f934ea1719
Tests user's hypothesis (Hold being FREE is the bug, not Hold itself) by
pricing Hold via ISV-driven adaptive controller targeting 20% Hold-rate.
11 new SP13 ISV slots [372..383). 5 new GPU kernels:
- aux_dir_acc_reduce_kernel.cu (correct/pos_pred/pos_label/valid → 3 scalars)
- hold_rate_observer_kernel.cu (packed batch_actions decode, count(Hold)/B)
- apply_fixed_alpha_ema_kernel.cu (preserves short/long timescale split that
Wiener-optimal apply_pearls_ad_kernel would collapse)
- aux_pred_to_isv_tanh_kernel.cu (mean(tanh(aux_pred)) → ISV[375])
- 3 reward-composition sites in experience_kernels.cu subtract isv[HOLD_COST]
on Hold actions (segment_complete pre-asymmetric-cap, positioned-non-event
per-bar, flat per-bar)
Host-side controller in training_loop.rs:
excess = max(0, observed - target)
hold_cost = HOLD_COST_BASE × (1 + 5 × excess), clamped [0.5×, 5.0×base]
Per-step observer + EMA chain in gpu_experience_collector.rs after
experience_action_select. Per-epoch HEALTH_DIAG emit:
aux_dir_acc target/short/long/pred_tanh
hold_pricing observed_rate/target/cost
4-way action space stays (ExposureLevel::Hold preserved). Replay buffer /
fxcache compatibility preserved. SP11 (11/11) + SP12 (14/14) tests no
regression. SP13 P0a oracle tests: 14/14 on RTX 3050 Ti.
Spec/plan: docs/superpowers/{specs,plans}/2026-05-04-sp13-redefine-success-for-predictive-skill.md (v3)
Audit: docs/dqn-wire-up-audit.md (SP13 P0a section appended)
v2 → v3 reframe: P0a.T3 v2 implementer's audit found DirectionAction enum
doesn't exist (codebase uses 8-variant fused ExposureLevel cascading through
77 files). v3 reframes from "eliminate Hold" (250 LOC + 32-test cascade) to
"price Hold" (additive, no contract change, no cross-crate cascade).
Tension with pearl_event_driven_reward_density_alignment acknowledged in spec
— per-bar Hold cost is exposure-NEGATIVE (pulls policy AWAY from Hold-default,
inverse of the pearl's failure mode), models real economic carry, ISV-bounded
by controller. Faithful reward modeling, not artificial shaping.
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.