Machine learning program

AQUILES

Data Mining Rule Induction & Extraction Reloaded

VTAlgo's 2026 flagship: induce robust, explainable trading rules from data using the newest AI techniques — with the same anti-over-optimization discipline that governs AEGIS.

$1,200

Selective admission 7 modules Live cohort English
Curriculum · 7 modules
Module 1
Analytical Data Preparation
PIT · gaps · quality
→
Module 2
Market Sampling
Time · volume · dollar
→
Module 3
Feature Engineering
Price · flow · regime · path
→
Module 4
Feature Search & Evidence
IC · FDR · stability
→
Module 5
Rule Discovery
Induction · extraction
→
Module 6
Rule Governance
Identity · lineage · budget
→
Module 7
Validation
Stability · evidence
Session counts and hours are not sealed. Lesson bodies are not authored.

What you'll learn

  • Engineer intradaily data under PIT discipline (timestamps, gaps, multi-asset sync, quality)
  • Build comparable bars — time, volume, and dollar — and compare sampling schemes
  • Engineer features across price, flow, structure, regime, cross-asset, and path
  • Select features under redundancy, dependence, IC, multicollinearity, FDR, and temporal stability
  • Discover explainable rules (induction, extraction, association, subgroup, sequential, RuleFit, ensembles)
  • Govern the rule (identity, lineage, complexity budget), then build the evidence package

Program content

Module 1 — Analytical Data Preparation 6 chapters
Module 1 — Analytical Data Preparation
Intraday data
PIT / timestamps
Cleaning
Gaps
Multi-asset synchronization
Data quality
Module 2 — Market Sampling 4 chapters
Module 2 — Market Sampling
Time bars: 1m, 5m, 15m, 30m, 60m
Volume bars
Dollar bars
Sampling-scheme comparison
Module 3 — Feature Engineering 9 chapters
Module 3 — Feature Engineering
Price / returns
Volatility
Volume / flow
Market structure
Temporal
Cross-asset
Cross-sectional
Regime
Path / sequence
Module 4 — Feature Search & Evidence 8 chapters
Module 4 — Feature Search & Evidence
Redundancy
Dependence
IC / predictive information
Stability
Multicollinearity
Univariate / multivariate selection
Multiple testing / FDR
Temporal stability
Module 5 — Rule Discovery, Induction & Extraction 9 chapters
Module 5 — Rule Discovery, Induction & Extraction
Rule Induction
Rule Extraction
Association Rules
Subgroup Discovery
Sequential Pattern Mining
Emerging Patterns
Trees → rules
RuleFit
Rule Ensemble
Module 6 — Rule Governance 7 chapters
Module 6 — Rule Governance
Rule representation
Rule identity
Canonicalization
Rule lineage
Candidate registry
Complexity budget
Rule lifecycle
Module 7 — Validation, Stability & Evidence 5 chapters
Module 7 — Validation, Stability & Evidence
Statistical evidence
Temporal evidence
Structural stability
Behavioral / economic stability
Robustness

Seven modules, the same names as the engine. Deployment is not a module. Lesson bodies are not authored. Chapter evaluations for Modules 6 and 7 are not authored. Session counts and hours are not sealed.

Description

AQUILES (formerly DMRI-2026) is VTAlgo's flagship 2026 program on Data Mining: Rule Induction & Extraction. It teaches how to induce robust, explainable trading rules from data with the latest AI, while refusing the shortcut of over-optimization. The curriculum is seven modules. Deployment is not one of them.

AQUILES is a paid program. It is not included with the ZEUS Engine.

Instructor

Elmer Niño

Elmer Niño

Founder, VTAlgo Group · Systems Engineer, MSc Computer Science

Algorithmic trader since 2008, 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.