da5e564ccf
Second half of Phase B4b. Wires the trade-outcome label column through
the replay buffer (struct field, allocation, scatter on insert, gather
on sample, direct-to-trainer pointer + setter) and connects the
trainer's aux_to_label_buf to receive per-batch sampled labels.
Completes the end-to-end producer → ring → trainer i32 path the K=3
sparse CE consumer reads.
Replay buffer changes (crates/ml-dqn/src/gpu_replay_buffer.rs):
- New struct fields: aux_outcome_labels (capacity-sized ring),
sample_aux_outcome_labels (mbs-sized fallback gather), trainer_aux_
outcome_labels_ptr (direct-path destination)
- New aux_outcome_labels_ptr field on GpuBatchPtrs
- insert_batch signature: new aux_outcome arg between aux_conf_in and
bs. Scatters via existing K-generic scatter_insert_i32 (same kernel
the K=2 aux_sign_labels uses).
- sample_proportional direct gather when trainer_aux_outcome_labels_ptr
!= 0; fallback gather otherwise. Mirrors aux_sign_labels direct/
fallback semantic exactly.
- New setter set_trainer_aux_outcome_labels_ptr mirrors
set_trainer_aux_conf_ptr.
Collector emission:
- New aux_outcome_labels field on GpuExperienceBatch
- Populated at end of collect_experiences_gpu via dtod_clone_i32 from
Phase B4b-1's per-(env, t) producer scratch.
Trainer wireup:
- aux_to_label_buf_ptr() accessor on GpuDqnTrainer (mirrors
aux_nb_label_buf_ptr)
- trainer_aux_to_label_buf_ptr() delegating accessor on
FusedTrainingCtx
- New set_trainer_aux_outcome_labels_ptr call in training_loop at the
same site where set_trainer_buffers + set_trainer_aux_conf_ptr fire.
Two call sites updated (lines ~835 + ~2857).
- insert_batch call in training_loop passes &gpu_batch.aux_outcome
_labels as new arg.
Test fixtures updated: 4 smoke test files + 3 unit-test fixtures in
gpu_replay_buffer.rs alloc zero-init aux_outcome i32 arg.
End-to-end chain complete:
trade_outcome_label_kernel (A2)
→ collector per-step launch (B3)
→ collector emission (B4b-1)
→ replay-buffer insert + scatter (B4b-2)
→ PER sample + direct gather (B4b-2)
→ trainer aux_heads_forward.loss_reduce (B4) reads sparse {-1,0,1,2}
→ trainer aux_heads_backward (B4) computes per-sample partials
→ Adam SAXPY (B1+B4) updates W1, b1, W2, b2 at [163..167)
The "degraded predict-Profit-everywhere" cold-start from Phase B4 is
resolved. K=3 head trains on real sparse trade-outcome labels.
Verification:
- cargo check -p ml clean (21 warnings, none new).
- cargo test -p ml --lib → 1016/0 on clean runs; pre-existing
NoisyLinear flake still surfaces ~30-50% of runs (unrelated to
vNext work — see ebc1b1502 / 20e1aea27 commit notes).
Remaining vNext work:
- B5: input concat 256 → 262 with plan_params
- Phase C: 3-slot state assembly (state[121..124])
- Phase D: 12-weight W atom-shift (4 actions × 3 outcomes)
- Phase E: dW backward + Adam for W[4, 3]
- Phase F: validation smoke at β=0.5 structural prior
Audit: docs/dqn-wire-up-audit.md Phase B4b-2 section.
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.