패키지 설치¶

매직명령어(!)를 사용하여 코랩리서치에 필요한 패키지를 설치한다

In [41]:

!pip install yahoo_fin
!pip install requests_html

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패키지 임포트¶

가장 먼저 할 일은 yahoo_fin에서 stock_info모듈을 임포트하는 것이다. 이 모듈과 함께 같이 필요한 것이 pandas패키지이다.

In [0]:

import yahoo_fin.stock_info as si
import pandas as pd

개별종목의 호가정보는 get_quote_table를 사용하여 구한다

In [0]:

quote = si.get_quote_table("aapl")

get_quote_table 메서드는 다음과 같이 여러 정보가 담긴 딕셔너리를 돌려준다.

In [44]:

quote

Out[44]:

{'1y Target Est': 308.91,
 '52 Week Range': '170.27 - 327.85',
 'Ask': '319.00 x 1100',
 'Avg. Volume': 51660098.0,
 'Beta (5Y Monthly)': 1.17,
 'Bid': '318.91 x 900',
 "Day's Range": '315.37 - 319.23',
 'EPS (TTM)': 12.73,
 'Earnings Date': 'Jul 28, 2020 - Aug 03, 2020',
 'Ex-Dividend Date': 'May 08, 2020',
 'Forward Dividend & Yield': '3.28 (1.04%)',
 'Market Cap': '1.382T',
 'Open': 315.77,
 'PE Ratio (TTM)': 25.05,
 'Previous Close': 316.85,
 'Quote Price': 318.8900146484375,
 'Volume': 20450754.0}

다음과 같이 키 값(여기선 "PE Ratio (TTM)")을 사용하여 값을 얻을 수 있다.

In [45]:

quote["PE Ratio (TTM)"] # 22.71

Out[45]:

25.05

PER을 구하는 또 다른 방법은 get_stats_valuation 메서드를 이용하는 것이다.

이것은 야후파이낸스의 종목 통계섹션의 내용이다.

In [0]:

val = si.get_stats_valuation("aapl")
val = val.iloc[:,:2]
val.columns = ["Attribute", "Recent"]

P/E 비율을 추출하면 다음과 같다

In [47]:

float(val[val.Attribute.str.contains("Trailing P/E")].iloc[0,1])

Out[47]:

24.77

주가매출액(Price-to-Sales) 비율

In [48]:

float(val[val.Attribute.str.contains("Price/Sales")].iloc[0,1])

Out[48]:

5.32

한번에 여러 재무 데이터 구하기¶

다우지수내 각 종목의 Price-to-Earnings 와 Price-to-Sales 비율을 구해보도록 하자.

tickers_dow메서드는 지수를 구성하는 종목의 티커를 돌려준다.

In [0]:

# get list of Dow tickers
dow_list = si.tickers_dow() 
 
# Get data in the current column for each stock's valuation table
dow_stats = {}
for ticker in dow_list:
    temp = si.get_stats_valuation(ticker)
    temp = temp.iloc[:,:2]
    temp.columns = ["Attribute", "Recent"]
 
    dow_stats[ticker] = temp 
 
# combine all the stats valuation tables into a single data frame
combined_stats = pd.concat(dow_stats)
combined_stats = combined_stats.reset_index()
 
del combined_stats["level_1"]
 
# update column names
combined_stats.columns = ["Ticker", "Attribute", "Recent"]

주가수익률(P/E) 비율

In [50]:

# get P/E ratio for each stock
combined_stats[combined_stats.Attribute.str.contains("Trailing P/E")]

Out[50]:

