cba9f25ed9
Two related blocks landing together (Bug 2 diagnostic forces the structural
conversion of touched files; pre-commit guard at 5275932f4 enforces zero
*_via_pinned helpers per `feedback_no_hiding`).
== Bug 2 instrumentation (training_loop.rs after collect_experiences_gpu) ==
One-shot AtomicBool-gated DIAG_BUG2 dump of first 5 samples × 5 cols of
both gpu_batch.states and gpu_batch.next_states + col-0 mean/std/mean_abs
across the full batch. Prints once at first rollout — pre-graph-capture so
no hot-path impact. Interpretation key embedded:
mean_abs ~0.001 = normalized log_return ✓
mean_abs ~5000 = raw price ✗ (Bug 2 confirmed)
Resolves the smoke-vs-production state[0] divergence question that pure
static code reading couldn't pin: production aux_label_scale=5300 traces
back through gather kernel to "feat[0] of next_states_buf" but the fxcache
shows feat[0] stddev=1.0 (z-normalized log_return). Either next_states_buf
is populated from a different source than fxcache feat[0], or some kernel
mutates state[0] post-gather. The diagnostic prints both states (post-gather)
and next_states (post-shift) to disambiguate.
== Structural conversion: targets_raw_cuda + features_raw_cuda + 10 sites ==
Field types (DQNTrainer in trainer/mod.rs:621/624):
Option<CudaSlice<f32>> → Option<MappedF32Buffer>
init_gpu_raw_buffers_from_slices (training_loop.rs): clone_to_device_f32_via_pinned
calls (lines 1308/1312) replaced with `MappedF32Buffer::new + write_from_slice`.
Consumer signatures (3 functions across 2 files):
- gpu_experience_collector::collect_experiences_gpu(market_features_buf,
targets_buf): &CudaSlice<f32> → &MappedF32Buffer (each)
- gpu_experience_collector::launch_timestep_loop: same
- gpu_experience_collector::compute_difficulty_scores(targets): same
- decision_transformer::build_dt_trajectories(features_gpu, targets_gpu): same
Launch sites: `.arg(buf)` → `.arg(&buf.dev_ptr)` at all 5 launch_builder
invocations in gpu_experience_collector.rs and the 2 in build_dt_trajectories.
== Cold-path init conversions (forced by guard touching shared files) ==
gpu_dqn_trainer.rs (9 sites → 4 buffer-migration groups):
- spec_u_s1/v_s1/u_s2/v_s2 (4 buffers, explicit)
- spec_u/v macro pairs (alloc_spec_pair! body, expands to ~22 buffers)
- graph_params (cross-branch graph message-passing, 60 floats)
- denoise_params (diffusion Q-refinement MLP, 1800 floats)
- qlstm_weights (xLSTM mLSTM-cell, 528 floats)
gpu_experience_collector.rs (1 site):
- upload_ofi_features → ofi_gpu field type Option<CudaSlice<f32>> → MappedF32Buffer
Inherited from prior worktree-agent attempts (compile clean, included here):
- sel_clip_buf in gpu_dqn_trainer.rs
- RmsNormWeightSet γ buffers in gpu_weights.rs
PPO trainer (trainers/ppo.rs) deferred — not on eval-collapse hot path.
== Validation ==
cargo check -p ml --offline: clean
pre-commit hook (check_no_dtod_via_pinned + Invariant 7 + GPU hot-path guard): pass
Bug 2 diagnostic will fire on next L40S run and print state[0] stats for
first batch. If mean_abs ~0.001 → state[0] is correctly normalized and Bug 2
is elsewhere (maybe label_scale_ema initialization). If mean_abs ~5000 →
state[0] really is raw price; the rollout state-builder is the bug.
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.