Metadata-Version: 2.1
Name: optionlab
Version: 1.1.0
Summary: Evaluate option strategies
Author: rgaveiga
Requires-Python: >=3.10,<4.0
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: holidays (>=0.44,<0.45)
Requires-Dist: matplotlib (>=3.8.3,<4.0.0)
Requires-Dist: pandas (>=2.2.1,<3.0.0)
Requires-Dist: pydantic (>=2.6.3,<3.0.0)
Requires-Dist: scipy (>=1.12.0,<2.0.0)
Description-Content-Type: text/markdown

# OptionLab

This package is a lightweight library written entirely in Python, designed to provide quick evaluation of 
option strategies.

The code produces various outputs, including the profit/loss profile of the strategy on a user-defined 
target date, the range of stock prices for which the strategy is profitable (i.e., generating a return 
greater than \$0.01), the Greeks associated with each leg of the strategy, the resulting debit or credit 
on the trading account, the maximum and minimum returns within a specified lower and higher price range 
of the underlying asset, and an estimate of the strategy's probability of profit.

The probability of profit (PoP) for the strategy is calculated based on the distribution of estimated 
prices of the underlying asset on the user-defined target date. Specifically, for the price range in the payoff 
where the strategy generates profit, the PoP represents the probability that the stock price will fall within 
that range. This distribution of underlying asset prices on the target date can be lognormal, log-Laplace, or 
derived from the Black-Scholes model. Additionally, the distribution can be obtained through simulations 
(e.g., Monte Carlo) or machine learning models.

Despite the code having been developed with option strategies in mind, it can also be used for strategies 
that combine options with stocks and/or take into account the profits or losses of closed trades.

If you have any questions, corrections, comments or suggestions, just [drop a message](mailto:roberto.veiga@ufabc.edu.br). 
You can also reach me on [Linkedin](https://www.linkedin.com/in/roberto-gomes-phd-8a718317b/).

## Installation

The easiest way to install **OptionLab** is using **pip**:

```
pip install optionlab
```

## Basic usage

Usage examples for several strategies can be found in the **examples** directory.

To evaluate an option strategy, an `Inputs` model needs to be created:

```python
from optionlab.models import Inputs
inputs = Inputs.model_validate(inputs_data)
```

The input data passed to `model_validate` above needs to be of the following structure: 

---

- `stock_price` : float
  - Spot price of the underlying.

- `volatility` : float
  - Annualized volatility.

- `interest_rate` : float
  - Annualized risk-free interest rate.

- `min_stock` : float
  - Minimum value of the stock in the stock price domain.

- `max_stock` : float
  - Maximum value of the stock in the stock price domain.

- `strategy` : list
  - A list of `Strategy`.

- `dividend_yield` : float, optional
  - Annualized dividend yield. Default is 0.0.

- `profit_target` : float, optional
  - Target profit level. Default is None, which means it is not calculated.

- `loss_limit` : float, optional
  - Limit loss level. Default is None, which means it is not calculated.

- `opt_commission` : float
  - Broker commission for options transactions. Default is 0.0.

- `stock_commission` : float
  - Broker commission for stocks transactions. Default is 0.0.

- `compute_expectation` : logical, optional
  - Whether or not the strategy's average profit and loss must be computed from a numpy 
  array of random terminal prices generated from the chosen distribution. Default is False.

- `discard_nonbusinessdays` : logical, optional
  - Whether to discard Saturdays and Sundays (and maybe holidays) when counting the 
  number of days between two dates. Default is True.

- `country` : string, optional
  - Country for which the holidays will be considered if 'discard_nonbusinessdyas' is True. Default is 'US'.

- `start_date` : dt.date, optional
  - Start date in the calculations. If not provided, days_to_target_date must be provided.

- `target_date` : dt.date, optional
  - Start date in the calculations. If not provided, days_to_target_date must be provided.

- `days_to_target_date` : int, optional
  - Days to maturity. If not provided, start_date and end_date must be provided.

- `distribution` : string, optional
  - Statistical distribution used to compute probabilities. It can be 'black-scholes', 'normal', 
  'laplace' or 'array'. Default is 'black-scholes'.

- `mc_prices_number` : int, optional
  - Number of random terminal prices to be generated when calculating the average profit and 
  loss of a strategy. Default is 100,000.

---

The `strategy` attribute can have be either of type `OptionStrategy`, `StockStrategy`, or `ClosedPosition`.

The `OptionStrategy` structure:

---

- `type` : string
  - Either 'call' or 'put'. It is mandatory.

- `strike` : float
  - Option strike price. It is mandatory.

- `premium` : float
  - Option premium. It is mandatory.

- `n` : int
  - Number of options. It is mandatory.

- `action` : string
  - Either 'buy' or 'sell'. It is mandatory.

- `prev_pos` : float
  - Premium effectively paid or received in a previously opened position. If positive, it 
  means that the position remains open and the payoff calculation takes this price into account, 
  not the current price of the option. If negative, it means that the position is closed and the 
  difference between this price and the current price is considered in the payoff calculation.

- `expiration` : string | int
  - Expiration date or days to maturity.

---
  
`StockStrategy`:

---
  
- `type` : string
  - It must be 'stock'. It is mandatory.

- `n` : int
  - Number of shares. It is mandatory.

- `action` : string
  - Either 'buy' or 'sell'. It is mandatory.

- `prev_pos` : float
  - Stock price effectively paid or received in a previously opened position. If positive, it 
  means that the position remains open and the payoff calculation takes this price into account, 
  not the current price of the stock. If negative, it means that the position is closed and the 
  difference between this price and the current price is considered in the payoff calculation.

