Building an AI Analyst in Python to Scale Your Research

Harness a Large Language Model (LLM) for Concise, Actionable Market Insights

Imagine it’s early Monday morning and you’re gearing up for a week in a volatile market. Your inbox is packed with research articles, each potentially containing crucial insights. However, there’s simply not enough time to read them all. What if you had a personal AI analyst capable of digesting these articles and delivering concise, actionable trading ideas? Today, we’ll build this tool—an AI-driven assistant designed to enhance your productivity by processing the bulk of data analysis.

We’ll demonstrate this using articles from SeekingAlpha, showing how to transform numerous reports into actionable insights, neatly organized in a table format for quick and informed decision-making.

Connecting to a Large Language Model

Portfolio managers and analysts often struggle with information overload. A Large Language Model (LLM) like OpenAI’s GPT can streamline this by automating the summarization of extensive research documents, focusing on the most pertinent information for your investment strategies. This approach greatly improves research efficiency.

Here’s how you connect to OpenAI’s model in Python:

from openai import OpenAI client = OpenAI() completion = client.chat.completions.create( model="gpt-3.5-turbo", messages=[ {"role": "system", "content": "You are an investment analyst, skilled in summarizing investment research into actionable items."}, {"role": "user", "content": "Summarize the latest findings on market trends in renewable energy."}, ] ) print(completion.choices[0].message.content) 

This configuration allows you to directly utilize OpenAI’s capabilities, tailored specifically to your analytical needs.

Customizing Our AI Analyst

Maximizing the utility of our AI analyst involves tailoring its responses to our specific requirements. This customization involves crafting precise “system” messages that guide the AI, directing it to focus on essential elements of the content and produce outputs that align with our analytical needs.

Implementing the ai_analyst_summarize Function

We’ll next implement the ai_analyst_summarize function to fetch structured summaries from OpenAI’s API, which can be easily integrated into a dataframe for swift decision-making:

def ai_analyst_summarize(article_text) -> dict...