c24cf7423b
LN sits between Mamba2 encoder output and the K-loop CfC consumer: Mamba2.h_enriched_seq [B,K,H] → LN fwd → ln_out_d [B,K,H] → transpose → h_enriched_seq_t_d [K,B,H] → CfC + heads Forward path: LN consumes raw Mamba2 output, writes ln_out_d + per-row stats (mean, inv_std). Transpose now reads from ln_out_d. Backward path: post-K-loop grad transpose produces grad_h_enriched_seq_d (now the LN OUTPUT grad). LN bwd consumes it + saved stats + Mamba2 fwd output (for normalised) + gain, writes: grad_ln_in_d (Mamba2's input gradient) per-row grad_gain/grad_bias scratches Two reducer launches collapse per-row scratches into [HIDDEN] param grads (block tree-reduce, no atomicAdd per feedback_no_atomicadd.md). AdamW state for ln_gain + ln_bias added (wd=0, lr=lr_cfc). Mamba2 bwd now consumes grad_ln_in_d, NOT grad_h_enriched_seq_d. Eval path mirrors training: LN fwd applied after Mamba2 fwd so eval sees the same distribution downstream layers trained on. Synthetic overfit smoke passes: BCE 0.37 → 0.0006 over 250 steps, confirming end-to-end Mamba2+LN+CfC+heads chain is wired correctly and all 9 AdamW optimisers (CfC×4 + heads×2 + LN×2 + Mamba2 grouped) move weights. 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.