DEV Community: Utsav Mehta (rockingUT)

🚀 Turning Data Into Wealth: How to Build a Mutual Fund Strategy with Python

Utsav Mehta (rockingUT) — Tue, 15 Jul 2025 07:07:07 +0000

Ever wondered how top investment firms balance risk and reward in their mutual fund plans? What if you could use Python and your favorite data science tools to build, analyze, and simulate your own portfolio—one that's tailored for long-term growth and backed by solid metrics?

If you love turning raw data into actionable insight, exploring market patterns, and designing smarter investment strategies, this is your playbook.

🧠 Build Your Own Mutual Fund Plan with Python

A modern mutual fund plan isn’t about guesswork—it’s about combining smart analysis with practical investment logic. Here’s a streamlined approach you can use (and that I’ve detailed in my latest project):

1. Pull and Clean Historical Data

Download historical closing prices for a basket of stocks (e.g., using pandas to load a dataset of Sensex companies).
Inspect and preprocess the data for quality—no nulls, mismatched dates, or surprises.

2. Compute Performance Metrics

ROI (Return on Investment)
Volatility (standard deviation / Sharpe ratio)
Average growth rate

3. Identify Winners

Filter for stocks with strong returns and relatively low risk.
Compare with established “defensive” (low volatility) and “growth” (high return but riskier) companies for a nuanced picture.

4. Simulate Investment Outcomes

Project how regular monthly investments (SIPs) would grow over periods like 1, 3, 5, and 10 years—showcasing the compounding effect.
Allocate weights favoring lower-volatility stocks, leveraging the “inverse volatility” principle.

📈 Key Findings from the Analysis

Defensive Picks: ITC, TATA STEEL, NTPC, and POWER GRID show very low volatility—ideal during choppy markets.
Steady Growers: BEL, NTPC, HDFC Bank, Infosys, Bajaj Finance, and Power Grid combine above-median ROI with below-median risk.
Portfolio Allocations: Using inverse volatility, more capital goes to the steadiest companies, achieving balance and resilience.
Compounding Results: A monthly SIP of ₹5,000 can grow from around ₹62,000 in 1 year to nearly ₹8.6 lakh in 10 years (assuming average returns and steady investing).

📊 Visual Insights

Risk vs. Return plots distinguish stable picks from high-growth, high-risk options.
Investment growth curves illustrate the power of compounding for disciplined, long-term investors.

🔬 Why This Matters

Transparency: Every number and stock selection is explained and justified by data.
Adaptability: Swap in your own data, change the risk threshold, or experiment with different allocation models.
Learning by Doing: This is a code-driven, hands-on method—not just theory but real analysis that mirrors what top analysts do.

👩💻 Get Started

Gather data: Use stock CSVs or download from public sources.
Fire up Python: Use pandas, NumPy, Plotly, and Jupyter Notebook for flexible analysis and stunning visuals.
Follow the workflow above—and iterate on your logic as markets change.

This isn’t about picking “the” best stock—it’s about understanding why some combinations work, balancing risk and reward, and building a system you control.

💡 Want to Learn More?

If this caught your interest and you’d like to dive deeper or follow this methodology step by step, click on my GitHub repo to explore the full notebook, code, and analysis:

utsavmehta24 / FundFlow

Analyze. Allocate. Grow.

🚀 FundFlow: Turning Data Into Wealth with Python

Analyze. Allocate. Grow.

Welcome to FundFlow—a data-driven project where you’ll learn to build and analyze mutual fund strategies using Python and real-world stock data. This project is perfect for anyone curious about how investment plans are crafted with data scienc e, from data wrangling to actionable strategy.

📊 Project Overview

This project guides you step-by-step through:

Extracting and cleaning historical stock data
Computing key performance metrics (ROI, volatility, Sharpe ratio, etc.)
Selecting a portfolio of stocks based on risk and return
Simulating long-term investment growth (e.g., SIPs)
Visualizing trends to support your decisions

You’ll go beyond theory—actually coding your own mutual fund strategy and seeing the results.

🧠 Workflow

Pull Historical Data
Use the provided dataset (Sensex Data.csv) containing daily closing prices for leading Indian stocks.
Data Cleaning & Exploration
Load, inspect, and preprocess the data for analysis.
Compute…

View on GitHub

The Looming AI Winter 2.0

Utsav Mehta (rockingUT) — Thu, 20 Mar 2025 20:14:23 +0000

How Tech Giants Like Google Are Fueling a New Crisis

Introduction: The Silent Battle for Tomorrow

Imagine a world where innovation dazzles and disrupts, yet beneath the surface, a silent arms race unfolds — a battle not of nations, but of ideas and ambition. In 2023, while the world celebrated ChatGPT’s meteoric rise, a covert struggle simmered among tech titans. Google, once the unchallenged monarch of search and invention, began to falter as Microsoft-backed OpenAI and agile startups like Perplexity AI redrew the rulebook. But this isn’t merely a contest of corporate muscle; it is the prelude to an AI Winter 2.0 — a looming era of stagnation sparked by corporate self-interest, stifling regulation, and a seismic shift in the job market that threatens to reshape society.

