1a05af803d
Cluster v5 alpha-rl-rjsjq step 371 revealed worst-account session_pnl
growing monotonically across cooldown windows (-$3.7k → -$9.3k from
step 100 → 371). Root cause: when DD triggers, actions_to_market_targets
forces {side=2, size=0} — a no-op that suppresses EVERY action type
including trail-stop-driven closes. If the account was holding a
losing position when DD fired, that position bleeds mark-to-market for
the entire 500-step cooldown with no exit path.
Fix: on first DD/cooldown step, emit a closing market order matched to
the current position (side=1 sell for long, side=0 buy for short, size
= |position_lots|). Once flat, subsequent cooldown steps emit the
existing no-op. Net effect: account closes its position at the moment
of DD trip, then sits flat until its recovery clock expires.
Validation: 13/13 risk_stack_invariants pass, 20/20 trade_management
pass, integrated_trainer_smoke passes. Local b=128 1k smoke:
qpa: +0.95 (best result of session)
mean_active: +$47k
worst: stabilizes at -$11.8k (vs unbounded growth pre-fix); residual
loss is from fat-tail single-step market moves that cross dd_limit
before the controller can react — out of scope for this fix.
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.