There are 4 repositories under modern-portfolio-theory topic.
Python financial widgets with okama and Dash (plotly)
Application and data for analyzing and structuring portfolios for climate investing.
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.
Exploratory analysis, visualization of stock market data along with predictions made on it using different techniques.
Mean-Variance Optimization using DL (pytorch)
Backtesting of different trading strategies by applying different Modern Portfolio Theory (MPT) approaches on long-only ETFs portfolios in Python.
Portfolio Optimization on a Quantum computer.
Modern Portfolio Theorem for portfolio optimization and asset allocation
Application for portfolio optimization with post-modern portfolio theory (PMPT)
Implementation of modern portfolio optimization (mean-variance portfolio optimization) using Monte Carlo simulation and sequential least squares programming (scipy package) in Python
An implementation of the Deep-portfolio-theory begins from working on the Modern Portfolio Theory by recreating the Markovian Efficient Frontier, then merges it with DeepFactors with the help of Kolmogorov Arnold Theorem.
ESG investing web app that takes user inputs to generate personalized equity portfolios and even comparative firm ESG rankings.
A MATLAB Realisation of Regime Switching Asset Allocation Strategy
🏦 Building a Minimally Correlated Portfolio with Data Science
Portfolio optimization is the process of selecting an optimal portfolio (asset distribution), out of a set of considered portfolios
Layout script interpreter and library to visualise markowitz's modern portfolio theory
This project implements MPT in Python to help investors optimize their portfolio by finding the ideal combination of assets for their investment. The primary goal is to create a portfolio that maximizes return and minimizes risk.
Stock Portfolio Optimization with Particle Swarm Optimization, using Modern Portfolio Theory
Optimize your Investment Portfolio using MPT
Asset allocation and portfolio optimization implementations to examine how each one differs and affects the overall portfolio.
A simple automated workflow for: 1) identifying investor indifference characteristics 2) strategic asset allocations with optimal risk-return
Quantitative Financial Risk Mangement
Design and build a reliable, large-scale trading data pipeline.
Investment Strategy to find the minimum risk portfolio combination/arrangement.
Modern Portfolio Theory and Dollar Cost Averaging simulation for the stock market
Tool to test the out-of-sample performance of portfolio optimization models
Constructing mean-variance efficient frontiers from MPT.
Six portfolio optimization strategies were considered, plus one benchmark, across 3 scenarios,. We considered methods relying both in ML and common statistical procedures; and we run an out-of-sample back-test for each strategy, for every scenario.
Master thesis project for the M.Sc. in Physics of Complex Systems @ Politecnico di Torino
Portfolio Selection, Weight Optimization, and Backtesting with Sentiment analysis and ML return predictions
Modern Portfolio Theory (MPT) and Monte Carlo simulations to optimize and backtest a portfolio of various financial assets
Modern Portfolio Theory (MPT) for predicting the performance of stocks
MPT in Python | Efficient Frontier modeling, Fama French 3 Factor Analysis, Monte Carlo Simulations and Portfolio Risk Analysis (Sharpe, VaR)
Portfolio optimization system that maximizes returns while effectively managing risk.