d2a27a0042
One-shot diagnostic to answer the SP13 root question: does the data have
predictable directional signal at the bar level?
Wires the existing `aux_next_bar_loss_reduce` kernel (which already had a
4-strip shmem reduction emitting `[dir_acc, pos_pred_frac, pos_label_frac]`
into a 3-float output) end-to-end:
- `gpu_aux_heads.rs`: launcher takes `dir_acc_out_ptr`, allocates 4×AUX_BLOCK
shmem to back the four parallel reductions.
- `gpu_dqn_trainer.rs`: adds `aux_nb_dir_acc_buf` (3 f32 device buffer),
threads it through the loss-reduce launch, exposes `read_aux_dir_acc()`
accessor for once-per-epoch DtoH readback.
- `training_loop.rs`: pins `aux_w = 1.0` (instead of the ISV-driven 0.05–0.3
clamp) so the supervised aux head dominates the loss; emits a new
`HEALTH_DIAG[ep]: aux_dir_acc accuracy=… pos_pred_frac=… pos_label_frac=…`
line per epoch.
Verdict thresholds:
* dir_acc > 55% by ep 5 ⇒ data has signal, DQN failing to use it
* dir_acc ≈ 50% throughout ⇒ data lacks signal at this timescale
* dir_acc 60–70% ⇒ strong signal we're not using
EXPERIMENT BRANCH — revert this commit after the investigation reads back the
5-epoch table from smoke logs. All five touch points are tagged
"SP13 data-investigation" / "EXPERIMENT" for clean revert.
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.