e09757419c
Forward W_Q SGEMM stored col-major [K, M] (lda=K) while backward dW_Q wrote col-major [M, K] (ldc=M). When M ≠ K (TLOB: M=16, K=32), Adam's element-wise update applied gradients computed at position (m, k) to weights stored at position (k, m) — silent learning corruption at every flat index ≠ 0 (511 of 512 W_Q slots updated using wrong-position gradients, matched in W_K/V; W_O is square so unaffected). Standardised backward dW_Q/K/V SGEMM output to col-major [K, M] (ldc=K) matching the forward layout (Strategy A from the audit brainstorm — the forward layout is the definitive weight storage; Adam's flat layout follows forward's allocation). The fix flips the cuBLAS strided-batched operands: backward now computes `dW^T = ofi @ d_proj^T` instead of `dW = d_proj @ ofi^T`. Same gradient values, just re-laid-out so flat indexing matches `params`. No new kernel; no kernel-internal layout change (the SDP forward/backward kernels still read `proj_qkv_buf` / `d_proj_qkv_buf` as [M, B] col-major — those buffers are untouched). The QKV-fusion `cublasSgemmStridedBatched(batch=3)` semantics are preserved: ofi is the new shared operand (strideA=0), d_proj is the per-batch operand (strideB=M·B), strideC=M·K=512 unchanged. Phase-1 reproduction (`tlob_dw_layout_alignment_repro`, #[ignore = "requires GPU"]) ran the broken and fixed cuBLAS dispatches side-by-side on identical sentinel inputs (`d_proj[m=0,b=0]=1`, `ofi[k=1,b=0]=1`, all else 0); broken `[M, K]` placed the `1.0` gradient at flat 16, fixed `[K, M]` placed it at flat 1 — O(1) cross-layout delta exactly matching the audit prediction. Pre-fix Adam would have updated `W_Q[m=0, k=16]` (the forward layout's flat-16 slot) using the gradient computed for `W_Q[m=0, k=1]` — the silent corruption. Phase-3 regression (`tlob_dw_layout_alignment_regression_full_chain`, #[ignore = "requires GPU"]) exercises the full forward → backward → Adam → forward chain with random Xavier-init weights (W_O seeded to break the production-zero-init that would collapse the gradient chain to all-zero in a synthetic test). Asserts (1) GPU dW_Q matches a CPU reference computed in the post-fix [K, M] layout within TF32 tolerance, and (2) the second forward Q matches the analytical [K, M] interpretation of the post-Adam W_Q — locks in cross-step layout agreement and would fail if any future refactor accidentally re-permutes `params` between Adam and the next forward. Existing inline `tlob_sgemm_parity_with_cpu_reference` still passes (its CPU dW_Q/K/V reference was updated in lockstep to the [K, M] layout per `feedback_no_partial_refactor`; pre-fix the GPU produced [M, K] and the new CPU reference would diverge element-wise — a clean no-skip parity check that locks the layout convention end-to-end). `tlob_qkv_fusion_equivalence` unchanged (the fix only touches the backward call, forward QKV fusion is bit-identical pre/post). Local verification (RTX 3050 Ti, batch=256 for fusion test): tlob_dw_layout_alignment_repro: PASS tlob_dw_layout_alignment_regression_full_chain: PASS tlob_qkv_fusion_equivalence: PASS (3.79× speedup retained) tlob_sgemm_parity_with_cpu_reference: PASS Fix 20 in docs/dqn-gpu-hot-path-audit.md updated FIXED with verdict + strategy + test list. Forward SGEMM call site got an inline comment block documenting the [K, M] convention and pointing at the `tlob_dw_layout_alignment_*` regression coverage. 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.