semvec/pypi-packages huggingface dataset:
langchain-google-calendar-tools 0.0.1
This repo walks through connecting to the Google Calendar API.
Requires Python: >=3.7

# Langchain Google Calendar Tools

This repo walks through connecting to the Google Calendar API.

## Installation

```console
pip install langchain-google-calendar-tools
```

For local development:

```console
pip install -e .
```

## How to use

- Create a Google Cloud project and enable Google Calendar API.
- To get Oauth credentials for the Desktop app, please refer <https://developers.google.com/calendar/api/guides/overview> for detail.
- Download the credentials file to `./notebooks` and rename it to `credentials.json`. If you want to keep its original file name, please replace the value of `client_secrets_file` in [demo.ipynb](notebooks/demo.ipynb) with the valid path which points to the credentials file.
- Run this notebook to perform the listed functions

## Limitations

Due to the short development time, some of the following parts have not been completed and will be improved in the future:

- Timezone: Currently being fixed to Vietnam's timezone, it will be taken from the user's Calendar or the system in the future
- Update recurring events:  has not been implemented yet
- Functions related to inviting others and responding to invitations have not been implemented

## License

`langchain-google-calendar-tools` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
dataforgelab 0.1.2
Quantitative Copilot - a helper in quant developping
Keywords: quant,framework,finance

# Quant Research

## What is this projet?

This is a project aimed for quant, or people want to devote themselves in quantative field. I concluded a lot of experience and sample codes in this project covering from data collection, quantative investment platform to actual factor digging process and so on. This project is still carrying on and ready to bring everyone in quantative field a better experience.

## How can we use this project?

Currently, I have no plan for publishing a independent pypi package for this project, for most of the code is created in research process. I you find some of the code in this project do help you, you can try to clone the project to your own computer, and import the corresponding package. More easily, just copy and paste the code in your project.

## References for QIML

Some paper references and quantative investment and machine learning

- [Double Ensemble](https://arxiv.org/pdf/2010.01265.pdf)

## Data

We provide data by [BaiduNetDisk](https://pan.baidu.com/s/1xOKAKKnpnWunhzIzPJ9qlg?pwd=wsyd).