91b48bc7a5
Adds val_sharpe_delta + saboteur_engagement + reward_component_mag_ratio
GPU producers for the SP11 reward-as-controlled-subsystem chain. Each is
a single-block producer chained with apply_pearls_ad_kernel for Pearls
A+D smoothing per pearl_first_observation_bootstrap.md +
pearl_wiener_optimal_adaptive_alpha.md. All three write to slots in
[350..360) which no consumer reads yet — Layer A is additive; consumer
migration lands atomically in Layer B.
A1.1 — val_sharpe_delta_compute_kernel.cu
Two-pass: writes raw delta + (delta - prev_delta_ema)^2 to scratch.
Chained Pearls A+D (n_slots=2) → ISV[VAL_SHARPE_DELTA_EMA_INDEX=350,
VAL_SHARPE_VAR_EMA_INDEX=351]. Host writes val_sharpe to mapped-pinned
history[1]; rotation handled in training_loop.rs at val emit boundary
(a literal already-computed value — no host-side compute, no htod_copy).
A1.2 — saboteur_engagement_compute_kernel.cu
Per-bar |Δreward| > 0.01 × ISV[PNL_REWARD_MAGNITUDE_EMA_INDEX] check
with block tree-reduce (no atomicAdd per feedback_no_atomicadd). The
per-bar Δreward signal is produced by experience_env_step's saboteur
perturbation site as `traded × |reward| × max(|eff_spread − 1|,
|eff_slip − 1|)` — a structural proxy for the cost-differential the
saboteur imposed on bars where the model traded. Single kernel-side
emit (no parallel reward computation), per spec §3.3.1.
Chained Pearls A+D → ISV[SABOTEUR_ENGAGEMENT_RATE_INDEX=358].
A1.3 — reward_component_mag_ratio_compute_kernel.cu
Reads ISV[REWARD_POPART_EMA_INDEX..+6) (the SP4 reward-component
magnitude EMAs), normalises to ratios, and mirrors popart magnitude
into scratch[6] as a side-output. ONE non-pointer parameter
(popart_ema_base_slot) — no _unused param per feedback_no_stubs.
Two chained Pearls A+D launches:
n_slots=6 → ISV[REWARD_COMPONENT_MAG_RATIO_BASE..+6)
n_slots=1 → ISV[PNL_REWARD_MAGNITUDE_EMA_INDEX=359]
(slots non-contiguous: 352..358 then 359.)
Wire-up (per feedback_wire_everything_up):
- 3 cubin entries appended to crates/ml/build.rs
- 3 kernel handles + val_sharpe_history_pinned (MappedF32Buffer[2]) +
saboteur_delta_reward dev-ptr cache fields on GpuDqnTrainer
- 3 launchers (launch_sp11_*) + 1 setter (set_sp11_saboteur_delta_reward_buf)
- saboteur_delta_reward_per_sample buffer field on GpuExperienceCollector
- experience_env_step kernel signature extended with the new buffer arg;
every call site in the same commit per feedback_no_partial_refactor
- training_loop.rs init wires collector→trainer setter; val emit boundary
invokes launch_sp11_val_sharpe_delta_compute; per-epoch metrics block
invokes launch_sp11_mag_ratio_compute then
launch_sp11_saboteur_engagement_compute (mag_ratio first so the
signal-relative threshold base is populated before the saboteur reader)
- SP5_SCRATCH_TOTAL grown 266 → 276 (10 new scratch slots: 2+1+7)
- docs/isv-slots.md SP11 section updated to reflect A1 producers
3 GPU oracle tests in crates/ml/tests/sp11_producer_unit_tests.rs
pass on RTX 3050 Ti via MappedF32Buffer fixtures (zero htod_copy /
dtoh_sync_copy / alloc_zeros — feedback_no_htod_htoh_only_mapped_pinned
compliant).
Note on Step 8a path: the plan offered two routes for the saboteur
Δreward producer — in-kernel diff emission OR a small dedicated
reader-of-existing-buffers. The existing reward path emits ONE reward
(not both with/without), so the dedicated-reader alternative was
infeasible. The in-kernel emission landed as a small write site at the
END of experience_env_step (after total_reward_per_sample is finalised),
threading saboteur_eff_spread/saboteur_eff_slip from the perturbation
site forward to the END via stack vars. Single new kernel parameter,
single new GPU-only buffer, single existing call site updated.
Spec: docs/superpowers/specs/2026-05-04-sp11-reward-as-controlled-subsystem.md §5
Plan: docs/superpowers/plans/2026-05-04-sp11-reward-as-controlled-subsystem.md (Task A1)
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.