🎬 AI-Powered Stock Price Movement Prediction: Playwright + Python + Claude Desktop LLM + MCP Server Demo

🎬 AI-Powered Stock Price Movement Prediction

Playwright + Python + Claude Desktop LLM + MCP Server Demo

🚀 Watch the AI-Powered Prediction System in Action!

In this demo, I showcase a complete end-to-end pipeline that predicts stock price movements using modern AI, machine learning, web automation, and Model Context Protocol (MCP) technologies.

The system analyzes Reliance Industries Ltd (RIL) stock data scraped from BSEIndia.com and transforms the collected information into easy-to-understand, human-readable predictions and insights.

🔧 TOOLS & TECHNOLOGIES USED

🎭

Playwright + Python

Web automation and scraping of live stock market data.

📈

Machine Learning

Predictive modeling for forecasting future closing prices.

🤖

Claude Desktop LLM

AI-powered analysis, interpretation, and summarization of results.

🔌

Local MCP Server

Custom MCP server connecting the automation, data, and AI components.

📊 WHAT THIS DEMO COVERS

🔷 Real-time data scraping from BSEIndia.com

🔷 Automated capture of market depth and financial data

🔷 Generation of analytical visualizations

⭐ Open / High / Low / Close Price Comparison Chart

⭐ Trading Volume & Spread Analysis

⭐ Future Close Price Predictions Table

🔷 AI-powered summarization into actionable insights

🛠️ MCP SERVER ARCHITECTURE

⚡

Tool 1: run_playwright_test

Executes the Playwright automation script to collect and process stock market data.

🧠

Tool 2: summarize_outputs

Processes analytical outputs and visualizations for interpretation by the Claude LLM.

🔄 END-TO-END AI WORKFLOW

🌐 BSEIndia.com   →   🎭 Playwright   →   🐍 Python   →   📊 ML Model   →   🔌 MCP Server   →   🤖 Claude Desktop   →   💡 AI Insights

⚠️ Disclaimer: This demonstration is for educational and technology demonstration purposes only. Stock price predictions generated by machine-learning or AI systems are not guaranteed and should not be considered financial or investment advice.

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💰 SAP FINANCE • TEST AUTOMATION

🎬 SAP S/4HANA Finance Demo: AR, AP & Financial Statements Automation with Tricentis Tosca

Explore end-to-end SAP S/4HANA Finance process automation covering Accounts Receivable, Accounts Payable and Financial Statements using Tricentis Tosca.

🏢 SAP S/4HANA 💳 Accounts Receivable 💰 Accounts Payable 📊 Financial Statements 🧪 Tricentis Tosca

▶️ SAP S/4HANA Finance Automation Demo

● WATCH DEMO

🔷 Overview

Demonstrating SAP S/4HANA's Accounts Receivable, Accounts Payable, Balance Sheet and Income Statement dashboards while automating financial processes using Tricentis Tosca.

💼

SAP S/4HANA Finance Processes

Three core finance scenarios demonstrated through automation

01

💳

Accounts Receivable

Automate and validate SAP S/4HANA receivables processes and verify corresponding financial information.

02

💰

Accounts Payable

Automate payable-related business processes and validate the resulting financial transactions.

03

📊

Financial Statements

Validate Balance Sheet and Income Statement overview dashboards as part of the automated finance workflow.

SAP S/4HANA → Tosca → Test Execution → Finance Validation

MODEL-BASED TEST AUTOMATION

🤖 Powered by Tricentis Tosca

The automation uses Tosca's model-based test automation approach to execute and validate SAP S/4HANA financial processes. The complete execution is performed directly through Tosca, enabling structured end-to-end validation of the finance workflows.

💳

Test Case 01 Receivables automation

💰

Test Case 02 Payables automation

📊

Test Case 03 Financial Statements validation

🧪

Automation Technology

Technologies used in the SAP Finance automation demo

🏢 SAP S/4HANA 🧪 Tricentis Tosca 🤖 Model-Based Testing 💳 Accounts Receivable 💰 Accounts Payable 📊 Financial Statements 🔄 End-to-End Automation

🚀 Key Takeaway

This demonstration shows how Tricentis Tosca can be used to automate SAP S/4HANA Finance scenarios across Receivables, Payables and Financial Statements, providing an end-to-end approach to business-process validation.

