4f82b74a51
Replaces the kernel's hardcoded `ema_alpha` with the shared `pearls_ad_update`
host-side helper, wires the host-side update across the trainer/collector
boundary (mirrors the A14/A15 Pearl C wiring path), and deletes the
trainer's orphan `launch_reward_component_ema` per
`feedback_wire_everything_up.md`.
Kernel + collector launcher:
- Kernel `reward_component_ema` signature: drops `(isv_out, ema_alpha,
isv_reward_base_slot)` for `(scratch_buf, scratch_first_index=63)`.
Single-block 6-thread (one per component); each writes mean|r_c| to
`scratch_buf[scratch_first_index + c]` with `__threadfence_system()`.
- 6 ISV slots wired with Pearls A+D: ISV[63..69) (Wiener offsets
189..207 — last slots in the post-A13 207-float wiener_state_buf).
- `GpuExperienceCollector::launch_reward_component_ema_inplace` now:
launches kernel → syncs stream → applies Pearls A+D in 6-iteration
loop → memsets reward_components_per_sample to zero (preserves
original behaviour). Degenerate-zero short-circuit per slot covers
the always-zero placeholder components (c=2 trail, c=5 bonus) plus
cold-start.
Cross-boundary wiring (mirrors A14/A15 precedent):
- 4 new fields on `GpuExperienceCollector`:
`reward_component_pearls_{wiener_host_ptr, scratch_dev_ptr,
scratch_host_ptr, isv_pinned_ptr}` — all NULL/0 until wired.
- New setter `set_reward_component_pearls_buffers(...)` on collector.
- New accessors on `GpuDqnTrainer`: `wiener_state_buf_host_ptr()`,
`producer_step_scratch_buf_dev_ptr()`,
`producer_step_scratch_buf_host_ptr()`, `isv_signals_pinned_ptr()`.
- New wire helper `FusedTrainingCtx::wire_reward_component_pearls_buffers`
pulls all 4 pointers from trainer, pushes into collector.
- Wired once in `training_loop.rs::init_gpu_experience_collector`
immediately after `set_curiosity_pearl_c_buffers`.
Orphan deletion (per `feedback_wire_everything_up.md`):
- Removed `GpuDqnTrainer::launch_reward_component_ema` (zero call sites
pre-deletion).
- Removed trainer-side `reward_component_ema_kernel: CudaFunction`
field (zero consumers post-launcher-deletion).
- Removed cubin loader + struct-init line.
- `REWARD_COMPONENT_EMA_CUBIN` static remains because the collector
still loads from it.
Tests:
- `sp4_reward_component_ema_writes_step_obs_via_pearl_a_then_converges_pearl_d`:
drives kernel with N=128 reward_components where r[i*6+c] = sign(i) ×
(c+1), asserts each slot ∈ ±1e-5 of (c+1), non-target slots remain 0,
Pearl A bootstrap + Pearl D convergence verified.
- The cross-boundary wiring path exercised in production by integration
smoke harness; this kernel-direct test isolates kernel signature +
Pearls A+D semantics.
- `cargo test -p ml --lib sp4_wiener_ema --offline` 6/6 passing.
Per `feedback_no_atomicadd.md`,
`feedback_no_htod_htoh_only_mapped_pinned.md`,
`feedback_no_partial_refactor.md`, `feedback_wire_everything_up.md`.
Build: `cargo check -p ml --lib --tests --offline` clean (11 pre-existing
warnings, no new warnings).
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.