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 using the Black-Sholes model, 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, expected profit and expected loss.
The probability of profit (PoP), expected profit and expected loss at the user-defined target date for the strategy are calculated by default using the Black-Scholes model. Alternatively, the user can provide an array of underlying asset prices following a distribution other than the normal (e.g. Laplace) or model other than the Black-Scholes model (e.g. Heston model) that will be used in the calculations.
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.
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The easiest way to install OptionLab is using pip:
pip install optionlab
The evaluation of a strategy is done by calling the run_strategy function provided by
the library. This function receives the input data either as a Python dictionary or an
Inputs object. 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.
input_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"
}
],
}After defining the input data as a Python dictionary, we pass it to the run_strategy
function as shown below:
from optionlab import run_strategy, plot_pl
out = run_strategy(input_data)
print(out)
plot_pl(out)The variable out is an Outputs object that contains the results from the
calculations. By calling print with out as an argument, these results are
displayed on screen. The plot_pl function, in turn, takes an Outputs object as
its argument and plots the profit/loss diagram for the strategy.
Usage examples for a number of popular options trading strategies can be found in the Jupyter notebooks in the examples directory.
Contributions are definitely welcome. However, it should be mentioned that this repository uses poetry as a package manager and git hooks with pre-commit to customize actions on the repository.
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.
Options are very risky derivatives and, like any other type of financial vehicle, trading options requires due diligence. This code is provided for educational and research purposes only.
Bugs can be reported as issues.
