6c4945fe16
Per the newly-saved memory pearl pearl_adaptive_carryover_discipline, diagnoses Fix D's "intentionally preserve train Kelly + inventory EMAs into eval" as the dominant cause of v8's eval-phase regression. Design — 3 layers at the train→eval boundary: Layer 1: Reset every adaptive EMA (Kelly wr/avg-win/avg-loss/ cumulative_dones, inventory β/variance, reward-clamp pos/neg/clip EMAs) to neutral sentinels. Lets the controllers re-bootstrap from eval-distribution observations rather than carrying poisoned train state. Layer 2: Defensive warmup window (500 steps) overrides risk-sizing controllers — Kelly safety_frac 0.5→0.25, IQN τ_min 0.10→0.30, entropy coef floor 0.01→0.05, PPO ε floor 0.05→0.10. Linear decay back to normal over additional 200 steps. Layer 3: 2× LR multiplier during warmup window. Network weights adapt fast to new-regime statistics; this is the slowest-adapting layer of the agent. Adds 8 new ISV slots (686-693), bumps RL_SLOTS_END 686→694. New small kernel rl_eval_warmup_decay applies overrides each step during warmup. Pure additive design — no kernel logic changes outside the new warmup-decay kernel. Validation requires running ALL 3 folds (vs v8's single fold-1) per pearl_single_window_oos_is_not_oos. Success criterion: mean eval pnl across 3 folds > v8 mean. No code change in this commit — design document only. Implementation deferred until decision is made on whether to ship v9 or first collect v8's fold-0 + fold-2 baseline for cleaner comparison.
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.