ab378327ec
The kernel block at experience_kernels.cu:698-704 divided market_features[0..3] by vol_normalizer at runtime — designed for the pre-Bug-1 pipeline where features arrived as RAW log returns (~±0.001). After Bug 1 fix (commit5a5dd0fed) moved z-normalization to the WRITER (precompute_features.rs::NormStats::normalize_batch before fxcache write, plus data_loading.rs DBN-fallback path applying the same op), features arrive already-z-normalized. The runtime division then created a 1000-13000× DOUBLE NORMALIZATION inflating column 0 of next_states to raw-price magnitude. DIAG_AUX_LABEL diagnostic ground truth (production train-multi-seed-bn42w): - features_raw_cuda col 0 mean_abs = 0.443 (clean z-norm at SOURCE) - aux_nb_label_buf mean_abs = 5398 (= 0.443 × inv_vol ~ 13245) - ratio matches 1 / vol_normalizer for ES 1-min realised vol ~7.5e-5 This bug caused label_scale=5481 in 50-epoch validation (cancelled train-multi-seed-bn42w) while smoke ran with smaller window producing smaller inv_vol → label_scale ~25-432. Both wrong, just different inflation factors. What changed: - experience_kernels.cu:698-704 block deleted; replaced with header comment explaining why. Kernel parameter `vol_normalizer` retained in signature with `(void)vol_normalizer;` to silence the unused-warning — removing the param would cascade through gpu_experience_collector.rs config struct + launcher + per-epoch Welford in training_loop.rs (60 lines). Bounded scope: leave the pipe wired, gut the consumer. - DIAG_AUX_LABEL diagnostic removed per its Fix 28 removal gate (gpu_dqn_trainer.rs ~12981 diagnostic block ~165 lines + the DIAG_AUX_LABEL_SOURCE_PTRS OnceLock static ~22 lines + training_loop.rs populate site ~22 lines). - Audit doc Fix 29 entry with audit results + open follow-ups. Bug-1 contract audit (this commit's exhaustive re-check, 18 sites classified): - 1 ⚠→✅: experience_kernels.cu:698-704 (this fix) - 13 ✅: env-step kernel body, mirror universe, feature mask/noise, target reads at col 2 raw_close (Fix-27 already correct), DT rewards kernel, curriculum/hindsight/portfolio_sim kernel target reads, kernel header docs, PS_PREV_CLOSE state-layout slot - 4 ⚠ Stale (deferred — separate triage commits): * scripted_policy_kernel.cu:59-65 — seed-phase momentum reads state[MARKET_START] as raw_close, but post-Bug-1 it is z-normed log-return. Affects seed-phase scripted policy quality only. * backtest_plan_kernel.cu:77-100 — val plan_isv reads features[bar*feat_dim+0] as raw_close. Corrupts val plan_isv slots [PNL_VS_TARGET]/[PNL_VS_STOP]. * metrics.rs:576 — val_data → window_prices reads target[0] as close (clones Fix-27 Bug B at host-side; affects val Sharpe). * hyperopt/adapters/dqn.rs:2492 — same target[0] pattern in HPO val_close_prices. - 1 ❓ Ambiguous: dqn_utility_kernels.cu:1089-1093 synthetic feature overlay; needs downstream-consumer contract verification. Verification: - SQLX_OFFLINE=true cargo check -p ml --offline (47.87s) clean. - SQLX_OFFLINE=true cargo build -p ml --release --offline --features cuda (1m 30s) clean; cubin recompiled via nvcc. - Next L40S production run should show label_scale ~0.8 (matching kernel docstring expectation). Refs: DIAG_AUX_LABEL diagnostic from Fix 28 in dqn-gpu-hot-path-audit.md (commit2683d4637) which captured the ground truth that pinned this bug. Cancelled validation runs train-multi-seed-p5qzw (label_scale=808) and train-multi-seed-bn42w (label_scale=5481) both blocked on this — Fix 27 cleared the host-side variant (Welford reading target[0] as raw_close); this Fix 29 closes the kernel-side variant. Bug 1 chain (label_scale=5443 from 4-month-old #193) closes here. feedback_trust_code_not_docs (the kernel comment said `#13 Vol normalization` for months — accurate-when-written, stale-after-Bug-1). feedback_no_partial_refactor does not apply because the kernel parameter is retained as a no-op, deliberately leaving the launcher/config-struct contract intact while the consumer is gutted. 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.