b26b189925
Atomic flip of the aux heads' input from Q's GRN trunk output `save_h_s2` to the SEPARATE aux trunk's output `h_s2_aux`. The aux trunk now trains its own w1/w2/w3/b1/b2/b3 from CE loss (next-bar + regime); Q's encoder is structurally protected by `aux_trunk_backward`'s missing `dx_in` output param (encoder boundary stop-grad enforced at the kernel-set level). Reverts the C.0 stop-grad band-aid commits (`872bd7392`, `411a30473`): the zero-fills in `aux_next_bar_backward` + `aux_regime_backward` Step 3 are replaced with the genuine SAXPY-back-to-input gradient (`dh_s2_aux[b,j] = sum_k sh_dh_pre[k] * w1[k,j]`). The leak that motivated stop-grad is now blocked structurally rather than by data zero-fill — aux gradient flows through the aux trunk's own params, never into Q's encoder. Wired in this commit (atomic, ~330 LOC): - 4 kernel signatures renamed `h_s2 → h_s2_aux` / `dh_s2_out → dh_s2_aux_out` (`aux_next_bar_forward`, `aux_regime_forward`, `aux_next_bar_backward`, `aux_regime_backward`); Rust wrappers in `gpu_aux_heads.rs` follow - Trainer fwd: insert `aux_trunk_forward_ops.launch(...)` in `aux_heads_forward` Step 0, populating `h_s2_aux` from `save_h_s1` (encoder layer-1 output, dim=shared_h1=256). Both head fwds redirect input pointer from `save_h_s2` to `h_s2_aux` - Trainer bwd: SAXPY both `aux_dh_s2_*_buf` into `dh_s2_aux_accum` (pre-zeroed each step via graph-safe `cuMemsetD32Async`); then `aux_trunk_backward_ops.launch(...)` propagates through w3/w2/w1 + b3/b2/b1; then `launch_aux_trunk_adam_update` applies global L2-norm clip + per-tensor Adam updates over 6 grad tensors - Collector fwd: insert `exp_aux_trunk_forward_ops.launch(...)` after `forward_online_f32`, reading `exp_h_s1_f32` and writing `exp_h_s2_aux`; redirect `exp_aux_heads_fwd.forward_next_bar` input from `exp_h_s2_f32` to `exp_h_s2_aux` - Pre-capture host-write of ISV-driven LR + grad-clip + step counter into mapped-pinned buffers in `launch_cublas_backward_to` (BEFORE `aux_heads_backward`); same `&mut self` pattern as `step_ofi_embed_adam` Verification: - `cargo check -p ml --tests` clean (1m02s, only pre-existing warnings) - `aux_trunk_oracle_tests` + `sp14_oracle_tests` 12/12 pass: - aux_trunk gradient check: max_rel_err=1.33e-2 (tol=2e-2) — matches C.4 baseline - aux_trunk_backward_does_not_write_dx: kernel source clean of dx_in/dx_in_out - aux_sign_label_lookahead_mask: 60/100 masked, 40/100 valid - 9 other oracle tests pass bit-identically Plan: docs/superpowers/plans/2026-05-07-sp14-layer-c-separate-aux-trunk.md §C.5b Audit: docs/dqn-wire-up-audit.md "SP14 Layer C Phase C.5b" section
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.