ebae67cb6b
The four biggest per-call scratch buffers in Mamba2Block::backward_from_h_enriched_seq were allocated fresh on every training step: d_a_per_channel [N, sh2, K, state_d] ~6 MB at B=8, K=96 d_b_per_channel [N, sh2, K, state_d] ~6 MB d_w_c_per_sample [N, sh2, state_d] ~64 KB d_h_s2 [N, sh2] ~4 KB For 2000 optimizer steps/epoch × 15 epochs = 30 000 alloc_zeros calls per training run, all on the hot path. New `Mamba2BackwardScratch` struct holds these as device-resident buffers, constructed once per (n_batch, seq_len, hidden_dim, state_dim) at trainer init. New `backward_from_h_enriched_seq_into` method takes the scratch by &mut and reuses the buffers each call. The smaller per-call buffers (d_a_proj_flat, d_b_proj_flat, dw_c) still allocate per call — they feed into ownership-transferring LinearGrads outputs, where pre-allocation would require refactoring ml-core's cuBLAS wrappers without proportional gain. The original `backward_from_h_enriched_seq` is preserved (Phase E.3 callers still use it). Trainer switches to the `_into` variant. Expected per-step savings: ~10-20 ms on L40S (4 cudaMalloc latencies per call × 2.5-5 μs each + cache pressure reduction). Over 2000 steps/epoch that's 20-40 sec/epoch. 77 ml-alpha tests pass. Synthetic overfit unchanged (0.27 → 0.0006). 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.