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Browse project ideas by Polygence mentors
AI Market Chart Pattern Recognition Engine App
Project Description: Teach AI to “see” and recognize patterns in real stock charts In this project, students will build a web-based AI platform that learns how to read stock market charts - just like a trader does. Users can choose a stock or crypto asset and a time scale (daily, weekly, or intraday), and the platform will generate interactive candlestick charts with volume and technical indicators layered on top. Students will then train AI models to recognize chart patterns and predict what might happen next. The AI models will attempt to forecast future price movements (for example: 1 hour ahead, 1 day ahead, or 1 week ahead). This project introduces students to: 1) Turning financial data into images AI can understand 2) Teaching computers to “see” patterns in charts using deep learning models like Convolutional Neural Networks (CNNs) and LSTMs, 3) Visualizing predictions in a clear, intuitive way, and 4) communicating the model findings using GenAI techniques (e.g., Visual Language Models). By the end, students will have built a real AI-powered pattern recognition engine and gained hands-on experience with deep learning in finance. Project Outcomes: - Build data pipelines for financial time series (OHLCV, indicators) - Transform market data into model-ready inputs (numerical + image-based) - Develop and test deep learning models (CNNs, LSTMs, or hybrid approaches) - Design prediction targets such as: Price direction (up/down/sideways), Strategy signals (trend-following, mean reversion), Visualizing predictions on interactive candlestick charts (Plotly), and integrating GenAI to generate human-readable feedback for predictions - At the end of the program, the student will deliver a working web app prototype that displays financial charts, generates model-based predictions, and provides clear, user-friendly prediction feedback with documentation of their approaches, experiments, and findings.
AI/ML
AI-Powered Trading Journal Dashboard App
Project Description: Turn trading habits into data, and data into smarter decisions This project challenges students to build an intelligent trading journal that does more than just store trades - it thinks with you. Users can log trades with details like entry price, exit price, strategy used, and confidence level. The platform automatically calculates key stats such as holding time, profit/loss, and reward-to-risk ratio. Students will also design features that capture the human psychology side of trading, including emotions, stress levels, and decision confidence. The exciting twist? Students will train AI models to: 1) Detect patterns in a trader’s behavior 2) Spot risky habits or emotional decision-making 3) Provide smart, real-time feedback such as “You’re holding this trade longer than usual - consider reviewing your plan.” These features can be accomplished using techniques from Machine Learning such as Recurrent Neural Networks (RNN) as well as integrations of GenAI models for LLM interactions. This project blends machine learning, psychology, and finance, showing students how AI can be used to improve real human decision-making. Project Outcomes: - Design structured trade data models that capture trade features like Entry/exit, stop loss, strategy, confidence, and emotional state. - Build a remote data storage system (e,g, via PostgreSQL) - Compute key trading metrics like Profit/loss, holding time, reward-to-risk ratio, which would be used by the machine learning models to identify behavioral patterns, detect risky habits or inconsistencies. - Process user-written trade notes using NLP/LLM techniques. - Build an interactive journaling interface (Streamlit or Dash in Python) which convey Pre-trade guidance, In-trade alerts, and Post-trade feedback. - At the end of the program, the student will deliver a working web app prototype that stores and analyzes trades, learns trade performances/ patterns, and provides AI-generated feedback at different trade stages with documentation of their approaches, experiments, and findings.
AI/ML
Agents of Alpha App Development
Project Description: What if you could hire an entire hedge fund team, except every "employee" is an AI agent you designed? You'll build an interactive web platform where users draft their dream team of AI agents, hand them an investing philosophy, and backtest the squad on historical market data to see whose fund would have crushed it. An example can be for them to train a "Warren Buffet" agent to select stocks, followed by a "Jim Simons" agent to trade them, then a "Ray Dalio" agent to manage the in-trade risks. Think fantasy football meets Wall Street, users can use your platform to simulate investing and trading strategies created by their agentic hedge fund team before deploying the team to make real financial decisions. Project Outcomes: - Build the dashboard experience: a "draft your team" screen, strategy configuration, a backtest mission control, and results dashboards. - Invent a roster of AI agent personas — value investor, technical chartist, risk manager, etc., each with its own prompt, personality, and decision-making style. - Write the backtesting engine: pull historical prices, simulate trades, track P&L, and score performance with metrics like total return, Sharpe ratio, and max drawdown. - Visualize the drama: equity curves, trade logs, agent "decision diaries," and team performance boards. - At the end of the program, the student will deliver a working web app prototype that trains and teaches the AI agents to behave like real-life traders.
