fb24614f07
Per train-multi-seed-pfh9n post-mortem: structural-Q-bias hypothesis (Adam's m/sqrt(v) prefers low-variance Hold over noisy direction Q-targets) needs direct per-action Q observations to verify or refute. WR plateaued at ~0.46 across both folds while Hold% climbed 15% → 52% and Q-mean climbed 0.19 → 0.41 — but per-action Q was unobservable. Adds HEALTH_DIAG emit each epoch: HEALTH_DIAG[N]: q_by_action [hold=X long=Y short=Z flat=W] Reads via host-side averaging of `q_out_buf [B, total_actions]` direction-branch columns [0..b0=4]. No new kernel needed — q_out_buf is already populated row-major by compute_expected_q. Modeled directly on the existing Task 0.3 magnitude-bucket diagnostic (same dtoh-and- average cold path). Action-index ordering canonical, see state_layout.cuh:123-126: DIR_SHORT=0, DIR_HOLD=1, DIR_LONG=2, DIR_FLAT=3. Emit slot order is [hold, long, short, flat] (Hold first because the hypothesis is about Hold's ascent dominating direction Q-magnitudes). New surface: - gpu_dqn_trainer.rs: q_dir_means_cached field + update_q_dir_means_cached method + q_direction_action_means accessor + free static helper compute_q_dir_means_from_host_buf (factored for testability). - fused_training.rs: FusedTrainingCtx wrappers parallel to q_mag pair. - training_loop.rs: emit block adjacent to q_var_per_branch. Behavioral test: sp16_phase0_q_by_action_diagnostic_reads_four_action_means seeds known per-column constants, asserts canonical-dir-idx → emit-slot mapping at 1e-3 tolerance, and includes sentinel-leak guard (non-direction columns at 99.0 fail any wrong index→slot mapping with margin >12). 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.