Foundations & Core Concepts

20 articles covering the evolution of AI in financial markets, big data concepts, statistical arbitrage, and fundamental machine learning approaches that form the foundation of modern quantitative finance.

The Evolution of AI in Financial Markets: From Rule-Based Systems to Self-Learning Agents

What Makes Market Data "Big Data"? Volume, Velocity, Variety, Veracity Explained

Combining Statistical Arbitrage with Modern ML: Complement or Cannibal?

Supervised vs Unsupervised Learning in Trading—When to Use Which Approach

Why Most Backtests Fail: Overfitting, Look-Ahead Bias, and Data Snooping

Feature Engineering 101 for Price Series: Lags, Rolls, Differencing & More

Ensemble Methods in Finance: Bagging, Boosting, Stacking for Alpha Generation

Curse of Dimensionality in Portfolio Models and How to Beat It

Crafting Robust Train/Validation/Test Splits for Non-Stationary Time-Series

Transfer Learning in Quant Research: Reusing Models Across Asset Classes

The Role of Bayesian Thinking in Modern Algorithmic Trading

Risk-Adjusted Performance Metrics Beyond Sharpe and Sortino

Understanding Market Regimes with Clustering Techniques

Combining Fundamental and Technical Signals in Hybrid ML Frameworks

Building an End-to-End Quant Research Pipeline in Python

Why Data Drift Matters More Than Concept Drift in Finance

Stationarity Testing: KPSS vs ADF vs Phillips–Perron in Practice

Hierarchical Time-Series Modeling for Multi-Asset Forecasts

The Bias-Variance Trade-Off Visualized with Real Market Data

Continual Learning: Updating Models Without Re-training From Scratch