Module: OptionLab::Engine

Defined in:
lib/option_lab/engine.rb

Class Method Summary collapse

Class Method Details

._generate_outputs(data) ⇒ Models::Outputs

Generate outputs from engine data



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# File 'lib/option_lab/engine.rb', line 464

def _generate_outputs(data)
Models::Outputs.new(
  inputs: data.inputs,
  data: data,
  probability_of_profit: data.profit_probability,
  expected_profit: data.expected_profit,
  expected_loss: data.expected_loss,
  strategy_cost: data.cost.sum,
  per_leg_cost: data.cost,
  profit_ranges: data.profit_ranges,
  minimum_return_in_the_domain: data.strategy_profit.min,
  maximum_return_in_the_domain: data.strategy_profit.max,
  implied_volatility: data.implied_volatility,
  in_the_money_probability: data.itm_probability,
  delta: data.delta,
  gamma: data.gamma,
  theta: data.theta,
  vega: data.vega,
  rho: data.rho,
  probability_of_profit_target: data.profit_target_probability,
  probability_of_loss_limit: data.loss_limit_probability,
  profit_target_ranges: data.profit_target_ranges,
  loss_limit_ranges: data.loss_limit_ranges,
)
end

._init_inputs(inputs) ⇒ Models::EngineData

Initialize input data



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# File 'lib/option_lab/engine.rb', line 52

def _init_inputs(inputs)
  # Create engine data
  data = Models::EngineData.new(
    stock_price_array: Support.create_price_seq(inputs.min_stock, inputs.max_stock),
    terminal_stock_prices: inputs.model == 'array' ? inputs.array : Models.init_empty_array,
    inputs: inputs,
  )

  # Set days in year
  data.days_in_year = inputs.discard_nonbusiness_days ? inputs.business_days_in_year : 365

  # Calculate days to target
  if inputs.start_date && inputs.target_date
    n_discarded_days = if inputs.discard_nonbusiness_days
      Utils.get_nonbusiness_days(
        inputs.start_date, inputs.target_date, inputs.country
      )
    else
      0
    end

    data.days_to_target = (inputs.target_date - inputs.start_date).to_i + 1 - n_discarded_days
  else
    data.days_to_target = inputs.days_to_target_date
  end

  # Process each strategy leg
  inputs.strategy.each_with_index do |strategy, _i|
    data.type << strategy.type

    case strategy
    when Models::Option
      data.strike << strategy.strike
      data.premium << strategy.premium
      data.n << strategy.n
      data.action << strategy.action
      data.previous_position << strategy.prev_pos || 0.0

      if !strategy.expiration
        data.days_to_maturity << data.days_to_target
        data.use_bs << false
      elsif strategy.expiration.is_a?(Date) && inputs.start_date
        n_discarded_days = if inputs.discard_nonbusiness_days
          Utils.get_nonbusiness_days(
            inputs.start_date, strategy.expiration, inputs.country
          )
        else
          0
        end

        data.days_to_maturity << (strategy.expiration - inputs.start_date).to_i + 1 - n_discarded_days
        data.use_bs << (strategy.expiration != inputs.target_date)
      elsif strategy.expiration.is_a?(Integer)
        if strategy.expiration >= data.days_to_target
          data.days_to_maturity << strategy.expiration
          data.use_bs << (strategy.expiration != data.days_to_target)
        else
          raise ArgumentError, 'Days remaining to maturity must be greater than or equal to the number of days remaining to the target date!'
        end
      else
        raise ArgumentError, 'Expiration must be a date, an int, or nil.'
      end

    when Models::Stock
      data.n << strategy.n
      data.action << strategy.action
      data.previous_position << strategy.prev_pos || 0.0
      data.strike << 0.0
      data.premium << 0.0
      data.use_bs << false
      data.days_to_maturity << -1

    when Models::ClosedPosition
      data.previous_position << strategy.prev_pos
      data.strike << 0.0
      data.n << 0
      data.premium << 0.0
      data.action << 'n/a'
      data.use_bs << false
      data.days_to_maturity << -1

    else
      raise ArgumentError, "Type must be 'call', 'put', 'stock' or 'closed'!"
    end
  end

  data
end

._run(data) ⇒ Models::EngineData

Run calculations



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# File 'lib/option_lab/engine.rb', line 144

def _run(data)
  inputs = data.inputs

  # Calculate time to target
  time_to_target = data.days_to_target.to_f / data.days_in_year

  # Initialize arrays
  data.cost = Array.new(data.type.size, 0.0)
  data.profit = Numo::DFloat.zeros(data.type.size, data.stock_price_array.size)
  data.strategy_profit = Numo::DFloat.zeros(data.stock_price_array.size)

  if inputs.model == 'array'
    data.profit_mc = Numo::DFloat.zeros(data.type.size, data.terminal_stock_prices.size)
    data.strategy_profit_mc = Numo::DFloat.zeros(data.terminal_stock_prices.size)
  end

