Essays · Operations Intelligence, AI and Quant
Hossein Narimani — Writing
In-depth writing on quant system design, operational AI, SaaS architecture, custom AI agents and founder execution systems.
Hybrid Quant System Architecture: Integrating Risk Management, Position Sizing, and Execution in a Unified Framework
Most quant systems that fail in production don't have a signal problem. They have an architecture problem. Their signals work. Their backtests are convincing. But when they hit a live environment, the internal structure can't handle real pressure — because...
Read more →
Quant Research vs Quant Trading: Signal Discovery vs Capital Execution in Quantitative Systems
Most people treat Quant Research and Quant Trading as different labels for the same profession. Operationally, they are two separate layers of the...
Read more →
What Is Edge in Quantitative Trading Systems? Calculation Methods, Practical Uses, and Common Failure Modes
Most traders believe edge is simply a high win rate. That assumption destroys more trading systems than market volatility.A strategy can win only...
Read more →
How Bad OHLCV Data Destroys Trading Strategies: A Practical Framework for Market Data Quality Assurance
Most trading strategy failures are blamed on poor signal design, weak indicators, overfitting, or flawed machine learning models. In practice, one...
Read more →
Complete Guide to OHLCV Data Cleaning in Big Data Pipelines: Frameworks, Failure Modes, and Production-Grade Implementation
Most quantitative trading failures do not begin with the model. They begin with the data. OHLCV datasets sit underneath backtesting engines, alpha...
Read more →