1961857c22
Empirical data from commit61ab27ff3across 14 captured epochs of the TD-propagation smoke test: HEALTH_DIAG[N]: ... gems [g6_branch_indep=X g10_temporal=Y g12_predictive=Z] G10 temporal_consistency: 0.000000 to 0.000002 (essentially zero) G6 branch_independence: 0.000102 to 0.000273 (~1e-4) G12 predictive_coding: 0.07 to 9657.83 (real signal) Verdict by V7-gem methodology — when an existing mechanism already covers the signal, wiring backward adds gradient noise without value: G10: spectral_norm (already wired on all 12 weight tensors) enforces a global Lipschitz constraint that subsumes per-pair Lipschitz on similar-state pairs. Cosine similarity between consecutive h_s2 samples is almost never > 0.95 anyway. Penalty value is below floating-point noise. G6: NoisyNets injects different parameter noise per layer; the 4 advantage branches are already 99.99% diverse. Penalty value is 4 orders of magnitude smaller than G12's working signal. G12: KEEP — backward already wired in commite72885e8b, demonstrably reduced Best Sharpe variance from [13, 22] to [17.99, 19.56] (~6× tighter). Real gem. Changes: - Removed compute_branch_independence + compute_temporal_consistency calls from submit_aux_ops (no per-step kernel launches for G6/G10) - Removed g6/g10 columns from HEALTH_DIAG (kept g12) - Added comment block in submit_aux_ops documenting WHY they were measured-then-removed (V7 methodology trail for future-readers) What stays for now (deletable in a follow-up cleanup): - branch_independence_penalty kernel + branch_indep_kernel field + branch_indep_penalty_buf alloc - temporal_consistency_penalty kernel + temporal_consistency_kernel field + temporal_per_sample_buf + temporal_penalty_buf allocs - branch_indep_loss_value() / temporal_loss_value() readback methods - read_gem_losses() pass-through (now returns (g6=0, g10=0, g12=value)) - compute_branch_independence + compute_temporal_consistency Rust fns Keeping these as scaffolding means: if future evidence (different env, different scale, different model) shows the underlying signal IS material in some regime, re-wiring is just adding the call back to submit_aux_ops. The measurement infrastructure stays in place. Files touched: crates/ml/src/trainers/dqn/fused_training.rs (-12 / +14) crates/ml/src/trainers/dqn/trainer/training_loop.rs (-13 / +9) Verified: cargo check passes. Smoke test should now match the G12-only baseline variance of [17.99, 19.56] (next session can verify). 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.