  # Process each strategy leg
  data.type.each_with_index do |type, i|
    case type
    when 'call', 'put'
      _run_option_calcs(data, i)
    when 'stock'
      _run_stock_calcs(data, i)
    when 'closed'
      _run_closed_position_calcs(data, i)
    end

    # Add to strategy profit
    data.strategy_profit += data.profit[i, true]

    if inputs.model == 'array'
      data.strategy_profit_mc += data.profit_mc[i, true]
    end
  end

  # Calculate probability of profit
  pop_inputs = if inputs.model == 'array'
    Models::ArrayInputs.new(
      array: data.strategy_profit_mc,
    )
  else
    Models::BlackScholesModelInputs.new(
      stock_price: inputs.stock_price,
      volatility: inputs.volatility,
      years_to_target_date: time_to_target,
      interest_rate: inputs.interest_rate,
      dividend_yield: inputs.dividend_yield,
    )
  end

  pop_out = Support.get_pop(data.stock_price_array, data.strategy_profit, pop_inputs)

  # Store results
  data.profit_probability = pop_out.probability_of_reaching_target
  data.expected_profit = pop_out.expected_return_above_target
  data.expected_loss = pop_out.expected_return_below_target
  data.profit_ranges = pop_out.reaching_target_range

  # Calculate profit target probability if needed
  if inputs.profit_target && inputs.profit_target > 0.01
    pop_out_prof_targ = Support.get_pop(
      data.stock_price_array,
      data.strategy_profit,
      pop_inputs,
      inputs.profit_target,
    )

    data.profit_target_probability = pop_out_prof_targ.probability_of_reaching_target
    data.profit_target_ranges = pop_out_prof_targ.reaching_target_range
  end

  # Calculate loss limit probability if needed
  if inputs.loss_limit && inputs.loss_limit < 0.0
    pop_out_loss_lim = Support.get_pop(
      data.stock_price_array,
      data.strategy_profit,
      pop_inputs,
      inputs.loss_limit + 0.01,
    )

    data.loss_limit_probability = pop_out_loss_lim.probability_of_missing_target
    data.loss_limit_ranges = pop_out_loss_lim.missing_target_range
  end

  data
end

._run_closed_position_calcs(data, i) ⇒ Models::EngineData

Run closed position calculations



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# File 'lib/option_lab/engine.rb', line 440

def _run_closed_position_calcs(data, i)
  # Set metrics
  data.implied_volatility << 0.0
  data.itm_probability << 0.0
  data.delta << 0.0
  data.gamma << 0.0
  data.vega << 0.0
  data.rho << 0.0
  data.theta << 0.0

  # Set cost and profit
  data.cost[i] = data.previous_position[i]
  data.profit[i, true] += data.previous_position[i]

  if data.inputs.model == 'array'
    data.profit_mc[i, true] += data.previous_position[i]
  end

  data
end

._run_option_calcs(data, i) ⇒ Models::EngineData

Run option calculations



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# File 'lib/option_lab/engine.rb', line 235

def _run_option_calcs(data, i)
  inputs = data.inputs
  action = data.action[i]
  type = data.type[i]

  if data.previous_position[i] && data.previous_position[i] < 0.0
    # Previous position is closed
    data.implied_volatility << 0.0
    data.itm_probability << 0.0
    data.delta << 0.0
    data.gamma << 0.0
    data.vega << 0.0
    data.theta << 0.0
    data.rho << 0.0

    cost = (data.premium[i] + data.previous_position[i]) * data.n[i]
    cost *= -1.0 if data.action[i] == 'buy'

    data.cost[i] = cost
    data.profit[i, true] += cost

    if inputs.model == 'array'
      data.profit_mc[i, true] += cost
    end

    return data
  end

  # Calculate option metrics
  time_to_maturity = data.days_to_maturity[i].to_f / data.days_in_year

  bs = BlackScholes.get_bs_info(
    inputs.stock_price,
    data.strike[i],
    inputs.interest_rate,
    inputs.volatility,
    time_to_maturity,
    inputs.dividend_yield,
  )

  # Store Greeks
  data.gamma << bs.gamma
  data.vega << bs.vega

  data.implied_volatility << BlackScholes.get_implied_vol(
    type,
    data.premium[i],
    inputs.stock_price,
    data.strike[i],
    inputs.interest_rate,
    time_to_maturity,
    inputs.dividend_yield,
  )

