KakiQuant

KakiQuant revolutionizes personal finance with AI-driven insights. Empowering users to make informed decisions and achieve financial success effortlessly.

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KakiQuant - Your personalized quantitative analysis platform

Traditional quant finance websites often lack the personalized touch needed to cater to individual users' unique financial situations. Many platforms rely solely on generic algorithms and broad market trends, failing to provide targeted insights tailored to each user's specific needs and goals. Moreover, the complexity of the data analysis tools often creates barriers for users without extensive financial knowledge, limiting accessibility. KakiQuant aims to revolutionize this landscape by harnessing the power of AI to deliver highly personalized finance analysis. By leveraging advanced machine learning algorithms, KakiQuant can decipher intricate financial data and provide actionable insights customized to each user's financial profile. Additionally, KakiQuant prioritizes user-friendly interfaces and intuitive tools, ensuring accessibility for users of all backgrounds. Through its innovative approach, KakiQuant empowers individuals to navigate the complexities of finance confidently and achieve their financial objectives with ease.

Features include:

  • Datasource service that can provide completed financial data at low cost, and local Databases to enjoy low latency streaming
  • Ready for AI, we will set up a framework where traditional ML toolkits, DNN-based solutions, and innovative models are introduced by Quant journals.
  • Easy to use, we will finally provide high-level wrappers, such as flowcharts, that people can drag blocks and assemble a strategy.

Developing Progress

This project is currently in the early stages, we are continuously adding more features and making the code base more robust. We will always maintain a publicly available branch for developers. In KakiQuant, you are able to find:

  • Profitable Strategies
  • Daily advice
  • AI portfolio management
  • More ...

Basic Features

  • Datafeeding: Downloading, Cleaning, Reading, Updating
  • Factor Analysis: Single Factor validation, Multi-Factor model, Barra, etc
  • Basic ML: K-Means, Lightgbm, Gplearn, Graph Neural Networks, Hidden Markov Models...

New Features

The following is a list of some of the features. Expect to see this list grow as we continuously work on implementing new features!

  • Similarity Candlestick to get the most correlated plots to the target.
  • Graph Neural Network to generate a list of the 10 most similar indexes.
  • Hidden Markov Model for market timing.
  • Access the data through our Database

What's next:

  • Factor analysis and database.
  • Access through an API.
  • Better web frontend
  • Optimizing backend processes.
  • And more...

Authors

  • Shengyang Wang - Data Science, class of 2026, Duke Kunshan University - Github
  • Nizar Talty - Data Science, class of 2026, Duke Kunshan University - Github
  • Renzo Balcazar Tapia - Data Science, class of 2025, Duke Kunshan University - Github
  • Zhengshan Zhang - Applied Mathematics, class of 2026, Duke Kunshan University - Github

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