6d433784f4
Closes defect #5 from the flawed Phase F+G arc (feedback_cpu_is_read_only violation in step_with_lobsim's host advantage + EMA loops) by landing the GPU primitives those loops will become in R6. Three new kernels, each with a GPU-oracle gate test: 1. ema_update_on_done.cu — done-gated EMA producer. - Slot-parameterised (one kernel, 3 callers in R5 covering mean_abs_pnl_ema, q_divergence_ema, td_kurtosis_ema). - Shared-mem tree reduce, no atomicAdd (feedback_no_atomicadd). - Per pearl_first_observation_bootstrap: sentinel-zero ISV → first observation replaces directly. Defers bootstrap if mean_obs == 0 to avoid writing a degenerate sentinel that would be re-bootstrapped next call. - Per pearl_wiener_alpha_floor_for_nonstationary: Wiener-α blend on subsequent calls; caller pre-floors α at 0.4. 2. ema_update_per_step.cu — per-step EMA producer (no done-gate). - Slot-parameterised (kl_pi_ema, entropy_observed_ema, advantage_var_ratio_ema, mean_trade_duration_ema in R5). - Same shared-mem tree reduce + bootstrap discipline as ema_update_on_done. 3. compute_advantage_return.cu — element-wise returns[b] = r + γ(1-done)·V(s_{t+1}); advantages[b] = returns − V(s_t). - Reads γ from ISV[400] (R1 bootstrap = 0.99). - Trivially parallel, one thread per batch entry; no atomics. Rust launchers added to IntegratedTrainer: - launch_ema_update_on_done(slot, alpha, obs_d, dones_d, b_size) - launch_ema_update_per_step(slot, alpha, obs_d, b_size) - launch_compute_advantage_return(rewards_d, dones_d, v_t_d, v_tp1_d, returns_d, advantages_d, b_size) 3 cubin includes, 3 module/function fields, loaders in new() between the rl_reward_scale_controller load and the with_controllers_bootstrapped call so the new fields are populated by struct construction. GPU-oracle tests in tests/r3_ema_advantage.rs (per feedback_no_cpu_test_fallbacks every oracle is either the kernel's documented bootstrap behaviour or an analytical property of the formula, not a CPU reference): R3.1: ema_update_on_done bootstrap path — sentinel-zero ISV + one observation k → ISV[slot] == k exactly. Negative invariant: hold-only step (dones all zero) preserves the EMA. R3.2: ema_update_per_step convergence — feed obs=5.0 for 50 steps with α=0.4 → ISV[slot] → 5.0 within 1e-4 (EMA of constant = constant). R3.3: compute_advantage_return formula — r=0, done=0, v_t=v_tp1=k, γ=0.99 → returns=γk=4.95, advantages=(γ−1)k=−0.05. Negative invariant: done=1 + r=0 zeros the future-value bootstrap (returns=0, advantages=−k). Build cache-bust v26. cargo check + cargo build --test r3_ema_advantage on ml-alpha green. Pre-existing heads_bit_equiv.rs index-out-of-bounds failure persists (unrelated; pre-Phase E). 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.