29b1d34c6d
Two latent bugs converged in the cancelled 50-epoch run (train-multi-seed-p5qzw at96769d171, label_scale=808 vs smoke baseline 22-28): Bug A (latent since063fd2716, 2026-04-19): TARGET_DIM was bumped 4→6 in fxcache (added raw_open at col 4 + mid_price_open at col 5), but N consumer kernels in experience_kernels.cu + dt_kernels.cu hardcoded stride 4 when indexing targets[bar*4+col]. Reading at the old stride against the stride-6 buffer slid every lookup into the wrong bar's data. Smoke harness paths (multi_fold_convergence) didn't exercise expert_action_override / compute_difficulty_scores / hindsight_relabel heavily; production 368k-bar run surfaced the drift via the compounded label_scale = 30× expected magnitude. Bug B (introduced today at5a5dd0fed, 2026-05-02): the Bug 1 fix changed targets[0] from raw_close to log-return-normalized preproc_close. training_loop.rs::epoch_vol_normalizer's Welford pass still read targets[0] expecting raw_close → output was ln(log_return/log_return) ~ stddev 0.6 → triggered sanity-band warning + default fallback (5e-4); but downstream label_scale consumers fed the corrupt value through. Sites fixed: HOST (1 site): - training_loop.rs:570-573, :603 — w[i].1[0] → w[i].1[TARGET_RAW_CLOSE] + warning message updated GPU (5 sites in experience_kernels.cu): - line 3768 — kernel param doc OHLCV → fxcache TARGET_DIM=6 - lines 3806-3808 — bar*4+3 → bar*6+2 (raw_close column) - lines 3974-3975 — i*4+2 → i*6+2 (stride-only) - lines 4015, 4020 — bar*4+2 → bar*6+2 (stride-only) - lines 3400-3404 — t_offset = global_idx*4 → *6 (stride-only) - lines 1553-1561 — kernel header doc updated to 6-column layout GPU (1 site in dt_kernels.cu): - line 793 — kernel param doc OHLC → fxcache TARGET_DIM=6 - lines 803, 807 — i*4+3 → i*6+2 (raw_close col 2) HOST docstring (decision_transformer.rs:1049) — [num_bars, 4] → TARGET_DIM=6 with reference to fxcache constant module. Structural prevention: 6 named column constants (TARGET_PREPROC_CLOSE / TARGET_PREPROC_NEXT / TARGET_RAW_CLOSE / TARGET_RAW_NEXT / TARGET_RAW_OPEN / TARGET_MID_OPEN) added to crates/ml/src/fxcache.rs co-located with the existing TARGET_DIM (promoted from private to pub). Co-locating in the contract-owner module means future renames or column adds force every consumer to update at the same call site. Compile-time density test asserts columns are dense and exhaustive. Host-side consumer training_loop.rs imports TARGET_RAW_CLOSE; GPU kernels reference the constant module in comments + use literal stride 6 with a header block documenting the contract (kernels can't import Rust constants). PPO consumer NOT in scope: ppo_experience_kernel.cu reads at stride 4 but PPO has its own set_raw_market_data path uploading 4 columns from Vec<f64>. PPO write/read pair is internally consistent at stride 4, unaffected by fxcache stride bump. Production train_baseline_rl.rs flow doesn't invoke set_raw_market_data, so PPO GPU collector is currently orphaned. Consolidating PPO onto fxcache target buffer is a separate refactor. Verification: - SQLX_OFFLINE=true cargo check -p ml --offline clean - cargo build -p ml --release --offline --features cuda clean (cubin compiles via nvcc; release build under 2 min) - cargo test -p ml --lib -- target_layout 1/1 pass (target_columns_dense_and_exhaustive) - audit grep "targets[var * 4 +]" returns ZERO hits in production - re-run of train-multi-seed-p5qzw will show label_scale ~22-30 (not 808) — gating production validation Refs: cancelled train-multi-seed-p5qzw at96769d171, 50-epoch validation blocker. Bug origins:063fd2716(target_dim 4→6 bump) and5a5dd0fed(Bug 1 column-0 semantic fix). feedback_no_partial_refactor (every consumer of shared buffer contract migrates in same commit), feedback_trust_code_not_docs (kernel doc OHLCV/OHLC strings stale post-bump), feedback_no_quickfixes (proper structural fix not one-line patch), feedback_wire_everything_up (column constants land co-located with fxcache::TARGET_DIM so writer + caller share single contract module). 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.