
Head Agents
ASLAN, WATCHER, BIG MAMA, and LISA as the decision layer.
Sector Scouts
Specialized collectors for Tech, Biotech, and Defense.
Impulse Database
Centralized storage and networking of all market signals.
Telegram Alerts
Real-time reporting of AI meetings and decisions.
Results
TA4U is my personal learning and moonshot project in AI and finance. The current focus is not perfection yet, but understanding agents and swarm agentics properly, implementing them in practice, and improving them iteratively. I am still at the beginning, learning with every step, and continuously refining the agents, prompts, and workflows.
Screenshots
Large preview on top, small thumbnails below. Click the large image to open the fullscreen gallery with next/close.
What I Learned
What this project is teaching me right now: agents and swarm agentics may look spectacular from the outside, but in practice they start with solid logic, clean data flows, and a lot of iteration. I am still at the beginning, learning every day, and improving step by step instead of pretending the system is already finished.