152f4c16d9
Pearl's do-calculus applied to RL: compute per-feature causal sensitivity by running intervened forward passes. For each of 14 active features, set it to 0 (do(X_k=0)) and measure how much Q-values change. High sensitivity = feature genuinely CAUSES different outcomes. Low sensitivity = spurious correlation (noise that breaks OOS). Implementation: - Intervened forward passes via cuBLAS (reuse existing infrastructure) - States copied to scratch buffer, feature k zeroed, forward pass run - Q-value delta computed: |Q_original - Q_intervened|² per feature - Mean sensitivity logged for interpretability - Runs every N steps (configurable, default 10) to limit overhead Architecture: - causal_states_scratch [B, state_dim_padded] bf16 — intervened copy - causal_sensitivity_buf [market_dim] f32 — per-feature sensitivity - Reuses existing activation scratch buffers (post-graph, no conflict) - ~10% compute overhead at interval=10 (42 extra cuBLAS GEMMs per step) Config: enable_causal_intervention=false (default). THE FOUR CROWN JEWELS ARE COMPLETE: Gem (#31): 2D Bottleneck — architecture defense Pearl (#32): Gradient Vaccine — optimization defense King (#33): Adversarial Self-Play — strategic defense Emperor (#34): Causal Intervention — epistemic defense Co-Authored-By: Claude Opus 4.6 (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.