0840fcfe64
Closes defect #1 from the flawed Phase F+G arc: ISV[400..406] were left at alloc_zeros sentinel 0 in production, causing bellman_target_projection (γ=0), ppo_clipped_surrogate (ε=0, entropy=0), and the C51 backward to train against degenerate targets that the MockLobEnv toy fixture (done=true every step, horizon=1) intrinsically could not detect. Three changes: 1. Port crates/ml-alpha/cuda/rl_reward_scale_controller.cu from the ml-alpha-phase-f-g-flawed reference branch (93 lines, unchanged). Add to build.rs KERNELS list; bump cache-bust to v25. 2. Extend src/rl/isv_slots.rs: add 7 new EMA-input slot constants (RL_MEAN_TRADE_DURATION_EMA_INDEX..RL_MEAN_ABS_PNL_EMA_INDEX), RL_SLOTS_END goes 417 -> 424. These are reserved for the EMA producer kernels Phase R3 lands; in R1 they stay at sentinel 0 (asserted by the G1 test). 3. Wire all 7 RL adaptive controllers (γ / τ / ε / entropy_coef / n_rollout_steps / per_α / reward_scale) into IntegratedTrainer: - 7 cubin includes + 7 module/function fields - All 7 loaded in new() via the existing load_cubin pattern - New fn launch_isv_controller_3arg() centralises the shared (isv*, alpha, scalar_input) launch signature - New fn with_controllers_bootstrapped() consumes self and fires each controller once against the freshly-zeroed isv_d; each kernel's first-observation-bootstrap path (per pearl_first_observation_bootstrap) sees sentinel zero in its slot and writes its canonical *_BOOTSTRAP value: ISV[400] γ = 0.99 ISV[401] τ = 0.005 ISV[402] ε = 0.2 ISV[403] entropy_coef = 0.01 ISV[404] n_rollout_steps= 2048 ISV[405] per_α = 0.6 ISV[406] reward_scale = 1.0 - new() ends with `.with_controllers_bootstrapped()?` so every trainer construction site picks this up automatically. This replaces the flawed Phase F approach of host memcpy_htod-ing canonical constants into ISV, which violated feedback_no_htod_htoh_only_mapped_pinned (tests not exempt) AND short-circuited the canonical pearl_first_observation_bootstrap pattern every other adaptive controller in the codebase uses. The launch_isv_controller_3arg helper is reused by Phase R5's per-step controller launches with real EMA inputs sourced from ISV[417..424] — at that point the Wiener-α blend kicks in and the slots adapt away from the R1 bootstrap defaults. Gate G1 (crates/ml-alpha/tests/isv_bootstrap.rs): - Construct IntegratedTrainer - memcpy_dtoh full ISV slice to host - Assert ISV[400..406] equal each kernel's #define *_BOOTSTRAP - Assert ISV[417..424] still at sentinel 0 (R3 wires producers) Per feedback_no_cpu_test_fallbacks: the oracle is the kernel's own *_BOOTSTRAP constant, not a CPU computation. Per pearl_tests_must_prove_not_lock_observations: the test asserts an invariant (bootstrap path wrote the canonical value defined by the kernel), not a tuned magic number. Build clean: cargo check + cargo build --test isv_bootstrap on ml-alpha both green. CUDA-required, #[ignore]'d for non-GPU CI. 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.