0a6a615d83
The eval policy used strict-argmax with an ISV-tied tie-break across all four
factored heads (direction, magnitude, order, urgency). The tie threshold was
`0.01 × isv_signals[V_HALF_*_INDEX]` — i.e. 1% of the C51 atom support range
(~40 for direction). That threshold did not match the actual per-sample
Q-spread (~1.5 once the IQN trunk gradient unstuck), so tie-break never fired
and eval became pure strict-argmax over a peaked Q distribution → val argmax
glued to one direction → 1-25 trades per 214k-bar window across cluster runs
`vg2r9` and `vnwtn`.
Replace with the same Boltzmann softmax training already uses: `tau =
max(q_range, floor)` where `q_range` is computed per-sample. Softmax is
mathematically bounded to `P(best) ≤ 47.5%` for 4 actions with `tau=q_range`,
so eval can never collapse to pure-greedy regardless of how peaked the
Q-values become. State-adaptive without tuned constants — confident states
(large q_range) still favour the best direction near-deterministically;
ambiguous states (q_range at floor) sample uniformly. The Philox stream is
seeded by (i, timestep) so eval remains bit-reproducible across runs at the
same checkpoint.
Three additions:
1. `experience_kernels.cu`: drop the four `else if (eval_mode)` strict-argmax
blocks; eval falls through to the existing Boltzmann path. Net -149 lines.
2. `cuda_pipeline/mod.rs`: add `test_eval_action_select_boltzmann_bounded`,
a focused unit test that exercises the kernel directly with peaked
synthetic Q-values and asserts the histogram matches Boltzmann theory
(P(best) ≈ 0.366, ≤ 0.6, ≥ 0.25). Runs in 1.65s after build, replaces
15-min smoke runs for kernel-level validation.
3. `trainers/dqn/trainer/metrics.rs`: log per-direction eval distribution
(`val_dir_dist [short hold long flat]`) to HEALTH_DIAG. The kernel-side
`dir_entropy` collapses Hold+Flat into one bucket, masking whether the
eval policy actually picks one direction or balances Hold/Flat.
Verified: unit test produces histogram short=0.146 hold=0.239 long=0.382
flat=0.233 — matches Boltzmann math, confirms the eval kernel produces
diverse picks for peaked Q-input.
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.