45b203e31e
Realizes the 2026-05-16 spec amendment merging Mamba2 + CfC into one
stacked architecture (vs the original "compete via gate" framing).
Forward chain:
snap_features × seq_len
-> window pack [1, seq_len, FEATURE_DIM]
-> Mamba2Block.forward_train -> (logit, cache.h_enriched [1, hidden_dim])
-> cfc_step(x=h_enriched, h_old=0) -> h_new
-> heads -> probs [5]
-> BCE(probs, labels)
Backward chain:
BCE -> grad_probs
-> heads_backward -> grad_h_new + grad_W_heads, grad_b_heads
-> cfc_step_backward -> grad_W_in, grad_W_rec, grad_b + grad_x (=grad_h_enriched)
-> Mamba2.backward_from_h_enriched(&cache, &grad_h_enriched_tensor)
-> Mamba2BackwardGrads (full 9-tensor gradient set)
Optimizers (6 total):
- 5 CfC AdamWs (W_in, W_rec, b, heads_w, heads_b) — reused from
PerceptionTrainer's per-param-group pattern
- 1 Mamba2AdamW for all 9 Mamba2 parameter tensors (existing
implementation in mamba2_block.rs)
Synthetic-overfit on constant +1 direction (seq_len=16, state_dim=8,
lr_cfc=3e-3, lr_mamba2=1e-3, 250 steps):
initial_avg=0.5951 → final_avg=0.1917 (68% drop, well past 40% gate).
Monotone descent at all 5 progress checkpoints.
Architectural note (v1): CfC runs with h_old=0 each step (no inter-
step recurrence). With h_old=0, the CfC layer is effectively per-cell
tau-scaled tanh FC. Inter-step CfC state (h_old carrying between
calls) is a v2 extension once the cluster gate validates the v1
foundation.
The cluster gate (Task 18) now has the actual stacked production
trainer to deploy, not a CfC-alone-vs-Mamba2-alone bench.
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.