#Backtesting

Articles about Backtesting — exploring patterns, best practices, and real-world implementations in production systems.

2 posts tagged with backtesting. ← All posts

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Pratik Dhanave · ·7 min read

Machine Learning in Finance — What Works and What Doesn't

Machine learning promises to find patterns humans miss, and finance is drowning in data — so surely ML should mint money? The reality is humbling: finance is the hardest domain to apply ML to, because the data is noisy, non-stationary, and adversarial, and the ways to fool yourself are endless. This post is an honest guide to where ML genuinely works in finance, where it reliably fails, and why the discipline here is mostly about not deceiving yourself.

Finance is the hardest domain to apply ML to — noisy, non-stationary, adversarial — and the ways to fool yourself are endless. An honest guide: where ML genuinely works (information, structure, credit, computation), where it reliably fails (market prediction), and why the discipline is mostly not deceiving yourself. (Educational, not financial advice.)

#AI Financial Engineering#Machine Learning#Quantitative Finance#Backtesting#AI Engineering
Pratik Dhanave · ·7 min read

Evaluation and Backtesting — Don't Let the Future Leak

The single most common way to build a forecaster that looks brilliant and fails in production is evaluating it wrong — and time-series evaluation is riddled with ways to accidentally let the future leak into the past. Getting evaluation right, through proper backtesting that respects time order, matters more than the choice of model. This post is about measuring forecast quality honestly, which is the skill that separates real forecasters from people who fool themselves.

The #1 way to build a forecaster that looks brilliant and fails in production is evaluating it wrong. Why random train/test splits are catastrophic (future leakage), how backtesting (walk-forward validation) respects time order, and why you must always compare against naive baselines. Evaluation matters more than the model.

#Time Series#Backtesting#Evaluation#Forecasting#Machine Learning#Testing

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse all posts or explore related topics using the tag cloud above.