Transformer Architecture · Gold Futures · Since 2000
Deep learning model trained on continuous gold futures order flow — 24 years of tick-level microstructure data, distilled into predictive signal.
Architecture
A custom transformer architecture designed specifically to ingest and reason over order flow sequences — bid/ask imbalances, trade aggression, volume clustering, and tick-level price dynamics.
Simultaneous attention across tick, minute, and session-level time horizons. The model learns which scale matters for each regime.
Proprietary tokenization of limit order book snapshots into dense vectors capturing bid/ask pressure, queue depth, and fill rate dynamics.
Session-aware positional embeddings that encode time-of-day, day-of-week, and macro calendar events directly into the attention mechanism.
Dedicated classification head that identifies market regimes — trending, mean-reverting, volatile, or compressed — to condition downstream predictions.
Training Data
Continuous GC futures from 2000 to present. Every tick, every trade, every order book event — roll-adjusted for seamless continuity across contract expirations.
Performance
Evaluated on held-out 2023–2024 data across multiple time horizons and market regimes. All metrics computed on out-of-sample data with realistic execution assumptions.
Output Signals
Real-time inference generates a suite of signals consumed by execution algorithms and risk systems.
Softmax over {up, down, flat} at configurable horizons. Calibrated probabilities, not raw logits.
Current market regime with transition probabilities. Trending, mean-reverting, volatile, or compressed.
VPIN-inspired metric enhanced with learned features. Measures probability of adverse selection in current flow.
Conditional volatility estimate at 1-min, 5-min, and 1-hour horizons. Adapts in real-time to regime shifts.
Normalized score of buyer vs. seller aggression. Leading indicator of short-term price movement direction.
Optimal entry/exit timing signal for minimizing slippage. Conditions on order book depth and recent fill patterns.
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