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Per spec §9.2 (3.5.3). After K consecutive losing trades, force Hold for M bars. Counter at COOLDOWN_BARS_REMAINING (slot 435) part of state — model can reason about it. Initial K=5, M=20 hardcoded sentinels. ISV-driven K via MEDIAN_STREAK_ LENGTH (slot 442) producer using two-heap median tracking is documented Phase 3.5.3 follow-up. ISV-driven M from vol_normalizer time-to-mean- reversion is also follow-up. 4 ISV slots: 433 K_THRESHOLD, 434 M_BARS, 435 BARS_REMAINING, 442 MEDIAN_STREAK_LENGTH. New sp15_cooldown_consecutive_losses MappedF32Buffer tracks streak counter persistent across kernel calls. 5 fold-reset registry entries + dispatch arms (4 ISV slots + scratch buffer reset). Per spec post-amendment-2 fix: streak counter only updates on trade-close events; per-bar non-close calls just decrement the cooldown counter. The trigger gate fires only when a trade-close lands during an inactive cooldown — re-arming mid-cooldown would extend the gate every closed trade during the freeze, which is not the spec. Per established Phase precedent: kernel + launcher land first; action- selection wiring (force Hold while cooldown_remaining > 0) deferred to follow-up commit per feedback_no_partial_refactor. Anchor test 2.12 cooldown_engagement (Phase 2C / Phase 3.5 paired) — green via this commit. 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.