QuantWise - Smarter Trades Through Data-Driven Backtesting

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Inspiration

We recognized a growing interest in personal finance and trading, but also a gap in accessible tools to help individuals make informed decisions. QuantWise was born from our desire to bridge that gap by empowering users to experiment with trading strategies, understand market indicators, and ultimately bolster their financial literacy. We wanted to create a product that demystifies complex financial concepts and allows users to gain hands-on experience through backtesting strategies in a safe, simulated environment.

QuantWise’s mission aligns with Capital One’s commitment to “change banking for good” by empowering individuals with accessible tools for financial decision-making and literacy. Through features that mirror Capital One’s own strategies—like risk management and portfolio optimization, where users can test different investment approaches to understand market behavior and refine their personal risk tolerance—QuantWise promotes financial confidence and informed decision-making. It also simulates credit and investment model testing, similar to how Capital One enhances its credit scoring and risk analysis, allowing users to explore strategies safely. Additionally, QuantWise incorporates market indicators, including interest rate changes, enabling users to grasp broader economic impacts on their investments, much like how Capital One applies interest rate modeling to manage lending risk. Finally, by familiarizing users with patterns that align with fraud detection principles, QuantWise helps users recognize irregularities, reinforcing financial security—a core Capital One value. In all, QuantWise complements Capital One’s mission by fostering a financially literate, empowered community equipped to navigate complex financial landscapes with clarity and confidence.

What it does

QuantWise allows users to design, test, and analyze trading strategies using a wide array of market indicators. Through an intuitive interface, users can select indicators such as Simple Moving Average (SMA), Relative Strength Index (RSI), and others, set parameters, and define trading conditions for entry and exit points. QuantWise then simulates trades on historical data, also known as backtesting, to evaluate the strategy’s performance. The platform provides valuable insights, such as win rate, profit factor, and Sharpe ratio, helping users gauge the strategy’s effectiveness and understand what works and what doesn’t in the selected dataset.

How we built it

The front end was built using Next.js, utilizing React components to deliver a smooth, responsive user experience. We incorporated UI elements from shadcn to create a cohesive and polished look, focusing on user-friendly and intuitive design. For charting, we implemented the TradingView library, enabling dynamic charting symbols that allow users to interactively visualize trade entries, exits, and indicator overlays on historical price data.

The back end, developed with Flask, hosts both the Yahoo Finance and backtesting APIs. This back end handles data requests, performs calculations, and powers the core backtesting logic. Data is fetched from Yahoo Finance via API and then processed with NumPy and Pandas for data cleaning, manipulation, and analysis. Additionally, TA-Lib is employed to calculate various technical indicators, which are applied to historical data to support the backtesting engine. This integration of shadcn components, dynamic TradingView charting, and robust back-end technology enabled us to create a seamless, data-driven platform that offers users powerful real-time insights and an engaging, interactive experience.

Challenges we ran into

One of our biggest challenges was managing and debugging the API communication between the front end and the Flask backend, especially with port configuration issues. Testing API requests required careful troubleshooting, and we used Postman extensively to simulate different scenarios and verify responses. Data handling also presented challenges, as we needed to ensure that all inputs, outputs, and calculations remained consistent and accurate across the entire system. Debugging these issues took considerable time, but ultimately led to a more reliable and functional product.

Accomplishments that we're proud of

We are proud to have a fully functional, deployed product that met nearly all of our initial goals. Despite the complexities of integrating multiple APIs, building a real-time charting interface, and managing large datasets, we were able to achieve a stable and user-friendly platform. We are particularly proud of the backtesting engine, which accurately simulates trade conditions and provides insightful metrics. Additionally, the seamless interaction between our front and back ends stands out as a technical achievement, allowing for smooth data flow and responsive updates.

What we learned

Building QuantWise taught us a lot about the intricacies of financial data processing and the challenges of integrating different technologies into a cohesive system. We gained a better understanding of API testing and debugging, learning to rely on tools like Postman to streamline development and quickly identify issues. The project also deepened our skills in using Python libraries like Numpy and Pandas to handle large datasets efficiently, and we learned about the challenges of financial data accuracy and precision. Working with Next.js also reinforced our understanding of front-end architecture and how to build a user-friendly interface in a data-intensive application.

What's next for QuantWise

Looking ahead, we aim to expand QuantWise by adding more indicators and refining the accuracy of those we encountered issues with during development. We are exploring additional data sources to enhance the backtesting experience and considering the integration of real-time market data for more dynamic analysis. To improve the user experience, we plan to introduce more customization options for strategies, a broader set of metrics, and detailed analytics to help users better understand their strategies' strengths and weaknesses. Expanding educational content to further support financial literacy is also on our roadmap, making QuantWise a comprehensive tool for both novice and experienced traders alike.