	Ticker	Attribute	Recent
2	AAPL	Trailing P/E	24.77
11	AXP	Trailing P/E	13.61
20	BA	Trailing P/E	NaN
29	CAT	Trailing P/E	12.22
38	CSCO	Trailing P/E	17.96
47	CVX	Trailing P/E	44.25
56	DIS	Trailing P/E	51.45
65	DOW	Trailing P/E	NaN
74	GS	Trailing P/E	9.77
83	HD	Trailing P/E	23.68
92	IBM	Trailing P/E	11.79
101	INTC	Trailing P/E	12.03
110	JNJ	Trailing P/E	22.89
119	JPM	Trailing P/E	10.19
128	KO	Trailing P/E	19.47
137	MCD	Trailing P/E	24.26
146	MMM	Trailing P/E	17.11
155	MRK	Trailing P/E	19.38
164	MSFT	Trailing P/E	30.94
173	NKE	Trailing P/E	33.59
182	PFE	Trailing P/E	13.44
191	PG	Trailing P/E	60.66
200	RTX	Trailing P/E	12.65
209	TRV	Trailing P/E	10.71
218	UNH	Trailing P/E	20.08
227	V	Trailing P/E	34.87
236	VZ	Trailing P/E	12.18
245	WBA	Trailing P/E	10.19
254	WMT	Trailing P/E	23.85
263	XOM	Trailing P/E	16.69

주가매출액비율(Price-to-Sales) 비율

In [51]:

# get P/S ratio for each stock
combined_stats[combined_stats.Attribute.str.contains("Price/Sales")]

Out[51]:

	Ticker	Attribute	Recent
5	AAPL	Price/Sales (ttm)	5.32
14	AXP	Price/Sales (ttm)	2.38
23	BA	Price/Sales (ttm)	1.11
32	CAT	Price/Sales (ttm)	1.27
41	CSCO	Price/Sales (ttm)	3.83
50	CVX	Price/Sales (ttm)	1.28
59	DIS	Price/Sales (ttm)	2.74
68	DOW	Price/Sales (ttm)	0.64
77	GS	Price/Sales (ttm)	2.02
86	HD	Price/Sales (ttm)	2.32
95	IBM	Price/Sales (ttm)	1.39
104	INTC	Price/Sales (ttm)	3.61
113	JNJ	Price/Sales (ttm)	4.75
122	JPM	Price/Sales (ttm)	2.50
131	KO	Price/Sales (ttm)	5.25
140	MCD	Price/Sales (ttm)	6.77
149	MMM	Price/Sales (ttm)	2.63
158	MRK	Price/Sales (ttm)	4.09
167	MSFT	Price/Sales (ttm)	10.31
176	NKE	Price/Sales (ttm)	3.53
185	PFE	Price/Sales (ttm)	4.19
194	PG	Price/Sales (ttm)	4.04
203	RTX	Price/Sales (ttm)	0.67
212	TRV	Price/Sales (ttm)	0.81
221	UNH	Price/Sales (ttm)	1.13
230	V	Price/Sales (ttm)	18.23
239	VZ	Price/Sales (ttm)	1.70
248	WBA	Price/Sales (ttm)	0.26
257	WMT	Price/Sales (ttm)	0.67
266	XOM	Price/Sales (ttm)	0.76

주가순자산(Price / Book) 비율

In [52]:

# get Price-to-Book ratio for each stock
combined_stats[combined_stats.Attribute.str.contains("Price/Book")]

Out[52]:

	Ticker	Attribute	Recent
6	AAPL	Price/Book (mrq)	17.51
15	AXP	Price/Book (mrq)	3.44
24	BA	Price/Book (mrq)	NaN
33	CAT	Price/Book (mrq)	4.41
42	CSCO	Price/Book (mrq)	5.37
51	CVX	Price/Book (mrq)	1.19
60	DIS	Price/Book (mrq)	2.35
69	DOW	Price/Book (mrq)	2.06
78	GS	Price/Book (mrq)	0.76
87	HD	Price/Book (mrq)	NaN
96	IBM	Price/Book (mrq)	5.29
105	INTC	Price/Book (mrq)	3.44
114	JNJ	Price/Book (mrq)	6.31
123	JPM	Price/Book (mrq)	1.19
132	KO	Price/Book (mrq)	10.68
141	MCD	Price/Book (mrq)	NaN
150	MMM	Price/Book (mrq)	8.27
159	MRK	Price/Book (mrq)	7.37
168	MSFT	Price/Book (mrq)	12.30
177	NKE	Price/Book (mrq)	15.65
186	PFE	Price/Book (mrq)	3.22
195	PG	Price/Book (mrq)	6.19
204	RTX	Price/Book (mrq)	2.31
213	TRV	Price/Book (mrq)	0.99
222	UNH	Price/Book (mrq)	4.78
231	V	Price/Book (mrq)	14.17
240	VZ	Price/Book (mrq)	3.71
249	WBA	Price/Book (mrq)	1.47
258	WMT	Price/Book (mrq)	5.21
267	XOM	Price/Book (mrq)	1.04

