74ed2f5008
Both kernels in `aux_heads_loss_ema_kernel.cu` retrofit in the same commit
per `feedback_no_partial_refactor.md` (single shared cubin, single producer
family).
- Kernel `aux_heads_loss_ema_update`: writes nb_loss/rg_loss scalars to
`producer_step_scratch_buf[41..43)` (slots 41=next-bar, 42=regime).
- Kernel `aux_label_scale_ema_update`: writes mean(|label|) to
`producer_step_scratch_buf[43]`.
- 3 ISV slots wired with Pearls A+D: ISV[113] (Wiener 123..126),
ISV[114] (Wiener 126..129), ISV[117] (Wiener 129..132).
- Launcher `AuxHeadsForwardOps::launch_loss_ema`: drops isv_dev_ptr +
isv_*_index + ema_alpha; takes scratch_dev_ptr + 2 scratch_idx args.
- Launcher `AuxHeadsForwardOps::launch_label_scale_ema`: same retrofit
pattern with a single scratch_idx arg.
- Wrapper `GpuDqnTrainer::launch_aux_heads_loss_ema(_ema_alpha_unused)`:
sync + Pearls A+D loop over both slots.
- `aux_heads_forward` mid-step launch (Step 2b — runs BEFORE
next_bar_loss_reduce + backward consume ISV[117]) gains inline sync +
Pearls A+D update so consumers see the up-to-date scale this step.
Behavior: stationary signals converge to the same value at adaptive rate
(Pearl D's α* derived from per-slot signal-vs-noise variance);
non-stationary signals respond Wiener-optimally faster. Slots stay
semantically identical (next-bar MSE, regime CE, label-scale mean_abs);
only the EMA blending logic changes.
Tests:
- `sp4_aux_heads_loss_ema_writes_step_obs_via_pearl_a_then_converges_pearl_d`:
nb_loss=0.5, rg_loss=1.2 stationary, both slots converge within 1%
after 1000 observations.
- `sp4_aux_label_scale_ema_writes_step_obs_via_pearl_a_then_converges_pearl_d`:
B=256 mixed-sign ±3.0 labels (mean_abs=3.0), step_obs ∈ ±1e-4 of
analytical mean, Pearl A bootstrap + Pearl D convergence verified.
Per `feedback_no_atomicadd.md`,
`feedback_no_htod_htoh_only_mapped_pinned.md`,
`feedback_no_partial_refactor.md`. Build: `cargo check -p ml --lib --tests
--offline` clean (11 pre-existing warnings, no new warnings).
Co-Authored-By: Claude Opus 4.7 (1M context) <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.