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PROJECT


Multi-Objective Evolutionary Optimization of Crypto Assets

Learn to optimize crypto assets using an evolutionary algorithm to minimize risk and maximize returns. Moreover, understand data loading and visualization using interactive graphs.

Multi-Objective Evolutionary Optimization of Crypto Assets

You will learn to:

Sample and transform the data.

Create interactive graphs using plotly in Python.

Create and view the percentage change in crypto values.

Use the pymoo module in Python to optimize multi-objective problems.

Skills

Data Visualization

Evolutionary Algorithms

Financial Optimization

Prerequisites

Intermediate knowledge of Python

Intermediate knowledge of interactive graphs

Intermediate knowledge of evolutionary algorithms

Technologies

Pymoo

Python

Plotly.js

Project Description

Crypto Asset Management (CAM) manages a portfolio of cryptocurrencies to maximize returns and minimize risk. Evolutionary Multi-objective Optimization (EMO) is used to find optimal solutions to multi-objective problems. In this project, EMO will be used to optimize a portfolio of cryptocurrencies by using risk, diversification, and returns as objectives. We will optimize the assets using SMS-EMOA, a multi-objective selection algorithm based on dominated hypervolume. This will be very useful for investors to make future investments.

In this project, we will cover the following:

  • Load the data for different cryptocurrencies.
  • Create interactive plots for data analysis.
  • Use an evolutionary algorithm to minimize the risks and maximize the returns.

Project Tasks

1

Data Preprocessing

Task 0: Get Started

Task 1: Import Modules

Task 2: Load the Dataset

Task 3: Clean the Data

2

Data Visualization

Task 4: Plot the Dataset

Task 5: Create an Interactive Graph for Currencies

Task 6: Plot the Comparative Interactive Graph

3

Data Modeling

Task 7: Get the Percentage Change

Task 8: Calculate the Covariance and Expected Returns

Task 9: Formulate the Problem

Task 10: Create an Evaluation Function

Task 11: Formulate the Repair Function

Task 12: Optimize the Objectives

Task 13: Print the Results

Congratulations!