Raul 's Projects
We will use python programming and machine learning to create an algorithmic trading bot that learns and adapts to new data and evolving markets.
This ledger should allow partner banks to conduct financial transactions (that is, to transfer money between senders and receivers) and to verify the integrity of the data in the ledger.
IS CRYPTO DECOUPLED FROM THE PRICE ACTION OF STOCKS AND WILL IT INCREASE/DECREASE IN THE FUTURE?
This is a web application that allows users to select a cryptocurrency, select a machine learning model, and pull historical data to predict its future price.
Analysis of the housing rental market data for San Francisco. The analysis will be complete with professionally styled and formatted interactive visualizations.
A financial analysis that study the arbitrage opportunities in bitcoin and other crypto currencies following the 3 phases in financial analysis.
A digital mortgage loan qualifier app.
Passive investing means that you invest in a basket of assets that’s called an exchange-traded fund (ETF).
Program that automates the tasks associated with valuing microlending loans.
Two financial analysis tools by using a single Jupyter notebook
go-flare is a modified version of avalanchego + coreth that incorporates the Flare Time Series Oracle (FTSO) and State Connector.
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
We will work as a risk management associate at Alphabet Soup, a venture capital firm.
A quantitative analyst for a FinTech investing platform. This platform aims to offer clients a one-stop online investment solution for their retirement portfolios that’s both inexpensive and high quality.
Bot that will recommend an investment portfolio for a retirement plan.
Python Projects: pandas, numpy
Credit risk poses a classification problem that’s inherently imbalanced. This is because healthy loans easily outnumber risky loans.
With over 200 million users, MercadoLibre is the most popular e-commerce site in Latin America. You've been tasked with analyzing the company's financial and user data in clever ways to make the company grow.
tpqoa is a Python wrapper package for the Oanda REST API v20 for algorithmic trading.
We will create a Jupyter notebook that clusters cryptocurrencies by their performance in different time periods. You’ll then plot the results so that you can visually show the performance to the client/ board.
Create a voting dapp that will take in a unique verified public address that will be given a single coin to place a vote. The coin will be sent to a unique account associated with the vote that was placed. The vote will be signed and sent to the unique account. Then using that we can obtain a balance of those minted tokens for each unique account to get a live chart for the poll.