fmilthaler Goto Github PK

followers: 102.0 following: 5.0 repos: 19.0 gists: 0.0

Name: Frank Milthaler

Type: User

Bio: Postdoctoral Researcher in Computational Physics gone rogue and turned into a Data Engineer. Open-Source developer at heart.

Location: Munich, Germany

Frank Milthaler's Projects

dakota-fluidity-examples

This repository contains a few examples on how to use DAKOTA and Fluidity

deep-reinforcement-stock-trading

A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.

finquant

A program for financial portfolio management, analysis and optimisation.

fmilthaler.github.io

fmilthaler project overview

fundamentalanalysis

Fully-fledged Fundamental Analysis package capable of collecting 10 years of Company Profiles, Financial Statements, Ratios and Stock Data of 13.000+ companies.

hpcmonitor

Automatisation of tasks associated with running jobs on HPC systems, e.g. estimation of required number of cores, data analysis/crunching, text processing, error checking, automatic e-mail reports. Essential when thousands of simulations are running simultaneously.

htmlparser

Python class to scrap and parse a webpage (using requests, BeautifulSoup4), mainly for converting tables to pandas.DataFrame

moderncvstyles

This packages provides a few tweaks/additions/styles to the popular LaTeX moderncv and moderntimeline packages.

numerical-methods-1

First numerical methods course for geoscience undergraduates

paigcp

Course content for Practical AI on the Google Cloud Platform

py2pgftable

This package provides a Python class to automate the process of generating LaTeX code displaying data in a table.

q-trader

Deep Q-learning driven stock trader bot

rpi-breadboard-tuts

Tutorial sessions with the Raspberry Pi and breadboard

self-driving-car

Self Driving car implementation using CNN

stanford-project-predicting-stock-prices-using-a-lstm-network

Stanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).

stock-selection-a-framework

This project demonstrates how to apply machine learning algorithms to distinguish "good" stocks from the "bad" stocks.

thesis-latex-template

This is a generic LaTeX template for dissertations (layout according to Imperial College London).

time-series-forecasting-of-amazon-stock-prices-using-neural-networks-lstm-and-gan-

Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.

turbine-geometry-automagic

This tool provides a Python class which allows the user to specify a number of parameters in order to automatically generate the geometry (and mesh) of a wind/tidal turbine in Cubit.

fmilthaler Goto Github PK

Frank Milthaler's Projects

Recommend Projects

Recommend Topics

Recommend Org