https://github.com/akashsingh3414/financial-portfolio-optimization

A medium-sized application that allows users to input investment options and constraints (budget and risk), run various optimization algorithms (knapsack, greedy, dynamic programming), and view results. It includes features like filtering, sorting, and graphical visualizations using Gnuplot.
https://github.com/akashsingh3414/financial-portfolio-optimization

cpp dynamic-programming gnuplot greedy-solution knapsack

Last synced: over 1 year ago
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A medium-sized application that allows users to input investment options and constraints (budget and risk), run various optimization algorithms (knapsack, greedy, dynamic programming), and view results. It includes features like filtering, sorting, and graphical visualizations using Gnuplot.

• Host: GitHub
• URL: https://github.com/akashsingh3414/financial-portfolio-optimization
• Owner: akashsingh3414
• License: mit
• Created: 2024-09-09T19:30:00.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-10-21T14:54:05.000Z (over 1 year ago)
• Last Synced: 2025-01-06T08:29:39.329Z (over 1 year ago)
• Topics: cpp, dynamic-programming, gnuplot, greedy-solution, knapsack
• Language: C++
• Homepage:
• Size: 1.94 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# Financial Portfolio Optimization Project (C++)

## Overview

This project provides an optimized solution for financial portfolio selection using different algorithmic techniques. The objective is to select a set of investments that maximize returns while adhering to constraints such as budget and risk tolerance.

## Input

- **Constraints**: 

  - Budget

  - Risk Type

  

- **Investment Dataset**:

  - Name

  - Cost

  - Return

  - Risk (0-1 scale)

  - Type (investment category)

## Features

1. **Knapsack with Greedy Approach (High Return-to-Cost Ratio)**:

   - Uses the knapsack algorithm to select an optimized set of investments based on the highest return-to-cost ratio.

   - The greedy approch also runs to offer a comparative set.

   - Both algorithms respect the input constraints of budget and risk type.

2. **Dynamic Programming for Maximum Return**:

   - This feature provides the most optimized result set, maximizing the return while adhering to constraints.

   - It avoids considering multiple periods for simplicity and focuses solely on maximizing returns in a single period.

3. **Filtering and Sorting**:

   - Filter and sort the investment dataset based on:

     - High return

     - Low cost

     - Low risk

4. **Graphical Visualization with Gnuplot**:

   - Plots a graph showing grouped investments by type.

   - Displays average cost, average return, and profit generated for each type using Gnuplot.

## How to Run

1. Compile the project using your preferred C++ compiler.

2. Input the investment dataset and define the constraints (budget, risk type).

3. Choose the desired feature to run: knapsack, dynamic programming, filtering/sorting, or plotting.

4. For graphical output, ensure Gnuplot is installed on your system.

## Dependencies

- C++ compiler

- Gnuplot (for graphical visualization)

## License

MIT License

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This project simplifies financial portfolio optimization, offering solutions ranging from quick, ratio-based selections to more complex dynamic programming models, all while considering key constraints like budget and risk.