ab4a7db33c
Threads `self.aux_conf_at_state_buf` into the `c51_loss_batched` launch
in `GpuDqnTrainer::launch_c51_loss`. Position matches the kernel's
appended trailing arg from the previous commit.
Tests added in `crates/ml-dqn/src/gpu_replay_buffer.rs::tests`:
- `aux_gate_high_confidence_passes_full_target` (CPU pure-math):
gate(aux_conf=0.5, threshold=0.10, temp=0.05) > 0.99 proves
high-confidence reward pass-through.
- `aux_gate_low_confidence_attenuates_reward` (CPU pure-math):
gate(aux_conf=0.02, threshold=0.10, temp=0.05) < 0.20 proves
the uncertain-state neutralizer semantic.
- `aux_gate_temp_floor_keeps_gate_finite` (CPU pure-math):
sweeps {temp, aux_conf, threshold} and asserts finite gate ∈ [0,1]
across the ISV-controllable parameter range — proves the
fmaxf(temp, 1e-3) floor keeps the kernel numerically safe.
- `aux_conf_direct_to_trainer_gather_populates_destination` (GPU
behavioral): wires a fresh CudaSlice<f32> as the trainer
destination, inserts 8 transitions with strictly-positive distinct
aux_conf values, samples 1, asserts the trainer destination
buffer post-sample holds a value from the inserted set (NOT the
alloc_zeros sentinel) — proves the direct-gather wiring actually
populates the trainer buffer with non-trivial data.
All 3 CPU math tests + 1 GPU integration test pass on RTX 3050.
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.