11b96dac6a
Top-1 Mixture-of-Experts gate per Switch Transformer. Three kernels
in one .cu file:
- regime_moe_gate_fwd: select top-1 expert from gate_logits, apply
its [H, H] linear to fused_ctx → routed_ctx [B, N_H, H].
- regime_moe_gate_bwd: chain-rule grads through the SELECTED
expert's W and bias (sparse-by-expert scratches), plus
d_fused_ctx accumulator. Inactive experts get zero contribution.
- regime_moe_gate_aux: softmax of gate_logits + load-balancing
auxiliary loss (frac · prob_mean × N_EXPERTS).
ARCHITECTURE:
- N_EXPERTS = 4. Each expert is a [H, H] linear with bias.
- Total expert params: 4 · 128 · 128 + 4 · 128 = 66 KB. Cheap.
- STE on gate: gate logit grad is zero from the expert path (top-1
is non-differentiable); the load-balance aux loss provides the
differentiable signal that pushes routing toward balanced usage.
PERFORMANCE:
- Grid (B, N_H, 1), block (HIDDEN_DIM). One block per (b, h).
- Forward: each thread computes one output channel via a dot
product over HIDDEN_DIM input dims (#pragma unroll 8).
- Backward d_fused_ctx: each thread accumulates over d_out
sequentially (HIDDEN_DIM iterations) since the weight matrix
column is naturally aligned to the thread's d_in index.
- Backward d_W/d_b scratches are sparse-by-expert; downstream
reduce_axis0 collapses over (B, N_H).
- Top-1 chosen by thread 0 per block, broadcast via shared mem.
NUMGRAD VERIFICATION (RTX 3050 sm_86):
forward_matches_host_reference_and_backward_matches_numgrad
PASSES 11 checks (4 on d_W, 3 on d_b, 4 on d_fused_ctx) within
5e-2 rel / 5e-3 abs.
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.