The goal was to develop a Python desktop application that goes beyond basic cryptocurrency price tracking by combining market monitoring, technical analysis, and strategy research in a unified interface. I built Pulse Quant using Python and Tkinter, with interactive financial charts and a dark, modern UI. The application integrates public Binance market data, candlestick visualizations, EMA trend analysis, RSI momentum monitoring, market-regime classification, and rule-based BUY/SELL/HOLD signal explanations. It also includes a strategy backtesting interface with configurable RSI thresholds, simulated transaction fees and slippage, performance metrics, and a strategy-versus-buy-and-hold comparison. One of the main challenges was presenting multiple analytical components without overcrowding the interface. I refined the layout, adjusted chart proportions, improved panel spacing, and resolved clipped headings to create a cleaner, more cohesive desktop experience. I also incorporated demo mode and CSV export to support offline exploration and data analysis. The result is a single-file Python application that brings market visualization and strategy experimentation together in an accessible research workspace, demonstrating practical desktop development, analytical logic, and UI design.