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K=3 backward kernel that closes the forward → loss → backward chain for the trade-outcome aux head. Mirrors `aux_next_bar_backward` (K=2 sibling) line-for-line because gradient flow is K-independent: `d_logits = (softmax − one_hot)/B_valid` propagated through `Linear → ELU → Linear` chain via standard softmax-CE derivative. Per-sample partials (caller reduces via existing K-generic `aux_param_ grad_reduce` kernel): dW1_partial [B, H=128, SH2=256], db1_partial [B, H] dW2_partial [B, K=3, H], db2_partial [B, K=3] dh_s2_aux_out [B, SH2] Mask handling: labels[b] == -1 zeros the K-vector → all downstream partials zero (chain rule's multiplicative zero). All-skip batch produces valid_count=0 → d_logits=0 for every row → zero gradients across the board, no NaN. Sparse-label gradient amplification: B_valid is typically ~1-5% of nominal batch (trade-close events are rare), so inv_B = 1/B_valid is much larger than the K=2 sibling's inv_B = 1/(~B). Per-trade-close gradients have proportionally higher magnitude — correct credit assignment (rare signal speaks louder) but Phase E's Adam may need class-weighted CE or per-group LR tuning. ELU backward via post-activation identity: f'(x) = (h_post > 0) ? 1 : 1 + h_post — recovers derivative without re-evaluating x_pre. SP14 Phase C.5b separation preserved: reads h_s2_aux (aux trunk output), writes dh_s2_aux_out SAXPYing into dh_s2_aux_accum. Q's encoder structurally protected (aux_trunk_backward has no dx_in output). Phase A5 (this commit) is dead code — no Rust launcher. Phase B will land the full launcher chain (gpu_aux_heads.rs parallel ops struct, collector struct fields for W1/W2/b1/b2/Adam-state/saved-tensors/dW- partials, wireup in collect_experiences_gpu). Cubin: aux_trade_outcome_backward_kernel.cubin (24.8 KB). ═══ Phase A complete ═══ A1: ISV slots (none needed — reuses padding 121-123) A2: trade_outcome_label_kernel.cu (label producer)26ce7ba69A3: aux_trade_outcome_forward + save-for-backward wireup07728f9efA4: aux_trade_outcome_loss_reduce_kernel.cu (sparse CE)3ddcfb886A5: aux_trade_outcome_backward_kernel.cu (this commit) All 5 GPU kernels compile, cubins built, build.rs registered. Contract chain composes end-to-end on the GPU side. Phase B (Rust launcher chain + 262-dim input concat) is next. Audit: docs/dqn-wire-up-audit.md Phase A5 section + Phase A summary. 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.