f01a292f6f
Phase 3.5 (hold_floor_kernel) + Phase 3.5.3 (cooldown_kernel) landed the producer + state machinery; both deferred the action-selection consumer wiring. This task wires both atomically. Architectural decision: hold_floor is now an INLINE __device__ computation inside experience_action_select reading ISV slots 426/427/428/429 directly. The standalone hold_floor_kernel.cu + launch_sp15_hold_floor + HOLD_FLOOR_CUBIN are deleted — launching a kernel to write one f32 just to read it back was unnecessary. ISV slots + state_reset_registry entries remain; only the launch path is removed per feedback_wire_everything_up + feedback_no_legacy_aliases. Entropy source: per-step Shannon entropy of softmax(e_dir) computed inline from the 4 e_dir floats already in registers (Pass 1 of the Thompson direction selector). High entropy = uncertain policy → Hold gets the floor lift; low entropy = confident policy → floor ≈ 0. q_eff_dir scratch preserves e_dir for downstream consumers (out_conviction, out_q_gaps, out_magnitude_conviction) — adding hold_floor there would corrupt the Kelly-cap warmup floor with a meta-confidence mask. cooldown mask: when ISV[COOLDOWN_BARS_REMAINING=435] > 0, action_select hard short-circuits to dir_idx = DIR_HOLD before Pass 2 — sidesteps the temperature-blend numerics where a finite-sentinel-on-non-Hold approach would let pure-Thompson (τ=1) samples dominate the masked direction. Cooldown supersedes hold_floor — when forcing Hold the floor is moot. 3 new oracle tests: - action_select_applies_hold_floor_inline (no cooldown) - action_select_forces_hold_during_cooldown - action_select_no_force_hold_when_cooldown_zero Atomic per feedback_no_partial_refactor: action_select changes + hold_floor_kernel deletion + cubin manifest update + 3 oracle tests + audit doc all in this commit. No parallel paths, no feature flags. Eliminates Phase 3.5 + Phase 3.5.3 deferred consumers. The cooldown_kernel itself remains (it maintains the consecutive_losses streak + decrements COOLDOWN_BARS_REMAINING per bar); only its consumer is now wired. Verified: cargo check -p ml --features cuda clean; ml lib suite holds 946 pass / 13 fail = baseline; all 6 oracle tests pass on RTX 3050 Ti (3 pre-existing cooldown + 3 new action_select). 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.