ab2133463e
Smoke smoke-test-dr2bn (commit 19b008e1c) F1-NaN'd at step 240 — earlier
than pre-fix smoke smoke-test-xvzgk (step 890). The fix made things
worse, indicating the ε floor `(1e6 × isv).max(1e3)` is actively
destabilizing F1 startup.
Diagnosis: at fold boundary, ISV[H_S2_RMS_EMA_INDEX=96] and
ISV[Q_DIR_ABS_REF_INDEX=21] reset to 0 (per StateResetRegistry).
Formula `(1e6 × 0).max(1e3) = 1e3` makes max_abs aggressively narrow.
F1 startup gradients can have natural magnitudes > 1e3 (post-fold
Bellman-target shift); clamping them to ±1e3 destabilizes Adam EMAs,
which then drive cuBLAS GEMM accumulators into pathological inputs
that overflow → slot 26 + 32 NaN.
New formula: `1e6 × isv.max(1.0)` guarantees max_abs ≥ 1e6 regardless
of ISV state:
- ISV = 0 → max_abs = 1e6 × max(0, 1.0) = 1e6
- ISV = 0.5 → max_abs = 1e6 × max(0.5, 1.0) = 1e6
- ISV = 2.0 → max_abs = 1e6 × max(2.0, 1.0) = 2e6
- ISV = 100 → max_abs = 1e8
F0 no-op intent preserved (F0 inputs ≪ 1e6 in all states; ISV[96]≈1.0
for converged F0 → max_abs = 1e6, well above F0-typical |iqn_d_h_s2|
≤ ~10²). F1 startup gradients ≤ 1e6 are un-clipped.
The 1.0 ε floor is on the ISV multiplier (Invariant 1 carve-out per
`feedback_isv_for_adaptive_bounds`), not on the bound itself — bound
is still ISV-driven when ISV is meaningful (≥ 1.0).
Two edit sites:
- gpu_dqn_trainer.rs:6967 (apply_iqn_trunk_gradient, slot 26 path)
- gpu_dqn_trainer.rs:18631 (launch_cublas_backward_to, slot 32 path)
F0 regression to 35.24 still unsolved — separate investigation thread
within SP1 (no deferral; Phase B instrumentation timing impact suspected).
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.