fab7758916
Comprehensive revision to match landed Plan 1+2+3 codebase state. Pattern
mirrors the Plan 3 second revision: per-task "Reality reconciliation"
blocks at the top of each task body identifying what's stale vs. landed,
with concrete file paths in the actual cuda_pipeline tree.
Added:
- Task dependency graph with recommended execution order:
5 (light ISV) → 4 (refactor) → 2 (GRN ADOPT) → 1 (Full VSN) →
6 (aux heads) → 3 (multi-Q IQN) → 7 (audit) → 8 (Argo)
- Per-task implementation surface with concrete trunk hooks:
- Task 1: pre-h_s1 VSN gate in batched_forward.rs::forward_online_raw
- Task 2: GRN audit confirms ADOPT branch (GRN absent from DQN trunk;
only in ml-supervised TFT). Replaces h_s1/h_s2 Linear blocks.
- Task 3: re-scoped — IQN already runs num_quantiles=32 with random τ.
Task is CONSTRAINING to fixed τ ∈ {.05, .25, .5, .75, .95}.
- Task 4: pure Rust API split (no kernel changes, no checkpoint break)
around existing forward_online_raw structure
- Task 5: split into Mode A (light, pre-Task-1, 3 ISV slots) and
Mode B (full, post-Task-1, 7 ISV slots). Mode A recommended first.
- Task 6: aux head loss scaled by ISV[LEARNING_HEALTH] per pearl
- Checkpoint-break consolidation note: Tasks 2/1/6/3 each break checkpoint
compatibility; land in sequence with no Argo run between (one fingerprint
shift per commit; final Argo at Task 8 amortizes retraining cost)
- Task 8 absorbs Plan 3's deferred Argo Tier 1 gate (combined validation)
No code changes.
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.