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Plan 3 Task 6a. Portfolio-state tail-append (shared-contract migration, all in same commit): - PS_INTRA_TRADE_MIN_PNL = 39 (symmetric to PS_INTRA_TRADE_MAX_PNL = 21) - PS_STRIDE 39 -> 40 - 6 PORTFOLIO_STRIDE hardcoded copies bumped in lockstep Producer (experience_kernels.cu): - MIN_PNL tracked per bar (fminf against pnl_pct) in the same block as MAX_PNL update - Reset to 0 at all 5 MAX_PNL reset sites (entry, reverse, 2x fold boundary) Consumer (experience_kernels.cu segment_complete): - Fires only on reward > 0 AND drawdown_depth > 1e-6 - persist_bonus = shaping x conviction x |min_pnl| x tanh(reward/|min_pnl|) - reward += persist_bonus; rc[5] += persist_bonus (accumulates with B.2 entry bonus + C.4 timing bonus — different (i,t) slots per trade) Self-scaling via tanh: no tuned coefficients. Saturates when recovery is large relative to drawdown; near-zero when recovery is trivial. Attribution lands in ISV[68] REWARD_BONUS_EMA via the Task 1 kernel. No new ISV slot. Smoke: multi_fold_convergence PASS (fold-2 best Sharpe 100.10, threshold >=80). HEALTH_DIAG reward_split bonus=17.21 (post-Task-5 rises with new D.4a credit firing on profitable drawdown recoveries). 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.