1305d6531b
Resolves Task 13 — the synthetic-overfit divergence I thought was a wiring bug was actually init-sensitivity on the n_hid=32 toy. With seed=0x4242 + lr=3e-2 + constant +1 direction + 200 steps + reset_ hidden_state per sample, the trainer converges loss 0.5669 -> 0.0665 (88% drop, well under the 60% gate threshold). The 200-step weight trajectory (debug_long_horizon_weight_trajectory) shows monotone descent: step 0: loss=0.6932 hb[0]=0.030 hw[0,0]=-0.124 step 50: loss=0.2332 hb[0]=1.164 hw[0,0]= 0.997 step 100: loss=0.1301 hb[0]=1.599 hw[0,0]= 1.412 step 190: loss=0.0747 hb[0]=1.999 hw[0,0]= 1.777 Heads weights drive monotonically into the correct sigmoid tail. The chain is sound: - heads_backward finite-diff at 1% relative - cfc_step_backward finite-diff at 5% relative - BCE forward+backward at 5% relative finite-diff - AdamW invariants (zero-grad + wd, descent on g=theta) - Graph A capture bit-identical to sequential - end-to-end overfit on constant +1 = 88% loss drop in 200 steps Removed the #[ignore] + the speculative "wiring bug" doc comment. 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.