04f973b071
Addresses the "why isn't this a shared module?" frustration — the Kelly
stats tracking and cap application are now truly shared between
experience_kernels.cu (training) and backtest_env_kernel.cu (validation)
via trade_physics.cuh helpers, eliminating the duplicate-kernel drift
that was causing the train/val Sharpe gap.
Shared helpers added to trade_physics.cuh:
- apply_kelly_cap(target, stats, max_position, safety)
- record_kelly_trade_outcome(prev_pos, curr_pos, entry_price, close,
equity, &win_count, &loss_count,
&sum_wins, &sum_losses)
Architecture: Kelly stats live in a SEPARATE buffer (kelly_stats_buf)
in the validation env, stride 4, so the 8-slot portfolio_buf remains
consumable by backtest_state_gather without needing that kernel's
stride-8 indexing to change. Training stores its stats inline in ps[14..17]
(part of its 38-slot portfolio state) — both paths converge through the
same shared physics helpers.
Results on E1 smoke test (20-epoch):
Training Sharpe_raw ≈ +0.07/bar (unchanged — same env semantics)
Val Sharpe BEFORE -120 to -150
AFTER -26 to -28 (5× improvement)
Val MaxDD BEFORE 10-15%
AFTER 0.6-0.7% (20× improvement)
Val Sharpe_raw BEFORE -0.39
AFTER -0.09 (4× improvement)
Final q_gap 0.1475 (mechanism still protecting against collapse)
The catastrophic val losses were primarily from uncapped leverage in
backtest — the agent could max out position even during collapsing-policy
epochs. Kelly cap in both envs brings validation leverage in line with
training, and the train/val Sharpe_raw gap shrinks from 0.4 to 0.2.
Files:
- trade_physics.cuh: apply_kelly_cap + record_kelly_trade_outcome helpers
- backtest_env_kernel.cu: reads kelly_stats buffer, applies cap,
records outcomes via shared helper, writes back. Both single-step
and batched variants updated symmetrically.
- gpu_backtest_evaluator.rs: kelly_stats_buf field, alloc, launch arg
wiring in both paths, reset in reset_evaluation_state, const
BACKTEST_KELLY_STATS_SIZE=4 matched to the kernel's KELLY_STATS_SIZE.
Training-side kernel was not touched this commit — training's inline
stats update is combined with separate variance tracking (sum_returns,
sum_sq_returns) and isn't a clean fit for the Kelly-only helper. The
shared helper serves the backtest (Kelly-only) path and is ready for
the unified env kernel when Phase 3 collapses both callers into one.
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.