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First half of Phase C. Lands the producer side of the K=3 trade- outcome aux head's state bridge: new kernel populates a per-env 3-slot cache from the K=3 softmax tile every rollout step. The consumer side (state gather reading from this cache → state slots [121..124)) lands in Phase C-2. Mirrors the K=2 head's existing aux_softmax_to_per_env_kernel exactly at K=3: - K=2: prev_aux_dir_prob[env] = 2*softmax[env, 1] - 1 (recentered) - K=3: prev_aux_outcome_probs[env, k] = softmax[env, k] for k in [0, 3) Changes: - state_layout.rs: 3 new constants AUX_OUTCOME_PROFIT_INDEX = 121, AUX_OUTCOME_STOP_INDEX = 122, AUX_OUTCOME_TIMEOUT_INDEX = 123. PROFIT_INDEX aliases AUX_DIR_PROB_INDEX (same value, different semantic). Phase C-2 flips slot 121's meaning from K=2's recentered p_up to K=3's p_Profit. - aux_outcome_softmax_to_per_env_kernel.cu: new kernel + cubin. - gpu_dqn_trainer.rs: new SP22_AUX_OUTCOME_SOFTMAX_TO_PER_ENV_CUBIN embed. - gpu_experience_collector.rs: 2 new struct fields (cache buffer + kernel handle); cubin load + alloc in constructor; struct-init; per-step launch in rollout loop after K=3 forward. - build.rs: kernel registered. Encoding shift K=2 → K=3: K=2 used recentered [-1, +1] to match "no signal = 0" baseline of every other slot. K=3 keeps raw softmax probabilities [0, 1]. Cold-start sentinel 0.0 for all 3 slots = "no prediction yet" (mask). The 3-slot natural distribution is more informative than a scalar. Dead-code status: producer populates cache every step but experience_state_gather doesn't read from it yet — state slot 121 still receives K=2's prev_aux_dir_prob write. Phase C-2 swaps the state gather's source from K=2 cache to K=3 cache (3-slot write). Why split C into C-1 + C-2: experience_state_gather is a hot-path kernel with many consumers. Updating it touches training collector, eval-side backtest evaluator, Rust launcher arg list. C-2 lands that as an atomic state-semantic flip; C-1 lands the GPU-side scaffolding independently so the producer chain can be validated first. Verification: - cargo check -p ml clean. - cargo test -p ml --lib → 1016/0 green. Audit: docs/dqn-wire-up-audit.md Phase C-1 section. 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.