2fb30f098e
The runtime DQN action space is 4×3×3×3 = 108 (dir×mag×order×urgency) per
the kernel's b0_size=4 (Short/Hold/Long/Flat) layout, but the monitoring
stack was still coded for the legacy 3×3=9-bin exposure layout. Flat
actions (dir=3) had exp_idx = 9 >= num_actions=9 and were silently
dropped by the monitoring_reduce kernel's bounds check.
Cascade:
- monitoring_kernel.cu: num_exposure_bins=12 (EXP_MAX constant), summary
layout 27 floats with bins [5..17), order [17..20), urgency [20..23),
N [23], trades [24], pad [25..27).
- gpu_monitoring.rs: MonitoringSummary.action_counts [usize; 12];
summary_buf 27 f32; reduce() now takes b0_size and validates
num_exposure_bins ≤ EXP_MAX.
- trainers/dqn/monitoring.rs: action_counts/q_value_sums/q_value_counts
all [_; 12], factored_action_counts [_; 108], EXPOSURE_NAMES with 4
dirs × 3 mags ("S_Small".."F_Full"); track_action now maps the 8-level
ExposureLevel enum onto the kernel's dir*3+mag layout by going through
direction()/magnitude() accessors (was indexing by raw `exposure as
usize` which is an entirely different scheme).
- trainer/training_loop.rs: total_action_counts [_; 12],
total_factored_action_counts [_; 108]; GPU monitoring reduce call
now passes b0 from agent.branch_sizes(); direction-entropy gains a
4th dir bin; per-action Q diagnostics names array extended to 12.
- trainer/metrics.rs: create_final_metrics arrays widened; q_diagnostics
per_action_avgs [_; 12]; combo_idx = d*3 + m (matching kernel layout);
action_space_size 108/12 and buy/sell/hold uses the new exp_idx
boundaries.
- financials.rs: action_counts [_; 12]; buy/sell/hold classification
updated — BUY = Long [6..9], SELL = Short [0..3], HOLD = Hold + Flat
([3..6] ∪ [9..12]). Tests updated for 12-bin layout.
Also fixes several collateral bugs exposed by the audit:
1. magnitude_action_dist_final conflated Hold (dir=1, mag-forced-to-0)
with "Quarter magnitude". New formula restricts the denominator to
tradable directions (Short + Long) and computes Q/H/F fractions only
over those, producing the real magnitude preference signal.
2. training_loop.rs:3012 flat_count indexed [3,4,5] (which is Hold in
the new layout, but was already the wrong bucket — "Flat" was never
at that index even under the legacy 9-bin reading, where [3,4,5]
was the Flat bin but mag was forced to 1 not 0). New code correctly
separates flat_count [9..12] from hold_count [3..6] and excludes
both from the directional denominator and diversity cell threshold.
3. monitoring.rs had q_value_sums: [f64; 7] but q_value_counts:
[usize; 9] — different sizes for what should be the same exposure
axis. Both now [_; 12].
4. log_action_distribution iterated over 7 indices when printing per-
action Q-values but the array is logically 12-wide — now uses
`0..12` with the 12-entry EXPOSURE_NAMES lookup.
Tests: all 7 financials tests pass. Monitoring cubin loads + default
summary sanity checks pass. Smoke test magnitude_distribution now
emits correct 12-bin breakdown — GPU summary shows (example epoch 20):
actions=[786, 157, 280, 377, 2, 7, 806, 156, 317, 303, 5, 4]
which decodes as Short (S) = 786/157/280 (~38%), Hold (H) = 377/2/7
(~12%, mag-forced), Long (L) = 806/156/317 (~40%), Flat (F) =
303/5/4 (~10%, mag-forced). Previously the Flat bucket (303+5+4 =
~10% of all actions) was silently dropped — policy was picking Flat
~10% of the time and nobody could see it.
No atomic ops added; all reductions remain via warp-scratch + lane-0
sequential merge. No feature flags, no stubs, no quickfixes.
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.