Marian Ignev's repositories
djongo
Django and MongoDB database connector
react-keyboard-event-handler
A React component for handling keyboard events
sklearn-json
A safe, transparent way to share and deploy scikit-learn models.
apns2
⚡ HTTP/2 Apple Push Notification Service (APNs) push provider for Go — Send push notifications to iOS, tvOS, and OSX apps, using the new APNs HTTP/2 protocol.
tsoa
Build swagger-compliant REST APIs using TypeScript and Node
BlogApp
This is a web application made using Streamlit. It is based on the Modern Portfolio Theory. Enter your stock tickers, and your optimal portfolio will be generated. Did I mention that there are some really pretty graphs?
django-ultracache
Drop-in replacement for Django's template fragment caching. Provides automatic cache invalidation.
portfolio-optimizer
A collection of various computational methods to optimize a user's investment portfolio using Modern Portfolio Theory and optimizing various factors such as Returns, Sharpe Ratio and Risk.
Stock-2
Portfolio optimisation through the application of the Modern Portfolio Theory (MPT)
home-assistant.io
:blue_book: Home Assistant User documentation
brain.js
🤖 Neural networks in JavaScript
modern-portfolio-theory
Basic implementation of modern portfolio theory to find the optimal portfolio for a set of stocks.
auth-server
OAuth 1.0 and 1.0a authentication server for signing requests, compliments hello.js
policies
Catalyze HIPAA Policy Documentation
httpstat
Higher level HTTP tracing for Go
pg-app-tynwrjdecdmr69ke5d8fec6ixljzx5
SashiDo App: Sashido-Demo
Portfolio-using-modern-portfolio-theory
inputs - 1) number of stocks in your portfolio and the name of them (use the ticker symbol not full name - (ex - AAPL)) 2) number of simulations required for calculating the optimal sharpe (higher the number of simulated portfolios better the results, i usually choose between 10,000 - 20,000 and it takes few secs in my dumb M3 surface pro) outputs - it generates a "portfolio.png" that contains the best percentage allocation it has found in the simulation with the sharpe ratio, expected return and standard deviation. so the outputs would be 1) percentage allocation 2) sharpe ratio 3) expected return
openshift-docs
OpenShift Documentation
apex-go
Golang runtime for Apex/Lambda.
openshift-scripts
Loose collection of different scripts for OpenShift.
speedtest
Self-hosted HTML5 Speedtest
caddy-cache
Caching middleware for caddy
vulcan
Vulcan extends Prometheus adding horizontal scalability and long-term storage
nodejs-ex
node.js example
parse-server-1
Parse-compatible API server module for Node/Express