https://github.com/systemathics/notebook-examples

Sample Jupyter notebooks targeting Ganymede gRPC API
https://github.com/systemathics/notebook-examples

analytics backtest csharp daily-data fsharp grpc indicators intraday-data jupyterlab mlnet normalized-data python raw-data reference-data tick-data vbnet

Last synced: 26 days ago
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Sample Jupyter notebooks targeting Ganymede gRPC API

• Host: GitHub
• URL: https://github.com/systemathics/notebook-examples
• Owner: systemathics
• Created: 2021-05-28T07:48:56.000Z (over 3 years ago)
• Default Branch: prod
• Last Pushed: 2024-05-03T08:11:08.000Z (9 months ago)
• Last Synced: 2024-05-03T21:17:11.562Z (9 months ago)
• Topics: analytics, backtest, csharp, daily-data, fsharp, grpc, indicators, intraday-data, jupyterlab, mlnet, normalized-data, python, raw-data, reference-data, tick-data, vbnet
• Language: Jupyter Notebook
• Homepage:
• Size: 10.2 MB
• Stars: 2
• Watchers: 1
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Ganymede | on-demand financial data

# About Ganymede

Built as cloud native, Ganymede is a fully managed platform, continuously updated with high-quality, curated,

and cross validated data. Ganymede is purpose built for the financial data markets ever increasing data volumes,

allowing clients to query against large data sets, including tick-by-tick, sampled, daily, corporate actions,

ESG and reference data. Packed with a broad range of analytics, the solution is optimized for fast response times,

supports full order book natively and allows fine grained/customizable trade and quote conditions mapping and filtering.

# About this repository 

This repository contains Jupyter notebooks to request analytics and retrieve bespoke datasets by calling Ganymede gRPC API. Samples and building blocks are available to help you designing your bespoke analytics and data requests:

- Reference data

- Corporate actions

- Historical financial data and calculations: daily, intraday and tick data

- Best execution

- Future roll strategies and trading strategies

- Monitoring dashboards including data storage and normalization metrics

Example are organized in a per language per topic way:

- [Python](/python/)

- [C#](/csharp/)

- [F#](/fsharp/)

There also are a few helpers to plug common development environments to Ganymede:

- Have a look [here](/remoteaccess/)

## Current Ganymede API version

[![PyPI version](https://badge.fury.io/py/systemathics.apis.svg)](https://badge.fury.io/py/systemathics.apis) [![NuGet version](https://badge.fury.io/nu/systemathics.apis.svg)](https://badge.fury.io/nu/systemathics.apis)

## Useful links

- [Ganymede](https://ganymede.cloud/) portal (registered users logon page is [here](https://ganymede.cloud/data/))

- Have a look at the [documentation](https://ganymede.cloud/api-documentation.html) (API reference and tutorials)

## Github setup

Create an ssh key in jupyterhub : 

`ssh-keygen -t ed25519 -C "your_name@email_provider.com"`

Get the public content of key:

`cat /home/jovyan/.ssh/id_ed25519.pub`

Add it to your [gihub ssh keys](https://github.com/settings/keys) idealy with the date so you can delete it later.

Configure the remote :

`git branch --set-upstream-to=origin/prod prod`

and configure the pull behaviour:

`git config pull.rebase true`

You can now push and pull as needed

## About us

[Systemathics](https://systemathics.com) is a French fintech founded in 2008 developing its innovative products with the highest quality standards 100% in France.

Our main mission is to provide global investors with a complete end-to-end solution to systematize alpha generation in a robust way.

From data pre and post trade analysis, back-testing, risk assessment and signal generation to day-to-day execution in production and everything in between.