---

For a non-determined previously opened position to be closed, which might consist of any combination of calls, 
puts and stocks, the `ClosedPosition` must contain two keys:

---

- `type` : string
  - It must be 'closed'. It is mandatory.

- `prev_pos` : float
  - The total value of the position to be closed, which can be positive if it made a profit or negative if it is a loss. It is mandatory.

---

For example, let's say we wanted to calculate the probability of profit for naked calls on Apple stocks with maturity on December 17, 2021. The strategy setup consisted of selling 100 175.00 strike calls for 1.15 each on November 22, 2021.

The inputs object must be passed to the `StrategyEngine` object as follows:

```python
from optionlab.engine import StrategyEngine

inputs_data = {
    "stock_price": 164.04,
    "start_date": "2021-11-22",
    "target_date": "2021-12-17",
    "volatility": 0.272,
    "interest_rate": 0.0002,
    "min_stock": 120,
    "max_stock": 200,
    "strategy": [
        {"type": "call", "strike": 175.0, "premium": 1.15, "n": 100, "action": "sell"}
    ],
}
st = StrategyEngine(Inputs.model_validate(inputs_data))
```

The calculations are performed by calling the *run()* method of the *StrategyEngine* object:

```python
out = st.run()
```

This method returns an `Outputs` object with the following structure:

---

- `probability_of_profit` : float
  - Probability of the strategy yielding at least $0.01.

- `profit_ranges` : list
  - A list of minimum and maximum stock prices defining ranges in which the strategy makes at least $0.01.

- `strategy_cost` : float
  - Total strategy cost.

- `per_leg_cost` : list
  - A list of costs, one per strategy leg.

- `implied_volatility` : list
  - A Python list of implied volatilities, one per strategy leg.

- `in_the_money_probability` : list
  - A list of ITM probabilities, one per strategy leg.

- `delta` : list
  - A list of Delta values, one per strategy leg.

- `gamma` : list
  - A list of Gamma values, one per strategy leg.

- `theta` : list
  - A list of Theta values, one per strategy leg.

- `vega` : list
  - A list of Vega values, one per strategy leg.

- `minimum_return_in_the_domain` : float
  - Minimum return of the strategy within the stock price domain.

- `maximum_return_in_the_domain` : float
  - Maximum return of the strategy within the stock price domain.

- `probability_of_profit_target` : float, optional
  - Probability of the strategy yielding at least the profit target.

- `project_target_ranges` : list, optional
  - A list of minimum and maximum stock prices defining ranges in which the strategy makes at least the profit target.

- `probability_of_loss_limit` : float, optional
  - Probability of the strategy losing at least the loss limit.

- `average_profit_from_mc` : float, optional
  - Average profit as calculated from Monte Carlo-created terminal stock prices for which the strategy is profitable.

- `average_loss_from_mc` : float, optional
  - Average loss as calculated from Monte Carlo-created terminal stock prices for which the strategy ends in loss.

- `probability_of_profit_from_mc` : float, optional
  - Probability of the strategy yielding at least $0.01 as calculated from Monte Carlo-created terminal stock prices.
---

To obtain the probability of profit of the naked call example above:

```python
print("Probability of Profit (PoP): %.1f%%" % (out.probability_of_profit * 100.0)) # 84.5%, according to the calculations
```

## Contributions

Although functional, **OptionLab** is still in its early stages of development. The author has limited time available 
to work on this library, which is why contributions from individuals with expertise in options and Python 
programming are greatly appreciated. 

### Dev setup

This repository uses `poetry` as a package manager. Install `poetry` as per the 
[poetry docs](https://python-poetry.org/docs/#installing-with-pipx). It is recommended to install poetry version
1.4.0 if there are issues with the latest versions.

Once poetry is installed, set up your virtual environment for the repo with the following:

```
cd optionlab/
python3.10 venv venv
source venv/bin/activate
poetry install
```

That should install all your dependencies and make you ready to contribute. Please add tests for all new features and 
bug fixes and make sure you are formatting with [black](https://github.com/psf/black).

Optionally, to use Jupyter, you can install it with: `pip install juypter`.

#### Git Hooks

This repo uses git hooks. Git hooks are scripts that run automatically every time a particular event occurs in a 
Git repository. These events can include committing, merging, and pushing, among others. Git hooks allow developers 
to enforce certain standards or checks before actions are completed in the repository, enhancing the workflow 
and code quality.

The pre-commit framework is a tool that leverages Git hooks to run checks on the code before it is committed to the 
repository. By using pre-commit, developers can configure various plugins or hooks that automatically check for 
syntax errors, formatting issues, or even run tests on the code being committed. This ensures that only code 
that passes all the defined checks can be added to the repository, helping to maintain code quality and prevent
issues from being introduced. 

To install the pre-commit framework on a system with Homebrew, follow these steps:

```
brew install pre-commit
```

Once pre-commit is installed, navigate to the root directory of your Git repository where you want to 
enable pre-commit hooks. Then, run the following command to set up pre-commit for that repository. This 
command installs the Git hook scripts that the pre-commit framework will use to run checks before commits.

```
pre-commit install
```

Now, before each commit, the pre-commit hooks you've configured will automatically run. If any hook fails, 
the commit will be aborted, allowing you to fix the issues before successfully committing your changes. This 
process helps maintain a high code quality and ensures that common issues are addressed early in 
the development process.

To check all files in a repository with pre-commit, use:

```
pre-commit run --all-files

```

## Disclaimer

This is free software and is provided as is. The author makes no guarantee that its results are accurate and is 
not responsible for any losses caused by the use of the code. Bugs can be reported as issues.