AI/ML
AI-Powered Quantitative Risk Dashboard App
Project Description: Ever wonder how Wall Street pros estimate how much money they could lose on a bad day, before it happens? You'll build an interactive web platform where users choose a risk personality, load a real or hypothetical portfolio, and go head-to-head against the machine to answer questions like "What's the chance this stock drops 10% next month?" Along the way you'll wield the same weapons professional risk managers use, such as Value-at-Risk, Expected Shortfall, Bayesian modeling, and even a neural network that can "look at" charts to turn scary market math into a game anyone can play. Project Outcomes: - Build the dashboard experience: portfolio entry, a risk-personality selector (risk-averse vs risk-taking), and results screens users actually enjoy using. - Implement the headline risk metrics: Value-at-Risk ("what's the most I'd expect to lose, 95% of the time?") and Expected Shortfall ("and when things DO go wrong, how bad is it on average?"). - Model the messy real world: fit heavy-tailed probability distributions (Student-t, Double Exponential, GED…) to market returns, and use Bayesian computing to estimate the distribution that best matches what's actually observed. - Arm users with decision tools: QQ plots, Kullback–Leibler (KL) divergence scores, scatter matrices, and correlation heatmaps, so they can pick their model like a pro, then compare their risk call against the machine's. - Crack portfolio optimization: use Mean-Variance Optimization and quadratic programming to find the mix of assets (long and short) that maximizes the Sharpe ratio or minimizes volatility. - AI implementation: teach a computer to read charts: train a CNN to evaluate QQ plots and efficient-frontier graphs (is this reward-to-risk tradeoff actually balanced?), including data augmentation to generate enough portfolio scenarios to train on. - User Interpretation: translate the machine's insights into friendly, personality-matched feedback that nudges users toward better distribution choices and smarter portfolio weights. - At the end of the program, the student will deliver a working web app prototype that qunatifies the uncertainties of a trading porfolio through probabilistic modeling and algorithmic optimization. Moreover, they will incorporate visualization techniques to make the technical implications easily understandable to any audience.
AI/ML
Predicting the Side Hustle: Building an AI Model to Forecast Gig Economy Success
Can machine learning predict which digital startups or freelancing paths will actually succeed? In this project, you will step into the shoes of a data scientist to build a predictive model that analyzes what makes digital creators and gig workers successful. Using Python, you will collect and clean real-world platform data (such as freelancer profiles, product listings, or social media engagement metrics) to identify key performance indicators. Depending on your coding experience, you will implement and compare different machine learning algorithms (ranging from simple regression and decision trees to classification models) to forecast income potential or business longevity.
Economics, Finance, Computer Science, Business, History
The Algorithm of Influence: How Gen Z Founders are Rewriting the Rules of E-Commerce
Traditional marketing is dead, and today's successful businesses are built on community, viral loops, and algorithm optimization. This project dives into the psychology and business strategies behind successful youth-led digital brands in the US and Southeast Asia. You will analyze how young founders use TikTok, community-building platforms, and AI-driven marketing to capture consumer attention on a tight budget. Your final project can take the form of a comparative market analysis paper or a comprehensive, investor-ready marketing and growth playbook for a modern digital startup.
Economics, Finance, Computer Science, Business, History
Coding for Change: Using Data Science to Map How AI is Reshaping Economic Mobility
Can a smartphone and an internet connection actually lift a young person out of poverty? This project merges data science with economics to find out. Using public platform data, surveys, or digital market trends from rapid-growth hubs like Singapore and Indonesia, you will analyze how digital tools are changing career pathways for youth. You will learn to use data analysis tools to track metrics like skill acquisition, income growth, or platform reach. Ultimately, you'll produce a data-driven research paper that proposes how tech platforms can be better designed to foster global equality and economic opportunity.
Economics, Finance, Computer Science, Business, History
Common modern predictive factors of anxiety
Much attention has been given to the idea that this generation is the most anxious alive today, yet most of the reasons why are based on opinions and conjecture. By examining the latest research and conducting meta-analysis on previous results, we hope to dive deeper into what we know about what is causing anxiety to increase in the 2020s-- as well as what the solutions could be.
Neuroscience, Psychology
Who Makes the Rules in the Twenty-First Century?
Climate change, artificial intelligence, cyber threats, and digital platforms are reshaping the world in ways that increasingly transcend national borders. Although researchers and policymakers have proposed a wide range of approaches to address these challenges, translating them into policies that work has often proved far more difficult. Why? In this project, we’ll compare how different countries are responding to these global problems and investigate why some strategies succeed while others struggle.
Economics, Quantitative, Social Science
How Is the Information Revolution Changing Society?
Modern markets and democratic institutions evolved in a world where information was relatively scarce, expensive to collect, and difficult to analyze. Today, digital platforms, artificial intelligence, and large-scale data collection have dramatically reduced the cost of producing, analyzing, and distributing information. These changes are transforming how businesses compete, how governments operate, how citizens participate in public life, and how societies balance innovation, privacy, competition, and democratic accountability. In this project, we'll investigate how the information revolution is reshaping relationships among individuals, businesses, and governments, and develop an evidence-based perspective on one of the defining transformations of the twenty-first century.
Economics, Quantitative, Social Science