647f15f9db
Activates the dormant Commit A scaffolding: forward + backward + ISV
producer + aux loss combination + HEALTH_DIAG observability. Two
auxiliary heads (next-bar return MSE + 5-class regime CE) train end-
to-end off the trunk's h_s2 activation, with their gradient flowing
into the trunk's bw_d_h_s2 accumulator (ISV-scaled by aux_weight)
before encoder_backward_chain consumes it.
Wire-points:
- Forward (launch_cublas_forward, after forward_online_raw saves
save_h_s2, BEFORE stochastic depth): regime label builder →
strided_gather (col 0 of next_states → dedicated aux_nb_label_buf,
NOT aliased) → next-bar forward → regime forward → next-bar MSE
reduce → regime CE reduce.
- Backward (launch_cublas_backward_to, AFTER backward_full fills
bw_d_h_s2, BEFORE encoder_backward_chain): per-head backward
emits per-sample partials + per-sample dh_s2; aux_param_grad_reduce
collapses partials → final, then SAXPY into grad_buf[119..127);
both dh_s2 buffers SAXPY into bw_d_h_s2 with alpha=aux_weight.
- ISV producer (training_loop.rs per-step): launch_aux_heads_loss_ema
alongside launch_h_s2_rms_ema / launch_vsn_mask_ema; updates
ISV[113][114] EMAs.
- Aux-weight refresh (training_loop.rs per-step): clamp(0.1 ×
LEARNING_HEALTH × (1 − tanh(0.1 × sharpe_ema)), 0.05, 0.3); pushed
via FusedTrainingCtx::set_aux_weight delegate.
- HEALTH_DIAG aux clause: emits per-epoch aux[next_bar_mse=…
regime_ce=… w=…] from the ISV slots populated by the producer.
Lessons applied from previous WIP attempt (stashed):
- Dedicated aux_nb_label_buf [B] f32 field (NOT aliased over
aux_partial_nb_b2) — partial-refactor invariant honoured cleanly.
- Init log "AuxHeadsForwardOps initialized: K_nb=1 K_rg=5 aux_h=32 …"
for observability sanity check.
- HEALTH_DIAG aux clause verified firing in smoke (5 lines, one per
epoch, with non-zero loss EMAs and ISV-driven aux_weight evolution
0.050 → 0.105 over 5 epochs).
Smoke (multi_fold_convergence --release, 3 folds × 5 epochs on RTX
3050 Ti, 684.37s, all 3 dqn_fold{N}_best.safetensors written):
F0=-9.7831 F1=71.5327 F2=65.9598
Within baseline noise band [F1: 61.10–74.56, F2: 61.57–88.20] from
two clean Commit A baseline runs. F0 is bit-reproducible across all
runs (deterministic cold-start collapse under --release profile —
pre-existing, not from this task).
HEALTH_DIAG observability (5 epochs):
aux [next_bar_mse=9.962e-5 regime_ce=2.099e-5 w=0.050]
aux [next_bar_mse=2.295e-4 regime_ce=3.924e-5 w=0.088]
aux [next_bar_mse=3.261e-4 regime_ce=6.893e-5 w=0.099]
aux [next_bar_mse=4.307e-4 regime_ce=6.819e-5 w=0.102]
aux [next_bar_mse=4.824e-4 regime_ce=6.634e-5 w=0.105]
Aux losses grow as expected (cold-start Xavier predictions diverge
from labels until trunk learns); aux_weight rises with LEARNING_HEALTH
and sharpe_ema; nothing pinned at clamp boundaries — formula working.
Constraints honoured: GPU-only (every reduce + SAXPY + EMA on-device,
zero DtoH in hot path; only HEALTH_DIAG cold-path read on CPU); no
atomicAdd; no stubs; no // ok: band-aids; no buffer aliasing tricks;
no tuned constants beyond the documented 0.1 aux base + [0.05, 0.3]
numerical-stability clamp; partial-refactor invariant honoured
(bw_d_h_s2 accumulator semantics extended, not replaced;
encoder_backward_chain consumes through unchanged pointer).
target_ema_update NOT extended for aux heads (Commit A design
decision preserved): aux heads are online-only supervised heads, NOT
used in Bellman bootstrapping. target_params_buf[119..127) remains at
Xavier init forever.
cargo check clean at 11 warnings (workspace baseline preserved); no
fingerprint change (Commit A already shifted to 0x26f7b1deb94cb226).
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.