2c0911981a
Path C investigation revealed the aux head at epoch 1 is severely
anti-predictive at H=60 bars:
- Aux predicts UP 83% of the time
- Labels are 17% UP, 83% DOWN
- Accuracy = 28% (vs 50% random)
This means H6 Phase 3 hypothesis cannot help WR — atom-shift on an
anti-predictive signal produces no discriminative bias. Confirmed by
full-mechanism smoke at b4e26a3b4 producing WR=0.4337 (statistically
identical to dormant baseline 0.4338).
Path D: revert mechanism to dormant state:
- aux_w_prior_init_kernel: W = [0, 0, 0, 0]
- state_reset_registry dispatch: scale_beta = 0.0
Infrastructure preserved (kernels, NaN guards, Step 8/11, Phase C1
setter, scratch buffers, dispatch arms). Re-activation requires fixing
aux head's directional prediction quality first.
Next-step options documented in audit doc:
1. Shorter horizon (H=1 or H=8)
2. Regression target instead of binary classification
3. Direct market features into aux head
4. Multi-epoch aux-only training
5. Bigger aux trunk
6. Use aux as confidence gate only (not direction signal)
Cargo check clean.
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.