75e94858c5
Retires ISV[117]=AUX_LABEL_SCALE_EMA_INDEX together with its producer
kernel (aux_label_scale_ema_update), launch site, backward pass-through,
StateResetRegistry entry, HEALTH_DIAG snapshot field, and unit test.
Why: labels at the data layer are z-normalised, so the
mean(|label|) EMA tracked by ISV[117] sits at ~1.0 empirically.
Dividing by max(scale, 1e-6) before the residual `(pred - label)`
reduces to `(pred - label)` within rounding. The divisor was a
defensive scaffold from when the data layer carried mixed-scale
labels (1e-3 log returns vs 5000 raw prices); z-normalisation made
that scaffold redundant.
This is a numerical bridge, NOT the final fix. B1.1 lands on top:
- Aux head 1→2 dim (next-bar regression → 2-class direction logit)
- MSE → CE loss flip
- aux_dir_acc reads softmax over the 2 logits
- aux_pred_to_isv_tanh rewrite as logit-diff
- Producer kernel that fills aux_sign_labels with real -1/0/1 from
the 30-bar price trajectory (B0 plumbing currently zero-init)
- dqn_param_layout fingerprint bump (head dim changes)
- aux_b1_diag HEALTH_DIAG metric
- 17+ GPU oracle unit tests
Cascade (atomic per feedback_no_partial_refactor):
- aux_heads_kernel.cu: aux_next_bar_loss_reduce + aux_next_bar_backward
drop `isv` + `isv_label_scale_index` params; residual is (pred - label)
- aux_heads_loss_ema_kernel.cu: aux_label_scale_ema_update kernel deleted
- gpu_aux_heads.rs: kernel field/loader + launch_label_scale_ema +
isv_* args from next_bar_loss_reduce / backward_next_bar all dropped
- gpu_dqn_trainer.rs: Step 2b producer launch + ISV slot uses dropped;
AUX_LABEL_SCALE_EMA=117 line retained in fingerprint seed
(no fingerprint bump in B1.0; B1.1 will bump on head-dim flip)
- gpu_health_diag.rs + health_diag.rs: aux_label_scale snapshot field
dropped; aux block 4→3 floats, downstream offsets shift down by 1,
WORD_TOTAL 150→149, snapshot_size_is_stable test 150*4 → 149*4
- health_diag_kernel.cu: WORD_AUX_LABEL_SCALE removed, downstream
offsets shift, static_assert(WORD_TOTAL == 149)
- state_reset_registry.rs: isv_aux_label_scale_ema FoldReset dropped
- training_loop.rs: reset_named_state arm + HEALTH_DIAG read +
aux line label_scale field all dropped
- sp4_producer_unit_tests.rs: load_aux_label_scale_ema_kernel helper +
sp4_aux_label_scale_ema_writes_step_obs_via_pearl_a_then_converges_pearl_d
test dropped
Hard rules upheld:
- feedback_no_partial_refactor: every consumer of ISV[117] migrates
atomically — kernel + Rust orchestrator + producer launch + backward
+ HEALTH_DIAG + reset registry + unit test all in this commit
- feedback_no_stubs: not a stub — divisor is removed at every site,
not aliased through a 1.0_const shim
- feedback_no_legacy_aliases: no legacy AUX_LABEL_SCALE_EMA_INDEX → 1.0
alias function
- feedback_no_hiding: doc comments forward to B1.1 explicitly; no
underscore suppression or #[allow(dead_code)]
Build: cargo check --workspace --tests clean.
Tests: snapshot_size_is_stable passes at 149*4=596 bytes.
cargo test -p ml --lib + cargo test -p ml-dqn --lib compile.
Net delta: 10 files, −288 LOC.
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.