c34650a241
Closes the last host orchestration in `step_with_lobsim`'s policy path
per `feedback_cpu_is_read_only` + `pearl_no_host_branches_in_captured_graph`.
The flawed Phase F+G shipped three host loops (Thompson sampling over
C51 atoms, argmax over expected Q, log-softmax for log π_old) that each
required a DtoH staging copy of the relevant Q/V/π device buffer per
step. R7b deletes them all in favour of three R4 kernels:
* `rl_action_kernel` — Thompson sampler with per-batch xorshift32
PRNG state (device-resident, no salt-based
host RNG seeding per step).
* `argmax_expected_q` — deterministic Bellman-target argmax over
expected Q per action; distinct selector
from Thompson per
`pearl_thompson_for_distributional_action_selection`.
* `log_pi_at_action` — per-batch log π(action_b) via log-softmax
+ lookup; feeds the PPO importance ratio.
Side effects of the lift:
* The host `read_slice_d` DtoH calls for `q_logits_host`,
`v_pred_host`, `pi_logits_host` are deleted (each was 4 × b_size ×
Q_N_ATOMS or b_size × N_ACTIONS floats per step). Three full
device→host→device roundtrips per step → zero.
* The host γ readback (ISV[400] mirror DtoH + bootstrap fallback
that the flawed branch kept "just in case") is also gone:
`compute_advantage_return` already reads γ on-device from
ISV[400] (R3). The fallback was a smell that R7a planned to
eliminate; R7b actually removes it.
* V(s_t) stays in `v_pred_d` throughout the hot path. The host
upload via the (now-deleted) `upload_f32` helper that round-tripped
V back to device for `compute_advantage_return` is gone. V(s_{t+1})
still reuses `v_pred_d` (h_t bootstrap) — true V(s_{t+1}) via
`forward_encoder(next_snapshots)` is explicitly scoped to R7c.
* Step-counter increment retained (drives the Thompson-vs-argmax
diagnostic ordering in the trainer's stats record), but the
ChaCha8Rng-from-step_counter that seeded the host loop is gone.
Per `feedback_no_hiding` the three retired helper functions
(`upload_f32`, `upload_i32`, `argmax_f32`) are DELETED rather than
`#[allow(dead_code)]`'d. The matching unused imports
(`MappedI32Buffer`, `DevicePtrMut`) are removed in the same commit.
The `actions_d` allocation that used to live in step_with_lobsim's
local scope (used only to bridge the host Thompson loop → the
GPU `actions_to_market_targets` kernel) is gone too — that kernel now
reads directly from `self.actions_d` written by `rl_action_kernel`,
closing the last action-path host upload.
What's still HtoD in the hot path (intentional, boundary data):
* `apply_snapshot(last_snap)` — the trainer-fed MBP-10 input
crosses the I/O boundary; mapped-pinned per
`feedback_no_htod_htoh_only_mapped_pinned`.
* HEALTH_DIAG ISV readback inside `step_synthetic` — explicit
diagnostic surface, not hot-path orchestration.
Falsifiability gate G4 (R4 kernel oracle tests + R5 controller +
soft-update tests) already passing on this branch. The R6/R7a smoke
(`integrated_trainer_step_with_lobsim_runs_without_panic`) re-builds
clean and the only host code remaining in `step_with_lobsim` is the
bounded `LaunchConfig`/`b_size_i` setup before each kernel launch.
R7c (next): PER buffer wiring (push transitions, sample, update
priorities) per `feedback_always_per`, and `next_snapshots` +
`forward_encoder(next_snapshots)` for true V(s_{t+1}) Bellman target
(replaces the h_t bootstrap reuse above).
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.