Stock Analysis Solution on AWS with AI

Introduction

Manual stock analysis can be time-consuming, error-prone, and difficult to scale. Automating this process not only saves time but also ensures consistent, repeatable insights, enabling better decision-making.

In this article, we’ll explore how to analyse stocks effectively and set up an automated workflow to support daily analysis. You’ll learn the end-to-end process—from understanding the analysis approach to deploying a solution that delivers actionable insights every day.

Additionally, we’ll demonstrate how to train a simple prediction model. Specifically, we’ll use a linear regression model to forecast stock performance using historical data and technical indicators.

Note: This is a proof-of-concept setup intended for learning purposes only and should not be used for actual investment decisions.

High-Level Architecture

This automated stock analysis system is designed to fetch, process, and analyse stock data, generate predictions, and deliver daily insights—all without manual intervention. Below is a high-level overview of the architecture, its layers, purposes, responsibilities, and the tools used.

This tabular format provides a clear overview of the architecture layers, their purposes, and responsibilities.

Layer	Purpose	Responsibilities	Tools/Tech
Automated Trigger	Initiates the workflow automatically	Schedule daily execution and ensure the workflow runs without manual intervention	AWS Lambda, CloudWatch
Data Ingestion Layer	Fetch and prepare stock data	Retrieve historical stock data from Yahoo Finance and store raw backups	Python, S3
Analysis Engine	Process data and apply analysis logic	Compute technical indicators (RSI, EMA, MACD, ATR) and run machine learning models (Linear Regression)	Python, Pandas, scikit-learn(ML)
Backtesting Module	Evaluate strategy performance	Simulate the strategy on historical data and calculate metrics like CAGR, Sharpe ratio, and drawdown	Python, Backtrader
Reporting Layer	Present results in a user-friendly format	Generate structured tables, format data, and create CSV reports for storage and distribution	Python, Pandas
Storage Layer	Persist data for long-term access	Store processed results in DynamoDB and maintain historical reports in S3	DynamoDB, S3
Deployment & Automation	Ensure scalability, reliability, and monitoring	Execute workflows, monitor system health, and manage storage	AWS Lambda, CloudWatch, S3, DynamoDB

Optional Feature: Users can also request the analysis of a single stock on demand with manual input.

Prediction Rules

Applying Prediction Rules as a Dataflow

This dataflow diagram represents the example as a step-by-step filtering process. Each stock characteristic is evaluated against the prediction rules, and only stocks passing all criteria are included in the final recommendation.

Summary

This article demonstrates how Python and AWS can be combined to build a scalable, automated workflow for analysing and forecasting stock performance. By automating the process, you can save time, ensure consistency, and generate actionable insights daily—without manual effort.

Key takeaways from this project include:

Automation: Daily stock analysis is fully automated using AWS Lambda and CloudWatch.
Scalability: The solution can handle increasing workloads efficiently by leveraging serverless and cloud-based services.
Cost-effectiveness: A serverless architecture minimises operational costs while providing reliable performance.
Machine Learning Integration: Linear Regression models provide a simple way to forecast stock trends based on historical data and technical indicators.
Flexibility: The system can be extended to include additional models, technical indicators, or even real-time data streams.

Note: This is a proof-of-concept setup intended for learning purposes only and should not be used for actual investment decisions.

By following this approach, you gain practical experience in combining data ingestion, analysis, machine learning, backtesting, reporting, and cloud deployment into a single, end-to-end solution. It’s an excellent foundation for further exploration in automated financial analysis or predictive modelling.