Markets as a
learning environment
A paper-trading system for NSE stocks combining entropy analysis, representation drift detection, and adaptive decision control.
Problem
Markets are chaotic and non-stationary, yet most trading systems assume stability. This leads to overfitting and failure under changing conditions.
Solution
Arbitrix evaluates not just signals, but whether the system should trust those signals. It integrates entropy (PIEC), drift detection (RLFS), and adaptive control (S-ADR).
System Architecture
DATA
Market Feed
Real-time + historical data
+ expand
ANALYSIS
Signal Engine
TA + entropy modeling
+ expand
CONTROL
Decision Layer
Adaptive execution
+ expand
EXECUTION
Trading System
Paper trading + AI reasoning
+ expand
Why this matters
In uncertain environments, knowing when not to act is more important than acting correctly.
Key Features
Build Journey
Concept
Explored RL + markets
System
Built simulation + TA engine
PIEC Integration
Added entropy modeling
Refinement
Improved robustness + modularity