27feb94a49
Closes defect #5 prerequisites for the GPU-pure step_with_lobsim that lands in R6. Replaces the host Thompson + host argmax + host log-softmax loops the flawed Phase F shipped in step_with_lobsim (which violated feedback_cpu_is_read_only with 5 DtoH copies + host per-action loops + HtoD action upload per training step). Three new kernels: 1. rl_action_kernel.cu — Thompson sampler over the C51 atom distribution. One block per batch, N_ACTIONS=9 threads. Each thread softmaxes its action's Q_N_ATOMS=21 atoms, samples one atom via CDF walk, writes sampled return to shared mem. Thread 0 argmaxes over per-action sampled returns + writes actions[b] + advances per-batch PRNG state. PRNG: per-batch xorshift32 state in prng_state_d (allocated + host-seeded from cfg.dqn_seed via ChaCha8 at trainer init per pearl_scoped_init_seed_for_reproducibility, with .max(1) guard since xorshift32 freezes at 0). Each per-action thread XORs its action index (golden-ratio mixed) into a thread-local copy of the per-batch state — no inter-thread race, reproducible by (cfg.dqn_seed, b_size, step_count). No cuRAND dep. 2. argmax_expected_q.cu — Bellman-target argmax over expected Q per action. Same layout as rl_action_kernel but deterministic (no PRNG). Per pearl_thompson_for_distributional_action_selection: Thompson for rollout (rl_action_kernel), argmax for Bellman target (this kernel) — distinct kernels, distinct ISV consumers. 3. log_pi_at_action.cu — per-batch log π(actions[b] | s_b) via log-softmax + lookup. One thread per batch entry (N_ACTIONS=9 is small enough for a per-thread sequential loop). Feeds the PPO importance ratio in R6. IntegratedTrainer gains: - 3 cubin includes (rl_action_kernel, argmax_expected_q, log_pi_at_action) - 3 module/function field pairs - 2 new device buffers populated at init: prng_state_d: CudaSlice<u32> of length n_batch atom_supports_d: CudaSlice<f32> of length Q_N_ATOMS=21, values [Q_V_MIN, Q_V_MIN + step, …, Q_V_MAX] = linspace(-1, +1, 21) - 3 launcher methods: launch_rl_action_kernel(q_logits_d, actions_d, b_size) launch_argmax_expected_q(q_logits_d, next_actions_d, b_size) launch_log_pi_at_action(pi_logits_d, actions_d, log_pi_out_d, b_size) GPU-oracle tests in tests/r4_action_kernels.rs (per feedback_no_cpu_test_fallbacks every oracle is analytical, not a CPU reference): R4.1: Thompson under sharp distribution (action 5 has logit=20 on atom 20 / support +1.0; others have logit=20 on atom 0 / support −1.0) collapses to argmax — per-action dominant-atom probability ≈ 1 − 4e-8, so 100/100 trials should pick action 5. Assert ≥99/100 (tolerates one fp-rounding edge near u ≈ 1.0 in CDF walk). R4.2: argmax_expected_q picks the rewarded action under the same sharp distribution. Negative invariant: swap dominant atom to action 2 → next_action follows. R4.3: log_pi_at_action with π logits dominant at action 3 (logit=20, others=0) → log π(3) ≈ 0 within 1e-4. Negative invariant: log π(other action) ≈ −20 within 1e-3. Build cache-bust v27. cargo check + cargo build --tests on ml-alpha green (heads_bit_equiv pre-existing failure persists). 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.