Finance

Abstract

1. Introduction

Finance is a Python toolkit for market data, technical indicators, financial analysis, stock screening, strategy research, backtesting, portfolios and statistical models. Calculations use explicit inputs, a small dependency set and tested execution conventions. Models and trading rules are research tools; runnable examples are the starting point.

Returns and rates are fractions; RSI is 0–100. Warm-up values remain missing. The modest backtester tracks cash, fractional shares, long/short fills, commission, slippage and borrow costs. Read the calculation and execution conventions before interpreting results.

2. Objective

Python toolkit for quantitative finance: stock analysis, technical indicators, strategy backtesting, portfolio optimization, and financial modeling.

This project demonstrates how Python, Machine Learning, Pandas can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/shashankvemuri/Finance.git
cd Finance

git clone https://github.com/shashankvemuri/Finance.git
cd Finance
python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
python -m pip install -e .

python -m pip install -e '.[data]'             # Public data, Finviz and financial statements
python -m pip install -e '.[portfolio,models]' # Optimization and statistical/ML experiments
python -m pip install -e '.[plot,sentiment]'   # Charts and VADER text scoring
python -m pip install -e '.[apps,reports]'     # Interactive app and Excel exports

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by shashankvemuri and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.