8f5f22fe4d
Doubles the trunk capacity. Forward chain:
snap_features → VSN → m1 → LN_a → m2 → LN_b → CfC → GRN heads
m1 = Mamba2Block { in_dim=FEATURE_DIM=40, hidden_dim=128 }
m2 = Mamba2Block { in_dim=128, hidden_dim=128 }
Both stacks share the SAME state_dim (cfg.mamba2_state_dim) and the
SAME hidden_dim. m2 reads m1's output (post LN_a). LN_a is a separate
LayerNorm instance from LN_b (the existing trunk-to-CfC normaliser).
Backward chain reverses the forward:
... grad_ln_in_d → m2.bwd → m2_grads_buffers.d_x_from_in (= LN_a output grad)
→ LN_a.bwd → grad_ln_a_in_d (= m1 output grad)
→ m1.bwd → m1_grads_buffers.d_x_from_in (= VSN output grad)
→ VSN.bwd → ...
Both Mamba2 stacks emit `d_x_from_in` (Phase 2D refactor already
exposed it on m1; m2 uses the same code path). LN_a uses the existing
layer_norm_fwd / layer_norm_bwd / layer_norm_reduce_param_grads
kernels — no new CUDA work, just a second instance with its own
gain/bias/stats/grad scratches.
New trainer state: ~17 fields (mamba2_l2 + its scratch + LN_a + LN_a
grads + opt_ln_a_*). All initialised in the construction order that
respects the `stream` move-into-Self at the end of new().
set_lr_mamba2 now updates BOTH stacks' AdamW configs. Total AdamW
instances on the trainer: 21 (CfC×4 + GRN heads×10 + LN×2 + LN_a×2 +
VSN×2 + Mamba2×2 grouped × 9 params each).
All 8 perception_overfit smokes pass: synthetic constant-direction
signal converges 0.31 → 0.0000 by step 100 (matches single-stack
trajectory — proves both stacks are wired forward + backward and all
21 AdamW optimisers 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.