842e90aeb0
CB1+CB2 swapped labels D→A+B; this swaps the kernels to match. aux_heads.cu — 4-output structure: - Forward: 12 outputs per snapshot = 4 per direction × N_HORIZONS (prof_long_logit, size_long_pred, prof_short_logit, size_short_pred, each [N_HORIZONS]). Linear projections; sigmoid applied in BCE kernel. - Backward: accepts 4 grad_y inputs, produces 8 grad_W + 8 grad_b + grad_h_aux. Cooperative h_aux staging in shmem once per block. - 8 weight matrices total, Xavier × 0.1 init under scoped_init_seed. aux_loss.cu — 2 kernels: - aux_bce_loss_fwd_bwd: class-weighted BCE+sigmoid fused. pos_weight in shared mem; scales positive-class gradient. NaN-mask y_true. - aux_huber_masked_fwd_bwd: Huber w/ NaN-mask. CB1's y_size=NaN at y_prof=0 provides the conditional-Huber semantics naturally — no separate mask buffer needed. aux_heads.rs: - AuxHeads + AuxHeadsWeights: 8 buffer fields (4 W + 4 b) - AuxBceLoss + AuxMaskedHuberLoss wrappers replace AuxHuberLoss - POS_WEIGHT_MIN/MAX = [1.0, 50.0] clamps per E3 - aux_heads_fwd_gpu/aux_heads_bwd_gpu/aux_bce_loss_gpu/aux_huber_masked_loss_gpu perception.rs (minimal compile-keeping signature updates only): - Renamed/added buffers: 4 prediction (prof/size × long/short), 4 label staging, 4 grad_y per-K, 8 head grad scratches, 8 head Adam optimizers, 2 pos_weight buffers (device + staging) - HOLDING PATTERN: bwd zeroes the 4 grad_y_per_K buffers each step so the head Adam updates are no-ops on grad=0 (no aux gradient signal this commit). CB5 wires the actual aux_bce + aux_huber_masked calls. - BCE direction signal + dir_acc readouts updated to use the new prof_long/prof_short prediction buffers (so existing perception_overfit aux test still passes). 7 GPU oracle tests on RTX 3050 sm_86, all pass in 2.43s: - fwd_matches_naive_reference, bwd_finite_diff_matches_bias_sample, aux_bce_loss_matches_naive_reference, aux_bce_pos_weight_scales_positive_gradient (verified: pos_weight=10 → 10× gradient ratio within 1e-4), aux_huber_masked_does_not_propagate, aux_huber_masked_covers_both_branches, aux_bce_nan_mask_does_not_propagate Cubins rebuilt: aux_heads (8736→10528 bytes, +20% for 4-head fwd/bwd), aux_loss (6944→13344 bytes, +92% for 2 kernels). 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.