88ae74ca73
Two SP1-Phase-C cuBLAS backward sanitisers at gpu_dqn_trainer.rs:7615
(bw_d_h_s2 input) and :20679 (d_value_logits + d_adv_logits) used
hardcoded `1e6 × signal.max(1.0)` formulas — outside SP4 mechanisms
1/2/5/6/9/10 but flagged by feedback_isv_for_adaptive_bounds review as
the last remaining hardcoded multipliers in the cuBLAS-backward
sanitiser consumer path after Layer A/B closed out the rest of SP4.
Migrated to the proper SP4 pattern:
- 2 new SP4 ISV slots [171, 172) extending [131..171) → [131..173):
BW_D_H_S2_BOUND_INDEX=171 (single-buffer p99) and
Q_DIR_GRAD_BOUND_INDEX=172 (multi-sub-buffer p99 over
d_value_logits ∪ d_adv_logits, n_sub=2).
- 2 new producer kernels:
bw_d_h_s2_p99_kernel.cu (mirrors h_s2_p99 single-buffer pattern)
q_dir_grad_p99_kernel.cu (mirrors param_group_oracle multi-sub
convention via shared sp4_histogram_p99_multi<256> template;
reuses oracle_subbuf_table_buf + oracle_subbuf_counts_buf).
- Pearls A+D wired via existing apply_pearls_ad_kernel chained on
same stream (GPU-only, graph-capture-compatible).
- Consumers read ISV[slot].max(EPS_CLAMP_FLOOR) directly.
Buffer growth (single source of truth in gpu_dqn_trainer.rs):
SP4_PRODUCER_COUNT 69 → 71, SP4_WIENER_TOTAL_FLOATS 207 → 213.
ISV_TOTAL_DIM 171 → 173. LAYOUT_FINGERPRINT_SEED extended (existing
checkpoints will fail-fast at load — re-train required).
StateResetRegistry: 2 new FoldReset entries (sp4_bw_d_h_s2_bound,
sp4_q_dir_grad_bound) + 2 matching reset_named_state dispatch arms;
both halves of Pearl A's sentinel contract reset together per
feedback_no_partial_refactor.
Unit tests: 2 new GPU-gated tests in sp4_producer_unit_tests.rs
exercising (single-buffer Pearl-A→Pearl-D convergence, multi-sub-
buffer p99 vs analytical |N(0,1)|). All 16 GPU tests pass on local
RTX 3050 Ti (rel-err 0.00000 / 0.00526).
Behaviour change: bound is now Pearls-smoothed p99 of the actual
gradient distribution, tighter than the pre-existing 1e6 ceiling but
appropriate-scale for observed values. Smoke validation must verify
F0/F1/F2 still converge.
Refs: task #260, baseline commit 1c150d190 (Layer A + B + fix-up +
L40S smoke pass).
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.