f97770b185
Layer 3 of the four-layer defense per pearl_wr_above_50_with_negative_pnl_loss_cutting.
The wr-targeting controller (Layer 4) successfully drives wr=0.55+ but
PnL spirals negative because Q can only learn exit timing from sparse
close-event rewards. Without per-step exit signal during open trades,
Q can't learn "close losing trade early".
Fix: add continuous drawdown penalty during open trades.
- rl_fused_reward_pipeline.cu PHASE 5 shaping:
if (current_lots != 0 && unrealized < 0)
r += unrealized * rate * reward_scale
- Penalty-only (no symmetric reward for unrealized gain) — avoids
exposure-positive bias per pearl_event_driven_reward_density_alignment
- Reads unrealized_pnl from PosFlat offset 24 (added this session)
ISV slot 592 RL_DRAWDOWN_PENALTY_RATE_INDEX, bootstrap 0.001.
Conservative rate to avoid "Q learns trading is punished" pathology
(observed when rate was 0.01 in earlier session).
Local smoke 1000 steps (b=128):
- wr trajectory: 0.31 → 0.46 (Layer 4 controller still working)
- pnl: -$105k (vs -$5M without layer 3 at cluster scale)
- Completes clean, no NaN
The per-step penalty magnitude (~1e-5 to 1e-3 scaled units at rate=0.001)
provides Q with the exit gradient it was missing. Cluster validation
will show if surfer + positive PnL emerges at 5k+ steps.
Co-Authored-By: Claude Opus 4.7 <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.