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.
Deep Learning Prediction Models for Stock OHLCV Data: A System Design Framework
The Real Problem: Why Deep Learning on OHLCV Data Usually DisappointsMost teams building deep learning models for stock prediction start with the wrong question: "Which architecture gives the highest accuracy?" That question is a trap.The right question...
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The Real Cost Equation of Quant Systems: A Component-Level Model
The Problem: Quant System Costs Are Always Higher Than the Spreadsheet ShowsMost teams calculate the cost of a quant system by adding up servers,...
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Look-Ahead Bias in Backtesting: How Future Data Silently Contaminates Your Strategy Test
If your strategy backtest is showing brilliant results, there's a good chance something is wrong. Not because the strategy is bad — but because...
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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...
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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...
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