73d68ab786
VSN sits between snap_feature_assemble and Mamba2 input:
snap_assemble → window_tensor_d [B, K, 40] (raw)
→ VSN fwd → vsn_out_d [B, K, 40] (gated) + vsn_gates_d [B*K, 40]
→ Mamba2 fwd → ...
Forward path: per-position softmax over FEATURE_DIM features, output[i] =
x[i] * gates[i]. Gates initialised near-uniform (W_vsn ~ N(0, 1/√FEATURE_DIM),
b_vsn = 0) so the model starts from "all features matter equally" and
learns regime-conditional gates.
Backward path: Mamba2 backward already computed d_x_from_in (gradient
w.r.t. its input) on its grads_buffers — previously labeled "unused but
allocated", now consumed by VSN bwd as grad_y. Zero refactor to
mamba2_block.rs.
VSN bwd writes:
grad_W_vsn [40, 40] → opt_vsn_w (AdamW, default wd)
grad_b_vsn [40] → opt_vsn_b (AdamW, wd=0)
vsn_grad_x_d [B*K, 40] → discarded (snap_features are non-trainable
transforms of raw MBP-10 data)
Param grads are explicitly memset_zeros before each VSN bwd call (the
kernel uses += semantics like the GRN bwd, but VSN runs ONCE per step
not K times, so zeroing makes the += a clean overwrite — matches
Adam's `step` expectations).
Eval path mirrors training (VSN fwd applied between snap_assemble and
Mamba2 fwd) so eval sees the gated features layers were trained on.
Trainer now manages 19 AdamW: CfC×4 + GRN heads×10 + LN×2 + VSN×2 +
Mamba2 grouped.
Synthetic overfit smoke: stride=1 initial=0.30 → final=0.00 in 50 steps
(faster than pre-VSN's 0.32, consistent with VSN's near-uniform init
giving a small head start). All 8 perception_overfit tests pass.
Note: the smoke proves VSN's W/b actually move via the Mamba2 input
gradient — if `d_x_from_in` were zero, VSN params wouldn't update and
the chain still converges but VSN remains identity. The fact that
initial loss DIFFERS (0.30 vs 0.32) shows VSN is in the forward path
end-to-end.
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.