dominic2017's starred repositories
Riskfolio-Lib
Portfolio Optimization and Quantitative Strategic Asset Allocation in Python
Stock_Analysis_For_Quant
Various Types of Stock Analysis in Excel, Matlab, Power BI, Python, R, and Tableau
Reddit-Stock-Trends
Fetch currently trending stocks on Reddit
wallstreet
Real time stock and option data.
awesome-CRISPR
List of software/websites/databases/other stuff for genome engineering
data-matching-software
A list of free data matching and record linkage software.
Machine-Learning-for-Finance
Machine Learning for Finance, published by Packt
pyOptionPricing
Option pricing based on Black-Scholes processes, Monte-Carlo simulations with Geometric Brownian Motion, historical volatility, implied volatility, Greeks hedging
sec-13f-filings
A nicer way to view SEC 13F filings data
dash-oil-and-gas-demo
Dash Demo App - New York Oil and Gas
Xpring-SDK
Xpring SDK is deprecated. See https://xrpl.org instead
Python_Portfolio__VaR_Tool
Python-based portfolio / stock widget which sources data from Yahoo Finance and calculates different types of Value-at-Risk (VaR) metrics and many other (ex-post) risk/return characteristics both on an individual stock and portfolio-basis, stand-alone and vs. a benchmark of choice (constructed with wxPython)
fund-protocol
A blockchain protocol for tokenized hedge funds
Semantic-Textual-Similarity
Natural Language Processing using NLTK and Spacy
Machine_learning_In_Finance
Built a trading algorithm in Python for the Tesla stocks returning in 39% higher returns than a simple buy and hold strategy, over a period of 2016-2018 . Designed random forest algorithm that combines CAPM, FAMA (French three factor model), Multi-Factor Linear Regression, Principal Component Analysis and Time series analysis to forecast stock prices . Generated trading signals using strategies such as Bollinger bands, Double crossover with evaluating risk and Sharpe ratio
Hedge-Fund-replication
This project tries to replicate hedge funds returns.
luminus-downloader
Downloads files and folders from LumiNUS
-Developed-Models-for-Hedge-Fund-with-Machine-Learning-
Deployed machine learning models (Logistic Regression, Random Forest, Gradient Boost, XGBoost, SVM, Neural Network) to manages an institutional grade long/short global equity strategy for the investors in hedge fund
ludwig-models
Models and examples built with Uber's Ludwig