7356e3c7bb
Closes 3 of 4 deferred items from commit 2665669b5; item 1 (encoder
backward) is deferred to a follow-up commit (see Notes below).
Item 2 — Bellman target projection kernel:
New `bellman_target_projection.cu` replaces the host-side stand-in
`build_synthetic_bellman_target`. Reads γ from
ISV[RL_GAMMA_INDEX=400] per pearl_controller_anchors_isv_driven.
Standard C51 categorical projection with linear interpolation onto
the discrete support [V_MIN, V_MAX]; no atomicAdd (per-source-atom
serial shared writes bracketed by __syncthreads, Q_N_ATOMS=21
inner-loop syncs).
Also adds `dqn_select_action_atoms` in the same TU — selects per-batch
action-row atoms from the full target-net output. Both kernels exposed
via DqnHead::project_bellman_target / DqnHead::select_action_atoms +
DqnHead::forward_target (new — target-network forward using
w_target_d / b_target_d).
IntegratedTrainer::step_synthetic now consumes the kernel path:
forward_target → select_action_atoms → project_bellman_target →
backward_logits. The `build_synthetic_bellman_target` host function is
DELETED.
Item 3 — Per-head LR ISV controller:
New `rl_lr_controller.cu` emits per-head learning rates to ISV slots
[412..417]: RL_LR_BCE_INDEX, RL_LR_Q_INDEX, RL_LR_PI_INDEX,
RL_LR_V_INDEX, RL_LR_AUX_INDEX (RL_SLOTS_END bumped 412 → 417).
Bootstrap 1e-3 per pearl_first_observation_bootstrap; Wiener-α blend
with floor 0.4 per pearl_wiener_alpha_floor_for_nonstationary.
IntegratedTrainer launches the controller at the top of step_synthetic
AFTER the encoder forward; it then DtoH-mirrors ISV and mutates each
per-head AdamW.lr field before the Adam steps. The PHASE_E2_DEFAULT_LR
constant is DELETED. The `lr` parameter on step_synthetic is gone
(greenfield — no caller override).
The controller currently emits the constant LR_BOOTSTRAP target; the
signal-modulated variant (gradient-norm EMA-driven LR adaptation) is
reserved for Phase E.3+ — kernel header documents the upgrade path
and the 5 diagnostic-signal args are already plumbed.
Item 4 — PPO V-loss canonicalization:
Deletes `returns_/v_pred/loss_v` from ppo_clipped_surrogate_fwd
(kernel + ppo.rs::surrogate_forward signature). V gradient now flows
ONLY through the dedicated `v_head_fwd_bwd` kernels per
feedback_single_source_of_truth_no_duplicates. PPO surrogate now
handles policy loss + entropy bonus only. The IntegratedTrainer's
surrogate_forward call site no longer passes returns/v_pred/loss_v.
ISV slot table (rl/isv_slots.rs):
408..411 loss-balance λ (BCE/Q/π/V) ← existing
412 RL_LR_BCE_INDEX ← new
413 RL_LR_Q_INDEX ← new
414 RL_LR_PI_INDEX ← new
415 RL_LR_V_INDEX ← new
416 RL_LR_AUX_INDEX ← new
417 RL_SLOTS_END ← was 412
Notes — item 1 (PerceptionTrainer::backward_encoder_with_grad_h_t)
deferred:
The IntegratedTrainer.step_synthetic path now COMBINES the Q/π/V
grad_h_t contributions into a dedicated `grad_h_t_combined_d` slot
via the existing `grad_h_accumulate_scaled` kernel (loss-balance λ
weighted), which is the prerequisite plumbing for the encoder
backward entry point. The PerceptionTrainer-side method that consumes
this slot — CfC bwd + Mamba2 bwd + encoder Adam step on caller-
provided grad_h_t — is a multi-thousand-line refactor of
perception.rs::dispatch_train_step (~7,400 lines) and is being
sequenced as a standalone follow-up commit to keep the kernel
signature changes here (items 2/3/4) reviewable in isolation.
All three landed items are greenfield replacements per project policy
(feedback_no_legacy_aliases, feedback_no_partial_refactor): no
deprecated fields, no const-or-ISV fallback shims, no parameter
backward-compat. ISV is the single source of truth for all adaptive
parameters per pearl_controller_anchors_isv_driven.
Validation:
cargo check -p ml-alpha --lib ✓
cargo check -p ml-alpha --tests --examples ✓
cargo check --workspace --lib ✓
cargo test -p ml-alpha --lib (63 passed, 6 ignored) ✓
cargo build -p ml-alpha --features cuda (new cubins built) ✓
Companion to E.2 (commit 2665669b5). Encoder backward + Phase E.3
LobSim integration follow in subsequent commits.
Co-Authored-By: Claude Opus 4.7 <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.