[Quant Lecture] Quantitative Modeling in Finance
Statistics for algorithmic traders
A structured approach to model formulation and choice
An ideal sequence guides a modeling investigation from falsifiable hypothesis through live deployment. At each stage—preliminaries, problem articulation, solution linkage, pipeline design, system construction, model taxonomy, and learning safeguards—statistical rigor and software‐engineering discipline transform raw ideas into quantifiable, robust algorithmic components.
What’s inside:
Problem articulation & hypothesis: Define the precise trading inefficiency and measurable outcome, grounding your research in a clear causal mechanism and avoiding “signal‑first” traps.
Modeling paradigms & probability foundations: Choose between theory‑driven (substantive) and data‑driven (empirical) approaches, and select the appropriate probabilistic structure—parametric, nonparametric, hierarchical—to focus inference on parameters of interest.
Model taxonomy: Differentiate the roles and uses of descriptive, explanatory, exploratory, predictive, and heuristic models to apply the right tool at each phase of discovery, validation, and deployment.
Research→Know→Do→Get Loop: Sustain and refine your edge via the four‑stage cycle:
Research: Hypothesis formulation
Know: Robust backtesting & statistical validation
Do: Modular production engineering
Get: Live performance feedback
End‑to‑end solution pipeline: Architect each module explicitly to your defined problem: data ingestion, signal extraction, signal calculation, sizing logic, risk overlays, and execution.
Layered trading system architecture: Translate validated models into a hierarchy of sub‑strategies—universe selection, alpha signal generation, position sizing, risk management, and execution—enabling independent development, testing, and diagnostics.
Algorithm vs. model & learning safeguards: Emphasize that the algorithm is the learning/execution procedure, while the model is the trained artifact; guard against survivorship bias, look‑ahead bias, spurious correlations, p‑hacking, and other pitfalls to ensure realistic, reliable performance.