Bridging Technology &
Capital.

Class 11 Student | Tech Enthusiast | Decoding Stocks using Tech

While my younger brother looks up at the stars to map constellations, I prefer looking at the ticker tape to find patterns.

Growing up in a home where "digital transformation" and "portfolio management" were dinner table topics, I had a front-row seat to how technology drives global finance. But I didn't want to just inherit the vocabulary; I wanted to build the tools.

Currently, I am bridging the gap between my "head start" and hard work. I spend my nights learning Python, building scripts to track stock volatility and translate market chaos into logic.

I am Dhruv Saxena. I am an athlete, a debater, and a coder. I’m not just here to watch the economy evolve; I’m learning to write the code that will drive it.

View My Work

Beyond the Vocabulary

Growing up, I had a distinct head start. With parents leading in Investment Banking and Pharma Tech, words like "scalability," "digital transformation," and "portfolio yields" were normal dinner conversation.

But knowing the words isn't the same as writing the code.

That realization was my turning point. I didn't want to rely on exposure; I wanted technical mastery. That drive sent me down the rabbit hole of Computer Science.

I have learned that the discipline required to master a complex chord progression on my guitar is the same discipline needed to debug an algorithm. The strategy required to predict an opponent’s bowling in cricket is the same mindset needed to spot a market trend.

Key Projects

Financial Analytics

The Hype-Check Algorithm

Problem: Novice investors often mistake high-volatility "hype" stocks for safe, stable investments, leading to capital loss.
Action: Coded a Python algorithm using Standard Deviation to analyze 30-day price history and classify stocks as "Safe" or "Speculative."
Result: Created a clear "Traffic Light" decision tool that helps beginners objectively assess risk before investing.

An AI-driven Portfolio Agent

Problem: Purely statistical trading models often fail because they cannot account for the sudden, unpredictable impact of breaking news and geopolitical shifts.
Action: Building a Python-based AI agent that evaluates historical stock trends (like variance and covariance) while simultaneously running sentiment analysis on current financial headlines.

Social Innovation

Donation Campaign

Problem: Hard-working daily wage earners were going hungry to preserve their dignity, refusing free charity despite severe financial hardship.
Action: Spearheading a digital advocacy campaign for "Change With One Meal," highlighting their ₹10 subsidized meal model to bridge the ₹28 operational deficit.
Result: Successfully mobilized social network community support to help sustain the daily distribution of over 1,200 nutritious meals.

Research & Policy

Designed for Debt

Problem: "Buy Now, Pay Later" apps often use deceptive "Dark Pattern" UI designs to trap students in impulse debt cycles.
Action: Conducted a behavioral audit and user survey of 50+ Gen Z peers to identify specific predatory design elements in top Fintech apps.
Result: Demonstrated a direct correlation between specific UI choices (like hidden fees) and increased financial default risk for youth.

Bridging Technology &Capital.