EOD Historical Data Python Library (Unofficial)

Overview

EOD Historical Data Python Library (Unofficial)

https://eodhistoricaldata.com

Installation

python3 -m pip install eodhistoricaldata

Note

Demo API key below is provided by EOD Historial Data for testing purposes https://eodhistoricaldata.com/financial-apis/new-real-time-data-api-websockets

Usage

None: """Main""" websocket = WebSocketClient( # Demo API key for testing purposes api_key="OeAFFmMliFG5orCUuwAKQ8l4WWFQ67YX", endpoint="crypto", symbols=["BTC-USD"] #api_key="OeAFFmMliFG5orCUuwAKQ8l4WWFQ67YX", endpoint="forex", symbols=["EURUSD"] #api_key="OeAFFmMliFG5orCUuwAKQ8l4WWFQ67YX", endpoint="us", symbols=["AAPL"] ) websocket.start() message_count = 0 while True: if websocket: if ( message_count != websocket.message_count ): print(websocket.message) message_count = websocket.message_count sleep(0.25) # output every 1/4 second, websocket is realtime if __name__ == "__main__": main() ">
"""Sample script"""

from time import sleep
from eodhistoricaldata import WebSocketClient

def main() -> None:
    """Main"""

    websocket = WebSocketClient(
        # Demo API key for testing purposes
        api_key="OeAFFmMliFG5orCUuwAKQ8l4WWFQ67YX", endpoint="crypto", symbols=["BTC-USD"]
        #api_key="OeAFFmMliFG5orCUuwAKQ8l4WWFQ67YX", endpoint="forex", symbols=["EURUSD"]
        #api_key="OeAFFmMliFG5orCUuwAKQ8l4WWFQ67YX", endpoint="us", symbols=["AAPL"]
    )
    websocket.start()

    message_count = 0
    while True:
        if websocket:
            if (
                message_count != websocket.message_count
            ):
                print(websocket.message)
                message_count = websocket.message_count
                sleep(0.25)  # output every 1/4 second, websocket is realtime

if __name__ == "__main__":
    main()
You might also like...
TE-dependent analysis (tedana) is a Python library for denoising multi-echo functional magnetic resonance imaging (fMRI) data
TE-dependent analysis (tedana) is a Python library for denoising multi-echo functional magnetic resonance imaging (fMRI) data

tedana: TE Dependent ANAlysis TE-dependent analysis (tedana) is a Python library for denoising multi-echo functional magnetic resonance imaging (fMRI)

Hatchet is a Python-based library that allows Pandas dataframes to be indexed by structured tree and graph data.
Hatchet is a Python-based library that allows Pandas dataframes to be indexed by structured tree and graph data.

Hatchet Hatchet is a Python-based library that allows Pandas dataframes to be indexed by structured tree and graph data. It is intended for analyzing

 🧪 Panel-Chemistry - exploratory data analysis and build powerful data and viz tools within the domain of Chemistry using Python and HoloViz Panel.
🧪 Panel-Chemistry - exploratory data analysis and build powerful data and viz tools within the domain of Chemistry using Python and HoloViz Panel.

🧪📈 🐍. The purpose of the panel-chemistry project is to make it really easy for you to do DATA ANALYSIS and build powerful DATA AND VIZ APPLICATIONS within the domain of Chemistry using using Python and HoloViz Panel.

Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code

Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code. Tuplex has similar Python APIs to Apache Spark or Dask, but rather than invoking the Python interpreter, Tuplex generates optimized LLVM bytecode for the given pipeline and input data set.

Python data processing, analysis, visualization, and data operations

Python This is a Python data processing, analysis, visualization and data operations of the source code warehouse, book ISBN: 9787115527592 Descriptio

Catalogue data - A Python Scripts to prepare catalogue data

catalogue_data Scripts to prepare catalogue data. Setup Clone this repo. Install

fds is a tool for Data Scientists made by DAGsHub to version control data and code at once.
fds is a tool for Data Scientists made by DAGsHub to version control data and code at once.

Fast Data Science, AKA fds, is a CLI for Data Scientists to version control data and code at once, by conveniently wrapping git and dvc

A data parser for the internal syncing data format used by Fog of World.
A data parser for the internal syncing data format used by Fog of World.

A data parser for the internal syncing data format used by Fog of World. The parser is not designed to be a well-coded library with good performance, it is more like a demo for showing the data structure.

