https://github.com/analitico-771/quantitative_analysis
This app helps you determine the fund with the most investment potential based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, and beta
https://github.com/analitico-771/quantitative_analysis
alpaca-trading-api analytics api conda-environment csv-files jupyter-notebook mathplotlib pandas pandas-dataframe python stocks
Last synced: 2 months ago
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This app helps you determine the fund with the most investment potential based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, and beta
- Host: GitHub
- URL: https://github.com/analitico-771/quantitative_analysis
- Owner: Analitico-771
- Created: 2022-01-24T21:39:06.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-01-26T02:40:06.000Z (over 3 years ago)
- Last Synced: 2025-06-30T14:49:32.658Z (3 months ago)
- Topics: alpaca-trading-api, analytics, api, conda-environment, csv-files, jupyter-notebook, mathplotlib, pandas, pandas-dataframe, python, stocks
- Language: Jupyter Notebook
- Homepage:
- Size: 1.45 MB
- Stars: 0
- Watchers: 2
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
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[![Forks][forks-shield]][forks-url]
[![Stargazers][stars-shield]][stars-url]
[![Issues][issues-shield]][issues-url]
[![LinkedIn][linkedin-shield]][linkedin-url]
Table of Contents
## About The Project
This app helps you determine the fund with the most investment potential based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, and beta.
### Built With
* [Python](https://www.python.org/)
* [Python CSV Reading/Writing](https://docs.python.org/3/library/csv.html)
* [Python pandas](https://pandas.pydata.org/)
* [Python matplotlib](https://matplotlib.org/)
* [Python conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html)
* [Python JupyterLab](https://jupyter.org/)
## Getting Started
* You don't need Python. You can install Anaconda and JupyterLab normally just like any other application on your computer. Follow the instructions for Anaconda, ensure that its working, then install JupyterLab.
* I have placed Comments throughout the code so that you can follow the code and be able to replicate the app on your own. Also, so that you're able to contribute in the future :-)
### Prerequisites
A text editor such as [VS Code](https://code.visualstudio.com/) or [Sublime Text](https://www.sublimetext.com/)
### Installation
1. Clone the repo
```sh
git clone https://github.com/AnaIitico/quantitative_analysis.git
```
2. You don't need to install pip - Conda comes with pip and you can also use the command
conda install 'package name'
3. Install Conda according to the instructions based on your operating system.
For windows users you MUST use the Administrator PowerShell. Users with AMD Processors MUST use the Administrator PowerShell 7 (X64) version
Once installed Conda has an Admin PowerShell version shortcut - look on your Start menu for it.
This shortcut will prove very useful at times when you need to install other apps or make adjustments to your installation
Once installed you will see (base) on your terminal
4. Activate Conda Dev environment
```sh
conda activate dev
```
You should now see (dev) on your terminal
5. Install JupyterLabs
```sh
pip install jupyterlab
6. Run JupyterLab
```sh
jupyter lab
```
A browser window should open on localhost:8889/lab
## Usage
This app helps you determine the fund with the most investment potential based on key risk-management metrics: the daily returns, standard deviations, Sharpe ratios, and beta.
It loads a database of 4 investment funds vs the S&P 500 and makes all calculations based on the code and the data. The csv file is located in the Resources folder.
## Roadmap
See the [open issues](https://github.com/AnaIitico/quantitative_analysis/issues) for a list of proposed features (and known issues).
## Contributing
Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are **greatly appreciated**.
1. Fork the Project
2. Create your Feature Branch (`git checkout -b feature/AmazingFeature`)
3. Commit your Changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the Branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## Contact
Jose Tollinchi - [@josetollinchi][linkedin-url] - jtollinchi1971@gmail.com
Project Link: [https://github.com/AnaIitico/quantitative_analysis](https://github.com/AnaIitico/quantitative_analysis)
## Acknowledgements
* [Img Shields](https://shields.io)
* [Choose an Open Source License](https://choosealicense.com)
[contributors-shield]: https://img.shields.io/github/contributors/AnaIitico/quantitative_analysis.svg?style=for-the-badge
[contributors-url]: https://github.com/AnaIitico/quantitative_analysis/graphs/contributors
[forks-shield]: https://img.shields.io/github/forks/AnaIitico/quantitative_analysis.svg?style=for-the-badge
[forks-url]: https://github.com/AnaIitico/quantitative_analysis/network/members
[stars-shield]: https://img.shields.io/github/stars/AnaIitico/quantitative_analysis.svg?style=for-the-badge
[stars-url]: https://github.com/AnaIitico/quantitative_analysis/stargazers
[issues-shield]: https://img.shields.io/github/issues/AnaIitico/quantitative_analysis/network/members?style=for-the-badge
[issues-url]: https://github.com/AnaIitico/quantitative_analysis/issues
[linkedin-shield]: https://img.shields.io/badge/-LinkedIn-black.svg?style=for-the-badge&logo=linkedin&colorB=555
[linkedin-url]: https://www.linkedin.com/in/josetollinchi/