CASE 2 - LIVE TRADING PLATFORM

BACKGROUND

The basis for this case is to explore different user cases of The Alpaca platform which is a modern, cloud-based trading platform that provides easy access to market data, order management, and trading APIs. The platform is designed to be highly scalable and customizable, making it an ideal foundation for a wide range of trading applications

PROBLEM DESCRIPTION

Participants in the hackathon will be tasked with creating new solutions based on the Alpaca platform that can help traders make better-informed decisions and execute trades more efficiently in addition to make powerful trading tools more available for a wide set of users. Solutions may include new trading algorithms, automated trading bots, mobile trading applications, or integrations with other financial platforms and services.

One of the key features of the Alpaca platform is its ease of use, and participants in the hackathon will be encouraged to build solutions that are equally user-friendly. Additionally, solutions should be designed to be highly scalable and adaptable to the changing needs of the market.

To help participants get started, they will have access to Alpaca's comprehensive documentation, trading APIs, and development tools. They may also have access to various datasets, market data feeds, and other resources to help them build their solutions.

GOALS AND OBJECTIVES

​​​​​The overall goal of this project is to create new tools, applications, and integrations around the Alpaca platform and provide users with new ways to trade.

Suggested goal - Use Alpaca's paper-trading API and create trading algorithm on top of it.

To achieve this the following activities are suggested:

• Project Specification

  • Obtain insights into the Alpaca platform.
  • Describe the business case of a proposed concept.

• System Architecture

  • Describe an overview of how a system can be built.

• Development of Minimum Viable Concept

  • ​​​Using Figma or Streamlit to demonstrate how the system can be deployed.

• Tips on what possible triggers to include in the trading algorithm

  • Volume of traded stocks
  • Real-time stock price
  • Historical data
  • Buy- and sell triggers