DOC: Annotate lib.random_ohlc_data() as a Generator (#1162)

* Update lib.py

Replacing pd.DataFrame with Generator[pd.DataFrame, None, None]
The reason for replacing pd.DataFrame with Generator[pd.DataFrame, None, None] is to better reflect the actual output type of the random_ohlc_data function. Here are the specific reasons and benefits:

Reasons:
Accuracy of Output Type: The original code declared that the function returns a pd.DataFrame, but in reality, the function is a generator that yields multiple pd.DataFrame objects. Using Generator more accurately describes the function's behavior.
Clarity of Type Hinting: Using Generator allows the code readers and users to more easily understand that the function returns a generator rather than a single DataFrame. This helps prevent potential misunderstandings and misuse.
Benefits:
Performance Improvement: Generators can generate data on-demand rather than generating all data at once, saving memory and improving performance, especially when dealing with large datasets.
Lazy Evaluation: Generators allow for lazy evaluation, meaning data frames are only generated when needed. This can improve the efficiency and responsiveness of the code.
Better Code Maintainability: Explicitly using generators makes the intent of the code clearer, enhancing readability and maintainability, making it easier for other developers to understand and maintain the code.

* Import typing.Generator

backtesting/lib.py

@@ -17,7 +17,7 @@
 from inspect import currentframe
 from itertools import compress
 from numbers import Number
-from typing import Callable, Optional, Sequence, Union
+from typing import Callable, Generator, Optional, Sequence, Union
 
 import numpy as np
 import pandas as pd
@@ -332,7 +332,7 @@ def wrap_func(resampled, *args, **kwargs):
 
 
 def random_ohlc_data(example_data: pd.DataFrame, *,
-                     frac=1., random_state: Optional[int] = None) -> pd.DataFrame:
+                     frac=1., random_state: Optional[int] = None) -> Generator[pd.DataFrame, None, None]:
     """
     OHLC data generator. The generated OHLC data has basic
     [descriptive statistics](https://en.wikipedia.org/wiki/Descriptive_statistics)