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.

Table of Contents

• Using
  • Streamlit Dashboard
  • Python Package
  • Python CLI
  • REST API
• Example
• Contributing

Using

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.

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.

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.

Similar projects

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

• Riskfolio-Lib
• PyPortfolioOpt