optitrader 0.2.1

Optitrader is an open-source Python package for portfolio optimization and algorithmic trading.

Optitrader

Optitrader is an open-source Python package designed for portfolio optimization, quantitative finance, and algorithmic trading. It empowers users with a wide array of tools to effortlessly construct personalized portfolios, thoroughly analyze optimization outcomes, and seamlessly execute trades using Alpaca's Trading API.

Accessible through an intuitive web-based Streamlit dashboard hosted at https://optitrader.streamlit.app, Optitrader enables users to interact with its rich features and harness the potential of portfolio optimization and algorithmic trade execution.

Using

Python package: to add and install this package as a dependency of your project, run poetry add optitrader.

Python CLI: to view this app's CLI commands once it's installed, run optitrader --help.

Rest API: to serve this application as a REST API, run docker compose up app and open localhost:8080 in your browser to see the documentation. Within the Dev Container, this is equivalent to running poe api.

Streamlit Dashboard: to use this application from a Streamlit dashboard, run optitrader dashboard. This is equivalent to running poe app and open localhost:8000 in your browser.

Example

Once the package has been installed you can use it as follows:

from optitrader import Optitrader
from optitrader.optimization.objectives import CVaRObjectiveFunction
from optitrader.enums import UniverseName

optimal_ptf = Optitrader(
        objectives=[CVaRObjectiveFunction()],
        universe_name=UniverseName.POPULAR_STOCKS,
    ).solve()

# Optimal Portfolio:
Portfolio(
    weights={
        'AAPL': 0.18168,
        'BABA': 0.00369,
        'BRK.B': 0.15119,
        'META': 0.04067,
        'MSFT': 0.01936,
        'ORCL': 0.16028,
        'PFE': 0.00207,
        'TSLA': 0.01057,
        'V': 0.02516,
        'WMT': 0.40532
        }, 
    objective_values={
        'Conditional Value at Risk': 0.007560866163075728
        }
)

and you can use the available methods of the Portfolio class, such as pie_plot:

optimal_ptf.pie_plot()

Contributing

This project has been boostrapped using this cookiecutter template.

Prerequisites

1. Set up Git to use SSH

1. Generate an SSH key and add the SSH key to your GitHub account.
2. Configure SSH to automatically load your SSH keys:

   cat << EOF >> ~/.ssh/config
   Host *
     AddKeysToAgent yes
     IgnoreUnknown UseKeychain
     UseKeychain yes
   EOF
   

2. Install Docker

1. Install Docker Desktop.
   • Enable Use Docker Compose V2 in Docker Desktop's preferences window.
   • Linux only:
     • Configure Docker to use the BuildKit build system. On macOS and Windows, BuildKit is enabled by default in Docker Desktop.
     • Export your user's user id and group id so that files created in the Dev Container are owned by your user:

       cat << EOF >> ~/.bashrc
       export UID=$(id --user)
       export GID=$(id --group)
       EOF
       

3. Install VS Code or PyCharm

1. Install VS Code and VS Code's Dev Containers extension. Alternatively, install PyCharm.
2. Optional: install a Nerd Font such as FiraCode Nerd Font and configure VS Code or configure PyCharm to use it.

Development environments

The following development environments are supported:

1. ⭐️ GitHub Codespaces: click on Code and select Create codespace to start a Dev Container with GitHub Codespaces.
2. ⭐️ Dev Container (with container volume): click on Open in Dev Containers to clone this repository in a container volume and create a Dev Container with VS Code.
3. Dev Container: clone this repository, open it with VS Code, and run Ctrl/⌘ + ⇧ + P → Dev Containers: Reopen in Container.
4. PyCharm: clone this repository, open it with PyCharm, and configure Docker Compose as a remote interpreter with the dev service.
5. Terminal: clone this repository, open it with your terminal, and run docker compose up --detach dev to start a Dev Container in the background, and then run docker compose exec dev zsh to open a shell prompt in the Dev Container.

Developing

• This project follows the Conventional Commits standard to automate Semantic Versioning and Keep A Changelog with Commitizen.
• Run poe from within the development environment to print a list of Poe the Poet tasks available to run on this project.
• Run poetry add {package} from within the development environment to install a run time dependency and add it to pyproject.toml and poetry.lock. Add --group test or --group dev to install a CI or development dependency, respectively.
• Run poetry update from within the development environment to upgrade all dependencies to the latest versions allowed by pyproject.toml.
• Run cz bump to bump the package's version, update the CHANGELOG.md, and create a git tag.

Similar projects

optitrader is built by keeping in mind the availability of other great open-source repositories, such as:

• Riskfolio-Lib
• PyPortfolioOpt