주가이익성장배율(Price / Earnings-to-Growth) 비율

In [53]:

# get PEG ratio for each stock
combined_stats[combined_stats.Attribute.str.contains("PEG")]

Out[53]:

	Ticker	Attribute	Recent
4	AAPL	PEG Ratio (5 yr expected) 1	2.22
13	AXP	PEG Ratio (5 yr expected) 1	1.07
22	BA	PEG Ratio (5 yr expected) 1	5.91
31	CAT	PEG Ratio (5 yr expected) 1	6.52
40	CSCO	PEG Ratio (5 yr expected) 1	2.85
49	CVX	PEG Ratio (5 yr expected) 1	7.07
58	DIS	PEG Ratio (5 yr expected) 1	10.17
67	DOW	PEG Ratio (5 yr expected) 1	NaN
76	GS	PEG Ratio (5 yr expected) 1	2.70
85	HD	PEG Ratio (5 yr expected) 1	2.16
94	IBM	PEG Ratio (5 yr expected) 1	9.56
103	INTC	PEG Ratio (5 yr expected) 1	2.04
112	JNJ	PEG Ratio (5 yr expected) 1	4.29
121	JPM	PEG Ratio (5 yr expected) 1	4.57
130	KO	PEG Ratio (5 yr expected) 1	3.77
139	MCD	PEG Ratio (5 yr expected) 1	4.21
148	MMM	PEG Ratio (5 yr expected) 1	4.37
157	MRK	PEG Ratio (5 yr expected) 1	1.64
166	MSFT	PEG Ratio (5 yr expected) 1	2.26
175	NKE	PEG Ratio (5 yr expected) 1	2.84
184	PFE	PEG Ratio (5 yr expected) 1	2.42
193	PG	PEG Ratio (5 yr expected) 1	2.84
202	RTX	PEG Ratio (5 yr expected) 1	1.97
211	TRV	PEG Ratio (5 yr expected) 1	0.91
220	UNH	PEG Ratio (5 yr expected) 1	1.40
229	V	PEG Ratio (5 yr expected) 1	2.83
238	VZ	PEG Ratio (5 yr expected) 1	3.78
247	WBA	PEG Ratio (5 yr expected) 1	6.89
256	WMT	PEG Ratio (5 yr expected) 1	4.22
265	XOM	PEG Ratio (5 yr expected) 1	10.47

Forward P/E 비율

In [54]:

# get forward P/E ratio for each stock
combined_stats[combined_stats.Attribute.str.contains("Forward P/E")]

Out[54]:

	Ticker	Attribute	Recent
3	AAPL	Forward P/E 1	26.74
12	AXP	Forward P/E 1	14.66
21	BA	Forward P/E 1	40.16
30	CAT	Forward P/E 1	22.57
39	CSCO	Forward P/E 1	14.93
48	CVX	Forward P/E 1	14.49
57	DIS	Forward P/E 1	60.61
66	DOW	Forward P/E 1	25.25
75	GS	Forward P/E 1	12.41
84	HD	Forward P/E 1	24.57
93	IBM	Forward P/E 1	10.99
102	INTC	Forward P/E 1	13.70
111	JNJ	Forward P/E 1	18.94
120	JPM	Forward P/E 1	14.16
129	KO	Forward P/E 1	22.68
138	MCD	Forward P/E 1	31.06
147	MMM	Forward P/E 1	17.70
156	MRK	Forward P/E 1	14.45
165	MSFT	Forward P/E 1	29.59
174	NKE	Forward P/E 1	33.67
183	PFE	Forward P/E 1	13.30
192	PG	Forward P/E 1	21.32
201	RTX	Forward P/E 1	14.73
210	TRV	Forward P/E 1	10.71
219	UNH	Forward P/E 1	17.61
228	V	Forward P/E 1	38.46
237	VZ	Forward P/E 1	11.32
246	WBA	Forward P/E 1	6.72
255	WMT	Forward P/E 1	25.19
264	XOM	Forward P/E 1	121.95

여러 종목의 기타 통계 구하기¶

야후파이낸스 종목 통계섹션에는“Valuation Measures” 테이블이 있는데

get_stats method를 통해 기타 통계 정보(Return on Equity (ROE),

Return on Assets, profit margin)를 구할 수 있다.

