1dc1f3563c
The merged Mamba2 -> CfC -> heads design from the 2026-05-16 spec
amendment supersedes the CfC-alone path. Removing the old CfC-only
PerceptionTrainer and renaming the stacked Mamba2CfcTrainer to
PerceptionTrainer (one trainer, clean naming).
Deletions:
- src/trainer/perception.rs (the OLD CfC-only trainer)
- tests/perception_overfit.rs (CfC-only smoke)
- tests/perception_debug_dump.rs (CfC-only trajectory print)
- tests/stacked_overfit.rs (replaced by perception_overfit pointing
at the renamed module)
Renames:
- src/trainer/stacked.rs -> src/trainer/perception.rs
- Mamba2CfcTrainer -> PerceptionTrainer
- Mamba2CfcTrainerConfig -> PerceptionTrainerConfig
- tests/stacked_overfit.rs content -> tests/perception_overfit.rs
CLI rewrite:
examples/alpha_train.rs now drives the stacked PerceptionTrainer.
Per-step inputs are sequences (Vec<Mbp10RawInput>) of length
seq_len; labels come from the LAST position of the window
(per-horizon). Flags: --seq-len, --mamba2-state-dim, --lr-cfc,
--lr-mamba2 (no more --n-hid since hidden_dim is fixed at 128 to
match Mamba2 and CfC by design).
Test status:
- 64 GPU tests pass on local sm_86 (31 lib unit + 33 integration)
- synthetic-overfit (rebranded perception_overfit): 250 steps,
initial=0.5951 -> final=0.1917 (68% drop, well above 40% gate)
- All bit-equiv / finite-diff / invariant tests still PASS
- One ignored test: the gate_artifact integration (waiting for
cluster-trained summary inputs)
The cluster gate (Task 18) now compares stacked-trained AUC vs a
Mamba2-baseline AUC (TBD: stacked vs a simpler "Mamba2 only" config
or an external reference baseline).
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.