aba8ec61b2
Honors R6's commit-message promise to close the host-work boundary
the partial GPU-purity left behind. After R7a, step_with_lobsim's
post-fill pipeline is GPU-resident through the entire training step
except for 3 small host-slice uploads (actions, next_actions,
log_pi_old) that R7b lifts via R4's Thompson/argmax/log_pi kernels.
CHANGES:
1. New cuda/abs_copy.cu — element-wise dst[b] = fabsf(src[b]).
Feeds the |reward| signal into ema_update_on_done for the
MEAN_ABS_PNL_EMA slot without mutating signed rewards_d.
2. New trainer-owned per-step device buffers (allocated once in
new(), reused every step — no per-call churn):
reward_abs_d, actions_d, next_actions_d, log_pi_old_d,
advantages_d, returns_d
3. step_synthetic signature change: drops the 7 synthetic_* host
slice args (synthetic_actions, synthetic_rewards, synthetic_dones,
synthetic_next_actions, synthetic_advantages, synthetic_returns,
synthetic_log_pi_old). The body now reads from trainer-owned
device buffers (self.actions_d, self.rewards_d, etc.) via the
disjoint-field borrow rule. The 7 upload_i32/upload_f32 calls
are deleted — caller (step_with_lobsim) populates the buffers
via GPU kernels before invoking step_synthetic.
4. step_with_lobsim Step 6 rewritten end-to-end:
- Upload host Thompson outputs (actions, next_actions, log_pi_old)
to trainer buffers — R7b removes these via R4's GPU kernels.
- GPU abs_copy(rewards_d) → reward_abs_d.
- GPU ema_update_on_done(MEAN_ABS_PNL_EMA, reward_abs_d, dones_d).
- GPU launch_rl_controllers_per_step (R5) — all 7 controllers
adapt to this step's EMA inputs.
- GPU apply_reward_scale(rewards_d) in-place — reads the freshly
updated ISV[406] from the controller.
- GPU compute_advantage_return(rewards_d, dones_d, v_t, v_tp1)
→ returns_d, advantages_d.
- step_synthetic(snapshots) consumes all trainer device buffers,
runs the training kernel chain, returns stats.
- dqn_head.soft_update_target(isv_d) — R5 target-net Polyak
update with τ from ISV[401].
R7a PARTIAL — work R7b lifts:
- Host Thompson sampler still produces actions/next_actions/log_pi
(R7b replaces with R4 rl_action_kernel + argmax_expected_q +
log_pi_at_action — eliminating the 3 remaining HtoD uploads).
- v_pred_host → v_pred_d HtoD round-trip (the host already has
v_pred_host from the action-sampling Thompson read; R7b keeps V
on device throughout).
- v_tp1_d uses v_pred_host (V(s_t) approximated as V(s_{t+1}) until
R7b wires next_snapshots + forward_encoder(next_snapshots)).
The 4 final DtoH copies the R6 commit message warned about are
GONE. Hot path: snapshot upload (boundary HtoD), ISV diagnostic
readback (HEALTH_DIAG), and 3 host-Thompson uploads (R7b removes).
cargo check + cargo build --tests on ml-alpha green. Tests still
compile against the new step_synthetic signature (no test calls it
directly — only step_with_lobsim does).
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.