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Lands the plan-conditioning concat kernel for the K=3 trade-outcome forward as reusable scaffolding. Phase B5b (integration) is deferred with rationale: Phase C (state slots) is more critical for testing the K=3 head's effect on policy behavior, and can land independently of the plan-conditioning refinement. NEW kernel aux_to_input_concat_kernel.cu: - Writes [B, SH2+P] from h_s2_aux [B, 256] || plan_params [B, 6] - Pure GPU map; one thread per output element, no atomicAdd - NULL-tolerant on plan_params (zeros trailing P cols when source unavailable, e.g., collector cold-start where the trade plan head doesn't run) - Registered in build.rs; cubin compiles (5.7 KB). Dead code at this commit — no Rust launcher yet. Why B5 is split + B5b deferred: Full Phase B5 (integration) requires three coordinated changes: 1. Forward path: bump aux_to_fwd.forward() to SH2=262 + 262-dim input 2. Backward stride mismatch: backward emits dh_s2_aux_to_buf [B, 262], but dh_s2_aux_accum (input to aux trunk backward) is [B, 256]. A direct SAXPY mismatches row strides (262 vs 256) and corrupts the trunk's upstream gradient. Needs a strided-SAXPY kernel. 3. Collector-path plan_params unavailability: trade plan head only runs trainer-side. Workarounds: zero-fill, add trade plan to collector, or skip K=3 forward in collector. All have trade-offs. Phase B5b would need (1) strided-SAXPY kernel and (2) collector plan_params decision. Real work but NOT on the critical path for testing the K=3 head's effect on WR. Why Phase C should land first: The K=3 head currently trains on real labels (post-B4b) but doesn't influence policy behavior. Phase C wires the head's softmax into state slots [121..124) = (p_Profit, p_Stop, p_Timeout), replacing the K=2 single-slot 121 = 2*p_up - 1. WITH Phase C the policy reads aux's outcome predictions as state features → behavior changes → testable. Without Phase C, validation runs would show "K=3 head trains and converges" but predictions don't reach the policy → WR signal isn't a function of K=3 at all. We'd be testing nothing. Recommendation: skip the full B5 for now, do Phase C next, then Phase D (atom-shift). Phase B5b (plan-conditioning) is a refinement we add IF Phase C/D's no-plan-params version shows promise but plateaus below the WR ≥ 0.55 target. Audit: docs/dqn-wire-up-audit.md Phase B5a 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.