Fuente en inglés — traducción pendiente — inglés

Research Stack

The research side answers one question with evidence: **is this candidate good

enough to trust — and can we prove it on data it never saw?**

Pipeline

DATA (canonical tick/bar datasets, provenance-fingerprinted)
  → FEATURES (same causal contract as live: 50D base, governed 70D assembly)
  → LABELING (triple-barrier, purged + embargoed)
  → TRAINING (deterministic seeds, walk-forward)
  → BACKTEST (friction-aware: spread / slippage / latency)
  → WALK-FORWARD VALIDATION
  → OOS GATE (hard: failure ⇒ REJECTED)
  → ROBUSTNESS STRESS
  → COUNTERFACTUAL / STRATIFICATION EVIDENCE
  → CANDIDATE REGISTRY (content-addressed)
  → SHADOW COMPARISON (zero order authority)
  → OPERATOR-GATED PROMOTION

Key guarantees

Leakage prevention (INV-008): purged + embargoed walk-forward

(Lopez de Prado); broker history REPLACE+ALIGN; liquidity features strictly

causal (confirmation bars, completed HTF buckets only). Purge/embargo

defaults are wired into the production research path (BUG-183 regression

suite).

Replay parity: the replay feature vector is bit-exact vs the dataset

(anti-leakage tests); live = replay = training semantics.

Determinism: seeded training, frozen datasets, fingerprinted artifacts

(dataset ID, schema hash, git commit in manifests and run snapshots).

Honest provenance: run snapshots record effective purge/embargo and

provenance fields — NOT_RECORDED is written when truthfully unknown, never

backfilled.

Components

Component	Module	Note
Dataset builder + quality gates	`research/dataset.py`, `model_generation/`	fingerprinted, versioned
Backtest engine	`research/backtest.py`	deterministic, friction-aware
Walk-forward	`research/walkforward.py`, `training/walk_forward_trainer.py`	purged/embargoed
OOS gate	`research/oos.py`	macro-F1 / balanced-accuracy / ECE floors
Streaming replay	`research/streaming_replay.py`	logical clock, zero sleeps; simulated fills on historical bid/ask; no `order_send` (test-enforced)
Forward tests	`research/forward_test.py`	freeze capture at cutoff (model/scaler/strategy fingerprints), strict `timestamp > cutoff` streaming
Tick datasets	`research/mt5_tick_dataset.py`	canonical adapter surface, offline after acquisition
Counterfactual engine	`research/counterfactual.py`	walks NO_TRADE decisions on canonical ticks (CHG-0041)
Evidence + observability	`research/evidence.py`, `research/observability.py`	gate model, events, evidence vault, worker health

The 70D case study

The 70D series (scalp_v3: Base 50 + News 10 + Liquidity 10) is the reference

example of the stack working as designed: full parity/validation infrastructure

built, fair A/B/C benchmark run on real data — and the candidate rejected

(OOS NOT_ELIGIBLE). The live contract stayed 50D. Details:

Status · internal reports under docs/70D_*.md.