varport 0.1.1

A library to calculate derivatives VAR portfolio and generate a report

VARPORT

VARPORT is a Python library to calculate Value at Risk (VaR) of a derivatives portfolio and generate reports .

Features

• Compute VaR using Monte Carlo simulations
• Generate PDF reports with portfolio summaries and charts
• Supports options and futures portfolios

Installation

#usage

pip install varport
from varport import ReportGenerator, MainVaRProcessor
import numpy as np

# Load your portfolio from a CSV file
portfolio_file = 'C:/xxx/portfolio.csv'

# Example user input for mu and Sigma
mu = np.array([0.05, 0.03, 0.07, 0.04, 0.06])  # Example mu vector (expected returns)
Sigma = np.array([[0.1, 0.02, 0.03, 0.01, 0.04],  # Example covariance matrix
                  [0.02, 0.08, 0.01, 0.03, 0.02],
                  [0.03, 0.01, 0.09, 0.02, 0.01],
                  [0.01, 0.03, 0.02, 0.07, 0.02],
                  [0.04, 0.02, 0.01, 0.02, 0.08]])

# Initialize MainVaRProcessor with the portfolio file path
var_processor = MainVaRProcessor(filepath=portfolio_file)

# Process the portfolio with user-provided mu and Sigma to calculate VaR and differences
portfolio, VaR, differences = var_processor.process(mu=mu, Sigma=Sigma)

# Generate the report with the calculated differences and VaR
report = ReportGenerator(differences=differences, VaR=VaR, portfolio=portfolio)

# Generate and save the PDF report
report.display_table_and_chart(pdf_filename="VaR_Report_Test.pdf")

print("Report generated successfully!")


# Portfolio Management Tool

## Overview

This tool allows users to manage their portfolio of financial instruments such as options and futures. You can upload your portfolio in a CSV file format that follows the required structure outlined below.

---

## Portfolio File Format

The portfolio file should be in `.csv` format and must include the following columns:

### Required Columns:

1. **date** (string, in `dd/mm/yyyy` format):  
   The date the trade was made.
   
2. **instrument_type** (string):  
   Specifies the type of financial instrument. Supported values:
   - `option`
   - `future`
   
3. **underlying_asset** (string):  
   The underlying asset for the trade (e.g., "sugar", "cocoa", "coffee").
   
4. **quantity** (integer):  
   The quantity of the asset in the trade. Positive values for long positions, negative values for short positions.
   
5. **lot_size** (integer):  
   Lot size for the contract. Typically `1`, but may vary.
   
6. **option_type** (string, optional for non-option instruments):  
   Type of option for option instruments. Supported values:
   - `put`
   - `call`
   
7. **strike_price** (numeric, optional for non-option instruments):  
   The strike price of the option contract.

8. **implied_volatility** (numeric):  
   The implied volatility of the option, typically represented as a decimal.

9. **expiry** (string, in `dd/mm/yyyy` format):  
   The expiration date of the option or future contract.
   
10. **underlying_future_expiry** (string, in `dd/mm/yyyy` format, optional for non-futures):  
    The expiry date of the underlying future, if applicable.

11. **risk_free_rate** (numeric):  
    The risk-free interest rate assumed in the valuation model, typically expressed as a decimal.

12. **volatility** (numeric):  
    The actual volatility of the underlying asset, represented as a decimal.

13. **underlying_price** (numeric):  
    The price of the underlying asset at the time of trade.

---

## Example CSV File

```csv
date,instrument_type,underlying_asset,quantity,lot_size,option_type,strike_price,implied_volatility,expiry,underlying_future_expiry,risk_free_rate,volatility,underlying_price
13/04/2023,option,sugar,27,1,put,200,0.478477817,31/10/2025,30/04/2025,0.031413423,0.215497254,429.494494
15/12/2023,option,cocoa,-83,1,call,300,0.41251842,30/04/2025,31/01/2025,0.019328656,0.17107249,478.6526844
28/09/2023,future,sugar,-76,1,,,0.472371642,31/08/2025,30/04/2025,0.028958798,0.206371496,467.5348595
...