🌟

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 Using MCP Server &Tools, executed Bank Deposit & Funds Transfer, with GitHub Copilot & Claude AI LLM

  ✅ MCP Server setup: Created a MCP server with three tools (deposit, withdraw, fund-transfer) that call the bank app APIs. 

✅ Code-base & Integration: Bank Application in Java + JavaScript, integrated with GitHub Copilot and Claude Desktop for orchestration. 

✅ Validation Layers: Every tool triggers API, database, and Selenium UI (POM) validations. 

🔵 ✔️ Example — Deposit: "Deposit 1000 → account A98D5": API, DB, and UI tests run; summary logged. 

🔵 ✔️ Example — Fund transfer: "Transfer 1000 → I6728C→ A98D5": API, DB (source & target), and UI tests run for both accounts; summary logged. 

🔵 ✔️ Claude Desktop runs the same flow — API, DB, UI validations, transaction history and overall test results reported. 

✅ Outcome: End-To-End demo showing LLM-driven orchestration of MCP Server & Tools + Multi-layer verification (API → DB → Selenium UI) with clear pass/fail summaries.

 

 

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📈 Using Selenium and Pandas to Evaluate Profitable Investment Decisions in DITQ Stock

 📈 Using Selenium and Pandas to Evaluate Profitable Investment Decisions in DITQ 

 

Stock Analyzing whether a stock such as DITQ is a profitable investment often requires up-to-date market data, historical patterns, and automated data extraction. By integrating Selenium, Pandas, and supporting Python libraries, investors can build a reliable pipeline for collecting, analyzing, and visualizing stock trends.  

This workflow combines web automation, data cleaning, and visual analytics to help you determine whether a stock is worth buying. 

🔷 Key Steps in the Selenium + Pandas Stock-Analysis Workflow 

🔹 Data Extraction with Selenium 

🔹 Using Python Requests (Where Possible) 

🔹 Data Cleaning and Structuring with Pandas 

🔹 Visualizing Stock Trends with Matplotlib 

🔹 Decision-Making for DITQ Stock 

🔷 Example Workflow Summary 

✔️ Step 1: Selenium loads a financial site and grabs live DITQ price data 

✔️ Step 2: Data is parsed and stored into Pandas DataFrames 

✔️ Step 3: Pandas computes indicators for trend evaluation 

✔️ Step 4: Matplotlib visualizes price patterns 

✔️ Step 5: Automated rules decide if DITQ is a potential buy

 

 

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VSCode Integration with Local MCP Server To Automate the P2P Business Process Flow in SAP S/4HANA

 

Overview: 

🔵 Integrate VS Code with a local AI LLM to automate the P2P (Procure-to-Pay) process flow in SAP S/4HANA. 

🔵 A local MCP server is created to host AI tools. 

🔵 The MCP server registers “test” as a tool for execution. 

🔵 A CLI interface is implemented to manage standard input/output (STD I/O). 

🔵 The CLI converts user commands into instructions understood by the MCP server. 

🔵 The MCP server receives the converted commands and executes the “test” tool. 

🔵 Results flow from the MCP server back through the CLI into VS Code, enabling automated workflow execution. 

 

Business Process Flows Automated in SAP S/4HANA: 

🔵 Purchase-to-Pay (P2P) 

 

 

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⚡ SAP • TEST AUTOMATION • AI

SAP S/4HANA Business Process Flow Automation using Playwright MCP Agent

Explore how SAP S/4HANA business process flows can be automated using Playwright, JavaScript, TypeScript and the Model Context Protocol (MCP) Agent.

🎭 Playwright 🤖 MCP Agent 🔧 MCP Server 📘 TypeScript ⚡ JavaScript 🏢 SAP S/4HANA

▶️ SAP S/4HANA Automation Demo

● END-TO-END DEMO

📋 Overview

SAP S/4HANA business process flows are automated using Playwright, which is controlled by the MCP Agent. This approach brings AI-assisted interaction and browser automation together for enterprise business-process testing.

