85ce295773
For seq_len K and horizon h with h ≫ K, the K position-supervised labels in a single sequence are near-identical (sequential positions' forward windows overlap by ~(h-1)/h). Per-position BCE therefore treats ~K highly-correlated labels as independent samples, inflating gradient pressure on long horizons by a factor of K. Concretely at K=96: h=30 → ~3 effective samples per seq (forward windows overlap ~97%) h=100 → ~1 (~99%) h=6000 → ~1 (~99.98%) Per-position supervision was paying 96× the natural signal density on h=6000, pulling the model toward fitting noise at long horizons. Fix: the fused BCE kernel now accepts an optional `loss_weights[N_HORIZONS]` (nullptr → uniform = no-op). Each (k, h) loss + grad contribution is multiplied by w_h; the normaliser is the sum of weighted valid entries instead of the raw valid count. `auto_horizon_weights(K, horizons)` computes `w_h = min(1.0, K/h)` so short horizons stay at full weight and long horizons collapse to their independent-sample density. Exposed via CLI: --auto-horizon-weights # K/h auto-derived --horizon-weights "1,1,0.5,0.1,0.02" # explicit floats Default behaviour is uniform (1.0) — apples-to-apples with the in-flight qf5mj baseline. Synthetic overfit still 0.6268 → 0.1144 in 250 steps (82% drop). 77 tests pass. 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.