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.
OHLCV Feature Pipeline Architecture: Designing Feature Registries and Caches for Scalable Trading Systems
Most trading systems do not fail because of the model. They fail because every component interprets market data differently.When a feature has one value in a backtest and another in live trading, the problem is not intelligence. It is architecture.The Real...
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OHLCV Pump Pattern Analysis: Detection, Limits, and Alert System Design
OHLCV Pump Pattern Analysis: Detection, Limits, and Alert System DesignThe hard problem is not finding a green candle.The hard problem is...
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OHLCV Data Quality Checks: What to Validate Before Backtesting or Trading
Most backtests do not fail because of the model. They fail because of the data.If your OHLCV feed is incomplete, inconsistent, or retrospectively...
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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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