8956c2fb77
Coordinated close-out of SP3 Q-learning numerical-stability per
feedback_no_partial_refactor.
REVERT Mech 8 (slow_ema fold-boundary reset). smoke-test-rxhjh on b8a7ac6f7
showed F1 NaN at step 2300 — WORSE than the 3720 ceiling with Mech 6 alone.
Persistent slow_ema across folds was providing unintentional F1 protection
(anchored at F0's smaller grad scale → tighter Mech 6 upper_bound during F1
ramp-up). Resetting loosened the clip and accelerated Adam saturation.
Removed: reset_grad_norm_slow_ema() method + reset_for_fold call site.
Kept: grad_norm_slow_ema_pinned field (consumed by Mech 6).
Kept: Mech 6 multiplier at 100× (already restored from the 5× experiment
in the Mech 8 commit; correct steady-state value).
ADD Mech 9 (post-Adam ISV-driven weight clamp). Root cause being targeted:
cuBLAS sgemm f32 accumulator overflow at slots 26 (iqn_trunk_m) + 32
(bn_d_concat_buf) at F1 step ~3540 with INPUTS clean (slots 27, 35
unflagged). The matmul output saturates because the WEIGHT matrices have
drifted to extreme-but-finite magnitudes via Adam updates over thousands
of steps. Mech 1 (gradient clamp) and Mech 6 (grad-norm clip) bound
gradients, not parameters — neither prevents this drift.
Mech 9 clamps |p_val| ≤ 100 × Q_ABS_REF.max(1.0) inside dqn_adam_update_kernel
after the L1 proximal step. ISV-driven (slot 16, ε on multiplier per SP1
pearl). 1 OOM below slot 44-45 diagnostic threshold (1e3 × Q_ABS_REF) so
the regression sentinel retains 10× firing headroom — symmetric with
Mech 1's clamp/diagnostic ratio.
Implementation:
- dqn_adam_update_kernel: new trailing arg `weight_clamp_max_abs`.
Clamp `p_val = fminf(fmaxf(p_val, -bound), bound)` after L1 step.
- All Adam launch sites in gpu_dqn_trainer.rs: compute bound from
read_isv_signal_at(Q_ABS_REF_INDEX).max(1.0) × 100.0 host-side, pass
as final arg.
- No new ISV slots. No new kernels. No graph topology change.
Validation: deferred to one L40S smoke at the new HEAD. Expected:
- F1 trains past step 3720 (Mech-6-only ceiling) — Mech 9 closes SP3.
- F1 still NaNs at similar steps — close SP3 honestly with documented
residual pointing at SP4/SP5.
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.