17cfbb2503
Three architectural changes in one atomic commit per spececab584c3: 1. Asymmetric bounded cap (REWARD_NEG_CAP=-10, REWARD_POS_CAP=+5): restores prospect-theory loss aversion (2:1 ratio) erased by SP11 symmetric cap. Per pearl_audit_unboundedness_for_implicit_asymmetry. 2. Min-hold soft penalty with temperature curriculum: patience requirement on voluntary exits via deficit/(deficit+T) soft factor. Temperature anneals 50->5 across ~100 epochs (curriculum, half-life 20 epochs). Trail-fire exits exempted (preserves stop-loss design). Penalty flows through r_popart so SP11 controller weighs it consistently with other voluntary-exit terms. 3. Zero per-bar shaping (Change 3): r_micro = 0 entirely on positioned-non-event bars (per-bar shaping is anti-pattern; Q-learning handles credit assignment via TD). Per-bar Flat opp_cost zeroed (was the symmetric counterpart to micro). r_opp_cost preserved as lump-sum at exit: r_opp_cost = -shaping_scale * holding_cost_rate * |position| * hold_time on segment_complete (both voluntary and trail-fire). Preserves carrying-cost economic concept without per-bar density bias. Per pearl_event_driven_reward_density_alignment. Empirical motivation: train-multi-seed-pmbwn 50-epoch on6a259942eshowed sharpe-gaming (PnL -30% over 8 epochs while sharpe held at 80). Three causes: lost loss aversion (Change 1), per-bar gradient (Change 3), no commitment (Change 2). Unified per-trade event-driven design fixes all three together. Constants live in state_layout.cuh as Invariant-1 numerical anchors (REWARD_POS_CAP, REWARD_NEG_CAP, MIN_HOLD_TARGET, MIN_HOLD_PENALTY_MAX, MIN_HOLD_TEMPERATURE_{START,END,DECAY}). Min-hold temperature is recomputed in Rust per epoch via min_hold_temperature_for_epoch in training_loop.rs and passed as a launch scalar. New HEALTH_DIAG line sp12_event_reward emits the constants per epoch alongside sp11_reward. Phase 1 = constants only. Phase 2 (ISV adaptive bounds per feedback_isv_for_adaptive_bounds) deferred until validation results indicate adaptive need. LOC: ~344 added (includes spec-required documentation comments) across experience_kernels.cu, state_layout.cuh, gpu_experience_collector.rs, training_loop.rs, dqn-wire-up-audit.md. Build: SQLX_OFFLINE=true cargo check -p ml --lib clean. Tests: SQLX_OFFLINE=true cargo test -p ml --lib — 938 passed, 13 failed (same 13 failures pre-existing on HEADecab584c3, none related to SP12 changes). 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.