exotx

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exotx provides a simple and user-friendly interface for pricing and analyzing financial derivatives using QuantLib's advanced numerical methods.

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# exotx

exotx is a Python wrapper for the [QuantLib library](https://www.quantlib.org/), a powerful open-source library for
quantitative finance. exotx provides a simple and user-friendly interface for pricing and analyzing financial
derivatives using QuantLib's advanced numerical methods.

## Installation

To install exotx, simply use pip:

```sh
pip install exotx
```

## Usage

### Define the product

#### Vanilla Option

```python
import exotx
from exotx.instruments import OptionType

strike = 90
option_maturity = '2016-05-04'
my_vanilla_option = exotx.VanillaOption(strike, option_maturity, OptionType.CALL)
```

#### Auto-Callable

```python
notional = 100
strike = 100.0
autocall_barrier_level = 1.0  # 100%
annual_coupon_value = 0.03  # 3.00%
coupon_barrier_level = 0.75  # 75%
protection_barrier_level = 0.75  # 75%
my_autocallable = exotx.Autocallable(notional, strike, autocall_barrier_level, annual_coupon_value, coupon_barrier_level, protection_barrier_level)
```

### Define the static data

The object that represents static data such as the calendar, the day counter or the business day convention used.

#### From the constructor

```python
my_static_data = exotx.StaticData(day_counter='Actual360', business_day_convention='ModifiedFollowing')
```

#### From JSON

```python
my_json = {
    'day_counter': 'Actual360',
    'business_day_convention': 'ModifiedFollowing'
}
my_static_data = exotx.StaticData.from_json(my_json)
```

### Define the market data

#### From the constructor

```python
reference_date = '2015-11-06'
spot = 100.0
risk_free_rate = 0.01
dividend_rate = 0.0
black_scholes_volatility = 0.2

my_market_data = exotx.MarketData(reference_date, spot, risk_free_rate, dividend_rate, black_scholes_volatility=black_scholes_volatility)
```

#### From JSON

```python
my_json = {
    'reference_date': '2015-11-06',
    'spot': 100,
    'risk_free_rate': 0.01,
    'dividend_rate': 0,
    'black_scholes_volatility': 0.2
}
my_market_data = exotx.MarketData.from_json(my_json)
```

### Price the product

#### Vanilla Option
```python
from exotx.enums import PricingModel, NumericalMethod

my_pricing_config = exotx.PricingConfiguration(PricingModel.BLACK_SCHOLES, NumericalMethod.ANALYTIC, compute_greeks=True)
exotx.price(my_vanilla_option, my_market_data, my_static_data, my_pricing_config)
```
```plaintext
>>> { 
    'price': 13.83328710, 
    'delta': 0.77183751, 
    'gamma': 0.01609460, 
    'theta': -7.01024983
}
```

#### Auto-Callable
```python
exotx.price(my_autocallable, my_market_data, my_static_data, model='black-scholes')
```

```plaintext
>>> 96.08517973497098
```

## Contributing

We welcome contributions to exotx! If you find a bug or would like to request a new feature, please open an issue on
the [Github repository](https://github.com/sebastieneveno/exotx).
If you would like to contribute code, please submit a pull request.

## License

exotx is released under the [MIT License](https://opensource.org/licenses/MIT).

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