3c61ee52ce
Implementation plans for the distributional-RL aggregation spec
(docs/superpowers/specs/2026-04-26-distributional-rl-aggregation-design.md).
Plan A — Phase 0: TDD hypothesis verification (8 tasks)
- Standalone GPU test kernel: sample_c51_inverse_cdf,
sample_iqn_quantile_interp, compute_e_c51, compute_e_iqn,
thompson_direction_test, argmax_eq_test
- 6 unit tests (5 GPU + 1 CPU synthetic edge)
- GPU integration on converged checkpoint (Test 0.F)
Plan B — Phase 1: existing-lever audit (8 tasks)
- 6 unit tests covering B.2/CF/PopArt/Q-target audit fixture
- Exit gate: all 6 PASS = no reward-shaping bug; Phase 2 unblocked
Plan C — Phase 2: Thompson sampling integration (11 tasks)
- Direction branch of experience_action_select rewritten:
eps-greedy + Boltzmann → Thompson (training) + argmax E[Q] (eval)
- C51 + IQN buffers wired to gpu_dqn_trainer + gpu_backtest_evaluator
- train_active_frac HEALTH_DIAG instrumentation
- 4 GPU-direct tests against production kernel
- Aggregation Contract table + memory pearl
Plan D — Phase 3: long verification + dead-code cleanup (8 tasks)
- Test 3.A: regression anchor against original C51 Flat bias
- L4: 1 seed × 6 folds × 30 epoch (~1 hour)
- L5: 5 seed × 6 fold matrix per Plan 5 Task 5 (Tier 1+2+3 PASS)
- Direction-only eps_dir adaptive boost + Boltzmann tau-floor cleanup
(per feedback_no_partial_refactor.md)
- nsys regression check; final architecture/spec footer
Plans gated sequentially: each phase exit gate must pass before next.
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.