Updated examples and blacked source

Author: rgaveiga

examples/naked_call.ipynb

@@ -88,7 +88,7 @@ def get_option_price(
     Parameters
     ----------
     option_type : str
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     s0 : float | numpy.ndarray
         Spot price(s) of the underlying asset.
     x : float | numpy.ndarray
@@ -140,7 +140,7 @@ def get_delta(
     Parameters
     ----------
     option_type : str
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     d1 : float | numpy.ndarray
         `d1` in Black-Scholes formula.
     years_to_maturity : float
@@ -217,7 +217,7 @@ def get_theta(
     Parameters
     ----------
     option_type : str
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     s0 : float
         Spot price of the underlying asset.
     x : float | numpy.ndarray
@@ -307,7 +307,7 @@ def get_rho(
     Parameters
     ----------
     option_type : OptionType
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     x : float | numpy.ndarray
         Strike price(s).
     r : float
@@ -432,7 +432,7 @@ def get_implied_vol(
     Parameters
     ----------
     option_type : str
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     oprice : float
         Market price of an option.
     s0 : float
@@ -474,7 +474,7 @@ def get_itm_probability(
     Parameters
     ----------
     option_type : str
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     d2 : float | numpy.ndarray
         `d2` in Black-Scholes formula.
     years_to_maturity : float

optionlab/black_scholes.py

@@ -24,11 +24,11 @@ class Stock(BaseLeg):
     Attributes
     ----------
     type : str
-        It must be 'stock'. It is mandatory.
+        It must be **stock**.
     n : int
-        Number of shares. It is mandatory.
+        Number of shares.
     action : str
-        `Action` literal value, which must be either 'buy' or 'sell'. It is mandatory.
+        `Action` literal value, which must be either **buy** or **sell**.
     prev_pos : float | None, optional
         Stock price effectively paid or received in a previously opened position.
         If positive, the position remains open and the payoff calculation considers
@@ -48,24 +48,23 @@ class Option(BaseLeg):
     Attributes
     ----------
     type : str
-        `OptionType` literal value, which must be either 'call' or 'put'. It is
-        mandatory.
+        `OptionType` literal value, which must be either **call** or **put**.
     strike : float
-        Option strike price. It is mandatory.
+        Strike price.
     premium : float
-        Option premium. It is mandatory.
+        Option premium.
     n : int
-        Number of options. It is mandatory
+        Number of options.
     action : str
-        `Action` literal value, which must be either 'buy' or 'sell'.
+        `Action` literal value, which must be either **buy** or **sell**.
     prev_pos : float | None, optional
         Premium effectively paid or received in a previously opened position. If
         positive, the position remains open and the payoff calculation considers
         this price instead of the current price of the option. If negative, the
         position is closed and the difference between this price and the current
         price is included in the payoff calculation. The default is None, which
         means this option position is not a previously opened position.
-    expiration : str | int | None, optional.
+    expiration : str | int | None, optional
         Expiration date or number of days remaining to maturity. The default is
         None.
     """
@@ -89,10 +88,10 @@ class ClosedPosition(BaseModel):
     Attributes
     ----------
     type : str
-        It must be 'closed'. It is mandatory.
+        It must be **closed**.
     prev_pos : float
         The total amount of the closed position. If positive, it resulted in a
-        profit; if negative, it incurred a loss. It is mandatory.
+        profit; if negative, it incurred a loss.
     """
 
     type: Literal["closed"] = "closed"
@@ -115,7 +114,7 @@ class BlackScholesModelInputs(TheoreticalModelInputs):
     Attributes
     ----------
     model : str
-        It must be either 'black-scholes' or 'normal'.
+        It must be either **black-scholes** or **normal**.
     stock_price : float
         Stock price.
     volatility : float
@@ -143,7 +142,7 @@ class LaplaceInputs(TheoreticalModelInputs):
     Attributes
     ----------
     model : str
-        It must be 'laplace'.
+        It must be **laplace**.
     stock_price : float
         Stock price.
     mu : float
@@ -168,7 +167,7 @@ class ArrayInputs(BaseModel):
     Attributes
     ----------
     model : str
-        It must be 'array'.
+        It must be **array**.
     array : numpy.ndarray
         Array of strategy returns.
     """
@@ -223,7 +222,7 @@ class Inputs(BaseModel):
         Number of business days in a year. The default is 252.
     country : str, optional
         Country whose holidays will be counted if `discard_nonbusinessdays` is
-        set to True. The default is 'US'.
+        set to True. The default is **US**.
     start_date : datetime.date, optional
         Start date in the calculations. If not provided, `days_to_target_date`
         must be provided.
@@ -234,8 +233,9 @@ class Inputs(BaseModel):
         Days remaining to the target date. If not provided, `start_date` and
         `target_date` must be provided.
     model : str, optional
-        Theoretical model used in the calculations. It can be 'black-scholes'
-        (the same as 'normal') or 'array'. The default is 'black-scholes'.
+        Theoretical model used in the calculations of probability of profit. It
+        can be **black-scholes** (the same as **normal**) or **array**. The default
+        is **black-scholes**.
     array : numpy.ndarray | None, optional
         Array of terminal stock prices. The default is None.
     """
@@ -491,7 +491,7 @@ def __str__(self):
 
 class PoPOutputs(BaseModel):
     """
-    Defineas the output data from a probability of profit (PoP) calculation.
+    Defines the output data from a probability of profit (PoP) calculation.
 