In [0]:

dow_extra_stats = {}
for ticker in dow_list:
    dow_extra_stats[ticker] = si.get_stats(ticker)   
 
combined_extra_stats = pd.concat(dow_extra_stats)
 
combined_extra_stats = combined_extra_stats.reset_index()
 
del combined_extra_stats["level_1"]
 
combined_extra_stats.columns = ["ticker", "Attribute", "Value"]

자기자본이익률(ROE)

In [56]:

combined_extra_stats[combined_extra_stats.Attribute.str.contains("Return on Equity")]

Out[56]:

	ticker	Attribute	Value
33	AAPL	Return on Equity (ttm)	62.09%
83	AXP	Return on Equity (ttm)	25.80%
133	BA	Return on Equity (ttm)	NaN
183	CAT	Return on Equity (ttm)	35.69%
233	CSCO	Return on Equity (ttm)	29.74%
283	CVX	Return on Equity (ttm)	2.51%
333	DIS	Return on Equity (ttm)	5.01%
383	DOW	Return on Equity (ttm)	-6.86%
433	GS	Return on Equity (ttm)	8.02%
483	HD	Return on Equity (ttm)	NaN
533	IBM	Return on Equity (ttm)	49.10%
583	INTC	Return on Equity (ttm)	30.31%
633	JNJ	Return on Equity (ttm)	28.55%
683	JPM	Return on Equity (ttm)	11.56%
733	KO	Return on Equity (ttm)	50.85%
783	MCD	Return on Equity (ttm)	NaN
833	MMM	Return on Equity (ttm)	49.92%
883	MRK	Return on Equity (ttm)	37.55%
933	MSFT	Return on Equity (ttm)	44.20%
983	NKE	Return on Equity (ttm)	47.96%
1033	PFE	Return on Equity (ttm)	25.41%
1083	PG	Return on Equity (ttm)	10.01%
1133	RTX	Return on Equity (ttm)	10.79%
1183	TRV	Return on Equity (ttm)	9.79%
1233	UNH	Return on Equity (ttm)	23.96%
1283	V	Return on Equity (ttm)	36.25%
1333	VZ	Return on Equity (ttm)	31.76%
1383	WBA	Return on Equity (ttm)	14.14%
1433	WMT	Return on Equity (ttm)	18.86%
1483	XOM	Return on Equity (ttm)	6.00%

총자산이익률(ROA)

In [57]:

combined_extra_stats[combined_extra_stats.Attribute.str.contains("Return on Assets")]

Out[57]:

	ticker	Attribute	Value
32	AAPL	Return on Assets (ttm)	12.38%
82	AXP	Return on Assets (ttm)	2.91%
132	BA	Return on Assets (ttm)	-2.27%
182	CAT	Return on Assets (ttm)	6.09%
232	CSCO	Return on Assets (ttm)	9.59%
282	CVX	Return on Assets (ttm)	2.49%
332	DIS	Return on Assets (ttm)	2.77%
382	DOW	Return on Assets (ttm)	NaN
432	GS	Return on Assets (ttm)	0.74%
482	HD	Return on Assets (ttm)	20.79%
532	IBM	Return on Assets (ttm)	4.30%
582	INTC	Return on Assets (ttm)	11.48%
632	JNJ	Return on Assets (ttm)	8.83%
682	JPM	Return on Assets (ttm)	1.02%
732	KO	Return on Assets (ttm)	7.31%
782	MCD	Return on Assets (ttm)	11.07%
832	MMM	Return on Assets (ttm)	10.40%
882	MRK	Return on Assets (ttm)	11.76%
932	MSFT	Return on Assets (ttm)	11.84%
982	NKE	Return on Assets (ttm)	13.18%
1032	PFE	Return on Assets (ttm)	5.78%
1082	PG	Return on Assets (ttm)	8.35%
1132	RTX	Return on Assets (ttm)	4.57%
1182	TRV	Return on Assets (ttm)	1.86%
1232	UNH	Return on Assets (ttm)	7.08%
1282	V	Return on Assets (ttm)	14.02%
1332	VZ	Return on Assets (ttm)	6.79%
1382	WBA	Return on Assets (ttm)	3.22%
1432	WMT	Return on Assets (ttm)	5.89%
1482	XOM	Return on Assets (ttm)	1.52%

