Remove strategy class (#26)

* Update version in pyproject.toml

* Update version in __init__.py

* Update getPLoption in support.py

* Add run_check directory and move run scripts to it

* Change .gitignore

* Update .gitignore

* Update test_core.py

* Add test_black_scholes() to test_core.py

* Update tests

* Update .gitignore

* Commented StrategyEngine class

* Updata README.MD

* Update CHANGELOG.md

* Update version to 1.3.0

* Update __init.py__

* Update engine.py

* Black source

.gitignore

@@ -1,4 +1,9 @@
-dist/
+.mypy_cache
+.pytest_cache
 __pycache__
-run_*.py
-.ipynb_checkpoints
+dist
+run_check
+examples/.ipynb_checkpoints
+optionlab/__pycache__
+tests/__pycache__
+tests/.benchmarks

CHANGELOG.md

@@ -1,5 +1,10 @@
 # CHANGELOG
 
+## 1.3.0 (2024-09-13)
+
+- Remove the deprecated `StrategyEngine` class (it remains commented in the code).
+- Update the README.md file to reflect the current state of the library.
+
 ## 1.2.1 (2024-06-03)
 
 - Add 1 to `time_to_target` and `time_to_maturity` in `engine.py` to consider the target and expiration dates as  trading days in the calculations

README.md

@@ -37,16 +37,13 @@ pip install optionlab
 
 ## Basic usage
 
-Usage examples for several strategies can be found in the **examples** directory.
+Usage examples for several strategies can be found in Jupyter notebooks in the **examples** 
+directory.
 
-To evaluate an option strategy, an `Inputs` model needs to be created:
+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.
 
-```python
-from optionlab import Inputs
-inputs = Inputs.model_validate(inputs_data)
-```
-
-The input data passed to `model_validate` above needs to be of the following structure: 
+The description of the input data follows: 
 
 ---
 
@@ -194,7 +191,7 @@ calls on Apple stocks with maturity on December 17, 2021. The strategy setup con
 of selling 100 175.00 strike calls for 1.15 each on November 22, 2021.
 
 ```python
-inputs_data = {
+input_data = {
     "stock_price": 164.04,
     "start_date": "2021-11-22",
     "target_date": "2021-12-17",
@@ -214,27 +211,18 @@ inputs_data = {
 }
 ```
 
-The simplest way to perform the calculations is by calling the `run_strategy` function 
-as follows:
+After defining the input data as a Python dictionary, we pass it to the `run_strategy` 
+function as shown below:
 
 ```python
-from optionlab import run_strategy
-
-out = run_strategy(inputs_data)
-```
-
-Alternatively, an `Inputs` object can be passed to the `StrategyEngine` object and 
-the calculations are performed by calling the `run` method of the `StrategyEngine` 
-object:
+from optionlab import run_strategy, plot_pl
 
-```python
-from optionlab import StrategyEngine
+out = run_strategy(input_data)
 
-st = StrategyEngine(Inputs.model_validate(inputs_data))
-out = st.run()
+plot_pl(out)
 ```
 
-In both cases, `out` contains an `Outputs` object with the following structure:
+The variable `out` contains an `Outputs` object with the following structure:
 
 ---
 
@@ -301,9 +289,12 @@ In both cases, `out` contains an `Outputs` object with the following structure:
 To obtain the probability of profit of the naked call example above:
 
 ```python
-print("Probability of Profit (PoP): %.1f%%" % (out.probability_of_profit * 100.0)) # 84.5%, according to the calculations
+print(f"Probability of Profit (PoP): {(out.probability_of_profit * 100.0):.1f}%%") # 84.5%, according to the calculations
 ```
 
+The `plot_pl` function takes an `Outputs` object as its argument and plots the profit/loss 
+diagram for the strategy.
+
 ## Contributions
 
 ### Dev setup

optionlab/__init__.py

@@ -1,7 +1,7 @@
 import typing
 
 
-VERSION = "1.2.1"
+VERSION = "1.3.0"
 
 
 if typing.TYPE_CHECKING:
@@ -35,7 +35,7 @@
         get_gamma,
         get_theta,
     )
-    from .engine import StrategyEngine, run_strategy
+    from .engine import run_strategy
     from .plot import plot_pl
     from .support import (
         get_pl_profile,
@@ -67,7 +67,6 @@
     "Action",
     # engine
     "run_strategy",
-    "StrategyEngine",
     # support
     "get_pl_profile",
     "get_pl_profile_stock",
@@ -109,7 +108,6 @@
     "Country": (__package__, ".models"),
     "Action": (__package__, ".models"),
     # engine
-    "StrategyEngine": (__package__, ".engine"),
     "run_strategy": (__package__, ".engine"),
     # support
     "get_pl_profile": (__package__, ".support"),

optionlab/engine.py

@@ -4,7 +4,9 @@
 import datetime as dt
 from typing import Any
 
-from numpy import array, ndarray, zeros
+from numpy import array, zeros
+
+# from numpy import ndarray
 
 from optionlab.black_scholes import get_bs_info, get_implied_vol
 from optionlab.models import (
@@ -28,7 +30,9 @@
     create_price_samples,
     get_pop,
 )
-from optionlab.utils import get_nonbusiness_days, get_pl, pl_to_csv
+from optionlab.utils import get_nonbusiness_days
+
+# from optionlab.utils import get_pl, pl_to_csv
 
