8aef59f735
Replaces eval-mode Boltzmann softmax with strict argmax + uniform-sample- among-tied-indices (|q_a − q_b| < 1e-6). Applied to all 4 branches (direction, magnitude, order, urgency) of experience_action_select. Uses the existing Philox state (same (i, timestep) seed used elsewhere in the kernel for CF-flip / exploration); eval mode is therefore deterministic per (sample, epoch) — no new atomics, no new RNG. Training mode keeps Boltzmann softmax unchanged (needed for exploration + gradient flow when C51 expected-Q structurally favors Flat/Quarter). Root-cause re-diagnosis (commit7e78bf4f8+41b0c559c): Task 2.0 instrumentation + bug fixes revealed Track 1 H4 was a measurement artefact produced by two silent bugs in per_branch_grad_norms. Real mag/dir gradient ratio is 50-400× (magnitude over-fed, NOT starved). The genuine observable problem is H10 — argmax tie-break at eval, which is what this commit closes. Why the previous eval-mode Boltzmann collapsed: with Q-range ≪ tau floor (0.01), softmax was effectively uniform at eval, but cumulative sampling with a single Philox draw deterministically picked the first index over every tied bin — hence eval_dist [eq=1.000 eh=0.000 ef=0.000] across all 20 baseline epochs despite training-mode ent_mag ≈ 0.99. Strict argmax honours the learned preference where it exists, tie-break only applies on genuine ties < 1e-6. Results from 20-epoch smoke (magnitude_distribution): Baseline (HEAD pre-fix): training dist [dist_q=0.577 dist_h=0.166 dist_f=0.257] eval dist [eq=1.000 eh=0.000 ef=0.000] Post-fix (this commit): training dist [dist_q=0.577 dist_h=0.166 dist_f=0.257] (unchanged — eval-only path) eval dist [eq=0.580 eh=0.420 ef=0.000] eh + ef = 0.420 ≥ 0.30 regression gate → PASS. Training-mode distribution is bit-identical as expected: the fix only touches the eval_mode == 1 branch of experience_action_select. Regression assertion in magnitude_distribution smoke requires eh + ef ≥ 0.30 (the production threshold from Task 2.9 §2.9 gate). Closes Track 1 H10 CONFIRMED verdict. With H4 REJECTED by Task 2.0's real data, H10 is now Phase 2's primary fix. If future training regresses this ratio, the fallback is the "delete magnitude branch" path (H9 spec §5.1 proposed fix — 108 → 36 action space).
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.