2cf9927647
Replaces half = max(10*q_gap, 3*std_ema, floor) tuned constants with quantile-based: half = max(|q_p95 - v_center|, |v_center - q_p5|) clamped to [min_half_floor, abs_half]. Covers observed Q distribution directly. New GPU kernel q_quantile_reduce reads per-sample Q-values from q_out_buf, sorts per branch (bitonic for power-of-2 N, quickselect otherwise), writes ISV[Q_P05_*=47..51) and ISV[Q_P95_*=51..55). Cold-path per-epoch (4 blocks, 1 thread each). No atomicAdd. ISV slots 47-54 added at tail (8 total). Fingerprint shifted to 55-56. ISV_TOTAL_DIM grows 49 -> 57. Fingerprint seed updated; recomputes hash at compile time (new value: 0xbf6c400c026d77e3). Launch order: reduce_current_q_stats (populates q_out_buf) -> q_quantile_reduce (writes ISV P5/P95) -> reduce_current_q_stats_per_branch -> update_eval_v_range (reads ISV P5/P95 for half-width). Bootstrap: q_p05 = v_min, q_p95 = v_max (matches current atom range at cold start). FoldReset: isv_q_quantiles dispatch arm resets to bootstrap values at fold boundary. StateResetRegistry: isv_q_quantiles as FoldReset; dispatch arm added in training_loop.rs::reset_named_state. Docs: isv-slots.md rows [47..57) updated, fingerprint tail reference corrected to [55..57). dqn-wire-up-audit.md: q_quantile_kernel.cu added. Plan 2 Task 1. Spec §4.C.1. Co-Authored-By: Claude Opus 4.7 (1M context) <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.