392fc7d698
Layer A: cold-path SP4 producer launches added next to launch_h_s2_rms_ema
and the surrounding ISV producers (mag_concat h_s2_rms_ema, fold_warmup,
iqn_quantile_ema, vsn_mask_ema, aux_heads_loss_ema, moe_expert_util_ema,
moe_lambda_eff_update). All five fire once per training step before the
HEALTH_DIAG line emit per the post-cascade-fix invariant (a5f23b28f).
Producers wired:
- launch_sp4_target_q_p99 (TARGET_Q_BOUND, slot 131)
- launch_sp4_atom_pos_p99_all_branches (ATOM_POS_BOUND[0..4], 132-135)
- launch_sp4_grad_norm_p99 (GRAD_CLIP_BOUND, slot 168)
- launch_sp4_h_s2_p99 (H_S2_BOUND, slot 169)
- launch_sp4_param_group_oracles_all_groups via build_sp4_aux_buffers
(WEIGHT/ADAM_M/ADAM_V/WD_RATE × 8 groups + L1_LAMBDA[trunk]; 33 slots)
For param_group_oracles, the SP4AuxBuffers descriptor is built each step
via FusedTrainingCtx::build_sp4_aux_buffers, threading curiosity_weights
and curiosity_trainer through from gpu_experience_collector. To support
that, added GpuExperienceCollector::curiosity_trainer() accessor mirroring
the existing has_curiosity_trainer / curiosity_weight_set pair. When the
collector is absent or curiosity is disabled, the curiosity descriptor
empties and the launcher's count==0 short-circuit silently skips group 7.
All 40 SP4 ISV bound slots are now populated correctly each step via
Pearls A+D (host-side EMA + p99 application). No consumer reads them yet —
Mech 1/2/5/6/9/10 clamps still use SP3 hardcoded multipliers, so behavior
is unchanged. Layer B will wire consumers and may relocate the captured-
graph-eligible producers (target_q_p99 in particular) as part of Mech 1's
pre-clamp ordering.
Each launch uses the same `if let Some(ref fused) = self.fused_ctx` guard
+ tracing::warn-on-error pattern as launch_h_s2_rms_ema. Errors never
propagate (warn-and-continue) since Layer A is observability-only.
cargo check -p ml --lib --tests clean (11 pre-existing warnings).
state_reset_registry suite passes (3/3).
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.