4913431406

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github

Committed by GitHub

Commit Message

Merge e204016c9 into c0827eaa4

Pull Request Pull Request #36: Add support to generate reports in different formats supported by pandas

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93 of 122 branches covered (76.23%)

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174 of 196 new or added lines in 7 files covered. (88.78%)

22 existing lines in 1 file now uncovered.

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91.3

/src/stocktracer/analysis/diluted_eps.py

import logging
from typing import Optional

import numpy as np
import pandas as pd

from stocktracer.data.sec import Filter as SecFilter
from stocktracer.data.sec import ReportDate
from stocktracer.data.sec import Sec as SecDataSource
from stocktracer.interface import Analysis as AnalysisInterface

logger = logging.getLogger(__name__)


def trendline(data: pd.Series, order: int = 1) -> float:
    """Calculate the trend of a series.

    >>> trendline(pd.Series((1,2,3)))
    1.0000000000000004

    Args:
        data (pd.Series): _description_
        order (int): _description_. Defaults to 1.

    Returns:
        float: slope of the trend line
    """
    coeffs = np.polyfit(data.index.values, list(data), order)
    slope = coeffs[-2]
    return float(slope)


class Analysis(AnalysisInterface):
    """Perform an analysis on the earnings per share over time."""

    def analyze(self) -> Optional[pd.DataFrame]:
        # Create the filter we'll use to scrape the results
        sec_filter = SecFilter(
            tags=["EarningsPerShareDiluted"],
            years=1,  # Over the past 5 years
            last_report=ReportDate(year=2023, quarter=1),
            only_annual=True,  # We only want the 10-K
        )

        # Create an SEC Data Source
        assert self.options is not None
        assert self.options.cache_path is not None
        sec = SecDataSource(storage_path=self.options.cache_path)
        data_selector = sec.select_data(tickers=self.options.tickers, filter=sec_filter)

        # # Show a quick dump of the data
        # logger.info(f"\n{data_selector.data}")

        # # Get only the EPSD Data
        eps_diluted = data_selector.data.query("tag == 'EarningsPerShareDiluted'")
        # TODO: Figure this out
        # return eps_diluted.groupby(["cik"]).apply(lambda x: pd.Series(trendline(x.value),
        #                                                               index=["trend"]))
        return eps_diluted.groupby(["cik"]).all()

    # Reuse documentation from parent
    analyze.__doc__ = AnalysisInterface.analyze.__doc__

1	import logging	2✔
2	from typing import Optional	2✔
3
4	import numpy as np	2✔
5	import pandas as pd	2✔
6
7	from stocktracer.data.sec import Filter as SecFilter	2✔
8	from stocktracer.data.sec import ReportDate	2✔
9	from stocktracer.data.sec import Sec as SecDataSource	2✔
10	from stocktracer.interface import Analysis as AnalysisInterface	2✔
11
12	logger = logging.getLogger(__name__)	2✔
13
14
15	def trendline(data: pd.Series, order: int = 1) -> float:	2✔
16	"""Calculate the trend of a series.
17
18	>>> trendline(pd.Series((1,2,3)))
19	1.0000000000000004
20
21	Args:
22	data (pd.Series): _description_
23	order (int): _description_. Defaults to 1.
24
25	Returns:
26	float: slope of the trend line
27	"""
28	coeffs = np.polyfit(data.index.values, list(data), order)	2✔
29	slope = coeffs[-2]	2✔
30	return float(slope)	2✔
31
32
33	class Analysis(AnalysisInterface):	2✔
34	"""Perform an analysis on the earnings per share over time."""
35
36	def analyze(self) -> Optional[pd.DataFrame]:	2✔
37	# Create the filter we'll use to scrape the results
38	sec_filter = SecFilter(	2✔
39	tags=["EarningsPerShareDiluted"],
40	years=1, # Over the past 5 years
41	last_report=ReportDate(year=2023, quarter=1),
42	only_annual=True, # We only want the 10-K
43	)
44
45	# Create an SEC Data Source
46	assert self.options is not None	2✔
47	assert self.options.cache_path is not None	2✔
48	sec = SecDataSource(storage_path=self.options.cache_path)	2✔
49	data_selector = sec.select_data(tickers=self.options.tickers, filter=sec_filter)	2✔
50
51	# # Show a quick dump of the data
52	# logger.info(f"\n{data_selector.data}")
53
54	# # Get only the EPSD Data
NEW 55	eps_diluted = data_selector.data.query("tag == 'EarningsPerShareDiluted'")	×
56	# TODO: Figure this out
57	# return eps_diluted.groupby(["cik"]).apply(lambda x: pd.Series(trendline(x.value),
58	# index=["trend"]))
NEW 59	return eps_diluted.groupby(["cik"]).all()	×
60
61	# Reuse documentation from parent
62	analyze.__doc__ = AnalysisInterface.analyze.__doc__	2✔

gyund / fundamental-analysis / 4913431406

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