a0abc3da35
Plan 3 Task 5.
Portfolio-state tail-append (shared-contract migration, all in same commit):
- PS_PEAK_PNL_BAR = 38 (hold_time snapshotted when MAX_PNL updates)
- PS_STRIDE 38 -> 39 in state_layout.cuh and ml-core/state_layout.rs
- PORTFOLIO_STRIDE 38 -> 39 in trade_stats_kernel.cu (hardcoded copy)
- PORTFOLIO_STRIDE 38 -> 39 in gpu_experience_collector.rs allocator
- ps_stride 38 -> 39 in gpu_dqn_trainer.rs launch_kelly_cap_update
Producer (experience_kernels.cu):
- Peak bar snapshotted alongside every MAX_PNL update (uses local
hold_time, not ps[PS_HOLD_TIME], because the portfolio-state commit
block runs later in the kernel).
- Peak bar reset to 0 at every MAX_PNL reset site: plan-entry (1856),
entering_trade (2014), reversing_trade (2019), fold hard-reset (2736),
trade-complete soft-reset (2751).
Consumer (experience_kernels.cu segment_complete block):
- bars_early = max(0, segment_hold_time - PS_PEAK_PNL_BAR)
- timing_bonus = shaping_scale x (bars_early / segment_hold_time)
x |final_pnl| x conviction_core
- reward += timing_bonus; rc[5] += timing_bonus
(accumulates with Task 3 B.2 entry bonus — different (i,t) slots).
No new ISV slot — rc[5] bonus semantics unchanged; B.2 and C.4 share it
via += accumulate semantics (defensively idempotent, but the two sites
fire at distinct (i,t) by construction: entry vs exit).
Self-scaling: shaping_scale x conviction_core x |pnl| keeps the bonus
proportional to trade magnitude, no tuned coefficients.
Smoke multi_fold_convergence (RTX 3050 Ti): all 3 folds complete,
fold-2 best Sharpe 84.44 at epoch 1 (expected ~85 range).
cargo check --workspace clean at 11 warnings baseline.
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.