0a32a3bb89
Closes defects #3 (controllers never launched) and #4 (target net never soft-updated) from the flawed Phase F+G arc. CONTROLLER SIGNATURE CHANGE (all 7 .cu files): Scalar input arg → int input_slot. Each controller now reads its EMA input from ISV[input_slot] directly inside the kernel, eliminating the 7 DtoH-per-step host roundtrips a scalar-arg signature would have required — per feedback_cpu_is_read_only (hot-path must be GPU-pure). Bootstrap path unchanged: kernel reads its output slot, sees sentinel zero, writes *_BOOTSTRAP, and returns BEFORE the input_slot read. So R1's launch_isv_controller_3arg(controller_fn, alpha=0.4, input_slot) works both at bootstrap (input read deferred via early return) and at per-step (input slot has real EMA observation from R3 producers). PER-STEP CONTROLLER LAUNCHER: New IntegratedTrainer::launch_rl_controllers_per_step() fires all 7 controllers in sequence, each with its dedicated EMA input slot: ISV[400] γ ← ISV[417] MEAN_TRADE_DURATION_EMA ISV[401] τ ← ISV[418] Q_DIVERGENCE_EMA ISV[402] ε ← ISV[419] KL_PI_EMA ISV[403] entropy_coef ← ISV[420] ENTROPY_OBSERVED_EMA ISV[404] n_rollout_steps← ISV[421] ADVANTAGE_VAR_RATIO_EMA ISV[405] per_α ← ISV[422] TD_KURTOSIS_EMA ISV[406] reward_scale ← ISV[423] MEAN_ABS_PNL_EMA R1's with_controllers_bootstrapped also updated to pass the input slot indices (the bootstrap path still ignores them via early return). TARGET-NET SOFT UPDATE (defect #4): New cuda/dqn_target_soft_update.cu — element-wise target[i] = (1-τ)·target[i] + τ·current[i] reading τ from ISV[401]. Trivially parallel, no atomicAdd. DqnHead gains target_soft_update_fn + _target_soft_update_module fields + soft_update_target(&isv_d) method that fires the kernel twice (weights + biases). R6 calls this from step_with_lobsim after the Q-head Adam update. GATE TESTS (tests/r5_controllers_and_soft_update.rs): G3: g3_per_step_controllers_move_isv_outputs_when_fed_real_emas - Verifies R1 bootstrap pre-conditions (all 7 output slots at documented bootstrap values; all 7 EMA-input slots at sentinel 0). - Populates each EMA-input slot with a distinct non-zero value via R3's ema_update_per_step bootstrap path (different values per slot so a wrong-slot wiring bug would produce out-of-range outputs). - Verifies the EMA producers wrote what we expected (sanity). - Fires launch_rl_controllers_per_step. - Asserts each output slot moved off its bootstrap value (catches "controller doesn't fire" / "reads wrong slot" / "dead kernel"). G4: g4_dqn_target_soft_update_implements_polyak_formula - Force-overwrite w_d with all-ones (breaks the w==target init symmetry so soft_update has something to blend). - Snapshot w_target (Xavier init values). - Fire dqn_head.soft_update_target with R1-bootstrapped τ=0.005. - For sample indices: assert target_after[i] equals (1-τ)·target_before[i] + τ·1.0 within 1e-6 (exact algebraic identity, not a CPU reference — kernel IS the kernel). - Negative invariant: at least one element changed. Per feedback_no_cpu_test_fallbacks: G3 oracle is the invariant "output != bootstrap after non-trivial input"; G4 oracle is the algebraic identity (1-τ)·a + τ·b applied to the SAME numbers the kernel saw — not a parallel CPU implementation. Build cache-bust v28. cargo check + cargo build --tests on ml-alpha green for all R-phase tests. 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.