047f175fbf
SP1 (F1 NaN root-cause investigation) closes at commit ab2133463 on
plan-c-phase-2-thompson with the surgical-fix-scope deliverables. Three
L40S validation smokes (smoke-test-xvzgk, dr2bn, ckgv8) characterize
both what SP1 CAN address (cold-start clamp pathology) and what it
CANNOT (structural Adam weight pathology — SP3 scope).
What SP1 produced
-----------------
1. Permanent diagnostic infrastructure verified working:
- nan_flags_buf 24→48 with backward-path coverage at slots 24-35.
- run_nan_checks_post_backward + 3 inline checks during backward
orchestration (slots 33/34/35 multi-point bw_d_h_s2 snapshots).
- Sentinel correctly fires on cuBLAS GEMM accumulator overflow.
2. F1 NaN entry kernels pinpointed (slots 26 + 32):
- apply_iqn_trunk_gradient cuBLAS sgemm (slot 26 = iqn_trunk_m)
- Bottleneck Linear backward sgemm (slot 32 = bn_d_concat_buf)
- IQN-internal buffers (slots 27, 28, 35) stayed CLEAN — the IQN
backward chain is NOT the seed; the cuBLAS consumer of clean
inputs produces NaN via accumulator overflow.
3. Cold-start clamp pathology fixed (ε floor formula):
- Original `(1e6 × isv).max(1e3)` clipped F1-startup gradients to
±1e3 when fold-boundary ISV reset put H_S2_RMS_EMA ≈ 0.
- New `1e6 × isv.max(1.0)` guarantees max_abs ≥ 1e6 in all states.
- Validated: F1 NaN moved from step 240 → 3540 (15× later).
What is SP3 territory (NOT fixed in SP1)
-----------------------------------------
F1 NaN at step 3540 has same `[6, 12, 26, 32]` signature with clean
inputs (slots 27, 35). The cuBLAS GEMM produces NaN/Inf because the
WEIGHTS being multiplied have accumulated NaN/Inf via Adam updates
over 3540 training steps. Surgical clamps on gradient inputs cannot
prevent this; root cause is Q-target inflation drives Adam EMA
saturation drives weight pathology drives GEMM overflow.
SP3 scope per spec:
- Target-Q clipping or pessimistic ensemble
- Atom-position growth bounds (C51)
- Conservative target sync at fold boundary
- Adam EMA reset frequency for fold transitions
What is SP2 territory (NOT fixed in SP1)
-----------------------------------------
F0 regression: 35.30 (Phase B+C+ε-fix) vs 55.87 baseline. Three
consecutive smokes show consistent F0 ≈ 35 across post-Phase-B
commits, indicating Phase B instrumentation has real F0 cost.
SP2 framework codification opportunity: consolidate the 11
per-step check_nan_f32 launches into a single fused reduce
kernel scanning all 12 backward-path slots in parallel.
Expected: F0 returns to ~55 baseline, regression sentinel preserved.
Pearls for memory
-----------------
1. ε-floor placement: when an ISV-driven adaptive bound has a cold-start
safety floor, the ε goes on the ISV MULTIPLIER (`isv.max(1.0)`),
not the BOUND (`bound.max(1e3)`). Putting ε on the bound clips
legitimate signal at cold-start; putting it on the multiplier
preserves the wide guard-band intent.
2. NaN diagnostic ordering: when adding sanitization (clamp,
isfinite-or-zero) to a buffer that's also NaN-checked by a
post-backward sentinel, the inline NaN check MUST fire BEFORE
the sanitization. Otherwise the sentinel sees zero'd buffers
and silently dies.
References
----------
- Spec: docs/superpowers/specs/2026-04-29-numerical-stability-investigation-design.md
- Plan: docs/superpowers/plans/2026-04-29-numerical-stability-sp1-f1-nan-root-cause.md
- Audit (durable for SP2/SP3): docs/dqn-backward-nan-audit.md
- Memory entry: ~/.claude/projects/-home-jgrusewski-Work-foxhunt/memory/project_sp1_f1_nan_root_cause_resolved.md
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.