Machine Learning & AI Models
Ethical & Regulatory Considerations for AI Trading Models in 2025 and Beyond
Guide for traders and quants on ethical and regulatory obligations for AI/ML trading models, covering EU AI Act, US guidance, and model‑risk controls. Now.
Hybrid Systems: Combining Rule‑Based EAs with ML Overlays for Safer Automation
Learn how to combine rule‑based Expert Advisors with ML overlays to reduce tail risks, add adaptability and meet modern model‑risk controls for FX trading.
Practical Guide to Feature Engineering for FX: Price, Order‑Book, Sentiment & Macro Inputs
Practical guide to engineering FX features—price, order-book microstructure, sentiment and macro inputs—for building robust ML and algorithmic trading models.
Transfer Learning Across Currency Pairs: Reuse Models When Data Is Scarce
Practical transfer‑learning recipes for FX: pretraining, self‑supervised representations, domain adaptation, meta‑learning and evaluation best practices.
Explainable AI for Forex: Interpretable Models and Why They Matter to Retail Traders
How Explainable AI improves trust, risk control and model performance for retail forex traders — practical XAI methods, workflows and deployment tips.
Reinforcement Learning in Forex: Reward Design, Risk Constraints and Real‑World Challenges
How to design rewards, enforce risk constraints and handle real‑world issues when applying reinforcement learning to algorithmic FX trading.
Using On‑Chain Crypto Signals to Improve FX Model Inputs
Practical on‑chain feature ideas—stablecoin flows, exchange netflow, DeFi metrics—and how to engineer and integrate them into FX ML models for better predictive inputs.
Data Vendors, Alternative Data and Cost‑Effective Feeds for FX Machine Learning
Compare FX data vendors, free & low‑cost feeds and alternative data (news, on‑chain) to build robust, cost‑efficient machine‑learning models for FX trading.
Model Risk Management for Retail Quants: Monitoring, Drift Detection and Retraining
Practical model risk guidance for retail quants: monitoring metrics, drift detection methods and cost‑aware retraining schedules to keep ML trading models robust.