FinancialComputingUCL/LOBFrame
We release `LOBFrame', a novel, open-source code base which presents a renewed way to process large-scale Limit Order Book (LOB) data.
This project helps quantitative traders and financial researchers analyze and predict stock price movements by processing large-scale Limit Order Book (LOB) data. It takes raw LOBSTER market data as input, then processes, transforms, and uses it to train and evaluate advanced forecasting models, ultimately producing model performance metrics and trading simulation results. The typical user is a quant researcher or algorithmic trader looking to build and test sophisticated trading strategies.
221 stars. No commits in the last 6 months.
Use this if you need to systematically process, model, and backtest trading strategies based on high-frequency Limit Order Book data.
Not ideal if you're dealing with low-frequency data, fundamental analysis, or require a simple, out-of-the-box trading bot.
Stars
221
Forks
48
Language
Python
License
—
Category
Last pushed
May 31, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/FinancialComputingUCL/LOBFrame"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
skfolio/skfolio
Python library for portfolio optimization built on top of scikit-learn
emoen/Machine-Learning-for-Asset-Managers
Implementation of code snippets, exercises and application to live data from Machine Learning...
WLM1ke/poptimizer
Оптимизация долгосрочного портфеля акций
jankrepl/deepdow
Portfolio optimization with deep learning.
baobach/mlfinpy
Mlfin.py is an advance Machine Learning toolbox for financial applications in Python.