Pinned Repositories
Algorithmic-Trading-Project
Algorithmic Trading project that examines the Fama-French 3-Factor Model and the Fama-French 5-Factor Model in predicting portfolio returns. The respective factors are used as features in a Machine Learning model and portfolio results are evaluated and compared.
Financial-Planning
This Python-written project creates two tools used for the purposes of financial planning: a Personal Finance Planner that allows for users to visualize their savings composed by investments in shares and cryptocurrencies, and a Retirement Planning Tool that uses the Alpaca API to fetch historical closing prices for a retirement portfolio composed of stocks and bonds. Monte Carlo Simulation run.
Machine-Learning-Credit-Risk
Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample the data. Evaluation metrics like the accuracy score, classification report and confusion matrix are generated to compare models and determine which suits this particular set of data best.
Multi-Blockchain-Wallet-in-Python
Creating Multi-Blockchain Wallet in Python that can hold hundreds of different cryptocurrencies and billions of addresses. In this specific example, Ethereum and Bitcoin Testnets are generated.
Natural-Language-Processing-Crypto
Sentiment Analysis surrounding Bitcoin & Ethereum utilizing Python Natural Language Processing (NLP) techniques.
NBA-Position-Predictor
Machine Learning project using 15 seasons of NBA data (2005-2020) to predict player position. Decision Trees, Random Forests, Support Vector Machines (SVMs) and Gradient Boosted Trees (GBTs) utilized. Example PCA transformation of X-data included as well. Specific predictions made at the end, leading to interesting insights into what players are out-of-position.
Portfolio-Analysis
Stock Portfolio Analysis using Python/Pandas
Python-Script-Automation
Python script that analyzes financial records. Automatically parses through a CSV and calculates Profit/Loss over entire period, average of the changes in Profit/Loss over entire period, month with greatest increase in profits, etc.
San-Fran-Housing-Market-Visualizations
This project utilizes Python visualizations packages, such as Plotly Express, HVPlot and PyPlot/Matplotlib to create an interactive dashboard exploring the San Francisco real estate housing market. Uses MapBox API.
Yen-vs-US-Dollar
Python-written project that utilizes Time Series analysis, along with a Linear Regression model, to forecast the price of the Japanese Yen vs. the US Dollar. ARMA, ARIMA, and GARCH forecasting models included, as well as decomposition using the Hodrick-Prescott filter. In-Sample and Out-of-Sample performance metrics used to evaluate Linear Regression model.
fischlerben's Repositories
fischlerben/Portfolio-Analysis
Stock Portfolio Analysis using Python/Pandas
fischlerben/Algorithmic-Trading-Project
Algorithmic Trading project that examines the Fama-French 3-Factor Model and the Fama-French 5-Factor Model in predicting portfolio returns. The respective factors are used as features in a Machine Learning model and portfolio results are evaluated and compared.
fischlerben/Financial-Planning
This Python-written project creates two tools used for the purposes of financial planning: a Personal Finance Planner that allows for users to visualize their savings composed by investments in shares and cryptocurrencies, and a Retirement Planning Tool that uses the Alpaca API to fetch historical closing prices for a retirement portfolio composed of stocks and bonds. Monte Carlo Simulation run.
fischlerben/Natural-Language-Processing-Crypto
Sentiment Analysis surrounding Bitcoin & Ethereum utilizing Python Natural Language Processing (NLP) techniques.
fischlerben/Machine-Learning-Credit-Risk
Machine-Learning project that uses a variety of credit-related risk factors to predict a potential client's credit risk. Machine Learning models include Logistic Regression, Balanced Random Forest and EasyEnsemble, and a variety of re-sampling techniques are used (Oversampling/SMOTE, Undersampling/Cluster Centroids, and SMOTEENN) to re-sample the data. Evaluation metrics like the accuracy score, classification report and confusion matrix are generated to compare models and determine which suits this particular set of data best.
fischlerben/Yen-vs-US-Dollar
Python-written project that utilizes Time Series analysis, along with a Linear Regression model, to forecast the price of the Japanese Yen vs. the US Dollar. ARMA, ARIMA, and GARCH forecasting models included, as well as decomposition using the Hodrick-Prescott filter. In-Sample and Out-of-Sample performance metrics used to evaluate Linear Regression model.
fischlerben/NBA-Position-Predictor
Machine Learning project using 15 seasons of NBA data (2005-2020) to predict player position. Decision Trees, Random Forests, Support Vector Machines (SVMs) and Gradient Boosted Trees (GBTs) utilized. Example PCA transformation of X-data included as well. Specific predictions made at the end, leading to interesting insights into what players are out-of-position.
fischlerben/Multi-Blockchain-Wallet-in-Python
Creating Multi-Blockchain Wallet in Python that can hold hundreds of different cryptocurrencies and billions of addresses. In this specific example, Ethereum and Bitcoin Testnets are generated.
fischlerben/San-Fran-Housing-Market-Visualizations
This project utilizes Python visualizations packages, such as Plotly Express, HVPlot and PyPlot/Matplotlib to create an interactive dashboard exploring the San Francisco real estate housing market. Uses MapBox API.
fischlerben/Python-Script-Automation
Python script that analyzes financial records. Automatically parses through a CSV and calculates Profit/Loss over entire period, average of the changes in Profit/Loss over entire period, month with greatest increase in profits, etc.
fischlerben/LSTM-Stock-Predictor
LSTM Stock Predictor
fischlerben/Crowdsale-With-ERC20-Token
This Blockchain example utilizes the OpenZeppelin Solidity library to create an ERC20 token that is minted through a crowdsale contract. The contract will allow users to send ETH and get back PUP, or PupperCoin, the hypothetical name of the company's token. The contract mints the tokens automatically and distributes them to buyers in one transaction. A real-world pre-production test is shown in which I deploy the crowdsale to the Kovan testnet.
fischlerben/Proof-of-Authority-Development-Chain
Setting up a Testnet Blockchain using MyCrypto
fischlerben/Smart-Contracts-With-Solidity
Blockchain example that creates an Ethereum-based Smart Contract which accepts Ether and divides it evenly among a hypothetical group of employees. This would allow a Human Resources department to pay employees quickly and efficiently.
fischlerben/SQL-Example
This is an example of the basics of the SQL language, done through the mySQL server but utilizing PopSQL to better visualize creating tables, running queries, etc. In this project, I create a database of movies and utilize SQL to create an overall schema, help organize the data in a unified manner, and run queries to give me specific pieces of information.
fischlerben/TrueAccord-Case-Study
Research Report on TrueAccord, a FinTech company founded in 2013 and based out of San Francisco, that is revolutionizing the debt collection industry.
fischlerben/unit13-challenge
Clustering Cryptocurrencies