OptionLab is a lightweight Ruby library designed to provide quick evaluation of options trading strategies. It aims to be a direct port of the popular Python library - OptionLab

docs page coming soon

• Calculate profit/loss profiles for options strategies
• Estimate probability of profit using Black-Scholes or custom models
• Calculate option Greeks (Delta, Gamma, Theta, Vega, Rho)
• Generate profit/loss diagrams
• Support for complex multi-leg strategies
• Handle stock positions and previously closed trades
• Support for different dividend yield and interest rate scenarios
• Business day calculations across different countries

Add this line to your application's Gemfile:

gem 'option_lab'

And then execute:

$ bundle install

Or install it yourself as:

$ gem install option_lab

OptionLab requires:

• Ruby 3.3.0 or higher
• numo-narray gem for numerical computations
• distribution gem for statistical calculations

The evaluation of a strategy is done by calling the run_strategy method provided by the library. This method receives the input data as a Ruby hash or an Inputs object.

Here's an example of evaluating a naked call strategy:

require 'option_lab'

# Define the strategy
input_data = {
  stock_price: 164.04,
  start_date: Date.new(2023, 11, 22),
  target_date: Date.new(2023, 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"
    }
  ]
}

# Run the strategy calculation
outputs = OptionLab.run_strategy(input_data)

# Export P/L data to CSV
OptionLab.pl_to_csv(outputs, filename: "covered_call_pl.csv")

The Outputs object returned by run_strategy contains a wealth of information:

# Key probability metrics
probability_of_profit = outputs.probability_of_profit
profit_ranges = outputs.profit_ranges
expected_profit = outputs.expected_profit
expected_loss = outputs.expected_loss

# Strategy costs
strategy_cost = outputs.strategy_cost
per_leg_cost = outputs.per_leg_cost

# Returns
min_return = outputs.minimum_return_in_the_domain
max_return = outputs.maximum_return_in_the_domain

# Option Greeks
delta = outputs.delta
gamma = outputs.gamma
theta = outputs.theta
vega = outputs.vega
rho = outputs.rho

# Print all metrics
puts outputs

1. Fork it
2. Create your feature branch (git checkout -b my-new-feature)
3. Commit your changes (git commit -am 'Add some feature')
4. Push to the branch (git push origin my-new-feature)
5. Create a new Pull Request

The gem is available as open source under the terms of the MIT License.

OptionLab.plot_pl(outputs)

## Common Strategies

OptionLab supports all standard options strategies, including:

- Covered calls
- Naked puts
- Bull/bear spreads
- Straddles/strangles
- Iron condors
- Butterflies
- Calendar spreads
- And more...

## Advanced Usage

The library also allows for more advanced use cases, such as:

```ruby
# Create a custom distribution model
bs_inputs = OptionLab::Models::BlackScholesModelInputs.new(
  stock_price: 168.99,
  volatility: 0.483,
  interest_rate: 0.045,
  years_to_target_date: 24.0 / 365
)

# Generate price array with 10,000 samples
prices = OptionLab.create_price_array(bs_inputs, n: 10_000, seed: 42)

# Run a strategy with the custom price array
input_data = {
  stock_price: 168.99,
  volatility: 0.483,
  interest_rate: 0.045,
  min_stock: 120,
  max_stock: 200,
  model: "array",
  array: prices,
  strategy: [
    { type: "stock", n: 100, action: "buy" },
    {
      type: "call",
      strike: 185.0,
      premium: 4.1,
      n: 100,
      action: "sell"
    }
  ]
}

outputs = OptionLab.run_strategy(input_data)