add: dynamic price increment.

optionlab/engine.py

@@ -66,7 +66,7 @@ def run_strategy(inputs_data: Inputs | dict) -> Outputs:
 
 def _init_inputs(inputs: Inputs) -> EngineData:
     data = EngineData(
-        stock_price_array=create_price_seq(inputs.min_stock, inputs.max_stock),
+        stock_price_array=create_price_seq(inputs.min_stock, inputs.max_stock, inputs.price_increment),
         terminal_stock_prices=inputs.array if inputs.model == "array" else array([]),
         inputs=inputs,
     )

optionlab/models.py

@@ -329,6 +329,14 @@ class Inputs(BaseModel):
     The default is an empty array.
     """
 
+    price_increment: float = Field(0.01, gt=0.0)
+    """
+    Price increment for generating the stock price array used in profit/loss calculations.
+    Smaller values create more data points but increase computation time.
+
+    The default is 0.01 (one cent increments).
+    """
+
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     @field_validator("strategy")

optionlab/support.py

@@ -174,30 +174,40 @@ def get_pl_profile_bs(
 
     return profile, n * cost - commission
 
-
 @lru_cache
-def create_price_seq(min_price: float, max_price: float) -> np.ndarray:
+def create_price_seq(min_price: float, max_price: float, increment: float = 0.01) -> np.ndarray:
     """
     Generates a sequence of stock prices from a minimum to a maximum price with
-    increment $0.01.
+    the specified increment.
 
     Parameters
     ----------
     `min_price`: minimum stock price in the range.
 
     `max_price`: maximum stock price in the range.
 
+    `increment`: price increment between consecutive values. The default is 0.01.
+
     Returns
     -------
     Array of sequential stock prices.
     """
 
     if max_price > min_price:
-        return round((arange((max_price - min_price) * 100 + 1) * 0.01 + min_price), 2)
+        if increment <= 0.0:
+            raise ValueError("Increment must be greater than 0!")
+        num_points = int((max_price - min_price) / increment) + 1
+        # Round to appropriate decimal places based on increment size
+        if increment >= 1.0:
+            decimal_places = 0
+        elif increment >= 0.1:
+            decimal_places = 1
+        else:
+            decimal_places = 2
+        return round((arange(num_points) * increment + min_price), decimal_places)
     else:
         raise ValueError("Maximum price cannot be less than minimum price!")
 
-
 def get_pop(
     s: np.ndarray,
     profit: np.ndarray,