53bc0bc505
Expands the NaN flag buffer from 24 to 48 slots to make room for
backward-path NaN checks (slots 24-35 per audit doc per-slot table)
plus 12 reserved headroom slots (36-47) for SP2 framework + SP3
observer hooks.
Touches:
- gpu_dqn_trainer.rs: alloc size 24→48 (line ~11178); read_nan_flags
signature [i32; 24]→[i32; 48] (line ~14982); field docstring updated
(line ~2658) to reflect 48-slot layout
- fused_training.rs: pub(crate) read_nan_flags signature [i32; 24]→[i32; 48]
- training_loop.rs: BOTH name table sites (halt_nan + halt_grad_collapse
block from commit d1808df14) updated to 48 entries
Slot names use audit allocation (docs/dqn-backward-nan-audit.md per-slot
table), which supersedes the plan's placeholder names per the audit's
plan-supersession note. Slots 24-35 cover production buffers:
d_value_logits_buf, d_adv_logits_buf, iqn_trunk_m, iqn_d_h_s2_ptr,
d_branch_logits_buf, cql_d_value_logits, aux_dh_s2_nb_buf,
ensemble_d_logits_buf, bn_d_concat_buf, bw_d_h_s2 (×3 call sites).
Slots 36-47 are rsv36-rsv47 (headroom).
No behavioral change — new slots stay at zero until Task 4 wires the
kernel-output NaN checks. Buffer size reviewable by SP2.
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.