Functional Data Analysis, or FDA, is the field of Statistics that analyses data that depend on a continuous parameter.
Comments
  • Syntax issue with query Parameter in get_calendar_ functions

    Syntax issue with query Parameter in get_calendar_ functions

    Hello,

    When using the get_calendar_XXX, functions we cannot use the query parameters defined by EOD as the word "from" is forbidden by Python, for instance : earning=client.get_calendar_earnings(from='2022-11-01', to='2022-11-30')

    will raise an issue.

    Should I pass the argument differently ?

    opened by ATCBGroup 1
  • dependency on matplotlib but it is not installed with pip

    dependency on matplotlib but it is not installed with pip

    dependency on matplotlib but it is not installed with pip

    [email protected]:~/git/traderai/eod$ cat test.py
    from eodhd import APIClient
    api = APIClient("DEMO")
    
    [email protected]:~/git/traderai/eod$ python3 test.py
    Traceback (most recent call last):
      File "/home/mshamber/.local/lib/python3.8/site-packages/eodhd/eodhdgraphs.py", line 5, in <module>
        import matplotlib.pyplot as plt
    ModuleNotFoundError: No module named 'matplotlib'
    
    [email protected]:~/git/traderai/eod$ python3 -m pip install eodhd
    Requirement already satisfied: eodhd in /home/mshamber/.local/lib/python3.8/site-packages (1.0.8)
    Requirement already satisfied: websocket-client==1.3.3 in /home/mshamber/.local/lib/python3.8/site-packages (from eodhd) (1.3.3)
    Requirement already satisfied: rich==12.5.1 in /home/mshamber/.local/lib/python3.8/site-packages (from eodhd) (12.5.1)
    Requirement already satisfied: websockets==10.3 in /home/mshamber/.local/lib/python3.8/site-packages (from eodhd) (10.3)
    Requirement already satisfied: numpy==1.21.6 in /home/mshamber/.local/lib/python3.8/site-packages (from eodhd) (1.21.6)
    Requirement already satisfied: pandas==1.3.5 in /home/mshamber/.local/lib/python3.8/site-packages (from eodhd) (1.3.5)
    Requirement already satisfied: requests==2.28.1 in /home/mshamber/.local/lib/python3.8/site-packages (from eodhd) (2.28.1)
    Requirement already satisfied: commonmark<0.10.0,>=0.9.0 in /home/mshamber/.local/lib/python3.8/site-packages (from rich==12.5.1->eodhd) (0.9.1)
    Requirement already satisfied: typing-extensions<5.0,>=4.0.0; python_version < "3.9" in /home/mshamber/.local/lib/python3.8/site-packages (from rich==12.5.1->eodhd) (4.3.0)
    Requirement already satisfied: pygments<3.0.0,>=2.6.0 in /home/mshamber/.local/lib/python3.8/site-packages (from rich==12.5.1->eodhd) (2.13.0)
    Requirement already satisfied: python-dateutil>=2.7.3 in /home/mshamber/.local/lib/python3.8/site-packages (from pandas==1.3.5->eodhd) (2.8.2)
    Requirement already satisfied: pytz>=2017.3 in /home/mshamber/.local/lib/python3.8/site-packages (from pandas==1.3.5->eodhd) (2022.5)
    Requirement already satisfied: charset-normalizer<3,>=2 in /home/mshamber/.local/lib/python3.8/site-packages (from requests==2.28.1->eodhd) (2.1.1)
    Requirement already satisfied: idna<4,>=2.5 in /usr/lib/python3/dist-packages (from requests==2.28.1->eodhd) (2.8)
    Requirement already satisfied: certifi>=2017.4.17 in /usr/lib/python3/dist-packages (from requests==2.28.1->eodhd) (2019.11.28)
    Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/lib/python3/dist-packages (from requests==2.28.1->eodhd) (1.25.8)
    Requirement already satisfied: six>=1.5 in /home/mshamber/.local/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas==1.3.5->eodhd) (1.16.0)
    
    opened by opme 1
Releases(1.0.8)
Owner
Michael Whittle
Solution Architect
Michael Whittle
PostQF is a user-friendly Postfix queue data filter which operates on data produced by postqueue -j.