 
 def run_strategy(inputs_data: Inputs | dict) -> Outputs:
@@ -202,7 +206,7 @@ def _run(data: EngineData) -> EngineData:
         data.profit_target_probability = get_pop(data._profit_target_range, pop_inputs)
 
     if inputs.loss_limit is not None:
-        data._loss_limit_rangesm = get_profit_range(
+        data._loss_limit_ranges = get_profit_range(
             data.stock_price_array, data.strategy_profit, inputs.loss_limit + 0.01
         )
         data.loss_limit_probability = 1.0 - get_pop(data._loss_limit_ranges, pop_inputs)
@@ -461,71 +465,72 @@ def _generate_outputs(data: EngineData) -> Outputs:
     )
 
 
-class StrategyEngine:
-    def __init__(self, inputs_data: Inputs | dict):
-        """
-        __init__ -> initializes class variables.
-
-        Returns
-        -------
-        None.
-        """
-        inputs = (
-            inputs_data
-            if isinstance(inputs_data, Inputs)
-            else Inputs.model_validate(inputs_data)
-        )
-
-        self.data = _init_inputs(inputs)
-
-    def run(self) -> Outputs:
-        """
-        run -> runs calculations for an options strategy.
-
-        Returns
-        -------
-        output : Outputs
-            An Outputs object containing the output of a calculation.
-        """
-
-        self.data = _run(self.data)
-
-        return _generate_outputs(self.data)
-
-    def get_pl(self, leg: int | None = None) -> tuple[ndarray, ndarray]:
-        """
-        get_pl -> returns the profit/loss profile of either a leg or the whole
-        strategy.
-
-        Parameters
-        ----------
-        leg : int, optional
-            Index of the leg. Default is None (whole strategy).
-
-        Returns
-        -------
-        stock prices : numpy array
-            Sequence of stock prices within the bounds of the stock price domain.
-        P/L profile : numpy array
-            Profit/loss profile of either a leg or the whole strategy.
-        """
-
-        return get_pl(self.data, leg)
-
-    def pl_to_csv(self, filename: str = "pl.csv", leg: int | None = None) -> None:
-        """
-        pl_to_csv -> saves the profit/loss data to a .csv file.
-
-        Parameters
-        ----------
-        filename : string, optional
-            Name of the .csv file. Default is 'pl.csv'.
-        leg : int, optional
-            Index of the leg. Default is None (whole strategy).
-
-        Returns
-        -------
-        None.
-        """
-
-        pl_to_csv(self.data, filename, leg)
+# TODO: Delete this class in the next version; for now, leave it as a comment
+# class StrategyEngine:
+#     def __init__(self, inputs_data: Inputs | dict):
+#         """
+#         __init__ -> initializes class variables.
+
+#         Returns
+#         -------
+#         None.
+#         """
+#         inputs = (
+#             inputs_data
+#             if isinstance(inputs_data, Inputs)
+#             else Inputs.model_validate(inputs_data)
+#         )
+
+#         self.data = _init_inputs(inputs)
+
+#     def run(self) -> Outputs:
+#         """
+#         run -> runs calculations for an options strategy.
+
+#         Returns
+#         -------
+#         output : Outputs
+#             An Outputs object containing the output of a calculation.
+#         """
+
+#         self.data = _run(self.data)
+
+#         return _generate_outputs(self.data)
+
+#     def get_pl(self, leg: int | None = None) -> tuple[ndarray, ndarray]:
+#         """
+#         get_pl -> returns the profit/loss profile of either a leg or the whole
+#         strategy.
+
+#         Parameters
+#         ----------
+#         leg : int, optional
+#             Index of the leg. Default is None (whole strategy).
+
+#         Returns
+#         -------
+#         stock prices : numpy array
+#             Sequence of stock prices within the bounds of the stock price domain.
+#         P/L profile : numpy array
+#             Profit/loss profile of either a leg or the whole strategy.
+#         """
+
+#         return get_pl(self.data, leg)
+
+#     def pl_to_csv(self, filename: str = "pl.csv", leg: int | None = None) -> None:
+#         """
+#         pl_to_csv -> saves the profit/loss data to a .csv file.
+
+#         Parameters
+#         ----------
+#         filename : string, optional
+#             Name of the .csv file. Default is 'pl.csv'.
+#         leg : int, optional
+#             Index of the leg. Default is None (whole strategy).
+
+#         Returns
+#         -------
+#         None.
+#         """
+
+#         pl_to_csv(self.data, filename, leg)

optionlab/support.py

@@ -352,8 +352,6 @@ def _get_pl_option(
     s: a numpy array of stock prices.
     x: strike price.
     """
-    if not isinstance(s, ndarray):
-        raise TypeError("'s' must be a numpy array!")
 
     if action == "sell":
         return opvalue - _get_payoff(option_type, s, x)

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "optionlab"
-version = "1.2.1"
+version = "1.3.0"
 description = "Evaluate option strategies"
 authors = ["rgaveiga"]
 readme = "README.md"