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THE K=3 HEAD NOW REACHES THE POLICY. Phase C-2 is the consumer-side flip — slot 121's semantic changes from K=2's recentered p_up to K=3's p_Profit, and slots [122, 123] gain new meaning as (p_Stop, p_Timeout). Architectural constraint: aux_dir_prob_per_env (K=2 buffer) is ALSO consumed by experience_env_step's β reward at lines 2335 + 3724-3725. Cannot repurpose that pointer to point at the K=3 [N, 3] buffer — env_step β consumer would read wrong-stride memory. This commit adds SEPARATE arg aux_outcome_probs_per_env [N, 3] to state_gather kernels with NULL fallback. Branching semantic at state_gather read site: - aux_outcome_probs_per_env != NULL → K=3 active path: writes slots [121..124) via new assemble_state_outcome_k3 helper - aux_outcome_probs_per_env == NULL → K=2 fallback: writes slot 121 via existing assemble_state from the recentered scalar Changes: - state_layout.cuh: NEW __device__ helper assemble_state_outcome_k3 — mirrors assemble_state except padding slots [121..124) get p_Profit/p_Stop/p_Timeout (raw softmax probs [0, 1]), slots [124..128) zero for 8-alignment. - experience_kernels.cu: training-side experience_state_gather + eval-side backtest_state_gather both get new trailing arg aux_outcome_probs_per_env (NULL-tolerant). Read site branches: K=3 reads 3 floats / env → assemble_state_outcome_k3; K=2 fallback preserves legacy assemble_state call. - gpu_experience_collector.rs: training launcher passes self.prev_aux_outcome_probs.raw_ptr() → K=3 active in training. - gpu_backtest_evaluator.rs: eval launcher passes NULL → K=2 fallback in eval (eval has no aux producer infra yet). K=2 head still alive: - prev_aux_dir_prob still populated by aux_softmax_to_per_env_kernel - experience_env_step still reads it for β reward (independent consumer untouched) - EGF chain still reads exp_aux_nb_softmax_buf - Only K=2's slot 121 contribution to policy state is suppressed End-to-end K=3 chain now active: Label producer (A2) → per-(env, t) ring (B4b-1) → replay buffer scatter (B4b-2) → PER direct gather → trainer aux_to_label_buf → loss_reduce (B4) sparse CE on real labels → backward (B4) per-sample partials → Adam SAXPY (B1+B4) updates W1/b1/W2/b2 at [163..167) PARALLEL: Collector rollout K=3 forward (B3) → softmax tile → C-1 producer → prev_aux_outcome_probs [N, 3] → C-2 state gather → state[121..124] → policy reads in next step The K=3 head closes the loop: learns from real labels via replay, AND predictions reach policy via state assembly. Trainable + observable. Verification: - cargo check -p ml clean. - cargo test -p ml --lib → 1016/0 green. Remaining vNext work: - Phase D: 12-weight W atom-shift (4 actions × 3 outcomes) - Phase E: dW backward + Adam for W[4, 3] - Phase F: Validation smoke at structural prior - B5b (deferred): plan_params input concat Audit: docs/dqn-wire-up-audit.md Phase C-2 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.