033906f213
While alpha-rl-8gtk2 (the B-7+B-8+B-9 20k+5k cluster run) was training, B-9's saturation diag exposed V_MAX_eff dropping from 856.82 (step 482) to 464.61 (step 817) within the same run. The kernel docstrings and slot doc-comments uniformly claimed "ratchet (monotone-grow)" semantics — contradicting the observation by 392 units. Root cause: wwcsz followup 2026-05-24 (commit landed in rl_reward_clamp_controller.cu Step 5 only) REPLACED the original ratchet with a slow symmetric EWMA (α=0.001, half-life ~700 steps) on `win_bound`/`loss_bound`. Static ratchet was wasting atom resolution on rare tails (avg rewards in [-5,+5] with span at [-60,+20] → Δz=4, Q couldn't distinguish "slightly winning" from "slightly losing"). The EWMA refocuses atom resolution on the ACTIVE range. The controller's own header was correctly updated at the time. The documentation drift was in 3 other places — fixed here: - bellman_target_projection.cu header (lines 47-49): "ratchet (monotone-grow)" → accurate EWMA description with cross-references + pearl_c51_v_max_freeze_required_for_surfer warning (V_MAX in 100-200 → trend-follower; past 1000 → degraded). - rl_atom_support_update.cu line 4: "Companion to the C51 atom-span ratchet" → "Companion to the C51 atom-span EWMA". - isv_slots.rs slot allocation table line 43: "C51 atom-span ratchet slots" → "C51 atom-span EWMA slots (α=0.001)". - isv_slots.rs RL_C51_V_MAX_INDEX / RL_C51_V_MIN_INDEX doc-comments (lines 649-668): replaced with accurate EWMA description, observed 857 → 465 drop example, and asymmetric tracking note (V_MIN_eff EWMAs -loss_bound NOT -V_MAX_eff — they only coincide when ratio≈1). Latent test bug also surfaced + fixed: c51_atom_saturation_diagnostic assumed `V_MIN_eff = -V_MAX_eff` (line 136). With observed Kelly EMAs avg_loss=$436 vs avg_win=$872 → ratio ≈ 0.5 → V_MIN_eff ≈ -0.5 × V_MAX_eff, the test's bot-rate consistency check would false-fail if saturation ever became non-zero. Dormant so far (sat=0% in both smoke and full run). Relaxed the bot-rate assertion to use the v_bound_floor (-1.0) as the necessary lower bound — correct for any asymmetric ratio and catches gross drift without false-failing on legitimate adaptation. No behavior change; pure docstring drift + dormant test bug repair per feedback_trust_code_not_docs. Compile clean. 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.