79b7d3fc8bd912d880dce21e833e46288b53197f
Revises Plan 2 to match the post-Plan-1 reality:
1. AdaptiveController → AdaptiveMonitor throughout (read-only observers
per spec §4.C.6 2026-04-24 revision; GPU kernels compute, CPU reads).
Applies to Task 3 (per-branch gamma) and Task 5 (liquid_mod audit).
2. Task 2 (D.1 Mamba2 backward) scope narrowed from "implement" to
"validate existing". Plan 1 A.5 audit confirmed
mamba2_scan_projected_bwd kernel + mamba2_backward host call are
already fully wired. Task 2 now adds a finite-difference grad-check
smoke + non-zero grad-propagation smoke; escalates if either fails.
3. Task 3 (D.2 per-branch gamma) rewritten for GPU-drives compliance:
- Replace scalar GAMMA_EFF_INDEX=43 with 4 per-branch slots at
43-46 (DIR/MAG/ORD/URG).
- New per_branch_gamma_update_kernel.cu reads v-range + health,
writes 4 slots. Deletes the Plan 1 gamma_update_kernel.cu.
- 4 read-only monitors (or one consolidated PerBranchGammaMonitor).
- ISV slots 44-48 shift downstream; fingerprint re-tails at 50-51;
ISV_TOTAL_DIM grows 49 → 52.
- c51_loss_kernel and iql_value_kernel read per-branch γ from ISV.
- No CPU-side γ computation anywhere.
4. Header architecture block updated: new ISV slot count target,
GPU-drives principle explicit, current Plan-1 layout table inline
for reference.
5. Pre-plan verification updated: expects AdaptiveMonitor trait (not
AdaptiveController); checks for 6 Plan-1 monitor files.
6. Removed "schema version bump 1 → 2" language — layout fingerprint
auto-recomputes from seed bytes; no integer version space exists.
Task 4 (D.5 soft fold transitions), Task 5 content (D.7 liquid audit),
Task 6 (D.3+D.6+D.8 coordinated state-layout migration), Task 7
(validation run) structure preserved. Internal slot numbers in those
tasks will reconcile to the new layout when they land (no pre-emptive
edits since those indices depend on whether Task 1 quantile slots or
Task 3 per-branch gamma slots land first — re-compute at exec time).
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.
Description
Languages
Rust
88.2%
Cuda
7.7%
Python
1.3%
Shell
1.1%
PLpgSQL
0.8%
Other
0.8%