c508d101c1
Bite-sized 22-task plan implementing the design at
docs/superpowers/specs/2026-05-17-kloop-parallelization-design.md.
Four atomic commits per the spec's Rollout section:
Commit 1: reduce_axis0 kernel + cfc_step refactor (Tasks 1-10)
Commit 2: GRN bwd refactor (Tasks 11-14)
Commit 3: VSN bwd refactor (Tasks 15-17)
Commit 4: attention_pool bwd refactor (Tasks 18-20)
Each commit covers kernel rewrite + per-batch scratch buffers +
reducer launches + memset_zeros wiring + smoke validation.
Tests added across commits:
- cfc_bwd_b1_oracle.rs (Task 6): B=1 oracle vs single-sample helper
within relative_eq 1e-5 (FP-tolerant, not bit-exact)
- stacked_trainer_loss_shrinks_at_batch_32 (Task 7): first test
that exercises the cross-batch reducer path
- cfc_bwd_scratch_clears_between_steps (Task 8): scratch-zero
regression guard
Acceptance gates 1-8 from the spec are mapped to Tasks 6, 7, 8, 9,
21, 22 (local + cluster A/B perf). Reference baseline for gate #8
is t6z89's per-epoch AUC trajectory.
Co-Authored-By: Claude Opus 4.7 <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.