b5d19c1004
Plan 3 Task 3.
ISV tail-append:
- [71] TRADE_ATTEMPT_RATE_EMA — Flat→Positioned transition rate EMA
(GPU-written by trade_attempt_rate_ema_update, adaptive α)
- [72] TRADE_TARGET_RATE — reference rate, CPU-frozen at epoch 5
- Fingerprint shifted [69,70] → [73,74]; ISV_TOTAL_DIM 71 → 75
Producer kernel (trade_rate_ema_kernel.cu):
- Single-block reduction of flat_to_pos_per_sample [N*L] (no atomicAdd)
- Adaptive EMA: α = α_base × (1 + 0.5 × |clamp(sharpe, -2, 2)|)
- α_base = 0.05 (matches reward_component_ema convention)
- Launched from training_loop alongside reward_component_ema
Consumer (experience_env_step):
- Flat→Positioned site: novelty = max(0, 1 - attempt/target)
- bonus = conviction_core × vol_proxy × novelty
- reward += shaping_scale × bonus; rc[5] captures bonus for ISV[68]
- Explicit freeze gate: target_raw > 1e-6f, so bonus is structurally
inert pre-freeze (prevents spurious novelty=1.0 on epoch-1 when
attempt_rate is still 0)
Epoch-5 freeze (training_loop):
- measured = ISV[TRADE_ATTEMPT_RATE_EMA]; floor at 0.001
- Prevents novelty from sticking at 1.0 post-freeze
StateResetRegistry: both slots registered as FoldReset with per-slot
reset dispatch arms in training_loop's fold-boundary path.
Smoke: multi_fold_convergence passes (fold 2 best Sharpe 87.55 —
slightly above Task 2 baseline 85.6, within noise; bonus inert
in 5-epoch smoke so training matches pre-B.2 baseline as designed).
cargo check 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.