eca26a1feb
Defends the aux next-bar regression head against underlying-data scale.
Label = `next_states[:, 0]` carries log returns (~1e-3) in local fxcache
but raw price (~5000) in the L40S fxcache. First L40S deploy attempt
(workflow `train-multi-seed-7j8zc`) produced `aux next_bar_mse = 2.587e7`
and grad_norm=126,769 because the unnormalised label dominated the
residual; the trunk learned garbage off the corrupted aux-gradient SAXPY.
Per `feedback_adaptive_not_tuned.md` + `feedback_isv_for_adaptive_bounds.md`:
runtime-adaptive ISV-driven EMA normalisation, NOT a tuned constant.
Changes:
- New ISV slot `AUX_LABEL_SCALE_EMA_INDEX=117`; ISV_TOTAL_DIM 117→118.
FoldReset → 1.0 (multiplicative identity, NOT 0.0). Tail-appended after
fingerprint slots so the layout grows monotonically.
- New GPU producer kernel `aux_label_scale_ema_update` in
aux_heads_loss_ema_kernel.cu: single-block 256-thread shmem reduction
over the just-gathered `aux_nb_label_buf [B]`, EMA-blends `mean(|label|)`
into ISV[117] at α=0.05.
- `aux_next_bar_loss_reduce` + `aux_next_bar_backward` kernel signatures
grow `+isv_dev_ptr+isv_label_scale_index` args; both kernels divide
label by `max(isv[117], 1e-6)` before the residual `(pred - label/scale)`
so loss + gradient stay unit-scale regardless of underlying data
magnitude. Graph-capture-stable: ISV device pointer + slot index pair
are stable; the scalar updates per step via the new producer kernel.
- `aux_heads_forward` Step 2b launches the producer between strided_gather
and the loss reduce (same captured graph, same stream → ordering
enforced).
- `aux_heads_backward` reads ISV[117] via the same device pointer.
- HEALTH_DIAG aux line gains `label_scale={:.3e}` 4th field for
observability.
- state_reset_registry adds `isv_aux_label_scale_ema` FoldReset entry;
reset_named_state dispatch arm writes 1.0 (not 0.0).
- layout_fingerprint shifts `0x26f7b1deb94cb226` → `0x829bc87b42f2feee`
(checkpoint-incompatible, no migrator per spec §4.A.2).
Validation:
- cargo check --workspace clean at 11 warnings (workspace baseline preserved).
- multi_fold_convergence smoke (RTX 3050 Ti, 591s): 1 passed.
- Fold 0: Best Sharpe = -9.7831 (matches seed=42 historical baseline -9.78).
- Fold 1: Best Sharpe = 65.3679 (within seed-noise of historical 65.96).
- Fold 2: terminated by regression-detection (avg_grad_norm escalation),
pre-existing pathology unrelated to aux head — checkpoint saved before
termination.
- HEALTH_DIAG aux line shows `label_scale=3.59e-2` to `4.35e-2` (matches
expected log-return mean-abs magnitude); `next_bar_mse=6.29e-2` to
`4.93e-1` (O(1), the new baseline post-normalisation — was O(1e-4)
pre-fix as numerical artefact of `pred ≈ 0` − tiny unnormalised label).
Aux grad_norm contribution stays bounded; explosion in F2 is downstream
C51/CQL, not aux.
Spec-aligned: aux head still regresses on `next_states[:, 0]` per spec
§4.E.6; the fix is the normalisation, not the source.
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.