# 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
```bash
#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
...
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"description": "# VARPORT\r\n\r\nVARPORT is a Python library to calculate Value at Risk (VaR) of a derivatives portfolio and generate reports .\r\n\r\n## Features\r\n\r\n- Compute VaR using Monte Carlo simulations\r\n- Generate PDF reports with portfolio summaries and charts\r\n- Supports options and futures portfolios\r\n\r\n## Installation\r\n\r\n```bash\r\n\r\n\r\n#usage\r\n\r\npip install varport\r\nfrom varport import ReportGenerator, MainVaRProcessor\r\nimport numpy as np\r\n\r\n# Load your portfolio from a CSV file\r\nportfolio_file = 'C:/xxx/portfolio.csv'\r\n\r\n# Example user input for mu and Sigma\r\nmu = np.array([0.05, 0.03, 0.07, 0.04, 0.06]) # Example mu vector (expected returns)\r\nSigma = np.array([[0.1, 0.02, 0.03, 0.01, 0.04], # Example covariance matrix\r\n [0.02, 0.08, 0.01, 0.03, 0.02],\r\n [0.03, 0.01, 0.09, 0.02, 0.01],\r\n [0.01, 0.03, 0.02, 0.07, 0.02],\r\n [0.04, 0.02, 0.01, 0.02, 0.08]])\r\n\r\n# Initialize MainVaRProcessor with the portfolio file path\r\nvar_processor = MainVaRProcessor(filepath=portfolio_file)\r\n\r\n# Process the portfolio with user-provided mu and Sigma to calculate VaR and differences\r\nportfolio, VaR, differences = var_processor.process(mu=mu, Sigma=Sigma)\r\n\r\n# Generate the report with the calculated differences and VaR\r\nreport = ReportGenerator(differences=differences, VaR=VaR, portfolio=portfolio)\r\n\r\n# Generate and save the PDF report\r\nreport.display_table_and_chart(pdf_filename=\"VaR_Report_Test.pdf\")\r\n\r\nprint(\"Report generated successfully!\")\r\n\r\n\r\n# Portfolio Management Tool\r\n\r\n## Overview\r\n\r\nThis 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.\r\n\r\n---\r\n\r\n## Portfolio File Format\r\n\r\nThe portfolio file should be in `.csv` format and must include the following columns:\r\n\r\n### Required Columns:\r\n\r\n1. **date** (string, in `dd/mm/yyyy` format): \r\n The date the trade was made.\r\n \r\n2. **instrument_type** (string): \r\n Specifies the type of financial instrument. Supported values:\r\n - `option`\r\n - `future`\r\n \r\n3. **underlying_asset** (string): \r\n The underlying asset for the trade (e.g., \"sugar\", \"cocoa\", \"coffee\").\r\n \r\n4. **quantity** (integer): \r\n The quantity of the asset in the trade. Positive values for long positions, negative values for short positions.\r\n \r\n5. **lot_size** (integer): \r\n Lot size for the contract. Typically `1`, but may vary.\r\n \r\n6. **option_type** (string, optional for non-option instruments): \r\n Type of option for option instruments. Supported values:\r\n - `put`\r\n - `call`\r\n \r\n7. **strike_price** (numeric, optional for non-option instruments): \r\n The strike price of the option contract.\r\n\r\n8. **implied_volatility** (numeric): \r\n The implied volatility of the option, typically represented as a decimal.\r\n\r\n9. **expiry** (string, in `dd/mm/yyyy` format): \r\n The expiration date of the option or future contract.\r\n \r\n10. **underlying_future_expiry** (string, in `dd/mm/yyyy` format, optional for non-futures): \r\n The expiry date of the underlying future, if applicable.\r\n\r\n11. **risk_free_rate** (numeric): \r\n The risk-free interest rate assumed in the valuation model, typically expressed as a decimal.\r\n\r\n12. **volatility** (numeric): \r\n The actual volatility of the underlying asset, represented as a decimal.\r\n\r\n13. **underlying_price** (numeric): \r\n The price of the underlying asset at the time of trade.\r\n\r\n---\r\n\r\n## Example CSV File\r\n\r\n```csv\r\ndate,instrument_type,underlying_asset,quantity,lot_size,option_type,strike_price,implied_volatility,expiry,underlying_future_expiry,risk_free_rate,volatility,underlying_price\r\n13/04/2023,option,sugar,27,1,put,200,0.478477817,31/10/2025,30/04/2025,0.031413423,0.215497254,429.494494\r\n15/12/2023,option,cocoa,-83,1,call,300,0.41251842,30/04/2025,31/01/2025,0.019328656,0.17107249,478.6526844\r\n28/09/2023,future,sugar,-76,1,,,0.472371642,31/08/2025,30/04/2025,0.028958798,0.206371496,467.5348595\r\n...\r\n",
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