🤖

Automation Technology

Modern browser automation powered by MCP

⚡

JavaScript & TypeScript

The automation implementation uses JavaScript and TypeScript to create maintainable and scalable browser automation workflows.

🎭

Playwright Automation

Playwright provides browser-level automation capabilities for interacting with SAP S/4HANA business applications.

🤖

MCP Agent

The MCP Agent controls and orchestrates the Playwright automation flow, enabling an AI-assisted automation approach.

🔧

MCP Server

The MCP Server acts as the integration layer between the automation capabilities and the agent-driven workflow.

⚡ JavaScript 📘 TypeScript 🎭 Playwright 🤖 MCP Agent 🔧 MCP Server 🏢 SAP S/4HANA

🔄

Business Process Flows Automated

Enterprise workflows automated inside SAP S/4HANA

01

🛒

Purchase-to-Pay

Automate the procurement lifecycle from purchasing activities through the payment process.

02

💰

Order-to-Cash

Validate the end-to-end sales process from customer order through fulfillment and financial processing.

03

📊

Project Management

Automate SAP project-related workflows and validate business-process execution through browser automation.

SAP S/4HANA → MCP Agent → Playwright → Business Flow

AI-ASSISTED TEST AUTOMATION

🚀 Connecting MCP Intelligence with Browser Automation

The combination of MCP Agent technology and Playwright creates an automation layer capable of interacting with enterprise applications while keeping the underlying browser automation implementation structured and reusable.

🌟

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Playwright OAuth 2.0 Security Tests Demo

 This demo illustrates how OAuth 2.0 is by passed and data are retrieved through REST API via Playwright

 

Critical Vulnerability Alert for Apache Tomcat

INTRODUCTION

Apache Tomcat, an essential open-source web server and servlet container, powers much of the web with its implementation of Jakarta Servlet, Jakarta Expression Language, and WebSocket technologies. Recent statistics reveal that 48% of developers rely on this "pure Java" HTTP web server environment, which allows Java code to run seamlessly. However, a critical vulnerability has surfaced that requires immediate attention of users and developers alike.

UNDERSTANDING THE VULNERABILITY: PATH EQUIVALENCE

The identified vulnerability, termed Path Equivalence, can lead to severe consequences, including Remote Code Execution (RCE) and information disclosure. The issue is particularly alarming due to its ability to allow malicious content to be added to uploaded files via the write-enabled Default Servlet in Apache Tomcat.

CONDITIONS FOR EXPLOITATION

The vulnerability manifests under specific conditions, primarily in certain GitHub projects that meet the following criteria:

1. Write permissions enabled for the default servlet.
2. Support for partial PUT.
3. Security-sensitive uploads directed towards a sub-directory of a public upload target URL.
4. Security-sensitive files being uploaded with a partial PUT.

Under these conditions, a malicious actor could exploit the vulnerability to view sensitive files or inject harmful content, thus executing arbitrary code.

IMPLICATIONS OF THE VULNERABILITY

The implications of this vulnerability are severe. An attacker could, if exploitation conditions are met, gain unauthorized access to sensitive information or compromise the integrity of files on the server. This necessitates a proactive approach to securing applications running under affected versions of Apache Tomcat.

AFFECTED VERSIONS AND REQUIRED ACTIONS

The after Apache Tomcat versions are affected:

• Apache Tomcat 11.0.0-M1 to 11.0.2 (fixed in 11.0.3 or later)
• Apache Tomcat 10.1.0-M1 to 10.1.34 (fixed in 10.1.35 or later)
• Apache Tomcat 9.0.0-M1 to 9.0.98 (fixed in 9.0.99 or later)

MITIGATION STEPS

To mitigate the risk associated with this vulnerability, it is highly recommended for users to update their installations to the fixed versions as mentioned above. Running outdated versions exposes systems to potential exploits that could be disastrous for both application integrity and data security.

CONCLUSION AND CALL TO ACTION

Considering this critical security vulnerability, it is imperative for all developers and organizations utilizing Apache Tomcat to take immediate action. Ensure your installations are updated to the latest patched versions.

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