2022-04-08 16:49:17 +00:00
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from .core import *
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2022-06-05 17:36:12 +00:00
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from .pyfacts import *
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2022-04-29 02:13:06 +00:00
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from .statistics import *
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2022-04-08 16:49:17 +00:00
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from .utils import *
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2023-03-25 05:48:25 +00:00
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__author__ = "Gourav Kumar"
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__email__ = "gouravkr@outlook.in"
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__version__ = "0.0.1"
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__doc__ = """
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PyFacts stands for Python library for Financial analysis and computations on time series.
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It is a library which makes it simple to work with time series data.
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Most libraries, and languages like SQL, work with rows. Operations are performed by rows
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and not by dates. For instance, to calculate 1-year rolling returns in SQL, you are forced
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to use either a lag of 365/252 rows, leading to an approximation, or slow and cumbersome
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joins. PyFacts solves this by allowing you to work with dates and time intervals. Hence,
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to calculate 1-year returns, you will be specifying a lag of 1-year and the library will
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do the grunt work of finding the most appropriate observations to calculate these returns on.
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PyFacts aims to simplify things by allowing you to:
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* Compare time-series data based on dates and time-period-based lag
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* Easy way to work around missing dates by taking the closest data points
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* Completing series with missing data points using forward fill and backward fill
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* Use friendly dates everywhere written as a simple string
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"""
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