Stock Fundamentals And Portfolio Optimization App

Stock Analysis & Portfolio Optimization – Ratios, Screeners & Forecasting

What you'll learn

Analyze key financial ratios such as Debt to Equity, ROE, and PE Ratio for stock evaluation.

Build a stock screener by extracting and processing company financial data

Conduct market analysis using market cap, risk distribution, and sector-based analysis.

Implement portfolio optimization techniques for better investment decisions.

Train and evaluate machine learning models for stock selection and classification.

Apply deep learning (LSTM) for stock price forecasting and trend prediction.

Requirements

Python knowledge for Implementation

Description

Are you looking to master stock fundamentals and build a data-driven portfolio optimization app? This course, Stock Fundamentals and Portfolio Optimization App, is designed to help you analyze financial ratios, screen stocks, and apply machine learning models for stock selection and forecasting.Starting with key financial ratios such as Debt to Equity, ROE, PE Ratio, and Interest Coverage, you'll learn how to evaluate a company's financial health. You'll then progress to developing a stock screener, where you'll extract company financials, perform data cleaning, and integrate key financial metrics.The capstone project will guide you through building an end-to-end portfolio optimization application. You'll work with daily market data, perform exploratory data analysis (EDA), and filter relevant stocks using Python. Advanced topics include market cap analysis, risk rating distributions, and stock selection techniques.To enhance decision-making, the course covers data visualization techniques for understanding financial trends. You will also implement feature engineering and one-hot encoding to refine stock data before applying machine learning models. Classifier models, SHAP-based feature importance, and ROC curve evaluations will be used to select the best predictive models.Finally, you’ll explore deep learning-based stock forecasting with LSTMs, helping you predict market trends and stock movements. By the end of this course, you’ll have the skills to analyze stocks, optimize portfolios, and make data-driven investment decisions using Python. Whether you're a beginner or an experienced trader, this course will equip you with the tools needed to leverage data science for financial success.

Overview

Section 1: Cash Ratio

Lecture 1 Cash Ratio - Demo

Section 2: Current Ratio

Lecture 2 Current Ratio - Demo

Section 3: Debt to Asset Ratio

Lecture 3 Debt to Asset Ratio - Demo

Section 4: Debt to Equity Ratio

Lecture 4 Debt to Equity Ratio - Demo

Section 5: Interest Coverage Ratio

Lecture 5 Interest Coverage Ratio - Demo

Section 6: PB Ratio

Lecture 6 PB Ratio - Demo

Section 7: PE Ratio

Lecture 7 PE Ratio - Demo

Section 8: Price to Cashflow Ratio

Lecture 8 Price to Cashflow Ratio - Demo

Section 9: Price to Sales Ratio

Lecture 9 Price to Sales Ratio - Demo

Section 10: ROA Ratio

Lecture 10 ROA Ratio - Demo

Section 11: ROCE Ratio

Lecture 11 ROCE Ratio - Demo

Section 12: ROE Ratio

Lecture 12 ROE Ratio - Demo

Section 13: Screener - Cashflow

Lecture 13 Cashflow Screener

Section 14: Screener- Profit Growth

Lecture 14 Profit Growth Screener

Section 15: Fundamentals Screener- Portfolio Optimization Capstone Project

Lecture 15 Smart Stock Screener Walkthrough

Lecture 16 Steps for Building the App

Lecture 17 Python Packages Info

Lecture 18 Extract Company Finance Information

Lecture 19 Extract Company Details

Lecture 20 Calculating Dividend Growth

Lecture 21 Calculating Debt to Equity

Lecture 22 Filtering Relevant Stocks

Lecture 23 Extracting Daily Market Data

Lecture 24 Date Manipulation

Lecture 25 Add Financial Metrics

Lecture 26 Scrubbing Data

Lecture 27 Preliminary Market Analysis

Lecture 28 Market Cap Analysis Part 1

Lecture 29 Market Cap Analysis Part 2

Lecture 30 Add long short Indicative variable

Lecture 31 Filter Stock Tickers

Lecture 32 Market Analysis EDA

Lecture 33 Excercise - Average Market Capitalization

Lecture 34 Risk Rating Distribution

Lecture 35 Stock Selection and Data Blending

Lecture 36 Data Visualization Part 1

Lecture 37 Data Visualization Part 2

Lecture 38 One hot encoding Dataset

Lecture 39 Model Training

Lecture 40 Classifier Models

Lecture 41 Model Results

Lecture 42 Best Model Selection

Lecture 43 ROC Curve

Lecture 44 Feature Importance using SHAP

Lecture 45 Model Evaluation

Lecture 46 Stock Forecasting

Lecture 47 LSTM Stock Forecasting

Beginner to Intermediate Investors – If you are new to stock markets or want to improve your ability to analyze financial statements and ratios, this course will provide structured, hands-on learning.,Finance Professionals & Analysts – Enhance your skill set by integrating data-driven stock screening, market analysis, and risk assessment into your workflow.,Data Scientists & Machine Learning Enthusiasts – Learn how to apply ML models for stock selection, feature importance analysis, and stock price forecasting using financial data.,Students & Academics – If you’re studying finance, economics, or data science, this course will help you understand how to apply quantitative techniques for investment strategies.,Algorithmic Traders & Quantitative Analysts – Gain insights into data preprocessing, financial data extraction, and predictive modeling for smarter trading decisions.,Python Developers Interested in Finance – If you have programming experience and want to explore financial data analytics and portfolio management, this course will teach you practical implementation techniques.