Python Jupyter Notebooks for Financial Portfolio Optimization

A collection of useful scripts originated or modified by Onri Jay Benally. This is where Onri's new repositories & many of his projects are born.

Blackjack Notebook (bjnb): Probabilistic analysis and simulation

Experiments with fastai

Selection of interactive (IPython) projects: Matrix multiplication, Capacitor 3D intensity plots, 3D muon tracker and detector using Monte-Carlo simulations, coupled oscillations and heat equation solutions.

Collection of Notebooks in Google Colab directly usable for study activities

Stock Portfolio Risk Assessment using Monte Carlo Simulation

Predicting prices of commodities by using moving monte carlo simulations.

Jupyter notebooks to call the VTS using Python

The Jet reconstruction Algorithm and the Jupyter Notebooks for plotting and comparing results with the data from the ATLAS Detector,CERN(PLB 790 108 (2019)).

Jupyter notebooks for Monte-Carlo simulation of SSPALS lifetime spectra

Notebook that implements a class that performs Monte Carlo simulations for a number of timeseries that may co-vary.

I’ll be creating two financial analysis tools with a single Jupyter notebook: A financial planner for emergencies and retirement.

A Jupyter Notebook that provides tools for analyzing and pricing stock options using the Implied Volatility and Monte Carlo Simulation.

A comprehensive Monte Carlo simulation for analyzing the Net Present Value (NPV) of AI implementation initiatives.

Financial planning and analysis in two notebooks, one that allows users to visualize savings by investment type, and the other uses Monte Carlo simulations for retirement projections.

In this jupyter notebook an attempt was made to predict interest rate movements by Monte Carlo Simulations using the Vasicek, Cox-Ingerson-Ross, and Hull & White Model

An approximation of π calculated via Monte Carlo method and proposed in Jupyter Notebook. A solution for Computer Simulation (40634-1 Sharif UT, Spring 2023) homework, the 1st series.

This notebook performs a Monte Carlo simulation to evaluate and compare the total supply chain cost (holding and shortage) of sourcing from three suppliers. Using synthetic delivery time data from a B2B order dataset, the simulation models daily inventory management over a 180-day period, considering uncertainty in customer demand.

Companion repository for the paper: On the Probability of Real Roots in a Quadratic Equation when its Coefficients are i.i.d. Uniform Variates. (pending publication). It contains the Jupyter notebooks used to perform the simulations and obtain the figures and data included in the paper.