5de5e546a4
Plan C as authored assumed:
- c51_probs_dir = post-softmax probabilities (didn't exist on collector;
only raw exp_b_logits is materialised)
- atom_values = single global linear support (production uses per-sample
per-direction adaptive [v_min, v_max, delta_z] + optional atom_positions)
- iqn_quantiles_dir = rollout-side IQN inference (doesn't exist; IQN is
training-only)
Amended kernel signature uses the production architecture:
- b_logits_dir [N, b0_size, n_atoms] — raw direction-branch logits
- per_sample_support [N, b0_size, 3] — adaptive per-direction support
- atom_positions [b0_size, n_atoms] — non-linear positions (NULL = linear)
- n_atoms
Direction-branch Thompson is now single-distribution over C51 (with adaptive
support), not joint C51+IQN. Single-distribution still provides the
principled posterior sample that fixes the UCB selector/target asymmetry —
the goal of Plan C is preserved, just sourced from the existing rollout-time
distribution instead of a non-existent IQN inference path.
New device-inline helpers: softmax_c51_inline, compute_atom_values_inline.
Plan + audit doc updated. Phase 0 standalone test kernel gained two new
entry points (direction_thompson_v2_test, argmax_eq_v2_test) matching the
amended production API; original Plan A entry points retained for tests
0.B-0.F. Tasks 3+4 (buffer wiring) unblocked — collector/evaluator already
have per_sample_support_buf and exp_b_logits in production.
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.