semvec/pypi-packages huggingface dataset:
black-sm 0.1.0
Option pricing using the Black Scholes Model
License: MIT
Requires Python: >=3.8

# BSM

This package is to be used for option price calculation using the Black Scholes Model

## Description

This package provides a simple and easy-to-use implementation of the Black Scholes Model for calculating European call and put option prices. It is designed for students, traders, and developers who want a lightweight and reliable tool for option pricing without the complexity of larger libraries.

## Getting Started

### Dependencies

Numpy,Scipy

### Installing

pip install bsm

### Executing program

from bsm import bsm

## example when time scale is in year
price=bsm(S=50,K=50,T=1,r=6,sigma=25,option_type = "call",time_scale="Year")
print(price)
## example when time scale is in month
price = bsm(S=50,K=50,T=6,r=6,sigma=25,option_type = "call",time_scale="Month")
print(price)

Note:
When time_scale="Year", enter T in years.
When time_scale="Month", enter T in months.
## Authors

Shreenivas Dani

shreenivasdani@gmail.com
## Version History

* 0.1
    * Initial Release

## License

This project is licensed under the MIT License - see the LICENSE.md file for details
Topsis-Naman-102303496 1.0.0
A Python package for TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)
License: MIT
Requires Python: >=3.7

# Topsis-Naman-102303496

A Python package implementing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for multi-criteria decision making.

## Installation

```bash
pip install Topsis-Naman-102303496
```

## Usage

### Command Line

```bash
topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>
```

### Example

```bash
topsis data.csv "1,1,1,2" "+,+,-,+" output.csv
```

### As a Module

```python
from topsis_naman_102303496 import topsis

topsis("data.csv", "1,1,1,2", "+,+,-,+", "output.csv")
```

## Input File Format

The input CSV file should have the following format:
- First column: Object/Alternative names
- Remaining columns: Numeric values for each criterion

| Fund Name | P1   | P2   | P3   | P4    | P5    |
|-----------|------|------|------|-------|-------|
| M1        | 0.67 | 0.45 | 6.5  | 42.6  | 12.56 |
| M2        | 0.6  | 0.36 | 3.6  | 53.3  | 14.47 |

## Output File Format

The output file includes all original columns plus:
- **Topsis Score**: The calculated TOPSIS score
- **Rank**: Ranking based on the score (1 = best)

## Parameters

- **InputDataFile**: Path to the input CSV file
- **Weights**: Comma-separated weights for each criterion (e.g., "1,1,1,2")
- **Impacts**: Comma-separated impacts, + for benefit, - for cost (e.g., "+,+,-,+")
- **OutputResultFileName**: Path for the output CSV file

## License

MIT