main.py

# import numpy as np

# from rolling_ta.logging import log

# from rolling_ta.data import CSVLoader, XLSXLoader, XLSXWriter

# from rolling_ta.trend import LinearRegressionR2, lr
# from tests.fixtures.data_sheets import lr_df

# # from ta.volume import VolumeWeightedAveragePrice


# def write_xlsx_file():
#     loader = CSVLoader()
#     btc = loader.read_resource()

#     btc_sliced = btc.iloc[:4000].reset_index(drop=True)

#     writer = XLSXWriter("btc-bop.xlsx")

#     for i, series in enumerate(btc_sliced):
#         writer.write(btc_sliced[series].to_numpy(dtype=np.float64), col=i + 1)
#         writer.save()


# if __name__ == "__main__":
#     lr_vwap_df = lr_df(XLSXLoader())
#     lr2 = LinearRegressionR2(lr_vwap_df)
#     # write_xlsx_file()
#     ...

from rolling_ta.momentum import BOP
from rolling_ta.data import CSVLoader

test_list = ["a", "b", "c"]
test_dict = {"a": 0}


if __name__ == "__main__":
    loader = CSVLoader()
    df = loader.read_resource()

    bop = BOP(data=df, init=True)

    df = bop.to_dataframe()

    df.info()

    # test_dict = {"a": 0, "b": 1, "c": 2}

    # # bop = BOP(data=df, init=True)
    # # print(bop._period_config)
    # # print(bop.to_series().name)
    # print(test_dict.fromkeys(["a", "b"]))