01 Systematic Quantitative Research

Research should preserve the reasoning and evidence behind every result.

Zeto was designed as a research environment for reproducible ML-driven quantitative experimentation. The system keeps transformations, diagnostics, and evaluation flow inspectable throughout the workflow.

validation.modewalk_forward
diagnostic.surfaceobservable
02 Research Workflow Fragmentation

Where research state breaks down.

Fragmentation appears when intermediate decisions disappear between notebooks, validation scripts, and final reports.

Conventional workflow failure

  • Disconnected notebooks
  • Opaque pipelines
  • Hidden intermediate states
  • Unreproducible experiments
  • Invisible transformations
  • Weak chronology
  • Metric-centric reporting
signals_final_v7_REAL.csvfeature lineage unknowndiagnostics missing

Zeto infrastructure response

  • Config-driven workflows
  • Explicit validation
  • Persistent artefacts
  • Visible diagnostics
  • Preserved chronology
  • Inspectable ML behaviour
03 Research System Structure

Rigorous experimentation depends on explicit research structure.

Zeto separates experiment specification, temporal data alignment, factor construction, signal research, walk-forward validation, research artefacts, and publication surfaces into explicit methodological layers. The objective is not infrastructure complexity, but disciplined research separation.

  1. 01EXPERIMENT SPEC
  2. 02TEMPORAL DATA ALIGNMENT
  3. 03FACTOR / FEATURE CONSTRUCTION
  4. 04SIGNAL RESEARCH
  5. 05WALK-FORWARD VALIDATION
  6. 06RESEARCH ARTEFACTS
  7. 07PUBLICATION SURFACE
Walk-forward validation separates signal research from publication as an explicit methodological boundary.
04 Research Lifecycle

Rigorous quantitative research depends on explicit chronological evaluation.

Research moves from controlled experiment specification through factor construction, signal evaluation, walk-forward validation, diagnostics, and publication. Each stage preserves temporal ordering and methodological separation.

  1. 01RESEARCH HYPOTHESIS
  2. 02EXPERIMENT SPECIFICATION
  3. 03TEMPORAL DATA ALIGNMENT
  4. 04FACTOR / FEATURE CONSTRUCTION
  5. 05SIGNAL RESEARCH
  6. 06WALK-FORWARD VALIDATION
  7. 07DIAGNOSTICS & STABILITY ANALYSIS
  8. 08RESEARCH ARTEFACTS
  9. 09PUBLICATION SURFACE
Signal research is evaluated only after temporal construction; diagnostics follow validation.
05 Research Visibility

Research should preserve the evidence behind its conclusions.

Zeto persists validation artefacts, coefficient behaviour, feature lineage, diagnostics, and generated reports as inspectable research evidence. The objective is not only reproducibility, but visibility into how conclusions were formed, validated, and interpreted.

canonical_ml_showcasesaved experiment state

walk-forward validation

split_01split_02split_03split_04

chronological train / test evidence retained

diagnostic sidecars

  • research/feature_registry.json
  • research/alignment_diagnostics.json
  • diagnostics/ml_model_diagnostics.json
  • diagnostics/split_metrics.json

model behaviour

coefficient behaviour and instability remain inspectable

publication surface

  • canonical_ml_showcase.md
  • canonical_ml_showcase_manifest.json
  • canonical_ml_showcase_provenance.json
Reports are rendered from saved artefacts rather than recomputed ad hoc.
06 Systematic Research

Rigorous quantitative research depends not only on predictive models, but on the visibility, validation, chronology, and reproducibility of the process that produced them.

Zeto was designed to make quantitative research inspectable, reproducible, and methodologically transparent. The objective is not infrastructure complexity, but disciplined experimentation and visible research reasoning.