my-NexusTradingForexBot/docs/architecture/research-stack.md

title, description, lang

title	description	lang
Research Stack	How the research engine turns historical data into validated (or rejected) strategy candidates.	en

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.