From Raw Data to Valuable Insights: How Generative AI is Transforming Market Analysis in Trading

In the dynamic field of trading, one of our clients sought to leverage the capabilities of generative AI to efficiently synthesize and summarize volumes of textual, graphical, and image information. The goal was to reduce these volumes from hundreds of pages to a few comprehensible pages, utilizing Cloud environments and existing DevOps tools.

“Quickly invoke AI models and execute inferences without technical complexity”

“Ensure tasks are performed in a secure environment controlled by the client”

“Easily configure processing steps to adapt to varying workloads”

“Provide a user-friendly graphical environment for configuring workflows and API actions”

To address our client’s challenges, we implemented a robust technical solution by leveraging advanced AWS services and cutting-edge AI frameworks. Here is an overview of the key components and their integration:

Cloud Infrastructure Utilizing AWS Bedrock : Bedrock is a platform offering a selection of foundational models provided by industry leaders such as Meta, Llama, Anthropic, A21Labs, Cohere, and Stability AI. We specifically chose Meta and Cohere models for their ability to handle large volumes of data and produce accurate summaries. By using Bedrock, we were able to fine-tune the model parameters to fit the client’s specific data, ensuring maximum relevance of the results.

Workflow Automation with AWS StepFunction AWS StepFunction is a visual workflow management service that facilitates the orchestration of microservices, process automation, and the creation of data and machine learning pipelines. We designed complex workflows with StepFunction to orchestrate various stages of data processing, including ingestion, transformation, analysis, and summary generation. By integrating AWS Lambda, StepFunction enabled parallel task execution and result merging before presentation. Additionally, this service offered real-time workflow monitoring and auditing capabilities to ensure process compliance and efficiency.

Model Deployment and Fine-Tuning: Hugging Face and Langchain We used Hugging Face to deploy advanced text processing models, which facilitated rapid and efficient model integration into the client’s cloud environment. The models were finely tuned to enhance inference accuracy. The Langchain framework was employed to develop interactive applications powered by LLMs (Large Language Models), creating intuitive user interfaces for interacting with model-processed data.

Generative AI-Specific Services For the generation of weekly market reports, each received document is stored in a vector database. Bedrock models analyze these documents to produce structured weekly summaries by product. Automation via StepFunction and AWS Lambda allows for parallel processing of hundreds of documents, optimizing speed and efficiency. Additionally, we developed a specialized chatbot using AI models to answer questions based solely on documents previously uploaded by the analyst, ensuring accurate and contextual responses based on the most recent and relevant data.