LeonardoBerti00/TLOB
This is the official repository for the paper TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data.
This project helps quantitative traders and financial analysts predict short-term stock price trends using raw Limit Order Book (LOB) data. It takes in LOB data (like bids, asks, and order sizes) for specific stocks and outputs predictions about whether the price will go up, down, or stay the same. This allows practitioners to anticipate market movements and inform their trading strategies.
121 stars.
Use this if you need to predict stock price trends over short horizons, especially in volatile market conditions, using detailed Limit Order Book data.
Not ideal if you are looking for long-term investment advice or if your primary data source is not granular Limit Order Book information.
Stars
121
Forks
43
Language
Python
License
MIT
Category
Last pushed
Feb 24, 2026
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/LeonardoBerti00/TLOB"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
rafa-rod/pytrendseries
Detect trend in time series, drawdown, drawdown within a constant look-back window , maximum...
TimRivoli/Stock-Price-Trade-Analyzer
This is a Python 3 project for analyzing stock prices and methods of stock trading. It uses...
JordiCorbilla/stock-prediction-deep-neural-learning
Predicting stock prices using a TensorFlow LSTM (long short-term memory) neural network for...
jcamiloangarita/stocker
Stock Price Prediction
stabgan/Recurrent-Neural-Networks-to-predict-Google-Stock-Price
I tried to predict google stock price using LSTMs