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.
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 of the most destructive failure modes sits much lower in the stack: market data quality.A strategy built on...
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Why Fixed LLM Reasoning Levels Are Inefficient: Designing Adaptive Token Allocation Architectures for Next-Generation AI Systems
Most discussions around LLM reasoning modes focus on quality. High reasoning is assumed to be better. Low reasoning is assumed to be cheaper. The...
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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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Execution Architecture: Why Poor Execution Matters More Than a Weak Idea
Most founders overestimate the value of ideas and underestimate the value of execution systems.The uncomfortable reality is that weak execution...
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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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