6dacde95ef
Adds envelope-detector floor on popart_sigma so single-account tail events within a batch are captured instead of diluted by the batch-mean variance. Kernel changes (rl_popart_normalize.cu): - warp_reduce_max helper alongside existing warp_reduce_sum - Pass 1 extension: per-thread local_max_abs via fmaxf(fabsf(r)) - 3rd shared-mem bank for max_abs reduction (s_max_abs[]) - envelope-detector inside tid==0 block: fast-up, slow-down (α_d=0.01) - new_sigma = fmaxf(new_sigma, max_r_ema) before write - CRITICAL (Issue β): Pass 3 broadcast slots moved block_dim*2 → block_dim*3 Trainer launch update: smem_bytes = (block_x * 3 + 3) * sizeof(f32). Per Theorem 6 (spec v3): at Run #8 step 7377 with r_tail=-19.45, the envelope captures 19.45 instantly via fast-up path; sigma jumps from 2.327 → 19.45 in one step. The popart_v_correct kernel handles the σ discontinuity affinely so V regression doesn't destabilize. Decay phase: α_d=0.01 (69-step half-life); max_r_ema returns to typical batch-max baseline over ~200 steps after a single shock. Co-Authored-By: Claude Sonnet 4.6 <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.