65c3083dec
`target_ratio[CQL][b] = ANCHOR_CQL_RATIO * (1 - flatness[b])` had the sign inverted: it pushed CQL HIGH when Q was flat (the collapse state) and LOW when Q had variance. Per pearl_2_budget_kernel.cu, `flatness = var_q / σ²`, so flatness HIGH means Q has variance — exactly when overconfidence-risk-driven CQL pressure should kick in. Symptom (T10-v3 train-multi-seed-x7sl2 logs): Once IQN Fix 34 woke IQN-mag/ord/urg branches and produced real Q-targets, the controller's inverted formula sent cql_budget to MAX_BUDGET=1.0 saturation on every branch, the conservative pull kept Q flat, the model collapsed to single-Flat-action eval (val_active_frac=0, dir_entropy=0, trade_count=1, sharpe=0). Fix is one operational character: `(1 - flatness)` → `flatness`. C51 formula was already correctly aligned (`flatness * ANCHOR_C51_RATIO`) and unchanged. Per pearl_controller_anchors_isv_driven.md: the inverted formula was masked for months because the IQN-dead regime (Fix 34) suppressed cql_raw on mag/ord/urg, never letting the controller engage with the inverted target. Fixing the IQN regime exposed the dormant formula bug. ANCHOR_CQL_RATIO=2.0 and MAX_BUDGET=1.0 remain hardcoded; Fix 36 will make those ISV-driven via a GPU train_active_frac canary signal per the pearl's "pick the canary that fires under the pathology" rule.
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.