797f8bf326
Adds 5 new pub(crate) accessor methods exposing backward-path buffer
device pointers for Task 4's per-step NaN checks (slots 24, 25, 28,
29, 30 per audit per-slot table at docs/dqn-backward-nan-audit.md
:530-548):
GpuDqnTrainer (4 new):
- d_value_logits_buf_ptr (slot 24 — post-c51_grad value gradient)
- d_adv_logits_buf_ptr (slot 25 — post-c51_grad branch advantage)
- cql_d_value_logits_ptr (slot 29 — CQL gradient output)
- aux_dh_s2_nb_buf_ptr (slot 30 — aux next-bar backward dh_s2)
GpuIqnHead (1 new):
- d_branch_logits_buf_ptr (slot 28 — production IQN backward output,
iqn_quantile_huber_loss)
Slot 27 (iqn_d_h_s2_buf) reuses existing GpuIqnHead::d_h_s2_raw_ptr()
at gpu_iqn_head.rs:1660 — no new method per feedback_no_legacy_aliases:
the existing accessor is already public and sufficient; renaming +
chasing the single call site adds churn without value.
Slots 26, 32, 33-35 reuse pre-existing handles:
- 26: self.ptrs.iqn_trunk_m
- 32: self.bn_d_concat_buf() (existing, returns &CudaSlice<f32>)
- 33-35: self.ptrs.bw_d_h_s2 (3 different Task 4 call sites)
Slot 31 (ensemble_d_logits_buf) deferred per Task 2 commit 387335e2b
(cross-struct on FusedDqnTraining).
DEVIATION FROM PLAN: the plan called for "delegate accessors on
GpuDqnTrainer for slots 27/28" — structurally invalid because
GpuDqnTrainer does NOT own GpuIqnHead. The IQN head is owned by
FusedTrainingCtx (fused_training.rs:289) alongside the trainer at
line 234. The audit's per-slot accessor table (lines 535-536) is
correct: accessors land on GpuIqnHead. Task 4's
run_nan_checks_post_backward will receive IQN pointers as u64
arguments from the FusedTrainingCtx call site — same pattern already
in use at gpu_dqn_trainer.rs:6843
(apply_iqn_trunk_gradient(&mut self, iqn_d_h_s2_ptr: u64, ...)).
NO new scratch buffer added — the plan's bw_d_h_s2_pre_saxpy scratch
+ DtoD-copy approach was superseded by the audit's 3-call-site
reformulation. Slots 33/34/35 are post-main / post-aux / post-iqn
snapshots of the same bw_d_h_s2 (one buffer, three Task 4 invocations).
Pattern follows commit e9096c7be's GRN-block accessors (concise
pub(crate) fn name_ptr(&self) -> u64 with doc-comment referencing
slot number + audit doc + buffer semantics). Additive — no behavioral
change; new accessors consumed by Task 4's NaN check call sites.
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.