453a22f47f
X11's original plan said "capture_graph_a covers full v2 forward" but
the X11 commit (4f888abbf) only shipped forward_only + from_checkpoint.
Graph capture is now actually implemented for the inference path.
Mirrors the pattern already in step_batched (perception.rs:1221-1257):
- First call: eager dispatch + set forward_warmed flag.
- Second call: begin_capture -> dispatch_forward_kernels -> end_capture
-> store CudaGraph.
- Subsequent calls: graph.launch() — captured replay.
forward_only now performs its own staging-fill of the mapped-pinned
host buffers (input data varies per call), then dispatches through
the three-state machine. The captured region is the new private
dispatch_forward_kernels helper: a copy of evaluate_batched's
forward chain (VSN -> Mamba2 x2 -> LN x2 -> attn-pool -> CfC K-loop
-> heads) that omits labels, BCE, and any stream syncs. The final
sync + dtoh of probs_per_k_d happens OUTSIDE capture in forward_only.
Per pearl_no_host_branches_in_captured_graph: no host branches /
scalar-arg-changes / host-mallocs inside the captured region; all
kernel launches use pre-bound device pointers stable across replays.
Per pearl_cudarc_disable_event_tracking_for_graph_capture: event
tracking is already disabled for the trainer's lifetime at
construction (see PerceptionTrainer::new ~line 529), so the captured
region is free of cuStreamWaitEvent / event.record() insertions.
Vestigial loader.rs:272 doc comment referencing the never-shipped
CfcTrunk::capture_graph_a updated to point at the now-real
PerceptionTrainer::forward_only warmup path.
Regression: forward_captured_matches_uncaptured — eager (call 1) vs
captured replay (call 3) agree within 1e-5 relative tolerance per
element. NOT strict bit-identity because CUDA Graph capture can
reorder kernel launches and flip f32 reductions by 1 ULP harmlessly.
Local RTX 3050 Ti run: 160 elements, max rel_err = 0e0.
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.