diff --git a/examples/naked_call.ipynb b/examples/naked_call.ipynb index 7359876..d4f3c6a 100644 --- a/examples/naked_call.ipynb +++ b/examples/naked_call.ipynb @@ -80,6 +80,9 @@ "interest_rate = 0.0002\n", "min_stock = stock_price - round(stock_price * 0.5, 2)\n", "max_stock = stock_price + round(stock_price * 0.5, 2)\n", + "profit_target = 100.0\n", + "loss_limit = -100.0\n", + "model = \"black-scholes\"\n", "strategy = [\n", " {\"type\": \"call\", \"strike\": 175.00, \"premium\": 1.15, \"n\": 100, \"action\": \"sell\"}\n", "]\n", @@ -92,6 +95,9 @@ " interest_rate=interest_rate,\n", " min_stock=min_stock,\n", " max_stock=max_stock,\n", + " profit_target=profit_target,\n", + " loss_limit=loss_limit,\n", + " model=model,\n", " strategy=strategy,\n", ")" ] @@ -110,8 +116,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: total: 172 ms\n", - "Wall time: 171 ms\n" + "CPU times: total: 156 ms\n", + "Wall time: 160 ms\n" ] } ], @@ -136,12 +142,12 @@ "text": [ "Profit/Loss diagram:\n", "--------------------\n", - "The vertical green dashed line corresponds to the position of the stock's spot price. The right and left arrow markers indicate the strike prices of calls and puts, respectively, with blue representing long and red representing short positions.\n" + "The vertical green dashed line corresponds to the position of the stock's spot price. The right and left arrow markers indicate the strike prices of calls and puts, respectively, with blue representing long and red representing short positions. The blue dashed line represents the profit target level. The red dashed line represents the loss limit level.\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -174,7 +180,9 @@ " 0.00 ---> 176.14\n", "Probability of Profit (PoP): 83.9%\n", "Expected profit: 115.00\n", - "Expected loss: -707.00\n" + "Expected loss: -707.00\n", + "Probability of reaching 100.00 or more: 82.0%\n", + "Probability of losing 100.0 or more: 14.3%\n" ] } ], @@ -188,7 +196,13 @@ "\n", "print(f\"Probability of Profit (PoP): {out.probability_of_profit * 100.0:.1f}%\")\n", "print(f\"Expected profit: {out.expected_profit:.2f}\")\n", - "print(f\"Expected loss: {out.expected_loss:.2f}\")" + "print(f\"Expected loss: {out.expected_loss:.2f}\")\n", + "print(\n", + " f\"Probability of reaching {profit_target:.2f} or more: {out.probability_of_profit_target * 100.0:.1f}%\"\n", + ")\n", + "print(\n", + " f\"Probability of losing {abs(loss_limit)} or more: {out.probability_of_loss_limit * 100.0:.1f}%\"\n", + ")" ] }, { diff --git a/optionlab/black_scholes.py b/optionlab/black_scholes.py index 3cc820d..f98e7a8 100644 --- a/optionlab/black_scholes.py +++ b/optionlab/black_scholes.py @@ -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 diff --git a/optionlab/models.py b/optionlab/models.py index 23dda4e..b812449 100644 --- a/optionlab/models.py +++ b/optionlab/models.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 @@ -48,16 +48,15 @@ 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 @@ -65,7 +64,7 @@ class Option(BaseLeg): 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 ---------- diff --git a/optionlab/support.py b/optionlab/support.py index 7d01f29..ef9f55e 100644 --- a/optionlab/support.py +++ b/optionlab/support.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. diff --git a/tests/test_core.py b/tests/test_core.py index b7f218a..52f875e 100644 --- a/tests/test_core.py +++ b/tests/test_core.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