8629a9e7c2
End-to-end investigation (2026-04-27) confirmed RegimeConditionalDQN is vestigial decoration — 3 heads constructed at training start but only trending_head ever receives gradient updates. GpuDqnTrainer (the actual production GPU trainer) has zero references to RegimeType/regime routing; experience replay inserts go to trending_head.memory only; ranging_head and volatile_head stay at random init for the entire training run. Several support APIs (get_count_bonuses_branched, config, get_state_dim) hardcode-delegate to trending_head, ignoring the regime split entirely. Per `feedback_no_hiding.md` (wire up or delete) and the user's preference to fix not delete: the design wires regime conditioning properly via Mixture-of-Experts replacing the vestigial 3-head architecture. Pearl introduced and saved as `pearl_learned_gate_subsumes_handcoded.md`: when the network already sees the heuristic's inputs, a learned gate strictly subsumes any hand-coded discretization. This is the load-bearing rationale — ADX/CUSUM are already at state indices 40/41, so threshold-based regime classification is a strict information bottleneck the gate can recover and improve on. Design summary: - Architecture: shared GRN trunk -> K=8 small expert MLPs (256->64->256 bottleneck per expert, ~33k params each) -> learned gating network (state[42]->64->8 softmax) -> mixed h_s2 -> existing branching heads + C51 + IQN dual head. Soft full mixture (no top-k hardcoding); gate emerges peaky or flat from data. Anti-collapse load-balancing aux loss with default lambda=0.01 (configurable hyperparameter, not a kernel constant) prevents init-noise-dominated single-expert lock-in without forcing uniform utilization. User-confirmed signal: "collapses don't recover well in this codebase". - 9 new ISV slots (118-126: per-expert utilization EMA + gate entropy EMA), GPU-driven producer per `pearl_cold_path_no_exception_to_gpu_drives.md`. - 3 new small CUDA kernels (moe_mixture_forward/backward, moe_load_balance_loss) + 1 ISV producer; everything else is cuBLAS- reusable. CUDA Graph capture compatible. - Atomic deletion (no fallback): regime_conditional.rs (~700 LOC), RegimeType enum, classify_from_features, RegimeMetrics, RegimeClassConfig, 4 DQNConfig regime threshold fields, per-regime 3-file checkpoint format. DQNAgentType becomes thin wrapper over single DQN. Old checkpoints fail loudly with layout-fingerprint mismatch. - 5-layer testing strategy (unit kernels, smoke, gate-differentiation validation, L40S production validation with explicit kill criteria, architecture-hash backward-incompat). Out of scope (explicit): top-k routing, per-expert action heads, hierarchical MoE, regime-conditional CountBonus/NoisySigma broadcast, expert warm-start from existing trending checkpoint, CVaR action selection on mixed C51 distribution. Precondition: the in-progress use_* flag cleanup + count_bonus [f32; N] refactor lands as its own commit before MoE implementation begins, per `feedback_no_partial_refactor.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.