Mephistopheles-0

finmath-lib

Mathematical Finance Library: Algorithms and methodologies related to mathematical finance.

Analyze-steps-monitoring-data-using-R

Analzye steps data using R

Branching-Diffusion-Method-for-Solving-PDEs-in-Matlab-

This Matlab code implements a branching diffusion method for solving partial differential equations (PDEs). The method uses Monte Carlo simulation and the branching process to approximate the solution of PDEs. The code provides a set of functions to calculate the mean, standard deviation, and L2 approximation error of the solution.

DeepBSDE

Python code for solving partial differential equations (PDEs) using deep learning. Specifically, we provide implementations for solving the following PDEs

Detection-of-Fraudulent-Transactions

Detection of Fraudulent Transactions using Machine learning and deep learning based classification models

Ethereum-Price-Prediction

This project aims to predict the future price of Ethereum using various machine learning models, including deep learning. The project is implemented using Python and Jupyter notebooks.

Extracting-and-Visualizing-Stock-Data

Extract some stock data, then display this data in a graph

NASDAQ-Volatility-Prediction

predict the future volatility of the NASDAQ index using various econometric and machine learning models, including ARCH, GARCH, EGARCH, SVM, and ANN

RL-trading-strategy

Utilizing reinforcement learning, this project implements a dynamic algorithmic trading strategy based on Q-learning with a deep Q-network. The Jupyter Notebook explores agent decisions on buying, selling, or holding Nasdaq stocks over a ten-year period (2014-2023).

SpaceX-Falcon-9-first-stage-Landing-Prediction

In this repository, we will predict if the Falcon 9 first stage will land successfully. If we can determine if the first stage will land, we can determine the cost of a launch. This information can be used if an alternate company wants to bid against SpaceX for a rocket launch.

Mephistopheles-0/DeepBSDE

Python code for solving partial differential equations (PDEs) using deep learning. Specifically, we provide implementations for solving the following PDEs

Mephistopheles-0/RL-trading-strategy

Utilizing reinforcement learning, this project implements a dynamic algorithmic trading strategy based on Q-learning with a deep Q-network. The Jupyter Notebook explores agent decisions on buying, selling, or holding Nasdaq stocks over a ten-year period (2014-2023).

Mephistopheles-0/Analyze-steps-monitoring-data-using-R

Analzye steps data using R

Mephistopheles-0/GBM

Generating a stock's geometric Brownian motion using C# and plot the result.

Mephistopheles-0/julia-PDE-solver

Julia file that solves a partial differential equation (PDE) using three parts: (1) setting up the PDE, (2) defining the numerical method for solving the PDE, and (3) running the simulation

Mephistopheles-0/Mephistopheles-0

Mephistopheles-0/modmesh

Toolkit for solving partial differential equations

Mephistopheles-0/Performance-Prediction-with-Wearable-Tech-Data

analyze data from accelerometers placed on the belt, forearm, arm, and dumbbell of six participants. These individuals were tasked with executing barbell lifts, both correctly and incorrectly, in five distinct manners

Mephistopheles-0/Air-Pollution-USA-1999-2012

Analyze the Pollution index from 1999 to 2012 in the USA using R.

Mephistopheles-0/Algorithms-Stanford-Programming-Assignments

Programming assignments for the Stanford University Algorithms Specialization

Mephistopheles-0/Data-Visualization-with-ggplot2

The Exercise solution of the 1st chapter (Data Visualization with ggplot2) of Hadley Wickham's book "R for Data Science"

Mephistopheles-0/ExData_Plotting1

Plotting Assignment 1 for Exploratory Data Analysis

Mephistopheles-0/Geometric-Brownian-Motion-Simulation

R code to generate and plot sample paths Geometric Brownian Motion for different volatilities

Mephistopheles-0/shields

Concise, consistent, and legible badges in SVG and raster format

Mephistopheles-0/toothgrow-stat-analysis

A Basic Statistical Analysis of the ToothGrowth Dataset (The Effect of Vitamin C on Tooth Growth in Guinea Pigs)

Mephistopheles-0/Bayesian-Network-for-Genetic-Inheritance

Constructing Bayesian Networks for Genetic Inheritance

Mephistopheles-0/bitcoin

Bitcoin Core integration/staging tree

Mephistopheles-0/datasciencecoursera

Mephistopheles-0/datasharing

The Leek group guide to data sharing

Mephistopheles-0/Galaxy-S-smartphone-wearable-data

Prepare tidy data from Samsung Galaxy S smartphone accelerometer data for wearable computing analysis in this project repository

Mephistopheles-0/Hospital-Ranking-Project

analyze hospital performance and ranking based on various healthcare outcome measures, such as 30-day mortality rates for different medical conditions (heart attack, heart failure, and pneumonia).

Mephistopheles-0/maximizing_a_normal_likelihood

GitHub repository for normal likelihood maximization and plotting in R

Mephistopheles-0/Motor-Trend-Data-Regression-Analysis

exploring the relationship between a set of variables and miles per gallon (MPG) (outcome)

Mephistopheles-0/Naive-bayes-text-classifier

Bernoulli and Multinomial Naïve Bayes classifiers for documents using Julia

Mephistopheles-0/ProgrammingAssignment2

Repository for Programming Assignment 2 for R Programming on Coursera

Mephistopheles-0/swirl_courses

:mortar_board: A collection of interactive courses for the swirl R package.

Mephistopheles-0/multiasset-option-pricing-3-diff

Three different examples of multi-asset option pricing problems using stochastic analysis and deep learning.

Mephistopheles-0/Python-Mini-games

Some python mini-games you can run in your terminal

Mephistopheles-0/wine-quality-prediction

An app to explore and model predictors of vinho verde quality using R, shiny, and more.

Mephistopheles-0/machine-learning-for-trading

Code for Machine Learning for Algorithmic Trading, 2nd edition.