ce841eb56e
Closes recs 2 + 3 in lookahead-bias-audit-2026-04-28.md.
Fix 2A (rec 2): add walk_forward::PURGE_BARS = 5 constant and insert
val_start = train_end + PURGE_BARS in all 4 fold-construction sites:
generate_walk_forward_indices, _from_timestamps, _windows (date-based,
drops first PURGE_BARS bars of val), and gpu_walk_forward::generate_folds
(both stratified variants preserve the gap on boundary shift). 5-bar
width matches max per-bar feature lookback (autocorr lags 1/5/10);
compile-time per feedback_isv_for_adaptive_bounds since the
feature-pipeline lookback is itself compile-time.
Fix 2B (rec 3): per-fold NormStats fit from train-only data.
- walk_forward.rs: add from_features_slice + denormalize/denormalize_batch
helpers (inverse of normalize_batch for clamp-bounded round-trip).
- train_baseline_rl.rs fold loop: load fxcache sidecar norm_stats.json,
denormalise back to RAW, refit per-fold via from_features_slice,
renormalise full dataset, re-upload via init_from_fxcache, save
per-fold norm_stats_fold{N}.json. DBN-fallback path keeps legacy
behaviour (no sidecar to denormalise from) with a warn! log.
Ensemble trainer block is documented follow-up (separate code path).
Behavioral tests:
- test_norm_stats_per_fold_fit_train_only: synthetic train-mean=1.0 /
val-mean=9.0 dataset; from_features_slice recovers train-only mean to
ε=1e-5 while from_features returns global 5.0
- test_norm_stats_denormalize_roundtrip: non-clamped features round-trip
bit-identically
- test_walk_forward_purge_gap_indices_from_timestamps: every fold has
val_start - train_end == PURGE_BARS
- test_fold_generation_basic (gpu_walk_forward): updated to expect the
purge gap
Validation: cargo check --workspace clean (12.30s); 20/20 walk_forward
+ gpu_walk_forward tests pass; audit_sp18_consumers.sh --check exit 0.
Pre-existing test failures on local RTX 3050 Ti are unrelated SIGSEGVs
(VRAM exhaustion in chunked Thompson tests, pre-existing on baseline).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Foxhunt
Production HFT trading system in Rust.
Architecture
The workspace contains 32 crates organized as follows:
Core Libraries (16)
| Crate | Purpose |
|---|---|
trading_engine |
Order processing, FIX 4.4, IB TWS, SIMD, RDTSC timing |
risk |
VaR, Kelly, circuit breakers, kill switches, compliance |
risk-data |
Risk data types and shared structures |
trading-data |
Trading data types |
ml |
DQN Rainbow, PPO, TFT, Mamba2, ensemble inference |
ml-data |
ML data types and feature definitions |
data |
Market data ingestion and storage |
backtesting |
Replay engine, strategy tester |
adaptive-strategy |
Ensemble execution, microstructure analysis |
common |
Shared types, resilience, error handling |
storage |
S3 and local model storage |
model_loader |
Model serialization and loading |
market-data |
Market data feed handlers |
database |
PostgreSQL access layer (SQLx) |
config |
Configuration management |
tli |
CLI commands and tooling |
Services (8)
| Service | Purpose |
|---|---|
backtesting_service |
gRPC backtesting service |
broker_gateway_service |
FIX routing, broker connectivity |
trading_service |
Core trading operations |
ml_training_service |
Model training orchestration |
data_acquisition_service |
Market data acquisition |
trading_agent_service |
Autonomous trading agents |
api_gateway |
gRPC API gateway with auth |
web-gateway |
Axum REST + WebSocket gateway |
Frontend
web-dashboard/ -- React 19 + TypeScript + Vite + TradingView charts.
Building
# Check compilation (no PostgreSQL required)
SQLX_OFFLINE=true cargo check --workspace
# Run tests for a specific crate
SQLX_OFFLINE=true cargo test -p <crate> --lib
# Clippy
SQLX_OFFLINE=true cargo clippy --workspace
ML Models
Four production model architectures on Candle v0.9.1 with CUDA:
- DQN Rainbow -- Deep Q-Network with prioritized replay, dueling heads, noisy nets
- PPO -- Proximal Policy Optimization with GAE and LSTM policies
- TFT -- Temporal Fusion Transformer for multi-horizon forecasting
- Mamba2 -- State space model for sequence prediction
Each model has a standalone trainer and a UnifiedTrainable adapter for the hyperopt pipeline.
Infrastructure
- Git: Gitea at
git.fxhnt.ai(Tailscale-only), Scaleway DEV1-S - Observability: OpenTelemetry OTLP (env
OTEL_EXPORTER_OTLP_ENDPOINT) - Database: PostgreSQL with SQLx offline mode for CI
License
Proprietary. All rights reserved.