이익률(profit margin)

In [58]:

combined_extra_stats[combined_extra_stats.Attribute.str.contains("Profit Margin")]

Out[58]:

	ticker	Attribute	Value
30	AAPL	Profit Margin	21.35%
80	AXP	Profit Margin	14.63%
130	BA	Profit Margin	-4.84%
180	CAT	Profit Margin	10.41%
230	CSCO	Profit Margin	21.32%
280	CVX	Profit Margin	2.86%
330	DIS	Profit Margin	6.88%
380	DOW	Profit Margin	-4.01%
430	GS	Profit Margin	21.40%
480	HD	Profit Margin	10.20%
530	IBM	Profit Margin	11.78%
580	INTC	Profit Margin	30.02%
630	JNJ	Profit Margin	20.75%
680	JPM	Profit Margin	29.42%
730	KO	Profit Margin	26.95%
780	MCD	Profit Margin	27.95%
830	MMM	Profit Margin	15.37%
880	MRK	Profit Margin	21.10%
930	MSFT	Profit Margin	33.36%
980	NKE	Profit Margin	10.46%
1030	PFE	Profit Margin	31.17%
1080	PG	Profit Margin	7.09%
1130	RTX	Profit Margin	5.34%
1180	TRV	Profit Margin	7.63%
1230	UNH	Profit Margin	5.58%
1280	V	Profit Margin	52.26%
1330	VZ	Profit Margin	14.00%
1380	WBA	Profit Margin	2.52%
1430	WMT	Profit Margin	2.84%
1480	XOM	Profit Margin	4.57%

재무상태표(balance sheets)¶

get_balance_sheet 메서드를 사용하여 재무상태표를 구할 수 있는 데, 유동현금, 자산, 부채등을 알 수 있다.

In [0]:

sheet = si.get_balance_sheet("aapl")

총현금(Total cash on hand)

In [60]:

sheet[sheet.Breakdown == "Total Cash"]

Out[60]:

	Breakdown	9/30/2019	9/30/2018	9/30/2017	9/30/2016

자본(Stockholders’ equity)

In [61]:

sheet[sheet.Breakdown == "Total stockholders' equity"]

Out[61]:

	Breakdown	9/30/2019	9/30/2018	9/30/2017	9/30/2016

총자산(Total Assets)

In [62]:

sheet[sheet.Breakdown == "Total Assets"]

Out[62]:

	Breakdown	9/30/2019	9/30/2018	9/30/2017	9/30/2016
0	Total Assets	338516000	365725000	375319000	321686000

여러 종목의 재무상태표 구하기¶

다우지수 구성종목들의 재무상태표를 다음과 같이 얻을 수 있다.

In [0]:

balance_sheets = {}
for ticker in dow_list:
    balance_sheets[ticker] = si.get_balance_sheet(ticker)

각 종목의 재무상태표를 묶어서 최근 데이터를 보여준다.

In [0]:

recent_sheets = {ticker : sheet.iloc[:,:2] for ticker,sheet in balance_sheets.items()}
 
for ticker in recent_sheets.keys():
    recent_sheets[ticker].columns = ["Breakdown", "Recent"]
 
# combine all balance sheets together
combined_sheets = pd.concat(recent_sheets)
 
# reset index to pull in ticker
combined_sheets = combined_sheets.reset_index()
 
# get rid of numeric index field
del combined_sheets["level_1"]
 
# update column names
combined_sheets.columns = ["Ticker", "Breakdown", "Recent"]

총자산(Total Assets)

