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awesome_retirement_portfolio_projector_tool

Financial tool that construct an optimized and diversified portfolio for a user given her level of risk and her current portfolio.

bitcoin_arbitrage

The present project uses Python and Pandas to analyzes opportunities for arbitrage in Bitcoin during the big crush of Q1 2018, when the bitcoin price fell by 65% in a month period. The analysis compares buying/selling on two major exchanges: Bitstamp and Coinbase.

blockchain-based-ledger-system

This code build a blockchain-based ledger system with a user-friendly web interface. This ledger allows 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.

crypto_clustering

Uses K-Means unsupervised machine learning algorithm and Principal Component Analysis to cluster cryptocurrencies based on performance in selected periods.

cryptocurrency_wallet

Python code that enables a user, such a human resources department of a company, to send cryptocurrency payments to its professionals

db_webapp_using_sql_voila

Build a financial database and web application by using SQL, Python, and the Voila library to analyze an ETF performance.

deep_learning_classifier_for_fraud

Preliminary deep learning model to predict transactions fraud.

dockerapp

Source code for James Lee's Docker course

financial_planning_tools

This project uses Json data from Alpaca APIs and Montecarlo Simulations for portfolio valuation and projections for financial planning..

fintech_python_tools

forecasting_net_prophet

Time series analysis showing trend, seasonality, and periodicity decomposition; and forecasting using Facebook Prophet. The analysis makes extensive use of indexing data tools and of the Pandas and Datetime libraries.

housing_rental_analysis_for_san_francisco

Use of visualizations with PyViz and Plotly Express to show results on geographical real estate data analysis.

jupyter_python_polls

Several exercises about specific topics in Python made in Jupyter Lab, some interesting work from other developers, and in general, useful material for Python.

large_data_sources_paola

COntains sources of data that use a lot of space

loan_qualifier

Phyton modular interactive application to determine the set of banks for which a user would qualify, based on her credit score and other financial information that will be asked through the terminal.

machine_learning_trading_bot

Apply machine learning SVM and AdaBoost algorithms to an algorithmic trading strategy.

money_examples

All the code examples I use in https://www.youtube.com/watch?v=0kzjD6jvfnk money representation video

paocarvajal1912

Config files for my GitHub profile.

python_polls

Python scripts with procedures for a wide variety of financial applications

risk_return_analysis

Quantitative analysis to select one fund among several, for inclusion in a suite of products for retirement portfolios, using key metrics, visualizations, and diversification considerations.

solidity_joint_saving_account

This project constructs a smart contract to automate an institutions’ financial process of hosting joint savings accounts.

spot_that_risk

Risk attribution report for portfolio management, using MatLab and Excel

supervised_credit_risk_classification

Uses Logistic Regression and various machine learning techniques to train and evaluate models with imbalanced classes applied to identify the creditworthiness of borrowers.

venture_funding_with_deep_learning

TensorFlow and Keras are used for the construction and evaluation of Deep Learning models to predict success of companies that receive funding from a venture capital fund.

vix_predictor

Machine learning model to predict the sign of the VIX Index for the next day.

vixm-algorithmic-strategy

An algorithmic trading strategy incursion using Adaboost machine learning classifier, to create the first volatility security suitable for long term investors.

vixx-token-dapp

Project description tbd...

yfinance

Yahoo! Finance market data downloader (+faster Pandas Datareader)