Chapter 1: The Fall of an Empire

Once upon a time, Google reigned supreme — a guardian of the web and the quiet innovator behind revolutionary tech. Rumor had it that Google had developed its own formidable AI model (the precursor to what we now know as Gemini) two years before ChatGPT burst onto the scene. Yet, overwhelmed by security concerns and a complacency born of past glories, the project was quietly shelved. By 2022, the world witnessed a new era as OpenAI’s ChatGPT, powered by Microsoft’s expansive Azure cloud, turned search into a dynamic conversation. In a matter of months, Google’s empire — built on traditional search — began to show deep, worrying cracks. Meanwhile, nimble startups like Perplexity AI emerged, blending real-time data with intelligent insights to challenge a monopoly that had long seemed invincible.

The lesson here? In a digital age defined by rapid evolution, even giants can stumble when they fail to adapt.

Chapter 2: The Diamond Paradox — Value in the Age of Abundance

Picture a world where value is not derived from scarcity, but rather from the abundance and diversity of options. Much like the De Beers cartel once manipulated diamond supply to create an aura of exclusivity, today’s tech giants are grappling with a similar paradox.

The Synthetic Surge: Open-source models such as Meta’s Llama and Mistral, along with country-specific AIs like India’s BharatGPT and the UAE’s Falcon, are democratizing technology at breakneck speed. As more players join the fray, the traditional power held by centralized, high-cost models begins to erode, much like diamonds losing their luster in a flood of synthetics.
A Counteroffensive: In response, Google and its peers have turned to regulation as their shield — lobbying for stricter rules like the EU AI Act. Framing AI as an “existential risk,” they aim to raise barriers to entry. But history warns us: excessive regulation can stifle the very innovation it seeks to control, reminiscent of the tech freeze during the 1980s AI Winter.

In this narrative, the diamond is not merely a symbol of wealth, but a metaphor for how value is redefined in an era of democratized technology.

Chapter 3: The Shifting Sands of Work and Expertise

The Fall of the White-Collar Bastions
The allure of high-skilled work — coding, legal analysis, radiology — has always promised prestige and stability. Yet, AI’s uncanny ability to recognize patterns and deliver rapid solutions is upending this expectation. A 2023 OECD report warned that 30% of high-skilled jobs could be automated by 2030. In healthcare, for instance, a groundbreaking study revealed that AI-human teams could diagnose breast cancer with 95% accuracy — a stark improvement over AI working alone.

As junior roles shrink and expertise becomes a luxury, society may soon find itself divided into two tiers: one where humans handle the complex and the other where machines manage the routine.

The Rise of the Unskilled Savior
In this brave new world, roles once dismissed as “unskilled” are emerging as society’s linchpin:

The Practical Pioneers: As AI encroaches on white-collar domains, the very tasks that require a human touch — fixing a burst pipe, repairing intricate machinery — become indispensable. According to projections by the U.S. Bureau of Labor Statistics, trade jobs like those of plumbers and electricians could see 10% growth by 2032.
Care in the Age of Machines: In a landscape where AI lacks empathy, the value of human connection in caregiving — nurses, therapists, and hospice workers — will soar.

Chapter 4: Charting a Course Away from Winter

Every crisis carries the seeds of opportunity. To avert the looming AI Winter, we must reimagine our path forward:

Reskilling for the Real World: Governments and institutions must pivot from a narrow focus on coding bootcamps to broader vocational training. Consider Germany’s apprenticeship model, where half of the youth learn trades like carpentry or electrical work — a blueprint for sustainable progress.
Reining in Monopolies, Not Innovation: It’s essential to dismantle the monopolistic grip on AI infrastructure. Breaking Big Tech’s stranglehold will not only prevent artificial scarcity but also promote a more level playing field for innovation.
Embracing the Wisdom of the Ages: As echoed in the ancient wisdom of the Bhagavad Gita, where the interplay of nature’s forces reminds us of our interconnectedness, our future depends on systemic support — be it through education, mentorship, or the unpredictable winds of luck. Success, whether in technology or life, is rarely about individual hustle; it is about the collective spirit that propels us forward.

Conclusion: An Invitation to Curiosity and Action

The saga of AI is not preordained; it is a narrative that we shape with every decision we make. The impending AI Winter 2.0 stands as a crossroads — a choice between allowing monopolies to dictate our future or democratizing technology to empower billions. As white-collar roles vanish into the digital ether, the once-overlooked trades may emerge as the true architects of tomorrow’s society.

“Forget the hype. The real AI crisis isn’t machines taking over — it’s humans forgetting the value of hands that build and hearts that care.”

In this unfolding story, every reader is invited to question, to learn, and to take action. Stay awake. Stay curious. The future is yours to write.

Sources:

Global HIV/AIDS Statistics: UNAIDS
U=U (Undetectable = Untransmutable): Prevention Access Campaign CDC
Global Health Funding: The Global Fund
Dr. Fauci Quote: NIH
Google’s AI Missteps: The Verge (2023) TechCrunch (2024)
Android Ecosystem Vulnerabilities: Reuters (2023)
Cloud Market Share: Statista (2024)
Diamond Cartel Analogy: The Atlantic (2018)
1980s AI Winter: MIT Technology Review (2020)
AI in Healthcare: Lancet Digital Health NIH Study
Trade Job Growth: U.S. Bureau of Labor Statistics (2023)
Germany’s Apprenticeship Model: OECD (2022)
AI Winter History: Stanford’s AI Index Report (2024)

Notes:

For studies (e.g., NEJM 2023 study on U=U, Lancet diagnostics), visit the journal websites and search with keywords like ‘AI diagnosis accuracy’ to find direct links. Replace generic links (e.g., NIH, OECD) with specific reports if required.