Essays · Operations Intelligence, AI and Quant
Hossein Narimani — Writing
In-depth writing on quant system design, operational AI, SaaS architecture, data, forecasting and founder execution systems.
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 same return-generation system. One discovers statistical edge. The other converts that edge into deployable...
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Can Artificial Intelligence Really Predict Markets? The Reality of AI in Trading and Investment Decisions
The short answer is yes—artificial intelligence can predict certain market behaviors. The longer and more useful answer is that markets are not...
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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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Why Profitable Backtests Fail in Production: The Hidden Gap Between Backtesting and Reality
Every quantitative researcher eventually encounters the same paradox. A strategy looks exceptional in backtesting, produces attractive...
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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...
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