Amir Kahriman
Backend Devleoper
Collaborated with mentors and peers for feedback and guidance
Developed in a few days as a side project
This project is a Python-based algorithmic trading tool leveraging the Jupyter environment. Using data from the S&P 500 stocks and the IEX Cloud API, it assists in making investment decisions by providing insights into stock prices, market capitalization, and suggesting the number of shares to buy based on a user-defined portfolio size. The tool generates a comprehensive report in an Excel format for easy analysis and decision-making.
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Utilize up-to-date stock data from the IEX Cloud API for informed investment decisions.
Determine the optimal allocation of funds across a diversified portfolio of stocks to maximize returns and minimize risk.
Implement algorithmic trading strategies to execute buy and sell orders based on predefined criteria, removing the need for manual intervention.
Tailor trading algorithms to specific investment goals and risk tolerance levels with adjustable parameters and thresholds.
Evaluate the performance of trading strategies using historical stock data to refine and improve algorithmic models.
Incorporate risk management techniques such as stop-loss orders and position sizing algorithms to protect against significant losses.
Explore the source code of the project on GitHub to go deeper into its structure, implementation, and contributions. Browse through commits, branches, and issues to gain insights into the development process.
The application is meticulously designed for effortless deployment and seamless usage across diverse devices. Please refer to the comprehensive guidelines provided in the GitHub readme, meticulously crafted to streamline the quick start process and ensure optimal user experience.
Last updated on March 19, 2024, 10:12 AM
