Building quantitative finance models and AI tools for equity research. Interested in how financial theory holds up against real data.
A machine learning model that reads a simulated price chart and labels each day as a bull market, bear market, or high-volatility period. Adjust the sliders to change simulation parameters and watch regimes shift in real time.
Reads the market's mood every day — bull, bear, or volatile — the way a trader does after years on the floor.
Stock markets cycle through three modes: trending up (bull), trending down (bear), and swinging wildly (volatile). These are called regimes. The problem is you can't directly observe which regime you're in — it's hidden. This model infers it from daily price movements and colours each day accordingly.
Step 1 — Geometric Brownian Motion simulates the price path. Each day's move = drift (μ) + random shock (σ·dW).
Step 2 — Hidden Markov Model. The transition matrix governs how likely each regime is to persist or switch tomorrow.
A full institutional-grade portfolio risk suite. Input any stocks and weights — get 8 risk metrics, 10,000 Monte Carlo simulations, a probability fan chart, correlation heatmap, and efficient frontier. Every metric explained in plain English.
Before you put money in, this tells you exactly how bad the bad days could get.
Most people think about investing as just "will this go up?" RiskLens asks the harder questions: how much could I lose, how likely is that, and is the return worth the risk? These are the exact questions risk managers at banks and hedge funds answer every day.
You input a portfolio of stocks with their weights, expected returns, and volatility. RiskLens runs three analyses to give you a complete picture of the risk.
Runs your portfolio through thousands of randomly generated futures. You get a distribution of possible outcomes — from worst case to best. Wide fan chart = high uncertainty.
The optimal portfolio curve. Your portfolio is plotted as a dot. On the curve = well-built. Below it = you can get more return for the same risk by rebalancing.
A complete, interactive discounted cash flow model — full income statement, cash flow statement, balance sheet, WACC build-up, and enterprise value bridge. Every assumption is live-editable. Includes sensitivity tables for WACC vs terminal growth rate and WACC vs revenue growth.
A company is worth the cash it'll make you over its lifetime. This model does that maths.
A company is worth the sum of all the cash it will ever generate, adjusted for the fact that money today is worth more than money tomorrow. That's the entire idea behind a DCF — Discounted Cash Flow.
You project how much free cash the business will produce over the next 5 years, then estimate what it'll be worth after that (the terminal value), then "discount" all of it back to today's dollars using a rate that reflects the risk involved. What you're left with is what the business is theoretically worth right now.
A live trading competition platform built for and used at Fountainhead School. Students compete in real-time simulated markets — buying and selling positions, tracking P&L, and competing on a live leaderboard. Built and deployed independently.
Built a live stock trading competition for my school. Real stakes, fake money, real lessons.
A live, full-screen portfolio simulator running 200 simultaneous Geometric Brownian Motion paths — each one a possible future. Adjust starting capital, expected return, volatility, and time horizon in real time. Every path draws itself live with a distinct colour. The cyan median cuts through the noise.
Nobody knows what the market does next. This runs 200 possible futures so you can see the range.
Nobody knows what the market will do tomorrow. But if we know roughly how fast something grows on average and how wildly it swings, we can simulate thousands of possible futures and see what the range of outcomes looks like. That's Monte Carlo.
Each coloured line is one possible future for your portfolio. Some go up a lot, some crash, most end up somewhere in the middle. The more paths you see, the clearer the true probability distribution becomes.
A structured 5-step equity research framework that turns Claude into a permanent, hallucination-free stock analyst. Enter a ticker and get every prompt auto-generated — grounded in Peter Lynch and Charlie Munger. Every claim traced to an exact quote from your uploaded filings.
Turns Claude into an analyst who's read every filing and won't say a word without a source.
Ask an AI "is Apple a good investment?" and it gives a confident answer from training data — possibly outdated, possibly wrong. This is hallucination. Stock Research Kit fixes this by locking Claude to your uploaded filings only. Every claim must start with an exact quote from the source document. No quote = no claim.
A full strategy backtest on AAPL 2020–2024. Adjust the fast and slow EMA periods and watch the equity curve, trade log, win rate, max drawdown, and Sharpe update instantly. Every trade is logged with entry, exit, return, and P&L.
Tests a simple rule — ride the trend up, exit when it turns — against five years of real AAPL data.
An Exponential Moving Average gives more weight to recent prices than old ones. When a fast-moving average (reacts quickly) crosses above a slow one (reacts slowly), it signals that momentum is building — so you buy. When it crosses back below, momentum is fading — so you sell. This backtest asks: would that rule have made money on AAPL over the last 5 years?
A portfolio construction tool that weights assets by inverse volatility so every asset contributes an equal share of total portfolio risk. Compare risk parity vs equal weight side by side — weights, risk contributions, and a full asset breakdown table.
