Financial econometrics program

HARMONIA

Multi-Asset Cointegration & Mean-Reverting Basket Engineering

Multi-Asset Cointegration & Mean-Reverting Basket Engineering

$1,800

6 phases · 18 modules · capstone3+ assets · not pairsRecommended: ARGOS or equivalent bivariate cointegration
Course roadmap · 6 phases
Phase 1
SYSTEM
Pair → N-asset
Phase 2
DISCOVERY
Space → basket
Phase 3
PORTFOLIO
Constraints + direct MRP
Phase 4
ECONOMICS
Speed vs amplitude
Phase 5
DYNAMICS
Stability + tracking
Phase 6
SCIENCE
Search + lab + OOS
Six technical phases. Eighteen academic modules (I–XVIII) live inside them. Granger–Lee is a special module, not in the 18. Coursework clock is not sealed.

Mission and vision

Mission

Teach discovery of multivariate relationships among three or more assets: cointegrating spaces, sparse baskets, and mean-reverting portfolios that can be defended statistically, temporally, and economically.

Vision

Researchers who extract small, interpretable baskets from a relationship space — not a pair spread, and not Granger–Lee strict multicointegration.

Why HARMONIA

HARMONIA is Multi-Asset Cointegration & Mean-Reverting Basket Engineering.

The wordmark is harmony among many parts: a cointegrating space turned into a small basket. It does not mean Granger–Lee strict multicointegration (I(2) accumulations of equilibrium errors). That distinction is a special module.

The object is `X_t = (A_1, …, A_N)` and `z_t = w' X_t` — relationships that may not be visible pair-by-pair, turned into small baskets. It is not the retired TAC stages (cointegration · failure · relations). It is not Forex-as-identity. It is not the two-asset pair path.

Who it is for

Quantitative researchers, systematic traders, statistical-arbitrage developers, and financial engineers who already command bivariate cointegration (ARGOS or equivalent). Linear algebra, time series, ADF, and spread construction are assumed.

What it is not

Not ARGOS (two-asset pairs). Not Granger–Lee. Not a five-name recipe. Lecture bodies are not authored yet.

What you'll be able to do

  • Move from (A, B) → spread to z = w'X on three or more assets
  • Estimate cointegration rank and a cointegrating space — not a unique basket
  • Extract sparse, constrained, tradable vectors from that space
  • Compare Econometric-First discovery with Objective-First (direct MRP)
  • Validate generating procedures under stability, costs, multiplicity, and causal OOS

Course content

Phase 1 — SYSTEM 3 modules
Module 1 — From Pair to Multi-Asset System Outline
Pair spread ≠ system · z = w'X Read
Module 2 — Johansen & VECM Outline
Johansen discovers a space, not the tradable basket Read
Module 3 — Cointegrating Spaces Outline
Cointegrating space ≠ unique basket Read
Phase 2 — DISCOVERY 3 modules
Module 1 — From Cointegrating Space to Tradable Basket Outline
Space → basis → tradable vector Read
Module 2 — Sparse Cointegration Outline
Sparse ≠ stable ≠ tradable Read
Module 3 — High-Dimensional Relationship Selection Outline
Variable screening ≠ full cointegrating-space recovery Read
Phase 3 — PORTFOLIO 3 modules
Module 1 — Constrained Cointegration Outline
Dollar / beta / factor neutrality as constraints Read
Module 2 — Cardinality & Small-Basket Engineering Outline
Statistical quality ↔ parsimony ↔ execution cost Read
Module 3 — Direct Mean-Reverting Portfolio Optimisation Outline
Econometric-First vs Objective-First Read
Phase 4 — ECONOMICS 3 modules
Module 1 — Mean-Reversion Characterisation Outline
A vector of evidence, not one score Read
Module 2 — Volatility Paradox & Variance Floors Outline
Fastest mean reversion ≠ best economic basket Read
Module 3 — Economic Opportunity Mapping Outline
Amplitude, frequency, holding time, cost, capital Read
Phase 5 — DYNAMICS 3 modules
Module 1 — Basket Stability Outline
Stable subspace ≠ stable tradable vector Read
Module 2 — Temporal Regularisation & Turnover Outline
Persistence vs artificial inertia Read
Module 3 — Dynamic Basket Tracking Outline
Kalman tracking ≠ relationship discovery Read
Phase 6 — SCIENCE 3 modules
Module 1 — Combinatorial Search & Candidate Generation Outline
Clustering ≠ cointegration Read
Module 2 — Multiple Testing & False Discovery Outline
Candidate count ≠ independent trial count Read
Module 3 — Multi-Asset Discovery Laboratory Outline
Which methodology systematically generates better baskets? Read
Special module — Strict Multicointegration (Granger–Lee) Outline · not in the 18
Strict multicointegration ≠ multivariate cointegration Read
Capstone — Multi-Asset Relationship Discovery Engine Outline
A reproducible discovery process, not a five-name strategy Read

Course content is Phase → Module → Chapter using HARMONIA's six phases (SYSTEM → DISCOVERY → PORTFOLIO → ECONOMICS → DYNAMICS → SCIENCE). Module numbers restart inside each phase. Granger–Lee is a special module, not in the 18. The capstone sits outside the six phases. Lecture bodies are not authored yet. Recommended prerequisite: ARGOS.

Instructor

Elmer Niño

Elmer Niño

Founder, VTAlgo Group · Systems Engineer · MSc in Computer Science

Algorithmic trader since 2009, running trading as an enterprise. Methodology built on walk-forward analysis, genetic optimization, PCA against over-optimization, the Triple Barrier Method, and 9–12 month live validation before capital.