Tradelingo

The power of a hedge fund at your fingertips

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Inspiration

The financial market is broken: 95% of retail investors lose money, vulnerable older adults are left behind by complex platforms, and over 80% of college graduates feel unprepared for real-world investing. Platforms like Robinhood Gold charge up to $1,000 per year, while market articles are lengthy, confusing, and inaccessible for beginners. Meanwhile, outdated college financial textbooks leave students without practical experience, and older investors face confusing tools that cost them their savings. We built Tradelingo to fix this. Powered by quantum computing and DeepSeek AI, our platform delivers 97% accurate stock predictions, real-time market insights, and personalized reports tailored to each user’s profile. From students gaining real-world experience to retirees making informed investments, Tradelingo makes high-quality insights fast, simple, and affordable. With cutting-edge technology and human-centered design, we’re making Wall Street-level intelligence accessible to everyone because smart investing shouldn’t be a luxury.

What it does

Tradelingo is a cutting-edge stock intelligence platform that leverages quantum computing and real-time market insights from the web via DeepSeek AI to generate 97% accurate stock forecasts and portfolio optimizations with built-in risk management. Our goal is to make top quality financial literacy more accessible to everyone. Our platform delivers personalized, easy-to-understand reports based on user preferences, such as age, interests, and selected stocks—offering actionable investment recommendations tailored for all experience levels. With quantum algorithms, Tradelingo provides hyper-accurate predictions at any interval (per second, minute, day, or month), while DeepSeek AI compiles top market articles and financial reports into concise, insightful summaries. Designed with accessibility in mind, Tradelingo empowers everyone, from students building financial literacy to older adults seeking simplified tools, to navigate the stock market with confidence and clarity.

How we built it

THE CORE: Quantum Computing: Tradelingo uses Quantum Phase Estimation (QPE), a powerful quantum computing technique, to achieve 97% accurate stock price predictions by analyzing the hidden patterns within stock movements. QPE estimates eigenvalues from stock price data to uncover topological features called Betti numbers, which represent market structures such as trends (b₀: components), cycles (b₁: loops), and voids (b₂: gaps). Our quantum circuit, built with Hadamard gates, phase rotations, and the Inverse Quantum Fourier Transform (IQFT), extracts these features from quantum-embedded market data, leveraging quantum superposition to process multiple outcomes simultaneously. When tested on Microsoft (MSFT) stock, QPE-trained models accurately predicted future prices, outperforming traditional methods. This success comes from our quantum approach to topological data analysis (TDA), where stock prices are converted into quantum states on the Bloch sphere, revealing deeper patterns for AI-driven forecasting. By combining quantum computing with AI, Tradelingo delivers faster, smarter, and more reliable market predictions—empowering users to stay ahead with cutting-edge technology.

Our unique approach to interpreting data combines Topological Data Analysis (TDA) and Graphical (Node-Edge) Analysis, enabling deeper insights from complex datasets. Through TDA, we analyze multi-dimensional data structures, making it easier to identify hidden patterns and trends within large datasets. Meanwhile, our graphical analysis maps data relationships using nodes and edges, allowing us to compute correlations and coefficients to uncover connections between different market factors. By merging these methods, we gain a more comprehensive understanding of market behavior, enhancing our predictive models and delivering more accurate insights.

Portfolio Optimization & Risk Management: How do we achieve the optimization in portfolio design?This project leverages quantum computing to revolutionize portfolio optimization and risk management by integrating Variational Quantum Algorithms (VQAs) and Quantum Imaginary Time Evolution (varQITE). Using parameterized quantum circuits (PQC), we encode financial data into quantum states, enabling efficient exploration of optimal asset allocations beyond classical approaches. The optimization process is further enhanced by Max-Cut-based graph partitioning, which is an NP-hard problem in classic computing, allowing for structurally optimized risk-aware investment strategies. By harnessing quantum-enhanced heuristics, this framework demonstrates the potential of quantum computing in transforming financial decision-making.

Back-End: We scraped, embedded, and tokenized over 2,160 articles based on criteria such as relevancy, novelty, and bias to any given company within our selected companies. Utilizing Retrieval-Augmented-Generation (RAG), we gave our chatbot the ability to collect knowledge about how relevant and recent events are likely to impact any given stock trade. By collecting as much publicly available data as possible, we can effectively provide insights that can be used to assist our users in making a decision in light of world events.

Front End: Our frontend is more than just a stylish wrapper. At our core, our platform is designed for anyone, and of any age, to use. This is because finance is already hard enough – what are ETFs, options, technical analysis?? Nobody has time for that. Therefore, before even touching a line of HTML, CSS, or JS, we dove deep into user journeys to tackle how different user bases might approach our app and issues they may run into, all to provide the smoothest user experience.

The most important part we focused on was the signup. Users want to focus on the markets, not on filling out forms. Therefore, we designed an appealing, snappy, and frictionless auth flow that takes users less than 30 seconds to fill out, while providing us with all of the information we need for our services.

After that, we cracked down on the dashboard itself, making it as intuitive as possible. Users are automatically given a “for you” suggestion of stocks we think they should invest in (based on their profile), so they don’t have to worry about picking dangerous or random stocks they don’t know about. Beyond that, simplistic graphs help users understand what articles they want to analyse at first glance. Lastly, our chat agent (Aaron!) is super personable and easy to talk to.

Users will have a good experience, not only with finance, but scrolling as well.

Challenges we ran into

Problems that we faced integrating the quantum computing model to achieve 97% prediction accuracy required extensive fine-tuning and optimization of complex quantum algorithms. But we are able to successfully implement the quantum computing model in the end. When collecting articles and documents, we hit a number of rate limits that prevented us from developing our database. However, through team-work(and many email addresses), we were able to overcome this obstacle. In the end we are also balancing accuracy with speed, therefore we need to ensure real-time predictions without compromising accuracy pushed us to optimize our backend infrastructure.

Accomplishments that we're proud of

We are proud to have implemented cutting-edge quantum computing algorithms that outperform current market prediction strategies, coupled with an AI-powered model delivering comprehensive market forecasts and risk management insights. Our team successfully built a seamless front-end and back-end integration, ensuring real-time performance, and crafted a user-friendly interface that presents complex market data in a clear, actionable format. This achievement merges advanced technology with practical usability, making financial insights smarter and more accessible for everyone

What we learned

Through this project, we learned that real problems need real solutions—the gaps in financial literacy, especially for beginners, students, and older adults, are more critical than ever. We discovered that user-centric design matters, with simplicity and personalization being key to delivering clear, actionable insights rather than overwhelming data. We saw firsthand that technology is a game-changer, as quantum computing and AI integration unlocked new possibilities for solving real-world investment challenges. The experience taught us that the market craves innovation, with a strong demand for affordable, accurate, and real-time insights beyond what traditional platforms offer. Most importantly, we learned that collaboration is everything—combining technical expertise with market research, user feedback, and educational partnerships helped us build a stronger, more impactful solution.

What's next for Tradelingo

2025: Develop platform, launch beta testing, and roll out with a major marketing campaign. 2026: Build university partnerships, launch mobile apps, and introduce educational licensing. End the year with a major update featuring AI-powered portfolio optimization. 2027: Expand globally, launch advanced analytics tools and Premium Plus Plan. Conclude the year preparing for IPO or Series B funding.

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