Goal

1. Options Tools
• Compare stock options to identify the best choice under various scenarios.
• Provide a calculator to determine net exposure across multiple positions for the same stock.
• Display volatility history and create a volatility surface.
2. Stock Financials
• Compile all relevant data required for comprehensive stock analysis.
• Automatically generate a spreadsheet for any stock, with predefined defaults.
• Classify stocks to identify similar companies for peer comparison:
• Currently using GICS Industry and Sub-industry classifications.
• Develop a custom classification model for improved accuracy.

Description

• A web-based platform with two components for stock and options analysis.
• Stock Financials and Peer Comparison:
  • Aggregates financial data from online sources such as SEC filings, financial APIs, and Wikipedia.
  • Includes peer comparison tools to evaluate company performance within its sector or industry.
  • Generates detailed stock report PDFs summarizing financial data, peer metrics, and key insights.
• Options Analysis and Strategy Builder:
  • Integrates my options package to retrieve and display option chains for selected stocks.
  • Provides tools for analyzing stock options and creating strategies, such as straddles, spreads, and more.
  • Visualizes option data and strategy outcomes for decision-making.
• Technology Stack:
  • Backend: Django with Celery.
  • Database: PostgreSQL for data storage, with Redis for caching and Celery communication.
  • Frontend: JavaScript and HTMX for better performance responsiveness.
  • Deployment: Docker for containerization, with Gunicorn and Nginx for serving content, deployed on Google Cloud Platform.

Description

As a web developer, I have created financial websites featuring tools like stock screeners, performance rankers, and technical analysis platforms. These projects use financial data to streamline analysis and decision-making for users.

Collected time series and financial data from various sources, including APIs, web scraping, SEC filings, and other platforms, while incorporating charts, graphs, and customizations to enhance user experience.

This tool is currently in my CD Finance project. You can try it by visiting this link - For better results, test it with using well-known American companies such as AAPL, AMZN, GOOG, and NFLX.

You can also check out a demonstration of the tool by viewing a generated report on NKE here.

Goal

• Create a program that will populate a spreadsheet with all financial data needed for stock analysis

Description

• Created a program that automatically updates stock information in a SQLite database, providing inputs for financial models, including financial statements, stock data, valuation multiples, and industry peer comparisons to streamline analysis.
• Collected data from multiple sources, including SEC.gov, EDGAR, Yahoo Finance, financial APIs, and web scraping.
• Added peer analysis with snapshot summaries featuring metrics like P/E and EBITDA for quick multiple comparisons.
• Compiled recent stock data and financial statements (quarterly/annual IS, BS, CF), along with additional metrics such as earnings CAGR, beta, and stock volatility.
• Designed for quick discounted cash flow (DCF) analysis and peer comparisons.

Description

• A web-based platform for stock analysis and scoring, designed for trading across major indices: TSX 60, TSX Composite, NASDAQ, S&P 500, and Dow Jones.
• Data Collection: Aggregates stock data from multiple sites and financial APIs, integrating fundamentals and sector information.
• Performance Metrics: Calculates performance history, moving averages, and technical indicators to evaluate stock trends.
• Stock Scoring: Uses index weights, calculated indicators, and sector data to generate scores for trading decisions.
• Technology Stack:
  • Backend: Uses Celery for tasks and AWS Lambda functions to support data gathering and processing.
  • Database: PostgreSQL for data storage, with Redis for caching and Celery communication, ensuring performance and scalability.
  • Framework: Django with Celery for task management and deployment.
  • Deployment: Combines Gunicorn and Nginx for serving content, deployed on Google Cloud Platform.

Progress: Currently Beta Testing

Goal

• Develop an interactive bot that integrates Alexa with ChatGPT.
• Uses this integration to transform Alexa into a dynamic game host that controls gameplay, interacts with players, and manages the game's flow.

Description

• Coco is an interactive host that responds to commands, engages with players, and guides both conversations and the flow of the game.
• Uses Amazon's Alexa Skill Kit for Echo integration and utilizes AWS Lambda to manage backend logic and communication with ChatGPT.

Goal

• Utilize publicly available data to develop models that accurately predict used car prices.
• Compare various predictive modeling techniques to determine the best-performing model based on predictive power (Lowest Mean Squared Error - MSE).

Description

Goal

• Create a guide for volunteers and employees to identify, price, and process donated goods across all regional stores.
• Standardize price for all locations.

Description

• Created a pricing guide for hundreds of items by analyzing current and historic prices and comparing against competitors.

Progress: Using in CD Project: Click Here

Goal

• Analyze how options and their Greeks are priced by the market, including the mathematical principles behind them.
• Explore the impact of inputs on pricing, focusing on volatility calculations and comparing historic versus implied volatility.

Description

• Use the appropriate pricing methods for options, considering factors such as American vs. European styles, dividends, implied volatility, and interest rates.
• Developed programs to implement and compare various option pricing methods:
• Black-Scholes: For Greeks and implied volatility calculations.
• Numerical Methods: Including Binomial Tree and Monte Carlo Simulation.
• Approximations: Such as Bjerksund-Stensland and Barone-Adesi-Whaley models.
• Conducted volatility calibration and generated volatility surfaces to view market dynamics.