0ca45ef61d
Spec v2 supersedes v1 with five major fixes from the critical review: - Drop alpha-vs-benchmark reward (mathematically tautological — Long trades had alpha = -costs always against always-long benchmark) - Split Phase 0 into 0a (Hold-only, clean test of user hypothesis) + 0b (aux amplification probe, only if 0a partial), avoiding the v1 confounded experiment - Bound direction-skill bonus relative to |alpha| (cap_ratio × |alpha|) to prevent reward gaming on small-alpha correct-direction losers; spec adds 8-quadrant worked-example matrix verifying the no-negative-EV invariant - Replace 5-epoch absolute-threshold gate with 10-epoch trajectory criterion to avoid false-negatives from aux head underconvergence - Aux_w controller adds dual-EMA stagnation detector (decays toward base when no improvement) — prevents permanent destabilization of Q-head in data-limited case Plan v2 mirrors spec changes: - Phase 0a (Hold elimination + dir_acc instrumentation, ~300 LOC, 1 atomic commit) - Phase 0b (aux_w controller replacement, conditional, ~80 LOC) - Layer B (aux head regression -> binary classification, ~120 LOC) - Layer C (skill bonus + luck discount with calibrated bounds, ~150 LOC) - Layer D (30-epoch validation + 3 new pearls) ISV slot allocation [372..380): drops slot 371 (BENCHMARK_PNL_CUMULATIVE), adds 374 (AUX_DIR_ACC_LONG_EMA — stagnation), 379 (SKILL_BONUS_CAP_RATIO). Plan integrates Explore-agent touch-list (50-70 sites) with corrected enum ordering: Short=0, Long=1, Flat=2 (preserves codebase Short-first convention, avoids 30+ stale-comment churn). Adds direction-bias signal handling at experience_kernels.cu:5159 (Hold's 0.5 softening gate vanishes -> [1, 1, 0]). Three new pearls planned for Layer D close-out: - pearl_redefine_success_for_predictive_skill - pearl_skill_bonus_must_be_alpha_bounded (calibration lesson) - pearl_reward_quadrant_audit_required (meta-pearl) 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.