Researched Models
This page presents evaluations of our market machine-learning models on historical data. Each model was tested on price data it had not encountered during training to approximate real-world usage. Results are presented for research and analytical purposes only.
What are models and how to use them
How to use EvalRun Models
1. Select your asset and go onto the "Train a Model" tab.
2. Choose one of our models, your chosen parameters and start running your first model.
3. View the results of your model's performance.
4. Jump back onto the chart's tab.
5. Select your chosen model.
6. Choose your model's configuration. And view your model predicting the next move (1 model can run infinite predictions).
LSTM (Long Short-Term Memory)
Deep LearningA deep learning model that predicts next-step log returns using an LSTM architecture trained on historical OHLCV data. Predictions are reconstructed back to price levels for evaluation and charting.
How It Works
- Pulls market data from Alpaca Bars (equities or crypto pairs)
- Builds sliding windows over the chosen lookback period
- Target at time t+1: r_{t+1} = log(close_{t+1} / close_t)
- Prediction made from window ending at t: predict r_{t+1}, then reconstruct close_{t+1}
Visualizations
Note: All models have been tested on out-of-sample data to ensure realistic performance metrics. Past performance does not guarantee future results. These models are implementations based on academic research and have been adapted for our trading platform. Terms of Use