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# 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:[email protected]).
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:[email protected]).

You can also reach me on [Linkedin](https://www.linkedin.com/in/roberto-gomes-phd-8a718317b/).

## Installation
Expand Down Expand Up @@ -80,35 +85,41 @@ The input data passed to `model_validate` above needs to be of the following str

- `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.
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'.
- Country for which the holidays will be considered if 'discard_nonbusinessdays' 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.
- 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.
- Target 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.
- Number of days until the target date, typically the maturity date of the options.
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'.
- 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.
- 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 `strategy` attribute can be either of type `OptionStrategy`, `StockStrategy`, or
`ClosedPosition`.

The `OptionStrategy` structure:

Expand All @@ -130,20 +141,21 @@ The `OptionStrategy` structure:
- 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.
- 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.

Expand All @@ -154,29 +166,32 @@ The `OptionStrategy` structure:
- 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.
- 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:
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.
- 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.
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.

```python
inputs_data = {
Expand All @@ -188,21 +203,29 @@ inputs_data = {
"min_stock": 120,
"max_stock": 200,
"strategy": [
{"type": "call", "strike": 175.0, "premium": 1.15, "n": 100, "action": "sell"}
{
"type": "call",
"strike": 175.0,
"premium": 1.15,
"n": 100,
"action":"sell"
}
],
}
```

The calculations can be run by using:
The simplest way to perform the calculations is by calling the `run_strategy` function
as follows:

```python
from optionlab import run_strategy

out = run_strategy(inputs_data)
```

Alternatively, the inputs object can be passed to the `StrategyEngine` object and the calculations are performed by calling
the `run` method of the `StrategyEngine` object:
Alternatively, an `Inputs` object can be passed to the `StrategyEngine` object and
the calculations are performed by calling the `run` method of the `StrategyEngine`
object:

```python
from optionlab import StrategyEngine
Expand All @@ -211,15 +234,16 @@ st = StrategyEngine(Inputs.model_validate(inputs_data))
out = st.run()
```

This method returns an `Outputs` object with the following structure:
In both cases, `out` contains 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.
- 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.
Expand Down Expand Up @@ -254,20 +278,24 @@ This method returns an `Outputs` object with the following structure:
- `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.
- `profit_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 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.
- 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.
- 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:
Expand All @@ -278,17 +306,15 @@ print("Probability of Profit (PoP): %.1f%%" % (out.probability_of_profit * 100.0

## 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.
[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:
Once `poetry` is installed, set up your virtual environment for the repository with
the following:

```
cd optionlab/
Expand All @@ -297,51 +323,56 @@ 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).
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.
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.
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.
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
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.
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.

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