Over the past few months, the amount of our users leveraging AI and our APIs to turn trading ideas into working projects has grown substantially Some are creating research workflows. Others are building paper trading systems, dashboards, or agentic workflows that connect market data, analysis, and execution logic.
What stands out is not one specific project. It is how quickly the community is experimenting, learning, and creating with Alpaca. In this first edition of The Weekly Roundup, we’re highlighting a few of the latest projects and the builders behind them.
What the Alpaca Community Shared This Week
- @Petko D. Petkov shared an experiment exploring an agentic trading system that executes paper trades through Alpaca while documenting the agent’s activity in a public blog. The project looks at how AI agents can support paper trading workflows while tracking activity, reviewing results, and refining the system over time.
- @Tzu-Yu Kao shared an AI SuperAgent for investors built during the AMS Tech Week Broker Hackathon that leverages Alpaca market data and news. Instead of centering the experience around stock recommendations, the project uses a portfolio knowledge graph to help users visualize relationships between holdings, explore different scenarios, and better understand portfolio risk.
- @Gaurav Mehta announced the launch of OptionX US, which allows US derivatives traders to connect their Alpaca accounts through the platform. The launch is another example of developers building investing applications on top of Alpaca’s infrastructure.
- @Jonto shared an AI-assisted trading workflow being tested with Alpaca’s paper trading environment, where users can define sources, select a strategy, and review proposed trades before execution.
- @Yago published an open-source Alpaca MCP library with Zod schemas generated from Alpaca’s OpenAPI specs, exposing tools across orders, positions, bars, snapshots, and portfolio history.
- @Rudraksh Mishra shared an engineering lesson on using Alpaca OCO bracket orders to manage risk logic when local trading bot processes stop running
Use Cases from the Alpaca Community
We’ve also worked with community members to showcase how they’ve used Alpaca’s APIs to bring their idea to life. Below is a deep dive on what they’ve built and what they learned while developing trading systems, AI workflows, backtesting tools, and interactive dashboards connected to their Alpaca account.
For readers looking to learn from other builders, here are a few examples:
- Agent M: An Autonomous Multi-Agent Trading Platform Using Alpaca
- Building NightWatcher V2: A Multi-Agent Trading System with Alpaca
- Building a Multi-Agent AI Trading System on Alpaca
- From Value Investing to Systematic Trading: Building a Multi-Strategy Backtesting Dashboard with AI and Alpaca
- How to Get Started with Machine Learning in Trading
Building with The Alpaca Community
At Alpaca, we focus on being the infrastructure layer behind builders. Our APIs help developers, traders, and builders develop applications that connect to market data, trading, account activity, and financial workflows.
Seeing the community building with Alpaca’s APIs gives us a closer look at how financial applications and services are evolving in real time. Some people are using paper trading to test ideas. Some are creating new interfaces. Others are exploring how AI agents can support research, strategy review, and operational tasks.
If you are building something with Alpaca’s APIs, tag us on LinkedIn or X, or drop a post in the /alpacamarkets Subreddit or Slack Community. We’d love to feature your project in a future edition of the Community Weekly Blog.
Alpaca’s AI Tools for Building
We’ve also been building features and products to support AI and agentic trading with Alpaca’s infrastructure. Check out Alpaca’s Skills Library for AI Agents, MCP Server, and CLI for more.
Sign Up for an Account
If you’re looking to get started with Alpaca, sign up for Alpaca’s Trading API account, connect to Alpaca’s Trading API, and start paper trading.
Please note that this article is for educational and general informational purposes only. The examples above are for illustrative purposes only. The views and opinions expressed are those of the author and do not reflect or represent the views and opinions of Alpaca. Alpaca does not recommend any specific securities or investment strategies. Testimonials and examples used are for illustrative purposes only and are not indicative of future performance or success.
Past hypothetical backtest results do not guarantee future returns, and actual results may vary from the analysis.
Insights generated by our CLI, MCP server, and connected AI agents are for educational and informational purposes only and should not be taken as investment advice. Alpaca does not recommend any specific securities or investment strategies. Past performance from models does not guarantee future results. Please conduct your own due diligence before making any decisions. All firms mentioned operate independently and are not liable for one another.
*The Paper Trading API is offered by AlpacaDB, Inc. and does not require real money or permit a user to transact in real securities in the market. Providing use of the Paper Trading API is not an offer or solicitation to buy or sell securities, securities derivative or futures products of any kind, or any type of trading or investment advice, recommendation or strategy, given or in any manner endorsed by AlpacaDB, Inc. or any AlpacaDB, Inc. affiliate and the information made available through the Paper Trading API is not an offer or solicitation of any kind in any jurisdiction where AlpacaDB, Inc. or any AlpacaDB, Inc. affiliate (collectively, “Alpaca”) is not authorized to do business.
Alpaca and the entities referenced in this article are not affiliated and are not responsible for the liabilities of the others.
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