d243a6f080
8 corrections from code-anchored critical review at HEAD eaf4adcb9:
1. Direction Q-head emits K=3 (Short/Flat/Long), not K=4. Aux head
emits K=2 (down/up). Gate 2 q_disagreement now uses K=3↔K=2 mapping
with Flat masking. Verified against gpu_dqn_trainer.rs:2438.
2. Forward launch order constraint added: aux forward must complete
before direction Q-head forward (new serial dep). Cited
pearl_canary_input_freshness_launch_order.
3. Adaptive sigmoid k formula fixed: v1 had unreachable k_max=50
because formula caps k ≤ k_base. v2 uses max(..., k_min) with
k_max = k_base implicit.
4. α_grad rate limiter promoted from nice-to-have to v1. Schmitt
state-flip introduces sigmoid discontinuity. β=0.9 EMA smoothing
added; new ALPHA_GRAD_SMOOTHED_INDEX slot.
5. q_disagreement_baseline drop: v1 had adaptive baseline as long-EMA
(feedback loop risk). v2 uses structural 0.5 (analytic K=3-with-
Flat-masked random alignment). Drop BASELINE_INDEX slot.
6. Backward gradient scaling clarified: α_grad scales dL/dx (input
gradient flowing back to aux), NOT dL/dW (Q-head's weight grad).
Q-head learns to use the wire freely; gate only controls upstream
flow.
7. 4 hard rules added: feedback_no_hiding,
feedback_no_htod_htoh_only_mapped_pinned,
feedback_kill_runs_on_anomaly_quickly,
pearl_canary_input_freshness_launch_order.
8. Smoke A2 explicit kill criteria table added (8 triggers).
Net ISV slot count unchanged (11), composition shifted: dropped
BASELINE, added SMOOTHED. Total impl cost ~1150 LOC (was ~1060).
Verified against current code:
- TARGET_DIR_ACC_INDEX=372, AUX_DIR_ACC_SHORT_EMA_INDEX=373,
AUX_DIR_PREDICTION_INDEX=375 (sp13_isv_slots.rs)
- set_aux_weight clamp(0.05, 0.3) at gpu_dqn_trainer.rs:14722
(confirms Bug 3 from Smoke A diagnostic)
- mag_concat_qdir precedent at experience_kernels.cu:4560
(direction-conditioning pattern; SP14's wire is the analog)
- state_reset_registry pattern at lines 913-922 (canonical
template for new EMA fold-reset entries)
- branch_0_size = 3 in production config (the K=3 finding)
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.