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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What Is Quant System Design? Architecture, Components, and How It Differs from a Trading Bot
Most trading bots do not fail because their code is poor. They fail because there is no system behind them.A buy-or-sell signal is not a quant...
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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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What Makes an Automated Trading Strategy Good? Real Criteria Beyond a Beautiful Backtest
Most strategies that look excellent in a backtest were never designed to survive production.A trading bot is not good because its equity curve...
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Can Quant Trading Strategies Be Trained From OHLCV Data Alone?
Most people asking "can you train a strategy on OHLCV data" are really asking something else: "can I train a model on past prices and make money...
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