Before you begin, remember that you have an index with the newsletter content organized by clicking on the image below.
From market theory to trading systems
This is a structured framework for transforming financial market problems into quantitative models and complete algorithmic trading systems. This book goes beyond individual trading signals to explore the principles of scientific research, statistical modeling, abstraction, and systems engineering. Its central objective is to develop robust, interpretable, and adaptive trading systems through rigorous hypothesis testing, sound model architecture, and continuous validation.
What’s inside:
Principles of quantitative research: Establish a systematic research methodology that transforms market observations into falsifiable hypotheses, measurable outcomes, and reproducible investigations.
Market variability and measurement: Understand non-stationarity, structural breaks, measurement errors, and market noise. Learn to distinguish genuine market behavior from statistical artifacts.
Research design and falsification: Develop precise hypotheses, pre-specify evaluation criteria, and design experiments that challenge trading ideas rather than merely confirm historical profitability.
Quantitative modeling in finance: Translate economic problems into mathematical representations. Explore descriptive, explanatory, exploratory, predictive, and heuristic models and their roles in trading.
Model selection and statistical learning: Compare substantive and empirical approaches, parametric and non-parametric models, feature selection, regularization, and the trade-off between complexity and generalization.
Alpha and risk modeling: Understand the distinct objectives of predictive alpha models and risk models. Integrate expected returns, covariance estimation, portfolio construction, and risk constraints into coherent trading decisions.
Systems-theoretic trading architecture: Design trading systems as interconnected hierarchies of specialized models. Examine how data processing, signal generation, portfolio optimization, risk management, and execution interact.
Abstraction and model validity: Learn how raw market observations become variables, inferential constructs, models, strategies, and complete systems. Evaluate internal consistency, external validity, and model–market correspondence.
Model development and implementation: Follow a structured workflow covering problem formulation, data acquisition, feature engineering, model selection, parameterization, backtesting, validation, deployment, and monitoring.
Adaptive modeling and continuous improvement: Explore feedback loops, causal relationships, model instability, and the simplicity–precision trade-off to develop trading systems capable of responding to changing market conditions.
