https://github.com/redinfinitypro/investorgenius
Rating: (5/10) InvestorGenius is a simulation project that simulates investment market dynamics using random decisions and statistical analysis, providing insights into investment strategies and market dynamics.
https://github.com/redinfinitypro/investorgenius
matplotlib random statistics
Last synced: 3 months ago
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Rating: (5/10) InvestorGenius is a simulation project that simulates investment market dynamics using random decisions and statistical analysis, providing insights into investment strategies and market dynamics.
- Host: GitHub
- URL: https://github.com/redinfinitypro/investorgenius
- Owner: RedInfinityPro
- Created: 2024-03-13T00:54:39.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-17T00:44:17.000Z (over 1 year ago)
- Last Synced: 2025-04-04T22:35:26.567Z (3 months ago)
- Topics: matplotlib, random, statistics
- Language: Python
- Homepage:
- Size: 7.81 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# InvestorGenius
InvestorGenius is a simulation project that simulates investment and financial market dynamics using random investment decisions and statistical analysis. It offers insights into investment strategies and market dynamics, but could benefit from more features like customizable parameters and an intuitive user interface.
## Table of Contents
- [About](#about)
- [Features](#features)
- [Imports](#Imports)
- [Rating: 5/10](#Rating)
# About
InvestorGenius is a simulation project that allows users to study investment and financial market dynamics using random investment decisions and statistical analysis. It simulates the behavior of investors and businesses over a specified period, allowing users to observe how factors affect investment value. Despite its basic nature, it offers insights into investment strategies and market dynamics. Improvements include customizable parameters, interactive visualization, and an intuitive user interface.
# Features
InvestorGenius is a simulation modeling tool that aims to understand complex systems and make informed decisions. The project involves several key phases: identifying the problem, formulating the problem, outlining a model, allowing users to customize key parameters, enhancing the versatility of the simulation, and providing interactive visualization.
The first step is to clearly define the problem, which ensures that the simulation efforts are focused and purposeful. The second step is to formulate the problem in more detail, quantifying the objectives of the study, identifying stakeholders, and determining if simulation is the right approach. The third step is to outline a model, constructing conceptual and logical models that represent investment dynamics, defining parameters, variables, and relationships.
The fourth step is to provide an interactive visualization, creating charts, graphs, or dashboards that allow users to explore investment scenarios, observe trends, and gain insights. The sixth step is to provide an intuitive user interface, ensuring that investors and analysts can navigate the simulation effortlessly.
Continual improvements can enhance the value of InvestorGenius, such as incorporating real-world data, refining algorithms, and expanding the scope. For more information, explore the [9 phases of a simulation project] and learn from other successful simulation tools in various domains.
# Imports
random, statistics, matplotlib.pyplot
# Rating
For its functionality, which generates a plot based on user input. However, there are areas for improvement, such as code organization, variable naming, magic numbers, global variables, redundant functions, comments, graph labeling, and user interaction. The code could benefit from better organization, clearer variable names, minimizing global variables, simplifying the `Investor()` function, and removing the `Business` class from a loop. Additionally, the code lacks clear comments explaining the logic behind certain operations, making it difficult to understand at first glance. The graph could also benefit from better labeling, such as the missing y-axis label and lack of a legend. Furthermore, more user interaction or feedback could improve the user experience. Addressing these issues can make the code more readable, maintainable, and user-friendly.