15b50ac38f
Adds the SP18 v2 Phase 0 D-leg observability scaffold per the plan's
"Phase 0 — diagnostic emit (NO functional change)" task:
- New kernel `reward_decomp_diag_kernel.cu`: block tree-reduce
(4 blocks × 256 threads, one block per direction-axis bin) reading
`reward_components_per_sample [N×6]` + `actions_out [N]` and emitting
5 per-bin stats (mean r_micro / mean r_opp_cost / mean r_popart /
mean |reward| / fire_rate) into a 20-float row-major output. Bin
order: Hold(0)→Long(1)→Short(2)→Flat(3); col order: micro→opp→
popart→abs→fire. Empty-bin guard emits 0.0 (NOT NaN) per the
consumer-side KILL CRITERION arithmetic contract.
- Cubin manifest entry in `crates/ml/build.rs` + `REWARD_DECOMP_DIAG_
CUBIN` re-export in `gpu_dqn_trainer.rs`.
- 20-float `MappedF32Buffer sp18_reward_decomp_diag_buf` field on
`GpuDqnTrainer` + accessor pair (`sp18_reward_decomp_diag_dev_ptr`
for the writer-side launcher; `read_sp18_reward_decomp_diag` for the
HEALTH_DIAG reader). Buffer is constructor-zeroed so cold-start
HEALTH_DIAG emits a deterministic zero block.
- `sp18_reward_decomp_diag_kernel` field on `GpuExperienceCollector` +
cubin load on the collector's stream + `launch_sp18_reward_decomp_
diag(n, b1, b2, b3, out_dev_ptr)` launcher. Wired in
`training_loop.rs` at the per-step boundary, BEFORE
`launch_reward_component_ema_inplace` (which `memset_zeros` the
source buffer after consuming it) per `pearl_canary_input_freshness_
launch_order`.
- New per-epoch HEALTH_DIAG line emit at the existing per-epoch
boundary (after the SP17 dueling line):
HEALTH_DIAG[N]: reward_decomp [hold(micro=X opp=Y popart=Z abs=W
fire=F) long(...) short(...) flat(...)]
Reads the mapped-pinned 20-float diag buffer directly via the
collector→trainer host_ptr — no DtoH copy.
- New `crates/ml/tests/sp18_hold_reward_oracle_tests.rs`:
* `reward_decomp_per_action_cpu_oracle` (CPU oracle pinning the
per-bin reduction math against a 4-sample synthetic batch).
* `reward_decomp_per_action_gpu_oracle` (GPU oracle, ignored unless
`--ignored`; asserts kernel matches CPU oracle bit-for-bit within
1e-6 f32 budget).
* `reward_decomp_empty_bin_emits_zero_not_nan` (empty-bin contract
guard).
Pure observability — no production-path consumer in this commit. No
reward changes, no Bellman target changes, no kernel modifications to
the action-selection or training paths. Per
`feedback_no_partial_refactor` the kernel + cubin manifest + buffer +
launcher + production wire-up + HEALTH_DIAG emit + GPU oracle test all
land atomically.
Verification:
SQLX_OFFLINE=true CUDA_COMPUTE_CAP=86 cargo check --workspace clean.
CPU oracle test passes; GPU oracle + empty-bin guard both pass on
RTX 3050 Ti (2.13s).
Plan: docs/superpowers/plans/2026-05-08-sp18-reward-shape-hold-attractor.md
§ Phase 0 Task 0.1.
Audit: docs/dqn-wire-up-audit.md § "SP18 v2 Phase 0 Task 0.1".
Co-Authored-By: Claude Opus 4.7 (1M context) <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.