This updated README.md incorporates the new advanced analytical features (NPV, EBITDA, Risk Analysis, and Simulations) while maintaining your existing structure and branding.

FinCalc Lib

A lightweight Python library for essential finance analytics, ranging from basic profitability metrics to advanced risk simulations and time-value-of-money calculations.

Features

• Customer Metrics: Calculate Net Promoter Score (NPS).
• Profitability: Gross, Operating, Net Profit Margins, and EBITDA/EBITA.
• Liquidity & Leverage: Current Ratio, Debt-to-Equity, and Return on Equity (ROE).
• Cash Flow: Free Cash Flow (FCF), Operating Cash Flow (CFO), and Cash Flow Margin.
• Investment Analytics: Net Present Value (NPV) and Future Value (FV).
• Risk & Simulation: Sharpe Ratio and Monte Carlo Price Simulations.
• Compliance: Built-in audit logging for financial reporting integrity.

Live URL

https://pypi.org/project/fincalc-lib/

Dashboard Image Reference

https://pypi.org/project/fincalc-lib/

List Of All calculated Function and Formulas

<img width="1153" height="638" alt="FinCalc Dashboard Reference" src="https://github.com/user-attachments/assets/304c5ae0-91d2-4fe3-9168-dc2cf410a35e" />

Installation

You can install the package directly from PyPI:

pip install fincalc-lib

Or install the wheel file locally:

pip install fincalc_lib-1.0.0-py3-none-any.whl

Quick Start

Here is a simple example of how to use the library's basic and advanced functions:

import fincalc

# 1. Calculate NPS
nps = fincalc.calculate_nps(80, 10, 100)
print(f"Your NPS is: {nps}")

# 2. Calculate Profit Margin
margin = fincalc.profit_margin(20000, 100000)
print(f"Profit Margin: {margin}%")

# 3. Advanced: Net Present Value (NPV)
# Initial investment of -1000, followed by cash flows of 300, 400, 500 at 5% rate
cash_flows = [-1000, 300, 400, 500]
npv = fincalc.calculate_npv(0.05, cash_flows)
print(f"Investment NPV: {npv:.2f}")

# 4. Advanced: Monte Carlo Simulation
# Predict stock price in 30 days with 10% return and 20% volatility
sim = fincalc.monte_carlo_simulation(start_price=150, days=30, mu=0.1, sigma=0.2)
print(f"Projected Median Price: {sim['median_final_price']:.2f}")

Available Formulas

Development

If you want to contribute or build the wheel yourself:

1. Clone the repo:

   git clone https://github.com/Akansha9821/finance_calculator.git
   

2. Build the package:

   python -m build
   

License

MIT