Portfolio optimization with deep learning.
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Updated
Jan 24, 2024 - Python
Portfolio optimization with deep learning.
Investment portfolio and stocks analyzing tools for Python with free historical data
A JavaScript library to allocate and optimize financial portfolios.
Markowitz portfolio optimization on synthetic and real stocks
Python financial widgets with okama and Dash (plotly)
Markowitzify will implement a variety of portfolio and stock/cryptocurrency analysis methods to optimize portfolios or trading strategies. The two primary classes are "portfolio" and "stonks."
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.
**Previsão de Preços de Ações e Otimização de Portfólio** Preveja preços de ações com LSTM e otimize seu portfólio de investimentos utilizando o modelo de Markowitz. Ideal para traders, cientistas de dados e entusiastas do mercado financeiro!
This Python script performs portfolio optimization based on different optimization criteria: 'sharpe', 'cvar', 'sortino', and 'variance'. The script uses historical stock price data downloaded from Yahoo Finance.
MVO and Monte Carlo Simulation for Financial Portfolio optimization
Constructing mean-variance efficient frontiers from MPT.
A Java implementation of the VBA code for the Critical Line Algorithm in the book "Mean-Variance Analysis in Portfolio Choice and Capital Markets" by Harry M. Markowitz
I did this project as a challenge for my Master's degree. The objective was to find the efficient frontier and the maximum sharpe portfolio using a simulation with a dataset of 70 thousand investment funds
Efficient frontier for different correlation coefficients between two assets
Efficient Frontier using R
This is a web app that helps people understand the main concepts from Markowitz's Modern Portfolio Theory
Maximize Your Investment Potential: Optimize Stock, Crypto, and ETF Allocations for Peak Portfolio Performance.
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