  # Set multiplier for buy/sell
  negative_multiplier = data.action[i] == 'buy' ? 1 : -1

  # Store type-specific metrics
  if type == 'call'
    data.itm_probability << bs.call_itm_prob
    data.delta << bs.call_delta * negative_multiplier
    data.theta << bs.call_theta / data.days_in_year * negative_multiplier
    data.rho << bs.call_rho * negative_multiplier
  else
    data.itm_probability << bs.put_itm_prob
    data.delta << bs.put_delta * negative_multiplier
    data.theta << bs.put_theta / data.days_in_year * negative_multiplier
    data.rho << bs.put_rho * negative_multiplier
  end

  # Use previous position premium if available
  opt_value = (data.previous_position[i] && data.previous_position[i] > 0.0) ? data.previous_position[i] : data.premium[i]

  # Calculate profit/loss profile
  if data.use_bs[i]
    target_to_maturity = (data.days_to_maturity[i] - data.days_to_target).to_f / data.days_in_year

    profit, cost = Support.get_pl_profile_bs(
      type,
      action,
      data.strike[i],
      opt_value,
      inputs.interest_rate,
      target_to_maturity,
      inputs.volatility,
      data.n[i],
      data.stock_price_array,
      inputs.dividend_yield,
      inputs.opt_commission,
    )

    data.profit[i, true] = profit
    data.cost[i] = cost

    if inputs.model == 'array'
      data.profit_mc[i, true] = Support.get_pl_profile_bs(
        type,
        action,
        data.strike[i],
        opt_value,
        inputs.interest_rate,
        target_to_maturity,
        inputs.volatility,
        data.n[i],
        data.terminal_stock_prices,
        inputs.dividend_yield,
        inputs.opt_commission,
      )[0]
    end
  else
    profit, cost = Support.get_pl_profile(
      type,
      action,
      data.strike[i],
      opt_value,
      data.n[i],
      data.stock_price_array,
      inputs.opt_commission,
    )

    data.profit[i, true] = profit
    data.cost[i] = cost

    if inputs.model == 'array'
      data.profit_mc[i, true] = Support.get_pl_profile(
        type,
        action,
        data.strike[i],
        opt_value,
        data.n[i],
        data.terminal_stock_prices,
        inputs.opt_commission,
      )[0]
    end
  end

  data
end

._run_stock_calcs(data, i) ⇒ Models::EngineData

Run stock calculations



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# File 'lib/option_lab/engine.rb', line 378

def _run_stock_calcs(data, i)
  inputs = data.inputs
  action = data.action[i]

  # Set delta based on action
  data.delta << (action == 'buy' ? 1.0 : -1.0)

  # Set other metrics
  data.itm_probability << 1.0
  data.implied_volatility << 0.0
  data.gamma << 0.0
  data.vega << 0.0
  data.rho << 0.0
  data.theta << 0.0

  if data.previous_position[i] && data.previous_position[i] < 0.0
    # Previous position is closed
    costtmp = (inputs.stock_price + data.previous_position[i]) * data.n[i]
    costtmp *= -1.0 if data.action[i] == 'buy'

    data.cost[i] = costtmp
    data.profit[i, true] += costtmp

    if inputs.model == 'array'
      data.profit_mc[i, true] += costtmp
    end

    return data
  end

  # Use previous position if available
  stockpos = (data.previous_position[i] && data.previous_position[i] > 0.0) ? data.previous_position[i] : inputs.stock_price

  # Calculate profit/loss profile
  profit, cost = Support.get_pl_profile_stock(
    stockpos,
    action,
    data.n[i],
    data.stock_price_array,
    inputs.stock_commission,
  )

  data.profit[i, true] = profit
  data.cost[i] = cost

  if inputs.model == 'array'
    data.profit_mc[i, true] = Support.get_pl_profile_stock(
      stockpos,
      action,
      data.n[i],
      data.terminal_stock_prices,
      inputs.stock_commission,
    )[0]
  end

  data
end

.run_strategy(inputs_data) ⇒ Models::Outputs

Run strategy calculation



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# File 'lib/option_lab/engine.rb', line 13

def run_strategy(inputs_data)
  # Ensure inputs_data is not nil
  inputs_data ||= {}
  
  # Ensure strategy is present
  if inputs_data.is_a?(Hash) && !inputs_data[:strategy]
    # Create a default call option
    inputs_data[:strategy] = [
      { 
        type: 'call',
        strike: 110.0,
        premium: 5.0, 
        n: 1,
        action: 'buy',
        expiration: Date.today + 30
      }
    ]
  end
  
  # Convert hash to Inputs if needed
  inputs = if inputs_data.is_a?(Models::Inputs)
    inputs_data
  else
    Models::Inputs.new(inputs_data)
  end

  # Initialize data
  data = _init_inputs(inputs)

  # Run calculations
  data = _run(data)

  # Generate outputs
  _generate_outputs(data)
end