1fc100ae74
The current λ controller boosts horizons by BCE-EMA z-score, which conflates
three distinct causes of high BCE — only one of which (under-trained
horizon) benefits from boosting. The other two (intrinsically harder
horizon, calibration drift) are unaffected by gradient-magnitude lifts.
Empirical motivation (gm67g fold-0, this branch's 3-fold run):
when λ_h6000 saturated at 2.0, h6000's next-epoch AUC went UP 2/4
times and DOWN 2/4 times. 50% hit rate ⇒ the BCE saturation signal
is misaligned with the optimization objective.
This spec replaces BCE-z-score with AUC-regret:
best_auc[h] = running max of per-horizon validation AUC
regret[h] = max(0, best_auc[h] - current_auc[h])
regret_max_ema = EMA of max_h regret[h]
λ[h] = clamp(1.0, 2.0, 1 + regret[h] / regret_max_ema)
Properties:
- Aligned with the objective (AUC, not BCE)
- Naturally bounded (regret ∈ [0, 1])
- "At personal best" → λ=1.0 (no wasted boost)
- Auto-saturation by design (max-regret horizon → ceiling)
- Cold-start clean (e0: best=current, regret=0, uniform λ)
- Zero hardcoded magic beyond bootstrap epsilons
Implementation surface ~250 LOC:
- Split horizon_lambda kernel: horizon_loss_ema (per-step) +
horizon_lambda (per-epoch, AUC-regret math)
- Trainer state: drop z_max_ema, add best_auc + regret_max_ema
- Per-epoch entry point: trainer.update_lambda_from_auc()
- Extend isv snapshot log line with best_auc_h* + regret_h*
Test plan:
- Local 9/9 perception_overfit
- New synthetic-AUC unit test (controller correctness)
- Cluster 5-epoch smoke + 3-fold A/B vs Option-2 baseline (004b662c8)
- Success: mean_auc lifts ≥ +0.005 AND median sat→next-AUC delta positive
Awaiting user review before plan handoff.
Co-Authored-By: Claude Opus 4.7 <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.