5d163e0e1d
Step 8 — c51_aux_dw_kernel (new):
- Per-action block tree-reduce: grid=(4,1,1), block=(256,1,1). One block
per W index a, tree-reduces dW[a] across batch via warp shuffle + shmem.
Zero atomicAdd per pearl_no_atomicadd.
- Per-sample contributions:
a == a_d: dW[a] += inv_batch × isw × (SP_b/dz) × state_121
a == a*: dW[a] += inv_batch × isw × (-γ(1-done)) × (SP_b/dz) × next_state_121
c51_loss_kernel forward — new scratch outputs:
- aux_target_a_dir_buf[B] (i32): saves best_next_a for d==0 after Step c
sampling.
- aux_proj_logdiff_dir_buf[B] (f32): saves SP_b = Σ_n p_target_n ×
(current_lp[upper_n] - current_lp[lower_n]) after Step d's projection
via re-derivation of lower_n/upper_n (matching Huber compression +
clamp arithmetic of block_bellman_project_f).
Step 11 — adam_w_aux_kernel (new):
- Standard Adam with bias correction, grid=(1,1,1), block=(4,1,1).
- Graph-capture-safe: lr via self.lr_dev_ptr pointer arg; step via
self.ptrs.t_buf pointer arg (matches main Adam pattern). beta/eps
as value args from sp5_isv_slots constants.
- Bias-correction denominator floored at 1e-30 to avoid /0.
Trainer wiring (submit_adam_ops):
- launch_c51_aux_dw + launch_adam_w_aux added right after
launch_adam_update. Both inside the captured adam_child graph.
- New trainer fields: aux_target_a_dir_buf, aux_proj_logdiff_dir_buf,
c51_aux_dw_kernel, adam_w_aux_kernel. Cubin statics SP22_C51_AUX_DW_CUBIN
+ SP22_ADAM_W_AUX_CUBIN added.
NULL-safety:
- aux_shift_active=false in c51_loss_kernel forward → both scratch
buffers stay at alloc_zeros 0 → dW reads 0 → Adam W is a no-op.
- aux_target_a_dir_out / aux_proj_logdiff_dir_out are NULL-tolerant.
Deferred (deliberate scope):
- dL/dstate_121 backward (c51 → aux head): refinement, not correctness;
aux head trains via own supervised CE loss.
- Phase C1 collector W ptr setter.
- Phase D (eval-side aux infrastructure).
Verification:
- cargo build -p ml --lib: 0 errors, 21 pre-existing warnings.
- nvcc full recompile clean (1m05s for sm_89 target).
- All forward atom-shift consumers + adaptive W backward + Adam now wired.
End-state: adaptive W trains from structural prior [-0.5, 0, +0.5, 0]
via projection log-diff gradient. Aux head trains independently via
supervised CE. Together they form learned cross-coupling from aux
direction predictions to dir-branch Q distribution shifts. Smoke can
now measure adaptive W's effect on WR.
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.