e72885e8b6
Found by V7-gem audit: predictive_coding_loss kernel existed (forward only,
per-sample MSE on consecutive trunk activations), and compute_predictive_coding_loss
Rust wrapper existed, but no backward and no caller. Pure scaffolding.
Self-supervised temporal smoothness on the enriched trunk h_s2 IS in the
"better form" by the V7 methodology — it operates at the gradient level on
the network representation, not as a reward shaping term. Worth wiring up.
Three changes:
1) New CUDA kernel `predictive_coding_backward` (experience_kernels.cu)
Loss: L = sum_{i=0..N-2} (lambda/SH2) * sum_j (h[i,j] - h[i+1,j])^2
Grad: dL/dh[i,j] = (2*lambda/SH2) * sum-of-affected-loss-terms
Each h[i,j] appears in TWO loss terms (interior i): "current" of term i
and "next" of term i-1. Boundaries (i=0, i=N-1) have one. Closed-form
gradient written directly with no atomicAdd — one thread per (sample,
feature) cell, each writes to a unique slot, plain += accumulates into
bw_d_h_s2. Bit-deterministic.
2) Rust glue (gpu_dqn_trainer.rs)
- Load `predictive_coding_backward` kernel via existing exp_module_for_mag
- New field on DQNTrainer (predictive_coding_backward_kernel)
- Extend `compute_predictive_coding_loss` to also launch backward as
step 3 (after forward + reduce). Now the function name accurately
describes what it does — both compute and accumulate gradient.
3) Integration (fused_training.rs::submit_aux_ops)
Inserted the call right after `launch_recursive_confidence_backward`,
before regime_scale_td_errors. Both spots accumulate into bw_d_h_s2 via
plain +=, so ordering is irrelevant for correctness — what matters is
that this runs INSIDE the aux_child CUDA-graph capture window AND
BEFORE the trunk W_s2 → W_s1 backward GEMMs read bw_d_h_s2.
Why not also G6 (branch_independence) and G10 (temporal_consistency)?
Per V7-gem methodology — check for redundancy first:
- G10 wants Lipschitz on Q for similar states. Spectral normalization
(already wired on all 12 weight tensors) achieves *global* Lipschitz.
G10 adds *local-pair* Lipschitz on top. Possibly redundant — needs
a measurement before wiring blindly.
- G6 wants the 4 advantage branches to stay diverse. NoisyNets already
adds different parameter noise per layer → naturally diverse heads.
Ensemble KL gradient pushes ensemble heads apart (different mechanism
but similar intent). Possibly redundant for the 4-branch case.
G12 is unambiguously useful — trunk smoothness is a genuine gem with no
existing equivalent in the codebase. G6/G10 land separately if measurement
shows they add real value beyond spectral_norm + NoisyNets + ensemble KL.
Files touched:
crates/ml/src/cuda_pipeline/experience_kernels.cu (+45)
crates/ml/src/cuda_pipeline/gpu_dqn_trainer.rs (+33 / -3)
crates/ml/src/trainers/dqn/fused_training.rs (+9)
Verified: cargo check passes. Smoke test running to verify training is
stable with G12 active (lambda_pred=0.1 — small enough that any regression
is from a real bug, not dominance over the C51/IQN gradient).
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.