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AlgoInvest&Trade
Invest with algorithms
Big O notation
Brute force
CSV
Dynamic programming
Knapsack algorithm
Python
About the project
This program implements two algorithmic approaches to solve the knapsack problem: it calculates the most profitable stock portfolio possible for a maximum budget of 500 EUR (use case for the fictional company AlgoInvest&Trade).
Main features
Exhaustive brute force - Explores every possible stock combination to guarantee the optimal profit, at the cost of exponential runtime (O(2^n)).
Dynamic programming - Optimized knapsack-style algorithm, much faster (approx. O(n * W)), recommended once the list exceeds about 20 assets and using memoization to avoid redundant calculations.
Invalid data report - Optional mode that detects and reports corrupted or unusable data in the provided datasets (for example negative costs or profits, missing values, and more).
Challenges encountered
Understand how the knapsack algorithm is constructed
Learnings
Big O notation, brute force, dynamic programming
Technical stack technique
B
Big O notation
Tool
B
Brute force
Tool
C
CSV
Tool
D
Dynamic programming
Tool
K
Knapsack algorithm
Tool
P
Python
Language
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