     Attributes
     ----------

optionlab/models.py

@@ -33,9 +33,9 @@ def get_pl_profile(
     Parameters
     ----------
     option_type : str
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     action : str
-        `Action` literal value, which must be either 'buy' or 'sell'.
+        `Action` literal value, which must be either **buy** or **sell**.
     x : float
         Strike price.
     val : float
@@ -80,7 +80,7 @@ def get_pl_profile_stock(
     s0 : float
         Initial stock price.
     action : str
-        `Action` literal value, which must be either 'buy' or 'sell'.
+        `Action` literal value, which must be either **buy** or **sell**.
     n : int
         Number of shares.
     s : numpy.ndarray
@@ -124,9 +124,9 @@ def get_pl_profile_bs(
     Parameters
     ----------
     option_type : str
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     action : str
-        `Action` literal value, which must be either 'buy' or 'sell'.
+        `Action` literal value, which must be either **buy** or **sell**.
     x : float
         Strike price.
     val : float
@@ -428,11 +428,11 @@ def _get_pl_option(
     Parameters
     ----------
     option_type : str
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     opvalue : float
         Option price.
     action : str
-        `Action` literal value, which must be either 'buy' or 'sell'.
+        `Action` literal value, which must be either **buy** or **sell**.
     s : numpy.ndarray
         Array of stock prices.
     x : float
@@ -459,7 +459,7 @@ def _get_payoff(option_type: OptionType, s: np.ndarray, x: float) -> np.ndarray:
     Parameters
     ----------
     option_type : str
-        `OptionType` literal value, which must be either 'call' or 'put'.
+        `OptionType` literal value, which must be either **call** or **put**.
     s : numpy.ndarray
         Array of stock prices.
     x : float
@@ -488,7 +488,7 @@ def _get_pl_stock(s0: float, action: Action, s: np.ndarray) -> np.ndarray:
     s0 : float
         Spot price of the underlying asset.
     action : str
-        `Action` literal value, which must be either 'buy' or 'sell'.
+        `Action` literal value, which must be either **buy** or **sell**.
     s : numpy.ndarray
         Array of stock prices.
 

optionlab/support.py

@@ -41,6 +41,28 @@
     "rho": [0.08536880237502181, -0.07509774107468528],
 }
 
+PROB_NAKED_CALL = {
+    "probability_of_profit": 0.8389215512144531,
+    "profit_ranges": [(0.0, 176.14)],
+    "expected_profit": 115.0,
+    "expected_loss": -707.0,
+    "per_leg_cost": [114.99999999999999],
+    "strategy_cost": 114.99999999999999,
+    "minimum_return_in_the_domain": -6991.999999999999,
+    "maximum_return_in_the_domain": 114.99999999999999,
+    "implied_volatility": [0.256],
+    "in_the_money_probability": [0.1832371984432129],
+    "delta": [-0.20371918274704337],
+    "gamma": [0.023104402361599465],
+    "theta": [0.091289876347897],
+    "vega": [0.12750177318341913],
+    "rho": [-0.02417676577711979],
+    "probability_of_profit_target": 0.8197909190785164,
+    "profit_target_ranges": [(0.0, 175.15)],
+    "probability_of_loss_limit": 0.14307836806156238,
+    "loss_limit_ranges": [(177.15, float("inf"))],
+}
+
 
 def test_black_scholes():
     stock_price = 100.0
@@ -209,6 +231,40 @@ def test_100_perc_itm(nvidia):
     ) == pytest.approx(PROB_100_ITM_RESULT)
 
 
+def test_naked_call():
+    inputs = Inputs.model_validate(
+        {
+            "stock_price": 164.04,
+            "volatility": 0.272,
+            "start_date": "2021-11-22",
+            "target_date": "2021-12-17",
+            "interest_rate": 0.0002,
+            "min_stock": 82.02,
+            "max_stock": 246.06,
+            "profit_target": 100.0,
+            "loss_limit": -100.0,
+            "model": "black-scholes",
+            # The naked call strategy is defined
+            "strategy": [
+                {
+                    "type": "call",
+                    "strike": 175.00,
+                    "premium": 1.15,
+                    "n": 100,
+                    "action": "sell",
+                }
+            ],
+        }
+    )
+
+    outputs = run_strategy(inputs)
+
+    assert isinstance(outputs, Outputs)
+    assert outputs.model_dump(
+        exclude={"data", "inputs"}, exclude_none=True
+    ) == pytest.approx(PROB_NAKED_CALL)
+
+
 def test_3_legs(nvidia):
     inputs = Inputs.model_validate(
         nvidia