d3a057af4f
Phase 5 plumbing — consumer-side wire-up. Allocates `aux_conf_at_state_buf: CudaSlice<f32>` ([batch_size]) on `GpuDqnTrainer`, exposes the raw_ptr via `aux_conf_at_state_buf_ptr()` and the `FusedTrainerCtx` delegating accessor `trainer_aux_conf_at_state_buf_ptr()`, and invokes `GpuReplayBuffer::set_trainer_aux_conf_ptr` at both fused_ctx init sites in training_loop.rs (init + re-init, atomic per `feedback_no_partial_refactor`). The PER `gather_f32_scalar` now writes the SAMPLED bar's per-batch aux_conf directly into the trainer's f32 buffer on every step — same direct-to-trainer pattern as the SP13 B1.1b `aux_nb_label_buf` (i32) wire-up immediately above the new call. The c51_loss_batched reward gate (lands in the next commit) reads this buffer to compute `gate = sigmoid((aux_conf - ISV[AUX_CONF_THRESHOLD]) / ISV[AUX_GATE_TEMP])` and applies `r_used = gate * reward` at the Bellman projection. `alloc_zeros` cold-start: 0.0 sentinel → at threshold ≈ 0.10 and temp ≈ 0.05 the gate is `sigmoid(-2) ≈ 0.12` → reward is mostly suppressed pre-population. This is the "graceful degradation" semantic from the Phase 3 Task 3.4 audit doc spec §4.4. Once PER's direct-gather populates from the producer ring on the first sample step, the per-bar aux_conf values drive the gate as designed. 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.