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Limexhub

Python version PyPi version PyPi downloads Star this repo

limexhub is a comprehensive Python library designed to simplify interactions with the financial data and services provided by Limex DataHub. It provides an easy-to-use interface for fetching various types of financial data, including instruments, candles, fundamentals, news, events, and predictive signals.

Features

  • Ease of Use: The limexhub library has a straightforward, intuitive interface that enables quick access to Limex DataHub.
  • Comprehensive Data Coverage: Access a wide range of financial data, from market instruments and historical candlestick charts to company fundamentals and latest news.
  • Predictive Signals: Leverage advanced machine learning models and signals to inform your financial decisions.
  • Customization: Easily customize your data queries with flexible parameters suitable for different analysis and trading strategies.
  • Efficiency: The library is designed for efficiency, minimizing the amount of code needed to make requests and handle responses.

Installation

Install limexhub with pip:

pip install limexhub

Getting Started

The library needs to be configured with an API key from your account. Sign up for free and you will automatically receive a set of API keys to start with.

import limexhub
api_token = 'your_api_key'
client = limexhub.RestAPI(token=api_token)



instruments = client.instruments(assets='stocks')

DJI_constituents = client.constituents(index = 'DJI')
SP500_constituents = client.constituents()


candles = client.candles(symbols = ["AAPL", "NVDA"],
                         start="2024-01-01", 
                         end="2025-01-01", 
                         interval = "1d",
                         pivot=False)
                         

fundamental = client.fundamental(symbol="AAPL", 
                                 from_date="2023-01-01",
                                 to_date="2024-01-01",
                                 fields='roa')

fundamental = client.fundamental(symbol="AAPL", 
                                 from_date="2010-01-01",
                                 to_date="2024-01-01",
                                 fields=None)

fundamental = client.fundamental(fields='roa',
                                 quarter='Q2-2023')
            
                                 
events = client.events(symbol="AAPL",
                       from_date="2023-01-01", 
                       to_date="2024-01-01",
                       event_type="dividends")

news = client.news(symbol="AAPL", 
                   from_date="2023-03-01",
                   to_date="2024-03-03")

models = client.models(vendor = 'boosted')

signals = client.signals(vendor="boosted", 
                         model=models.iloc[0]['id'], 
                         symbol="AAPL", 
                         from_date="2023-01-01")