AI Research
Teaching a machine
to read the market.
We are building a Temporal Fusion Transformer trained on live tick, orderbook, and candle data from futures markets. This is long-term, open research. We document everything as we go.
The Architecture
Temporal Fusion
Transformer
The TFT was built at Google DeepMind for exactly this kind of problem — forecasting complex, multi-variate time series where different inputs matter at different times.
Unlike a black-box neural network, the TFT uses variable selection networks and multi-head attention to show you exactly which inputs it weighted most heavily for each prediction. Every decision is interpretable.
The Roadmap
Data Collection
Tick-level trade data, full orderbook depth (bid/ask ladders), and multi-timeframe candle data collected continuously from live futures markets. Every microsecond of market activity is captured, timestamped, and stored. This is the raw material the model will train on.
Feature Engineering
Raw market data is transformed into meaningful signals — orderbook imbalance, trade flow toxicity, bid/ask pressure, volume delta, price displacement relative to VWAP. The model learns which features matter at which moments in time.
Model Training
A Temporal Fusion Transformer (TFT) — an architecture built specifically for multi-horizon time series forecasting. It combines LSTM sequence encoders with multi-head attention, learning which inputs are predictive at each point in time. No black box: every attention weight is interpretable.
Signal Generation
The trained model outputs a probability distribution over future price displacement — not a single prediction, but a range of outcomes with confidence intervals. These signals are used to filter and validate existing rule-based strategies before execution.
Live Deployment
The validated model runs alongside the existing bot infrastructure, providing a continuous AI signal layer. Over time, as confidence in the model grows, it takes on more of the decision making — evolving from filter to co-pilot to primary signal source.
Follow the Research
We document everything.
Every experiment, every result, every failure — published openly as the research progresses.
Follow the Journey →