9fb980da2b
Post-commit kernel-formula audit of `compute_min_hold_penalty` at
trade_physics.cuh:567-577 showed the original `temp = TEMP_MIN +
(TEMP_MAX - TEMP_MIN) × skill` mapping was BACKWARDS for the
WR-plateau scenario this 8-commit chain targets.
Formula recap:
soft_factor = deficit / (deficit + T)
HIGH T → soft_factor → 0 → forgiving (no exit penalty)
LOW T → soft_factor → 1 → sharp (max exit penalty)
The deleted schedule's `START=50 → END=5` therefore meant
"permissive early, strict late" (classic explore-then-exploit), not
"permissive when committed, strict when uncertain" as the
substituted mapping assumed.
WR-plateau scenario: dir_acc ≈ 0.46-0.48 (model committed but
wrong) — the model needs a permissive (HIGH T) exit ramp to bail
out of bad committed directions, not maximum exit penalty (LOW T)
locking it into them.
Inverted the mapping using `confusion = 1 - skill`:
- dir_acc ≈ 0.5 (random / plateau) → confusion=1 → temp=50
(permissive, plateau exit ramp)
- dir_acc → 1.0 (saturated skill) → confusion=0 → temp=5
(strict, force informed commitment)
This preserves the deleted schedule's epoch-0 anchor (T=50
cold-start) while reacting to actual realized skill instead of an
epoch-time anchor.
Files touched (atomic per `feedback_no_partial_refactor`):
- min_hold_temperature_update_kernel.cu — formula + header doctring.
- sp14_isv_slots.rs — slot-doc comment expanded with new mapping
+ fixup-rationale paragraph.
- gpu_aux_trunk.rs — `MinHoldTemperatureUpdateOps` docstring.
- gpu_dqn_trainer.rs — cubin docstring + ISV_TOTAL_DIM doc-comment.
- tests/sp14_oracle_tests.rs — Tests 3 + 4 flipped (high dir_acc
now expects temp=TEMP_MIN; low dir_acc now expects TEMP_MAX);
Tests 1, 2, 5 numerically unchanged (sentinel guard fires before
the mapping; midpoint dir_acc=0.75 has skill=confusion=0.5 so
pre/post-fixup numerics match).
- docs/dqn-wire-up-audit.md — Item 4 entry rewritten with the
fixup rationale + mapping table updated.
Verification:
cargo check -p ml --tests --all-targets PASS
cargo test sp14_audit_4b (lib) 4/4 PASS (slot layout
locks unchanged)
GPU oracle re-run deferred to next L40S smoke (kernel was rebuilt
in-place so cubin contents change; the 5 oracles encode the new
mapping and will exercise the new cubin).
Per `pearl_first_observation_bootstrap.md` (sentinel→target
replace; sentinel anchors unchanged) +
`pearl_symmetric_clamp_audit.md` (bilateral clamp on target_temp
preserved) + `feedback_no_partial_refactor.md`.
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.