In [65]:

combined_sheets[combined_sheets.Breakdown == "Total Assets"]

Out[65]:

	Ticker	Breakdown	Recent
0	AAPL	Total Assets	338516000
13	AXP	Total Assets	198321000
28	BA	Total Assets	133625000
43	CAT	Total Assets	78453000
58	CSCO	Total Assets	97793000
71	CVX	Total Assets	237428000
86	DIS	Total Assets	193984000
100	DOW	Total Assets	60524000
115	GS	Total Assets	992968000
131	HD	Total Assets	51236000
146	IBM	Total Assets	152186000
161	INTC	Total Assets	136524000
174	JNJ	Total Assets	157728000
188	JPM	Total Assets	2687379000
203	KO	Total Assets	86381000
217	MCD	Total Assets	47510800
232	MMM	Total Assets	44659000
247	MRK	Total Assets	84397000
261	MSFT	Total Assets	286556000
275	NKE	Total Assets	23717000
287	PFE	Total Assets	167489000
302	PG	Total Assets	115095000
318	RTX	Total Assets	139716000
333	TRV	Total Assets	110122000
346	UNH	Total Assets	173889000
359	V	Total Assets	72574000
373	VZ	Total Assets	291727000
388	WBA	Total Assets	67598000
402	WMT	Total Assets	236495000
416	XOM	Total Assets	362597000

손익계산서(income statements)¶

손익계산서는 get_income_statement 메서드를 사용하여 얻을 수 있다

In [0]:

income=si.get_income_statement("aapl")

매출액(total revenue)

In [67]:

income[income.Breakdown == "Total Revenue"]

Out[67]:

	Breakdown	ttm	9/30/2019	9/30/2018	9/30/2017	9/30/2016
0	Total Revenue	267981000	260174000	265595000	229234000	215639000

매출총이익(gross profit)

In [68]:

income[income.Breakdown == "Gross Profit"]

Out[68]:

	Breakdown	ttm	9/30/2019	9/30/2018	9/30/2017	9/30/2016
2	Gross Profit	102127000	98392000	101839000	88186000	84263000

여러 종목의 손익계산서 구하기

In [0]:

income_statements = {}
for ticker in dow_list:
    income_statements[ticker] = si.get_income_statement(ticker)

각 종목의 손익계산서도 하나로 묶어 필요한 계정만 볼 수 있다

In [0]:

recent_income_statements = {ticker : sheet.iloc[:,:2] for ticker,sheet in income_statements.items()}
 
for ticker in recent_income_statements.keys():
    recent_income_statements[ticker].columns = ["Breakdown", "Recent"]
 
combined_income = pd.concat(recent_income_statements)
 
combined_income = combined_income.reset_index()
 
del combined_income["level_1"]
 
combined_income.columns = ["Ticker", "Breakdown", "Recent"]

매출액(Total Revenue)

In [0]:

combined_income[combined_income.Breakdown == "Total Revenue"]

개별종목의 현금흐름표 다운로드¶

현금흐름표(cash flow statements)는 get_cash_flow 메서드를 사용하여 구한다

In [0]:

flow = si.get_cash_flow("aapl")

여러 종목의 현금흐름표 다운로드

In [0]:

cash_flows = {}
for ticker in dow_list:
    cash_flows[ticker] = si.get_cash_flow(ticker)

다운받은 현금흐름표 묶기

In [0]:

recent_cash_flows = {ticker : flow.iloc[:,:2] for ticker,flow in cash_flows.items()}
  
for ticker in recent_cash_flows.keys():
    recent_cash_flows[ticker].columns = ["Breakdown", "Recent"]
  
combined_cash_flows = pd.concat(recent_cash_flows)
 
combined_cash_flows = combined_cash_flows.reset_index()
 
del combined_cash_flows["level_1"]
 
combined_cash_flows.columns = ["Ticker", "Breakdown", "Recent"]

잉여현금흐름(free cash flow)

In [0]:

combined_cash_flows[combined_cash_flows.Breakdown == "Free Cash Flow"]

부채정보(debt information)

In [0]:

combined_cash_flows[combined_cash_flows.Breakdown == "Issuance of Debt"]

In [0]: