0b3e401500
K=3 policy mixture + regime-gated routing head, with gate reading
REGIME_DIM=6 features DIRECTLY (bypassing the VSN softmax bottleneck
per the regime-attenuation empirical finding). Components are
unconditional but not yet wired into the trainer — Phase 2A-B
adds backward + aux KL prior, 2A-C wires Q-distill grad routing
to the mixture.
* 4 new ISV slots 761-764 (K, gating entropy floor, head entropy
floor, aux prior β) + RL_SLOTS_END 761→765.
* multi_head_policy_forward.cu: per-batch K-head logits + mixture
combination. Grid=(B), Block=(N_ACTIONS=11). Persists pi_logits_k
+ pi_probs_k for backward.
* multi_head_policy_gate_forward.cu: per-batch K-thread softmax
over W·regime + b. Reads regime_h directly (parallel channel —
bypass VSN). Stores pre-softmax logits to gmem BEFORE the
in-place exp (caught during impl — plan pseudocode would have
corrupted gate_logits).
* MultiHeadPolicy struct with Option A asymmetry-break init:
Head 0 → ShortLarge bias (+0.5), Head 1 → LongSmall+LongLarge
bias (+0.5 each), Head 2 → Hold bias (+0.5). Indices verified
against rl/common.rs:56-70 N_ACTIONS=11 enum. htod via local
upload() helper using MappedF32Buffer + raw_memcpy_dtod_async
(mirrors rl/ppo.rs, rl/dueling_q.rs).
* 5 GPU-oracle invariant tests, all PASS:
- gate_probs_sum_to_one
- pi_probs_mixture_sums_to_one
- k1_reduces_to_single_head (bit-equal vs reference Linear)
- gate_responds_to_regime_change (TVD > 0.5, modal head flips)
- forward_is_deterministic_across_contexts (bit-equal across
two fresh CUDA contexts)
* Determinism preserved: ./scripts/determinism-check.sh --quick
exits 0 (pipeline unchanged, components inert).
* No memcpy_htod/memcpy_dtoh on regular slices, no atomicAdd, no
scoped-init-seed bypass.
Empirical motivation (3-seed mid-smoke at HEAD 779c03b9d, b=128):
mean -$6.41M ± $1.39M σ; 62× spread in popart_envelope quartile
(Q1 -$1.99M / Q4 -$32k); policy TVD asymmetry 0.17 on popart
axis vs 0.02 on vol axis. Regime-direct gating targets the
attenuation chokepoint; mixture provides regime-conditional
capacity that single-head softmax cannot express.
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.