cdd3dc6edb
Completes the F-3 observability story. Smoke runs now print
aux_trade_outcome_ce_ema every epoch in the standard HEALTH_DIAG output
— no ISV inspector required.
Changes:
health_diag.rs:
- New field aux_trade_outcome_ce: f32 appended at END of
HealthDiagSnapshot per "Field order is stable; adding fields
appends to the end" doc rule. Existing fields' byte offsets
preserved → no kernel-side word offset re-validation.
- snapshot_size_is_stable pin: 149 × 4 → 150 × 4 = 600 bytes.
health_diag_kernel.cu:
- New WORD_AUX_TRADE_OUTCOME_CE = 149 (appended at end).
- WORD_TOTAL = 150 (was 149); static_assert bumped.
- New kernel arg aux_trade_outcome_ce_idx appended after
moe_lambda_eff_idx.
- New mirror write after the existing MoE mirrors. Stream-implicit
ordering: aux_outcome_ce_ema_update (F-3b) fires before
health_diag_isv_mirror, so the read picks up the just-updated EMA.
gpu_health_diag.rs:
- launch_isv_mirror gets new aux_trade_outcome_ce_idx: i32 arg.
gpu_dqn_trainer.rs:
- launch_health_diag_isv_mirror passes
AUX_TRADE_OUTCOME_CE_EMA_INDEX as the new arg.
training_loop.rs:
- Per-epoch metrics push appends ("aux_trade_outcome_ce_ema",
ISV[538]) to the standard out vec. Console / CSV automatically
includes the new column.
End-to-end F-3 chain now closed:
K=3 fwd → aux_to_loss_scalar_buf → aux_outcome_ce_ema_update
→ ISV[538] → health_diag_isv_mirror → snap.aux_trade_outcome_ce
→ training_loop metrics → console.
Operator sees CE every epoch:
- Cold-start: 0.000
- After bootstrap: ~1.098 (= ln(3))
- After training: ideally 0.5-0.7 (head learning)
Phase F end-to-end ready. The vNext stack has:
- Full GPU kernel chain (A2-A5 + D + plan-conditioning)
- Full Rust wireup (B0-B4, B4b-1/2, C-1/C-2, B5b)
- Real labels reaching trainer
- K=3 → policy via state slots + atom-shift
- End-to-end CE observability
Verification:
- cargo check -p ml clean.
- cargo test -p ml --lib → 1016/0 green (incl. bumped
snapshot_size_is_stable byte-size pin test).
Audit: docs/dqn-wire-up-audit.md Phase F-3c 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.