UNDEGREED: DAY-26 "SIGMOIDS, QUANT & MARKETS"

Day 26 was about building systems and discipline across domains. I coded logistic regression from scratch in Python, breaking it down from the sigmoid function to gradient descent, prediction logic, and visualizing the decision boundary to understand what the model is actually learning. Alongside that, I worked through core quant finance functions—bond pricing, loan and mortgage pricing, preferred and common stock valuation, portfolio returns, and forward pricing—connecting formulas to real market intuition. In my DSA session, I wrapped up graphs by revisiting Flood Fill and Course Schedule, focusing on how traversal and cycle detection actually work in practice, then implemented and submitted both successfully. I also returned to trading with a disciplined demo mindset after losses, focused on risk control rather than emotions, followed by language practice to extend my streak and a push-day gym session to end the day strong. This series is about showing the real process—thinking, failing, learning, and building—one day at a time.

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