About This Project

This site is mainly dedicated to my kids, to help them learn about investing. I've always enjoyed the stock market and I'm heavily involved with data visualization at my work place. It made sense for me to develop something like this so my kids could see for themselves how investing works. My goal is to provide a few fun, interactive tools for analyzing two of my favorite strategies: Dollar Cost Averaging (DCA) and Buy-on-Dip (BOD). I've tried to develop a several calculators and visualizations to help people understand different investment methodologies and their historical performance. I do not guarantee the accuracy of the data or calculations, so please use this site for educational purposes only.

Disclaimers

This is not financial advice or investment recommendations.
This is a personal project to help me learn web development and financial data analysis.
This site was developed as a learning project using historical market data for educational purposes only.
I am not a licensed financial advisor or professional investment manager.
Past performance does not guarantee future results.
All investment strategies carry risk, including potential loss of principal.
Please consult with a qualified financial advisor before making investment decisions.

Project: Stock Market Investment Strategy Analysis

Data Collection: Retrieve historical stock price data using Yahoo Finance API (yfinance) for major ETFs and individual stocks.
Strategy Implementation: Implement Dollar Cost Averaging (DCA) and Buy-on-Dip (BOD) investment strategies with configurable parameters.
Performance Analysis: Calculate portfolio values, gains/losses, and compare strategy effectiveness across different market conditions.
Visualization: Create interactive charts using ECharts library to display portfolio growth, purchase timing, and strategy comparisons.
Advanced Calculators: Provide customizable tools for testing different investment amounts, timeframes, and trigger conditions.

Investment Strategies Analyzed

Dollar Cost Averaging (DCA): Regular periodic investments of fixed dollar amounts regardless of market conditions. Default strategy invests $25 weekly with customizable investment days.
Buy-on-Dip (BOD): Purchase shares when stock price declines by specified percentages from previous day's close. Default strategy buys 1 share per 1-5% decline with configurable decline thresholds.
Multi-Ticker Analysis: Compare strategy performance across multiple ETFs such as 'QQQ' (NASDAQ 100), 'SPLG' (S&P500), and 'XLG' (S&P500 top 50) simultaneously with unified charting.
Advanced Calculators: Fully customizable tools allowing users to test their own investment amounts, frequencies, decline percentages, and date ranges.
Market Pattern Analysis: Advanced metrics including up/down day analysis, Monday-Friday success rates, and week-over-week profitability patterns.

Advanced Analytics Features

Up Days vs Down Days: Analysis of daily market direction by comparing opening to closing prices within selected date ranges, showing trend patterns and market volatility.
Monday-Friday Success Rate: Measures how often buying at Monday's close and selling at Friday's close would be profitable, revealing weekly market cycles and trading patterns.
Week-over-Week Profitability:
- DCA Strategy: Analyzes market performance between consecutive weeks based on total weeks available in the selected date range, not just investment weeks.
- BOD Strategy: Tracks individual trade profitability after one week from execution, measuring trade timing effectiveness.
Date Range Filtering: All metrics are calculated based on user-selected start and end dates, ensuring accurate analysis within chosen time periods.
Customizable Parameters: DCA tools allow selection of investment day (Monday-Friday) and any investment amount; BOD tools support up to 10 different decline percentages with custom share quantities.
Real-time Calculations: Instant strategy recalculation as users modify parameters, with live chart updates and immediate metric refreshes.
Detailed Trade Tracking: Complete transaction history with precise purchase prices, decline percentages, and cumulative performance metrics.

Technical Implementation

Data Processing: Python ETL pipeline using pandas and yfinance for historical data collection, with enhanced CSV generation including weekday analysis and price averaging.
Frontend Framework: Responsive HTML/CSS/JavaScript with ECharts 5.x for interactive visualizations and professional design system.
Calculation Engine: Advanced JavaScript-based portfolio calculations with precise currency formatting, trade tracking, and market pattern analysis.
User Interface: Professional design system with unified CSS styling, consistent navigation classes (.main-nav, .nav-link), and standardized title/subtitle formatting across all 8 analysis pages for seamless user experience.
Mobile Optimization: Responsive design ensuring accessibility across all device types with adaptive navigation and optimized layouts.
Advanced Metrics: Real-time calculation of market patterns including daily trends, weekly cycles, and short-term profitability analysis.
Data Export: Comprehensive CSV export functionality with detailed transaction history, trade execution details, and performance metrics.

Data Sources and Methodology

Description: This project analyzes historical stock market data to compare the effectiveness of different investment strategies. The analysis uses historical price data from Yahoo Finance to simulate how these strategies would have performed over various time periods. All calculations are based solely on historical price data and do not account for dividends, transaction fees, taxes, or other real-world trading costs. The goal is to provide educational insights into long-term investment strategies and help users understand the potential benefits and risks of systematic investing approaches. This toolkit is designed for educational purposes and to demonstrate data analysis and web development skills.

Data Source: Yahoo Finance historical price data via yfinance Python library.
Analysis Period: Configurable date ranges with focus on multi-year performance comparisons.
Strategy Simulation: Precise calculation of share purchases, portfolio values, and performance metrics.
Visualization: Interactive charts showing portfolio growth, purchase timing, and comparative analysis.
Export Functionality: CSV download capability for detailed transaction history and further analysis.
Mobile Optimization: Responsive design ensuring accessibility across all device types.

ETF & Stock Summary

Below is an overview of selected ETFs and equities in our portfolio:

Ticker	Name & Strategy
TQQQ	ProShares UltraPro QQQ – 3× daily leveraged exposure to NASDAQ-100 (short-term use only).
UPRO	ProShares UltraPro S&P 500 – 3× daily leveraged exposure to the S&P 500 (short-term use only).
GGLL	Direxion Daily GOOGL Bull 2× – Leverages Alphabet's daily returns.
MSFU	Direxion Daily MSFT Bull – 2× (or possibly 1.5×) leveraged returns of Microsoft stock.
QQQ	Invesco QQQ – Nasdaq-100 index fund; tech-heavy, low-cost, long-term exposure.
FBCG	Fidelity Blue Chip Growth ETF – Actively managed large-cap growth equity ETF.
QTOP	iShares Nasdaq Top 30 ETF – Tracks top 30 NASDAQ-100 companies.
MGK	Vanguard Mega Cap Growth ETF – Mega-cap U.S. growth stocks exposure.
VGT	Vanguard Information Technology ETF – Broad tech-sector index fund across all market caps.
SPLG	SPDR Portfolio S&P 500 ETF – Low-cost S&P 500 index tracker.
XLG	Invesco S&P 500 Top 50 ETF – Concentrated top-50 S&P 500 companies exposure.
TOPT	iShares Top 20 U.S. Stocks ETF – Focuses on the 20 largest U.S. companies.
MSFT	Microsoft Corp – Large-cap tech and cloud services leader.
GOOGL	Alphabet Inc. (Class A) – Parent of Google; ad and AI giant.
HSBC	HSBC Holdings plc – Global banking and financial services.
ALLY	Ally Financial Inc. – Digital-first auto finance and banking services.

Educational Purpose

This project serves as both a learning exercise in web development and a practical tool for understanding investment strategies. It demonstrates skills in data analysis, JavaScript programming, responsive web design, and financial calculation methodologies. The interactive nature of the tools helps users visualize how different approaches to investing might perform under various market conditions.