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.
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 rises. It is good because its decision logic, execution, and risk controls remain coherent under real market...
Read more →
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...
Read more →
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...
Read more →
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...
Read more →
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...
Read more →