PostQF Copyright © 2022 Ralph Seichter PostQF is a user-friendly Postfix queue data filter which operates on data produced by postqueue -j. See the ma

Ralph Seichter 11 Nov 24, 2022
Renato 214 Jan 02, 2023
Feature engineering and machine learning: together at last

Feature engineering and machine learning: together at last! Lambdo is a workflow engine which significantly simplifies data analysis by unifying featu

Alexandr Savinov 14 Sep 15, 2022
Very useful and necessary functions that simplify working with data

Additional-function-for-pandas Very useful and necessary functions that simplify working with data random_fill_nan(module_name, nan) - Replaces all sp

Alexander Goldian 2 Dec 02, 2021
Repositori untuk menyimpan material Long Course STMKGxHMGI tentang Geophysical Python for Seismic Data Analysis

Long Course "Geophysical Python for Seismic Data Analysis" Instruktur: Dr.rer.nat. Wiwit Suryanto, M.Si Dipersiapkan oleh: Anang Sahroni Waktu: Sesi 1

Anang Sahroni 0 Dec 04, 2021
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 09, 2023
Big Data & Cloud Computing for Oceanography

DS2 Class 2022, Big Data & Cloud Computing for Oceanography Home of the 2022 ISblue Big Data & Cloud Computing for Oceanography class (IMT-A, ENSTA, I

Ocean's Big Data Mining 5 Mar 19, 2022
First steps with Python in Life Sciences

First steps with Python in Life Sciences This course material is part of the "First Steps with Python in Life Science" three-day course of SIB-trainin

SIB Swiss Institute of Bioinformatics 22 Jan 08, 2023
Python ELT Studio, an application for building ELT (and ETL) data flows.

The Python Extract, Load, Transform Studio is an application for performing ELT (and ETL) tasks. Under the hood the application consists of a two parts.

Schlerp 55 Nov 18, 2022
Python tools for querying and manipulating BIDS datasets.

PyBIDS is a Python library to centralize interactions with datasets conforming BIDS (Brain Imaging Data Structure) format.

Brain Imaging Data Structure 180 Dec 18, 2022
A meta plugin for processing timelapse data timepoint by timepoint in napari

napari-time-slicer A meta plugin for processing timelapse data timepoint by timepoint. It enables a list of napari plugins to process 2D+t or 3D+t dat

Robert Haase 2 Oct 13, 2022
pyhsmm MITpyhsmm - Bayesian inference in HSMMs and HMMs. MIT

Bayesian inference in HSMMs and HMMs This is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and expli

Matthew Johnson 527 Dec 04, 2022
Stream-Kafka-ELK-Stack - Weather data streaming using Apache Kafka and Elastic Stack.

Streaming Data Pipeline - Kafka + ELK Stack Streaming weather data using Apache Kafka and Elastic Stack. Data source: https://openweathermap.org/api O

Felipe Demenech Vasconcelos 2 Jan 20, 2022
Data imputations library to preprocess datasets with missing data

Impyute is a library of missing data imputation algorithms. This library was designed to be super lightweight, here's a sneak peak at what impyute can do.

Elton Law 329 Dec 05, 2022
sportsdataverse python package

sportsdataverse-py See CHANGELOG.md for details. The goal of sportsdataverse-py is to provide the community with a python package for working with spo

Saiem Gilani 37 Dec 27, 2022
pyETT: Python library for Eleven VR Table Tennis data

pyETT: Python library for Eleven VR Table Tennis data Documentation Documentation for pyETT is located at https://pyett.readthedocs.io/. Installation

Tharsis Souza 5 Nov 19, 2022
simple way to build the declarative and destributed data pipelines with python

unipipeline simple way to build the declarative and distributed data pipelines. Why you should use it Declarative strict config Scaffolding Fully type

aliaksandr-master 0 Jan 26, 2022
Implementation in Python of the reliability measures such as Omega.

OmegaPy Summary Simple implementation in Python of the reliability measures: Omega Total, Omega Hierarchical and Omega Hierarchical Total. Name Link O

Rafael Valero Fernández 2 Apr 27, 2022
Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code

Tuplex is a parallel big data processing framework that runs data science pipelines written in Python at the speed of compiled code. Tuplex has similar Python APIs to Apache Spark or Dask, but rather

Tuplex 791 Jan 04, 2023
4CAT: Capture and Analysis Toolkit

4CAT: Capture and Analysis Toolkit 4CAT is a research tool that can be used to analyse and process data from online social platforms. Its goal is to m

Digital Methods Initiative 147 Dec 20, 2022