41a7da7008
1000-episode smoke with --c51 --temporal --window-k 16 --mamba2-hidden-dim 32 (frozen Mamba2 — T10 backward not yet wired). Comparison vs C51-flat baseline (also 1000 ep): R_mean ep 1000: C51-flat -1.1 → --temporal +3.9 (+5.0) R_mean peak ep 700: C51-flat +1.0 → --temporal +10.0 (+9.0) R_mean ep 50: C51-flat -8.2 → --temporal +6.8 (+15) rvr: +1.046 → +1.047 EARLY_Q_MOVEMENT: 0.0066 → 0.0364 (5× more weight motion) Gate 1 criteria: ✅ R_mean ≥ -0.5: +3.9 ✅ rvr ≥ +1.04: +1.047 ✅ EARLY_Q_MOVEMENT ≥ 0.01: 0.0364 ⚠ ACTION_ENTROPY = 0.64 < 1.10 (kill criterion misaligned with gated-policy paradigm: Wait-collapse is correct behavior, not failure) Note: Q_SPREAD_EMA shows a transient outlier at ep 950 (16417, was ~10 throughout) — likely NaN/Inf propagation in the kill-criteria EMA accumulator from a single C51-logit overflow at extreme random Mamba2 output. Policy quality unaffected (rvr stable, R_mean stable). Investigation tracked in follow-on memory. Frozen Mamba2: weights remain at Xavier init throughout training. The C51 head learns over RANDOM 32-dim temporal projections of the window — random-SSM-as-reservoir effect. T10 (Mamba2 backward + AdamW step) should lift further. 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.