eaf4adcb98
Designs the SP14 chain on top of SP13 Layer B (HEAD 6657e5626):
1. Sub-project A — stability fixes (3 small bugs found in Smoke A)
- C51 atom-probability floor (ISV-driven from SP4 atom_pos_p99)
- aux_w setter clamp lift [0.05, 0.3] → [0.15, 1.5]
- Stagnation warmup gate at fold boundary
2. Sub-project B — the architectural piece (THIS spec)
- Forward wire: aux_softmax_diff per-bar into direction Q-head input
concat (in_dim+1, fingerprint bump, zero-init new column)
- Earned Gradient Flow pearl — adaptive ISV-driven gradient gating:
* Gate 1 (aux competence) — Schmitt-trigger hysteresis
* Gate 2 (Q-head disagreement) — NEW signal, EMA per-step argmax
mismatch
* ISV-adaptive sigmoid steepness (variance-driven k_aux, k_q)
* Per-epoch warmup ramp
* Anti-gradient-hacking circuit breaker (mesa-opt defense)
- 11 new ISV slots, 3 new GPU kernels, ~1060 LOC total
- HEALTH_DIAG pearl_egf_diag observability line
3. Sub-project C — Adaptive LR (deferred until A+B effects measured)
Motivation from Smoke A diagnostic:
- aux_dir_acc reached 0.61 (signal extraction works)
- val_win_rate stuck 45-48% (no path to action selection)
- WR-flat-while-aux-varies = Q-head directional weights frozen
- 1109 GRAD_CLIP_OUTLIER events (chronic; not noise)
Three parallel diagnostic agents triangulated three interlocking root
causes:
- Slot 375 has zero readers (the wire was scoped but never built)
- C51 raw grad reaches 9.5e6, saturates SP7 budget controller
- aux_w controller muzzled by SP11-era [0.05, 0.3] clamp
The Earned Gradient Flow pearl is a new application of the codebase's
pearl pattern: ISV-driven adaptive controller, but applied to backward-
pass gradient flow instead of forward-pass features. The wire is one-
way (stop-gradient) by default; co-training is earned by both:
(a) aux head demonstrating label competence, AND
(b) Q-head showing it's actually fighting aux signal (informative
disagreement above baseline).
Stability additions hardened against:
- Oscillation around target (Schmitt hysteresis)
- Numerical sigmoid saturation (argument clipping ±30)
- Stale variance EMAs across folds (state-reset-registry)
- Discontinuous warmup transitions (linear ramp)
- Mesa-optimization (gradient-hacking circuit breaker)
- Cold-start sentinel-state spurious gate openings (Pearl-A bootstrap)
Awaiting user review before invoking superpowers:writing-plans for
sub-project B (and a separate small plan for sub-project A).
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.