<aside> 💡 Skills applied in this project: Python BeautifulSoup, Pandas, Team Collaboration

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Project Background and Objectives

To enable more efficient lending decisions, I developed a Telegram BOT that allows lenders on the Bitfinex platform to receive real-time updates on lending rates and earnings.

Project Benefits

• Users can get the current lending rates through TGBOT without logging into the Bitfinex platform.
• Users can receive daily reports on their earned interest and annualized returns via TGBOT, displayed with visual charts.
• When placing a lending order, users need to set their desired interest rate and repayment duration, but Bitfinex's Floating Rate Return (FRR) might underestimate the returns. This project allows users to get the optimal rate and duration directly from TGBOT.

Project Content

In this project, I was responsible for developing the function that provides "the most suitable interest rate and repayment duration based on the lender's desired loan amount" as a reference when they place an order. I used Python's BeautifulSoup to scrape the last 200 transaction records from the Bitfinex platform, performed statistical calculations using Pandas, and returned the results. Although Bitfinex provides an FRR dynamic rate, it overlooks the correlation between interest rates and duration. To improve rate prediction accuracy, I calculated rates using correlation-weighted averages and determined the duration using the K-nearest neighbors (KNN) model.