In the era of AI and quantitative trading, how can you leverage artificial intelligence to build your own stock investment tools? This article breaks down the four core steps—from data collection and strategy model development to building an automated trading system—and discusses the barriers and risks developers face.
Google has launched the open-source Agent Development Kit (ADK), a code-first AI Agent development toolkit supporting multiple languages like Python and TypeScript. It empowers developers to define Agent roles, connect tools, and architect predictable and stable multi-Agent collaboration systems as easily as writing traditional software.
Streamlit is an open-source Python framework that allows data scientists and AI engineers to transform analysis scripts, machine learning models, or AI apps into interactive web pages with just a few lines of Python code, without needing to learn frontend technologies. It features simple installation, rapid development, and is perfect for quick demonstrations and sharing data tools, now also offering free cloud deployment services.
Laravel is currently one of the most popular PHP frameworks, while Filament is an admin panel package built on top of Laravel. By combining the two, developers can not only rapidly generate management interfaces but also leverage the official Laravel AI SDK and community plugins to allow AI tools to participate directly in content generation, data queries, and even automated backend operations.
Wagtail is an open-source Content Management System built on Django. With Python at its core and a clean, intuitive backend interface, it has recently added the wagtail-ai package. This allows editors to use AI directly in the backend to assist with writing, generate SEO summaries, and automatically create image alt text, all while keeping human review in the loop.
Codex Record & Replay allows users to demonstrate a workflow on a Mac, which Codex then compiles into a reusable skill. This article introduces its use cases, limitations, suitable scenarios, and how to record more stable AI automated workflows.
Integrating third-party payments and logistics has always been a hurdle in e-commerce development. Using Taiwan's market-leading ECPay as an example, this article explains how to use AI tools to read official API docs, generate integration code, debug, and achieve secure and robust payment and logistics integration.
Faced with complex commercial sales data, how can you use AI for efficient analysis? This article compiles practical AI prompts across five major stages—from data cleaning and structure transformation to pattern modeling and qualitative interpretation—helping you turn chaotic raw data into high-value business decisions.
Decoupled architectures are now mainstream for modern e-commerce websites. This article explores how to use AI techniques to accelerate the development of decoupled e-commerce platforms, including defining API specifications, generating frontend UI and state management, and designing backend databases and APIs.
E-commerce operations require integrating data from multiple sources like orders, GA4 traffic, and ad spend. This article introduces how to use AI to write API integration documentation and establish scheduled data pipelines with Python to consolidate fragmented data into a local database, ultimately visualizing key metrics with Python charts.