98dd464480
Branch worktree-agent-a7a1d9df, commit 746b8b675. Documents the
diff between TLOB's 51-dim feature set and Foxhunt's current 20-dim
OFI + 42-dim market features + 12-dim MicrostructureState.
Key findings:
- 23 of TLOB's 51 slots are placeholders (hardcoded constants, sine
waves, time_since_update=0.5). Unusable as-is.
- 12 duplicate Foxhunt's existing slots (VPIN, Kyle's λ, depth
imbalance variants). Already persisted.
- 10 initially "novel," collapsing to ~4 after removing intra-TLOB
redundancy.
- Critical insight: most of those 10 are ALREADY COMPUTED in
Foxhunt's own ofi_calculator.rs::MicrostructureState::snapshot()
at slots [0..10] — realized variance, Hawkes intensity, weighted
book pressure, spread dynamics, aggression ratio, queue-depletion
asymmetry, order-count flux, intra-bar momentum, regime score,
OFI trajectory. They are discarded before reaching fxcache.
Bonus production bug flagged: OFI slots [18..20) (ofi_acceleration,
toxicity_gradient) are written to fxcache but never consumed by
the OFI embed kernel (experience_kernels.cu:6148-6173 reads only
[0..18)). Dead data every bar.
Phase B (persist the already-computed features + fix the [18..20)
gap) is a vastly smaller scope than importing TLOB would have been.
Phase B agent dispatched separately.
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.