9dbd8d7e9f
Design document for the follow-up to the adaptive-learning-rootcause session. Lays out the evidence, root cause, options considered, and recommended path for closing the 70% structural gap between training and validation Sharpe that remained after the distillation collapse fix landed. Key findings documented: - Reward-shaping ablation (2026-04-20) closed ~30% of the gap; ~70% remains architectural - Two env kernels (experience_env_step vs backtest_env_step) have drifted: spread scaling, fill model, saboteur noise, reward terms, action selection, position dynamics all differ - Hint from history: experience_kernels.cu:1418 comment "Regime- adaptive scaling removed to eliminate train/eval mismatch" shows someone aligned *some* things previously Recommended path (Option C, "unified env with layered reward"): - Single unified_env_step kernel replaces both - Core reward = pure P&L; shaping is additive and P&L-units-aligned - Validation = training with exploration_scale=0 AND shaping_scale=0 - Scale factors are pinned device-mapped scalars (same pattern used by the distillation alpha fix) Phased implementation plan with ~8-day budget and concrete success criteria: validation Sharpe_raw within 0.05 of training Sharpe_raw by epoch 30 on L40S production run. Rejected alternatives: backtest-matches-training (hides real issue), training-matches-backtest (regresses stability), two-environment with divergence as metric (fallback only). 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.