Most portfolios are secretly 90% stocks risk. This one actually splits the risk equally.
A 60/40 portfolio sounds balanced. But stocks are three times more volatile than bonds — so 90% of the risk is actually coming from the 60% in stocks. Risk Parity fixes this by asking: instead of equal dollar weights, what if every asset contributed the same amount of risk? Lower-volatility assets get bigger positions. Higher-volatility assets get smaller ones. The result is a genuinely balanced portfolio. Ray Dalio's All Weather fund is built on this idea.
Runs thousands of random portfolio weight combinations to find the one with the highest Sharpe Ratio — the Maximum Sharpe (Tangency) Portfolio. Includes a live Sharpe gauge, efficient frontier scatter, optimal vs equal weight bar chart, and full asset breakdown.
Tries thousands of portfolio combinations and finds the one where the return-to-risk tradeoff is best.
There are infinite ways to split your money between assets. The Sharpe Optimizer tries thousands of them randomly and finds the split that gives you the most return per unit of risk — that's the Maximum Sharpe Portfolio. It sits at the exact point on the efficient frontier where a line from the risk-free rate is tangent to the curve. Every other portfolio is either taking too much risk or leaving return on the table.
A full market intelligence platform with four modules: a live dashboard with real-time price simulation across 13 assets, an AI analysis engine powered by Claude that generates institutional-grade stock analysis on any ticker, a smart alerts system for price and signal-based triggers, and a clean landing page. Every design decision matches the terminal aesthetic of this portfolio.
Bloomberg Terminal meets Claude — type a ticker, get an analyst's view in seconds.
Most retail investors either get overwhelming data terminals (Bloomberg, Reuters) or dumbed-down apps with no real analysis. MarketPulse sits in the middle — a clean, fast interface that shows live market data and lets you ask Claude to analyse any stock like an institutional analyst would.
The AI analysis uses Claude's actual reasoning to generate bull cases, bear cases, risk assessments, and technical reads — not pre-written templates. Every analysis is generated fresh for the ticker you enter.
Market data is simulated with realistic GBM-based price drift. The AI analysis calls Claude's API directly — in a production deployment this would require a backend proxy for API key security. The architecture mirrors real market data products: live feed layer, analysis layer, alert engine.
A full Indian market terminal modelled after Bloomberg — live NIFTY 50, SENSEX, BANK NIFTY, and NIFTY IT feeds, real-time candlestick charts with EMA overlays, live order book, sector heatmap, active trading strategies with signals, geopolitical intelligence feed, and Zerodha Kite Connect integration. Built entirely in vanilla JS.
A Bloomberg Terminal for Indian markets — because real data shouldn't require a $24,000/year subscription.
Bloomberg Terminal costs $24,000 a year. It's the gold standard for professional traders — live data, charts, analytics, news, and execution all in one place. This is a full recreation of that experience for Indian markets, built in vanilla JS, free to use, deployed on GitHub Pages.
Every piece of data you see — NIFTY prices, order book depth, FII/DII flows, strategy signals — updates in real time. The charting engine handles multiple timeframes (1m to 1D) with full technical indicator overlays.
During my internship at Phoenix Investments I used professional trading terminals every day. Coming back to school and not having access to that tooling was frustrating. So I built my own — designed specifically for the Indian market, with the indicators and data flows that actually matter for NSE trading.
In Grade 10 I interned at Phoenix Investments — a forex trading firm. I spent weeks backtesting strategies on gold, watching how real positions are sized, and getting direct exposure to how professional traders think about risk. I came back with a lot of questions and no clean way to answer them.
The projects on this site are my attempt to build the tools I wish had existed — things that make financial models interactive, visual, and honest about their assumptions.
I completed Coursera's Investment Risk Management and DCF Modelling courses and Outskill's Generative AI Mastermind — not to collect certificates, but because I wanted the vocabulary to go with the intuition I'd built from real market exposure.
Every course I've taken has a corresponding project on this site that tries to go beyond what the syllabus covers.
I founded and run my school's Investment & Trading Club, host financial literacy sessions for teachers and support staff, and built the intraschool trading simulation — the same platform you can see as Project 004 on this site.
Teaching something is the fastest way to find out what you don't actually understand about it.
I'm aiming for UPenn with the goal of working in quantitative research or investment banking. The projects here are my attempt to demonstrate that interest isn't theoretical — it's been tested against real data, real markets, and real people trying to learn.
Most of what's here is Grade 11 work. I'm just getting started.
Manit Bhasin. Grade 11, IB Diploma Programme, India.
Interested in quantitative finance and economics — specifically how financial models are constructed and whether they hold up against real data. Most of my projects start from a question I can't find a clean answer to.
Aiming for UPenn with the goal of working in quantitative research or investment banking.
Whether it's a collaboration, an opportunity, or a conversation about markets and models — I'm open.