5a5dd0fed1
Bug 1 of the eval-Hold-collapse diagnosis. The fxcache target schema documented in experience_kernels.cu:1556 + cuda_pipeline/mod.rs:508 specifies: target[0] = preproc_close — log-return-normalized close (network input) target[1] = preproc_next — log-return-normalized next close target[2] = raw_close — raw price for portfolio simulation target[3] = raw_next — raw price target[4] = raw_open — raw price target[5] = mid_price_open — MBP-10 midpoint (fallback raw_open) Both writers — `precompute_features.rs:360` and `data_loading.rs:510` — violated the contract by storing raw OHLCV close prices in slots [0:1]. Empirical fxcache inspection: target[0..4] mean=$5967, stddev=$582 (raw prices throughout). The raw-price values at target[0:1] were never directly consumed by training (production aux head reads next_states[i][0] = MARKET feat[0] = log_return), but they corrupted any code reading targets per the documented contract. Both writers now compute (raw_curr / prev_close).ln() and (raw_next / raw_curr).ln() for the preproc columns. FXCACHE_VERSION bumped 7→8 to invalidate existing caches and trigger ensure-fxcache regen. A second bug — eval label_scale=5300 (raw price magnitude) at production binary despite source state[0] tracing back to z-normalized log_return — remains unresolved. Bug 2 instrumentation lands in the next commit; that runtime trace will pin which production-binary code path injects raw_close into state[0] post-gather. 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.