b98dc2730d
Adds 11 buffer fields + 2 orchestrator ops handles (fwd + bwd) to the
trainer struct, mirroring the existing aux_nb_* / aux_partial_nb_*
pattern at K=3 instead of K=2.
Trainer struct additions:
- aux_to_fwd: AuxTradeOutcomeForwardOps (Phase B0 scaffold)
- aux_to_bwd: AuxTradeOutcomeBackwardOps
- aux_to_hidden_buf [B, H=128] saved post-ELU
- aux_to_logits_buf [B, K=3] saved logits
- aux_to_softmax_buf [B, K=3] saved softmax (3 future consumers)
- aux_to_label_buf [B] i32 sparse {-1, 0, 1, 2}
- aux_to_loss_scalar_buf [1] mean CE
- aux_to_valid_count_buf [1] B_valid for backward
- aux_dh_s2_to_buf [B, SH2] SAXPYs into dh_s2_aux_accum
- aux_partial_to_w1 [B, H, SH2] per-sample dW1
- aux_partial_to_b1 [B, H] per-sample db1
- aux_partial_to_w2 [B, K=3, H] per-sample dW2
- aux_partial_to_b2 [B, K=3] per-sample db2
Memory: aux_partial_to_w1 = 256 MB at B=2048 — identical to K=2 head's
partial size (same SH2, same H). Total new aux-to footprint ≈ 260 MB.
The existing aux_param_grad_final_buf scratch is sized to the largest
tensor across all aux heads; trade-outcome head's largest is W1 [H, SH2]
= 32,768 floats — identical to K=2/K=5 W1s. No resize needed.
Cold-start label semantics: alloc_zeros yields label 0 (Profit) for
every sample. Until the producer wires in (B3), the trainer's CE loss
treats every sample as "should have predicted Profit" — degraded but
well-defined (no NaN). Mirrors the K=2 head's known-degraded state
between B1.1a and B1.1b.
No FoldReset registration: these buffers are overwritten every batch
— no stale-state-leak risk across folds (matches the existing aux_nb_*
pattern).
Phase B3 next: collector-side rollout buffers + forward chain wireup
into collect_experiences_gpu (per-env softmax → per-(i, t) fan-out
scatter for trainer's aux_to_softmax_buf population).
Audit: docs/dqn-wire-up-audit.md Phase B2 section.
Cargo check clean (21 warnings, none new on aux_to_* fields).
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.