0f5d5c7b4a
CB3+CB4 shipped the kernels with a holding-pattern (grad-zero) call site.
CB5 wires the real calls + per-loss ISV signals.
perception.rs:
- step_batched body: pos_weight computed host-side from
self.last_pos_fraction (n_neg/n_pos clamped to [1.0, 50.0] per E3),
uploaded mapped-pinned to stg_aux_pos_weight_{long,short}
- 4 kernel calls per step (slab mode, K*B*N_AUX_HORIZONS):
* aux_bce_loss_gpu × 2 (prof_long, prof_short) — class-weighted
* aux_huber_masked_loss_gpu × 2 (size_long, size_short) — NaN-mask
from CB1's y_size=NaN at y_prof=0 gives conditional-Huber for free
- Per-loss EMAs replace single aux_huber_ema:
* aux_prof_bce_ema_per_h (BCE EMA per horizon)
* aux_size_huber_ema_per_h (Huber EMA per horizon)
* aux_dir_acc_ema_per_h (unchanged)
- Stop-grad lift condition: aux_prof_bce_ema < 0.4 AND aux_dir_acc > 0.85
for ALL horizons (uses BCE not Huber per E3 — size Huber is
observability-only since the regression scale varies more than the
binary classification quality)
- aux_lift_huber_threshold renamed to aux_lift_prof_bce_threshold
alpha_train.rs:
- AlphaTrainSummary: final_aux_huber_ema_per_h split into
final_aux_prof_bce_ema_per_h + final_aux_size_huber_ema_per_h
- Per-epoch tracing: aux_prof_bce_h{100,300,1000} + aux_size_huber_h{...}
+ aux_dir_acc_h{...} + stop_grad_aux_to_encoder
perception_overfit.rs: synthetic test asserts BCE finite + below ln(2)
chance baseline, size Huber finite, dir_acc >= 0.5.
Synthetic test on RTX 3050:
aux_prof_bce_ema = [0.0142, 0.0142, 0.0142] (30× below threshold)
aux_size_huber_ema = [0.1213, 0.1213, 0.1213] (small)
aux_dir_acc_ema = [1.0, 1.0, 1.0] (perfect)
stop_grad lifted = false (lift fired)
Known limitation (follow-up CB6): both kernels return single joint scalar
over the [K × B × N_AUX_HORIZONS] slab — all per-horizon EMA entries
carry the broadcast joint mean. Per-h gating would require kernel split.
cargo check --workspace --all-targets clean.
cargo test -p ml-alpha --lib: 43 passed.
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.