2665669b58
Replaces Phase E.1's placeholder host-side loss scalars with actual GPU kernel calls. Each of Q, π, V heads now runs: - Forward kernel on h_t → head outputs (q_logits / pi_logits / v_pred) - Backward kernel chain → grad_w / grad_b / grad_h_t (per-batch scratch reduced by reduce_axis0) - Adam step using the existing project-wide adamw_step cubin via the AdamW wrapper (each head owns 2 instances — one for w, one for b) New CUDA kernels: - v_head_fwd_bwd.cu: scalar V head forward + MSE backward. Per-batch loss scratch + per-batch grad_w / grad_b scratch — caller reduces via reduce_axis0. Per-(batch, c) grad_h_t writes are sole-writer so no atomicAdd needed. - grad_h_accumulate.cu: element-wise grad_h_encoder += λ × grad_h_head combiner. Loaded and ready; consumed by Phase E.3 once the encoder backward entry point lands. Extends existing kernels (additive, doesn't break prior callers): - dqn_distributional_q.cu: adds `dqn_grad_w_b_h_t` that maps the C51 bwd kernel's grad_logits output into per-batch grad_w / grad_b scratch + OVERWRITE grad_h_t. Mirrors aux_heads_bwd's thread-role pattern (one thread per HIDDEN_DIM channel, sole writer per slot). - ppo_clipped_surrogate.cu: adds `ppo_policy_logits_fwd` (standalone linear-projection forward producing pi_logits for the surrogate kernel to consume) AND `ppo_grad_w_b_h_t` (same backward pattern as the DQN extension, with output dim N_ACTIONS=9). All new kernels honour feedback_no_atomicadd: per-batch grad scratch reduced by the existing reduce_axis0 path, never atomics. The Phase C/D loss-scalar atomicAdds (dqn / ppo bwd accumulators) stay as their loud-flagged deferral. Per-head Adam state: - DqnHead: 2 AdamW instances (w_d, b_d). Re-uses the existing adamw_step cubin from `trainer::optim::AdamW`. - PolicyHead: same. - ValueHead: same. - Each AdamW owns independent m / v buffers and a device-resident step counter (per pearl_no_host_branches_in_captured_graph: counter advancement is a captured kernel, not host scalar). PerceptionTrainer: adds `h_t_view()` accessor — borrows the h_t_d field that `forward_encoder` populates. IntegratedTrainer uses this to dispatch Q/π/V head kernels on the same encoder representation without re-borrowing self.perception mutably between the encoder forward and the head dispatches (disjoint field borrows). ENCODER BACKWARD: Phase E.2 DEFERS the encoder-side gradient combine to Phase E.3 (where the LobSim integration provides a clean entry point for a new PerceptionTrainer backward-encoder method). The Q/π/V kernels DO emit grad_h_t but it is not yet wired into the encoder backward path. The encoder still learns from BCE+aux only in E.2; the new heads update their own weights via per-head Adam. E.3 lands the missing piece via `launch_grad_h_accumulate` (wired and ready). Bellman target distribution: Phase E.2 uses a deterministic single-atom-mass projection (host-side, mapped-pinned upload). Phase E.3 replaces with the proper categorical projection kernel that consumes γ from ISV[400] and the target net's bootstrap atom values. build.rs: registers `v_head_fwd_bwd` + `grad_h_accumulate` cubins. Cache-bust v21 → v22 with a verbose changelog entry covering this phase's contract changes. The integrated_trainer_smoke #[ignore]-gated GPU test remains gated; Phase E.3 activates it alongside LobSim. Non-GPU host tests (loss_balance default + bootstrap) continue to pass. Companion to Phases A-E.1 (commits6a46ded7d9ec43fdb956efd96cb9732a667c729f110e0).
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.