d94696620d
iTransformer-style cross-variate attention pass: each of HIDDEN_DIM
features becomes a "variate token" with its K-trajectory as embedding.
FORWARD:
X_inv[h, k] = ln_out[k, h] # transpose
scores[h, j] = inv_scale · Σ_k X_inv[h, k] · X_inv[j, k]
attn[h, j] = softmax_j(scores)
pooled[h] = Σ_j attn[h, j] · mean_k_X_inv[j] # mean-K commutes out
BACKWARD: three independent chains into ln_out:
- value path: (1/K) · attn[h', my_h] · d_pooled[h'] (per-k constant)
- query path: inv_scale · Σ_j d_scores[my_h, j] · X_inv[j, k]
- key path: inv_scale · Σ_h' d_scores[h', my_h] · X_inv[h', k]
Softmax bwd: d_scores[h, j] = attn · (d_attn - Σ_l attn · d_attn)
IMPLEMENTATION NOTES:
- First attempt cached attn [H, H] = 64 KB in shared mem → tripped
the 48 KB dynamic-shared limit on sm_86 (CUDA_ERROR_INVALID_VALUE).
- Fixed by moving d_scores to a DRAM scratch buffer; shared mem
holds only X_inv [H, K] (≤ 16 KB at K = 32). One block-wide barrier
between the d_scores write and the value/query/key accumulation.
- All per-batch slice writes; no atomicAdd, no cross-block race.
- Pooled computation uses the mean-K commute (Σ_k attn · X_inv =
attn · mean_k_X_inv), saving an entire H×K accumulation pass.
LOCAL VERIFICATION (RTX 3050 sm_86):
forward_then_backward_matches_central_difference PASSES 6 numgrad
checks on ln_out positions 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.