aa16ca31f2
Task 0.3 (Track 1 magnitude):
* q_mag_full/half/quarter: plumbed via GpuDqnTrainer::q_magnitude_bucket_means()
→ FusedTrainingCtx::q_magnitude_bucket_means() → HEALTH_DIAG. Phase-0
returns [0.0; 3]; per-mag Q reduction kernel deferred per plan §0.3 step 2
(goal here is the accessor chain, swap-in without touching emit formatting).
* var_scale_mean: STUB 0.0 — kernel computes `1/(1+sqrt(Var[Q]))` per-sample
inside experience_env_step but does not persist to a device buffer; it is
consumed in-place to shrink effective_max_pos. Exposing requires a
dedicated per-sample output buffer + launch arg + kernel write; deferred
to Phase 1+. Documented in code.
* kelly_f_mean / avg_win_ratio: REAL — computed host-side from
self.trade_stats_history.last() using the SAME formula the kernel uses
(b = avg_win/avg_loss, kelly = (b·p − (1−p))/b clamped [0,1] × 0.5
for half-Kelly; avg_win_ratio = (sum_wins/win_count) /
max(sum_losses/loss_count, 0.001) per plan formula line 239).
One-epoch lag (trade_stats_history is appended later in the same
epoch body); zero until first epoch's stats land. Documented in code.
Task 0.6 (Track 1 noisy):
* vsn_mag / vsn_dir: REAL per-branch — mean |w| across each branch's VSN
projection weight pair (tensors 26..34 grouped by branch:
26/27=dir, 28/29=mag, 30/31=order, 32/33=urg). The live VSN gate mask is
computed per-sample inside variable_select_bottleneck and consumed
immediately, not materialized into a device buffer we can cheaply read;
projection-weight mean is the closest stable H7 surrogate. Documented
in accessor rustdoc. Higher-fidelity measurement would require a
per-sample mask output buffer following the Task 0.5 pattern — deferred.
* drift_mag / drift_dir: REAL per-branch — RMS ‖target_w − online_w‖ across
each branch's 4 weight tensors (indices 8..12 dir, 12..16 mag, 16..20
order, 20..24 urg). Host-computed cold path via two memcpy_dtoh + normal
Vec<f32> reduce loops; stream-sync'd before read. NO atomics (project rule).
~2.7 MB × 2 DtoH per epoch, negligible diagnostic cost.
NoisyNets sigma_mag/sigma_dir already wired in 999bb2fa0 — preserved untouched.
Accessor chain (mirrors Task 0.4 grad_ratio_mag_dir pattern):
* GpuDqnTrainer::per_branch_target_drift() -> Result<[f32; 4], MLError>
* GpuDqnTrainer::per_branch_vsn_mean() -> Result<[f32; 4], MLError>
* GpuDqnTrainer::q_magnitude_bucket_means()-> [f32; 3] (plumbing stub)
* FusedTrainingCtx wrappers → delegate, fallback to zeros on MLError.
* training_loop.rs HEALTH_DIAG block: populate slots via
self.fused_ctx.as_ref().map(|f| f.…).unwrap_or(…) — same pattern
used for grad_ratio_mag_dir.
HEALTH_DIAG `mag` and `noisy` groups now carry real signal (or plan-sanctioned
plumbing stubs for the q_mag_* / var_scale slots documented in commit + code).
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.