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.