5161244252

1	"""Piotroski F-score is a number between 0 and 9 which is used to assess strength of company's financial position."""
UNCOV 2	import logging	×
UNCOV 3	from typing import Optional	×
4
UNCOV 5	import pandas as pd	×
UNCOV 6	from beartype import beartype	×
7
UNCOV 8	import stocktracer.collector.sec as Sec	×
UNCOV 9	from stocktracer.analysis.annual_reports import create_normalized_sec_table	×
UNCOV 10	from stocktracer.interface import Analysis as AnalysisInterface	×
11
UNCOV 12	logger = logging.getLogger(__name__)	×
13
14
UNCOV 15	@beartype	×
UNCOV 16	class Analysis(AnalysisInterface):	×
17	"""
18
19	The score is used by financial investors in order to find the best value
20	stocks (nine being the best). The score is named after Stanford accounting professor Joseph Piotroski.
21
22	Calculation procedure
23	=====================
24
25	The score is calculated based on 9 criteria divided into 3 groups.[2]
26
27	- Profitability
28	- Return on Assets (ROA) (1 point if it is positive in the current year, 0 otherwise);
29	- Operating Cash Flow (1 point if it is positive in the current year, 0 otherwise);
30	- Change in Return of Assets (ROA) (1 point if ROA is higher in the current year compared to the previous one, 0 otherwise);
31	- Accruals (1 point if Operating Cash Flow/Total Assets is higher than ROA in the current year, 0 otherwise);
32	- Leverage, Liquidity and Source of Funds
33	- Change in Leverage (long-term) ratio (1 point if the ratio is lower this year compared to the previous one, 0 otherwise);
34	- Change in Current ratio (1 point if it is higher in the current year compared to the previous one, 0 otherwise);
35	- Change in the number of shares (1 point if no new shares were issued during the last year);
36	- Operating Efficiency
37	- Change in Gross Margin (1 point if it is higher in the current year compared to the previous one, 0 otherwise);
38	- Change in Asset Turnover ratio (1 point if it is higher in the current year compared to the previous one, 0 otherwise);
39
40	Some adjustments that were done in calculation of the required financial ratios are discussed in the original paper.[2]
41
42	The score is calculated based on the data from financial statement of a company. A company gets 1 point for each met criterion. Summing up of all achieved points gives Piotroski F-score (number between 0 and 9).
43	"""
44
UNCOV 45	under_development = True	×
UNCOV 46	years_of_analysis = 2	×
47
UNCOV 48	def analyze(self) -> Optional[pd.DataFrame]:	×
49	# Create the filter to scrape the data we need for processing
UNCOV 50	sec_filter = Sec.Filter(	×
51	tags={
52	"EarningsPerShareDiluted",
53	"CommonStockSharesIssued",
54	"AssetsCurrent",
55	"LiabilitiesCurrent",
56	"Assets",
57	"OperatingIncomeLoss",
58	"NetCashProvidedByUsedInOperatingActivities",
59	},
60	years=self.years_of_analysis,
61	last_report=self.options.final_report,
62	only_annual=True, # We only want the 10-K
63	)
64
NEW 65	table = create_normalized_sec_table(sec_filter, self.options.tickers, False)	×
UNCOV 66	table.data.fillna(0, inplace=True)	×
67
UNCOV 68	assert (	×
69	self.options.final_report.year + 1
70	not in table.data.index.get_level_values(1)
71	)
72
UNCOV 73	logger.debug(f"filtered_data:\n{table.data}")	×
UNCOV 74	max_year = int(table.data.index.get_level_values("fy").max())	×
75
76	# Do calculations
UNCOV 77	table.calculate_return_on_assets("ROA")	×
UNCOV 78	table.calculate_net_income("net-income")	×
UNCOV 79	table.calculate_delta(column_name="delta-ROA", delta_of="ROA")	×
UNCOV 80	table.calculate_debt_to_assets("debt-to-assets")	×
UNCOV 81	table.calculate_current_ratio("current-ratio")	×
