aab13a83f2
Add per-epoch HEALTH_DIAG line measuring how far current online params
have drifted from the best-Sharpe checkpoint. The fold-transition
restore-best fix preserves peak weights across folds, but within-fold
edge-decay still happens (Adam keeps stepping after peak Sharpe). The
drift diagnostic surfaces the trajectory so operators can correlate
Sharpe-peak decay with parameter movement.
HEALTH_DIAG line:
HEALTH_DIAG[N]: weight_drift [norm_l2={:.6} relative={:.6} branch_max={:.6}]
Where:
- norm_l2 = ||params_flat - best_params||₂ (absolute L2)
- relative = norm_l2 / max(||best_params||₂, EPS) (relative drift)
- branch_max — currently mirrors `relative` (single-scalar form per spec).
Per-branch breakdown is a deferred follow-up: branch heads are
protected from S&P (skip_start..skip_end), so trunk drift dominates
the L2 in any case.
Cold-start: when best_params_snapshot is None (first fold or any fold
without a Sharpe improvement yet), launcher emits [0.0, 0.0] directly
(kernel skipped, mapped-pinned host slice written from CPU).
Distinguishable from "snapshot matches current" via best_epoch elsewhere.
Kernel (weight_drift_diag_kernel.cu): single block × 256 threads. Two
block tree-reductions sharing one shmem tile sequentially:
Pass 1: ||params - best||₂² over (params - best)
Pass 2: ||best||₂² over best
Thread 0 finalizes sqrt + EPS-floored ratio + writes via
__threadfence_system() for PCIe-visible coherence.
Per feedback_no_atomicadd — block tree-reduce only.
Per feedback_no_htod_htoh_only_mapped_pinned — output is
MappedF32Buffer<2>; cold-start bypass uses host_slice_mut.
Wire-up (atomic):
- crates/ml/build.rs: cubin manifest entry
- crates/ml/src/cuda_pipeline/weight_drift_diag_kernel.cu (NEW)
- crates/ml/src/trainers/dqn/fused_training.rs: field + constructor
+ launch_weight_drift_diag() + read_weight_drift_diag()
- crates/ml/src/trainers/dqn/trainer/mod.rs:
DQNTrainer::read_weight_drift_diag() public wrapper (CPU-only path
emits zeros)
- crates/ml/src/trainers/dqn/trainer/training_loop.rs: per-epoch
HEALTH_DIAG emit adjacent to SP17 dueling block
GPU oracle test (crates/ml/tests/sp18_weight_drift_test.rs, NEW):
4 cases, all #[ignore = "requires GPU"]:
1. weight_drift_matches_cpu_oracle_4096 — N=4096, ε=1e-5
2. weight_drift_is_zero_when_params_equals_best
3. weight_drift_eps_floor_when_best_is_zero (catches NaN/Inf edge)
4. weight_drift_n_zero_emits_zeros (degenerate guard)
All 4 pass on local RTX 3050 Ti.
Pre-commit Invariant 7: docs/dqn-wire-up-audit.md updated with kernel
algorithm, wire-up table, cross-pearl invariants, and oracle-test
inventory.
Per:
- feedback_no_atomicadd (block tree-reduce in drift kernel)
- feedback_no_htod_htoh_only_mapped_pinned (MappedF32Buffer<2>)
- feedback_wire_everything_up (kernel + launcher + emit + test atomic)
- pearl_no_host_branches_in_captured_graph (cold-path, outside graph)
- feedback_no_partial_refactor (single atomic commit)
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.