8de65265ee
Atomic removal of 5 boolean feature flags from DQNConfig per `feedback_no_feature_flags.md`, all of which gated dead or redundant code. (use_iqn already removed in da632446c.) - `use_dueling`, `use_distributional`, `use_noisy_nets`, `use_branching`: pure-cosmetic always-on flags. Only metadata strings remained. 4 `if true /* always on */` blocks in dqn.rs collapsed to live arms; dead else arms (legacy non-branching code paths, 5-flat ExposureLevel) deleted. - `use_count_bonus`: redundant with `count_bonus_coefficient` (the live numeric kill-switch). Recording made unconditional; coefficient is the single dial. - `use_cvar_action_selection`: dead post-use_iqn cleanup (only consumers were inside the deleted IQN arms). cvar_alpha retained for cuda_pipeline CVaR loss usage. count_bonus.rs API tightened: `bonuses_branched_f32(&self, exp_out: &mut [f32], ord_out: &mut [f32], urg_out: &mut [f32])` write-into API alongside the existing Vec<f64> form (kept for tests). Production DQN::get_count_bonuses_branched() returns fixed `([f32; 4], [f32; 3], [f32; 3])` arrays — allocation-free, f32 throughout, fed directly to GPU action selector via stack-allocated buffers. Test 0.F outputs bit-identical vs pre-cleanup (sigma_C51, argmax, Thompson counts) — confirms legacy use_* arms were unreachable as expected. `docs/dqn-wire-up-audit.md` updated per Invariant 7. Precondition for the MoE regime redesign per `docs/superpowers/specs/2026-04-27-moe-regime-redesign-design.md`. 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.