82
83	# fscore = pd.DataFrame(index=table.data.index)
84	# logger.debug(f"\n{fscore}")
85
UNCOV 86	f_score_tags = []	×
87
88	# - Profitability
89	# - Return on Assets (ROA) (1 point if it is positive in the current year, 0 otherwise);
UNCOV 90	table.data["ROA>0"] = (table.data["ROA"] > 0).astype(int)	×
UNCOV 91	f_score_tags.append("ROA>0")	×
92	# TODO: select last year by ROA and assign a 1 if it's positive
93	# logger.debug(f"\n{fscore}")
94
95	# - Operating Cash Flow (1 point if it is positive in the current year, 0 otherwise);
96
UNCOV 97	table.data["NetIncome>0"] = (table.data["net-income"] > 0).astype(int)	×
UNCOV 98	f_score_tags.append("NetIncome>0")	×
99	# - Change in Return of Assets (ROA) (1 point if ROA is higher in the current year compared to the previous one, 0 otherwise);
UNCOV 100	table.data["delta-ROA>0"] = (table.data["delta-ROA"] > 0).astype(int)	×
UNCOV 101	f_score_tags.append("delta-ROA>0")	×
102
UNCOV 103	logger.debug(f"ROA:\n{table.data['ROA']}")	×
UNCOV 104	logger.debug(f"delta ROA:\n{table.data['delta-ROA']}")	×
105
106	# - Accruals (1 point if Operating Cash Flow/Total Assets is higher than ROA in the current year, 0 otherwise);
UNCOV 107	table.data["accruals"] = (	×
108	table.data["NetCashProvidedByUsedInOperatingActivities"]
109	/ table.data["Assets"]
110	)
UNCOV 111	table.data["CF/Total-Assets>ROA"] = (	×
112	table.data["accruals"] > table.data["ROA"]
113	).astype(int)
UNCOV 114	f_score_tags.append("CF/Total-Assets>ROA")	×
115
UNCOV 116	logger.debug(f"accruals:\n{table.data['accruals']}")	×
UNCOV 117	logger.debug(f"CF/Total-Assets>ROA:\n{table.data['CF/Total-Assets>ROA']}")	×
118
119	# - Leverage, Liquidity and Source of Funds
120	# - Change in Leverage (long-term) ratio (1 point if the ratio is lower this year compared to the previous one, 0 otherwise);
UNCOV 121	table.calculate_delta("debt-to-assets-delta", delta_of="debt-to-assets")	×
UNCOV 122	table.data["debt-to-assets<last-year"] = (	×
123	table.data["debt-to-assets-delta"] < 0
124	).astype(int)
UNCOV 125	f_score_tags.append("debt-to-assets<last-year")	×
126
UNCOV 127	logger.debug(f"debt-to-assets-delta:\n{table.data['debt-to-assets-delta']}")	×
UNCOV 128	logger.debug(	×
129	f"debt-to-assets<last-year:\n{table.data['debt-to-assets<last-year']}"
130	)
131
132	# - Change in Current ratio (1 point if it is higher in the current year compared to the previous one, 0 otherwise);
UNCOV 133	table.calculate_delta("current-ratio-delta", delta_of="current-ratio")	×
UNCOV 134	table.data["current-ratio>last-year"] = (	×
135	table.data["current-ratio-delta"] > 0
136	).astype(int)
UNCOV 137	f_score_tags.append("current-ratio>last-year")	×
138
UNCOV 139	logger.debug(f"current-ratio-delta:\n{table.data['current-ratio-delta']}")	×
UNCOV 140	logger.debug(	×
141	f"current-ratio>last-year:\n{table.data['current-ratio>last-year']}"
142	)
143
144	# - Change in the number of shares (1 point if no new shares were issued during the last year);
UNCOV 145	table.data["shares-issued==0"] = (	×
146	table.data["CommonStockSharesIssued"] == 0
147	).astype(int)
UNCOV 148	f_score_tags.append("shares-issued==0")	×
149	# - Operating Efficiency
150	# - Change in Gross Margin (1 point if it is higher in the current year compared to the previous one, 0 otherwise);
151	# - Change in Asset Turnover ratio (1 point if it is higher in the current year compared to the previous one, 0 otherwise);
152
UNCOV 153	return table.slice(year=max_year, tags=f_score_tags)	×
154
155	# Reuse documentation from parent
UNCOV 156	analyze.__doc__ = AnalysisInterface.analyze.__doc__	×

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