파이썬 (python) 라이브러리 - finpie

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# finpie

- 링크/문서 : pypi.org/project/finpie/#A2

- 용도 : 재무/경제 데이터를 다운로드 가능

MarketWatch, The Motley Fool, Finviz와 Macrotrends 사이트에서 데이터 스크랩할 수 있는 함수 제공

. Company Fundamental data : 야후 파이낸스, finbiz 및 OBOE 에서 재무 Data 수집

. Economic data : OECD database에서 수집,

. news module : 과거의 뉴스 해드라인을 수집 : FT, NYT, WSJ, Barrons, Seeking Alpha, Reuters 에서

. other : 모든 나스닥 종목의 ticker 와 시세 가져오기 가능

# Required

- 파이썬 버전 : Python3
- 구글크롬 버전 : 85.*.****.*** 이상 버전 필요(Selenium 관련 일부 기능 사용을 위함)
- 구글 크롬 87 버전을 써야지 수행이 가능한 함수가 존재함.
Failed to start driver: Message: session not created:

This version of ChromeDriver only supports Chrome version 87

- Selenium은 방화벽과 함께 Chrome 사용 못할 수 있음.

# Module & Method

Output	Data Output	Runtime
Company Fundamentals
fd = Fundamentals( ticker, source, freq )
<u>Financial statements</u>
fd.income_statement()	up to 2005	Slow, depends on source
fd.balance_sheet()	up to 2005	Slow, depends on source
fd.cashflow_statement()	up to 2005	Slow, depends on source
<u>Financial ratios and key metrics</u>
fd.ratio()	up to 2005	Slow
fd.key_metrics()	Most recent data	Fast
<u>Earnings and revenue estimates</u>
fd.earnings_estimate()	Most recent data	Fast
fd.earnings_estimates_trends()	Recent trend	Fast
fd.earnings_history()	4 quarters	Fast
fd.revenue_estimates()	Most recent data	Fast
fd.growth_estimates()	Most recent data	Fast
<u>Earnings call transcripts</u>
fd.transcripts()	up to 2018	Slow
<u>Insider transactions and analyst ratings</u>
fd.insider_transactions()	Most recent data	Fast
fd.analyst_ratings()	Most recent data	Fast
<u>ESG data</u>
fd.esg_score()	Most recent data	Fast
fd.corporate_governance_score()	Most recent data	Fast
<u>Company profile</u>
fd.profile()	Most recent data	Fast
fd.executives_info()	Most recent data	Fast
Price data
historical_prices(ticker)	Timeseries	Fast
yahoo_option_chain(ticker)	Most recent data	Fast
cboe_option_chain(ticker)	Most recent data	Fast
historical_futures_contracts(date_range)	Timeseries	Very slow
futures_contracts(date)	Any date	Fast
Economic data
*OECD Data*
oecd = OecdData( country_code, args )**
<u>Composite leading indicators</u>
oecd.cli(subject = 'amplitude)	Timeseries	Not that slow
oecd.cci()	Timeseries	Not that slow
oecd.bci()	Timeseries	Not that slow
<u>Financial indicators</u>
oecd.monetary_aggregates_m1()	Timeseries	Not that slow
oecd.monetary_aggregates_m3()	Timeseries	Not that slow
oecd.interbank_rates()	Timeseries	Not that slow
oecd.short_term_rates()	Timeseries	Not that slow
oecd.long_term_rates()	Timeseries	Not that slow
oecd.all_share_prices( )	Timeseries	Not that slow
oecd.share_prices_industrials()	Timeseries	Not that slow
oecd.usd_exchange_rates_spot()	Timeseries	Not that slow
oecd.usd_exchange_rates_average( )	Timeseries	Not that slow
oecd.rer_overall()	Timeseries	Not that slow
<u>Trade indicators</u>
oecd.exports_value()	Timeseries	Not that slow
oecd.imports_value( )	Timeseries	Not that slow
<u>Labour market indicators</u>
oecd.unemployment_rate( )	Timeseries	Not that slow
<u>Price indices</u>
oecd.cpi_total()	Timeseries	Not that slow
oecd.cpi_city_total( )	Timeseries	Not that slow
oecd.cpi_non_food_non_energy()	Timeseries	Not that slow
oecd.cpi_energy( )	Timeseries	Not that slow
<u>Business tendency and consumer opinion </u>
oecd.business_tendency_survey( sector )	Timeseries	Not that slow
oecd.consumer_opinion_survey( measure = ‘national' )	Timeseries	Not that slow
<u>National accounts </u>
oecd.gdp_deflator()	Timeseries	Not that slow
oecd.gdp_total()	Timeseries	Not that slow
oecd.gdp_final_consumption()	Timeseries	Not that slow
oecd.gdp_government_consumption()	Timeseries	Not that slow
oecd.gdp_fixed_capital_formation()	Timeseries	Not that slow
oecd.gdp_exports()	Timeseries	Not that slow
oecd.gdp_imports()	Timeseries	Not that slow
<u>Production and sales </u>
oecd.total_manufacturing_index()	Timeseries	Not that slow
oecd.total_industry_production_ex_construction()	Timeseries	Not that slow
oecd.total_construction()	Timeseries	Not that slow
oecd.total_retail_trade()	Timeseries	Not that slow
oecd.passenger_car_registration()	Timeseries	Not that slow
oecd.construction_permits_issued()	Timeseries	Not that slow
<u>OECD Business Tendency Survey </u>
oecd.economic_situation_survey(sector)	Timeseries	Not that slow
oecd.consumer_confidence_survey()	Timeseries	Not that slow
oecd.consumer_prices_inflation_survey()	Timeseries	Not that slow
<u>OECD Balance of Payments </u>
*Current Account*
oecd.current_account(percent_of_gdp = False)	Timeseries	Not that slow
oecd.goods_balance( xm = ‘balance’ )	Timeseries	Not that slow
oecd.services_balance( xm = ‘balance’ )	Timeseries	Not that slow
*Financial account*
oecd.financial_account(assets_or_liabs = None)	Timeseries	Not that slow
oecd.direct_investment(assets_or_liabs = None)	Timeseries	Not that slow
oecd.portfolio_investment(assets_or_liabs = None)	Timeseries	Not that slow
oecd.other_investment(assets_or_liabs = None)	Timeseries	Not that slow
oecd.financial_derivatives()	Timeseries	Not that slow
oecd.reserve_assets()	Timeseries	Not that slow
*EIA Data*
eia = EiaData()
eia.eia_petroleum_series()	Timeseries	Not that slow
<u>Weekly balances</u>
eia.weekly_balance()	Timeseries	Not that slow
eia.last_weekly_balance()	Most recent data	Not that slow
<u>Crude oil supply</u>
eia.crude_production()	Timeseries	Not that slow
eia.crude_supply_and_disposition()	Timeseries	Not that slow
eia.rig_count()	Timeseries	Not that slow
eia.crude_reserves()	Timeseries	Not that slow
<u>Refining and Processing</u>
eia.weekly_refinery_inputs()	Timeseries	Not that slow
eia.refinery_utilisation()	Timeseries	Not that slow
eia.refinery_yield()	Timeseries	Not that slow
eia.crude_acquisition_cost()	Timeseries	Not that slow
eia.crude_inputs_quality()	Timeseries	Not that slow
eia.refineries()	Timeseries	Not that slow
<u>Imports and Exports</u>
eia.weekly_xm()	Timeseries	Not that slow
eia.monthly_xm()	Timeseries	Not that slow
eia.weekly_imports_by_country()	Timeseries	Not that slow
eia.crude_imports_quality()	Timeseries	Not that slow
<u>Stocks</u>
eia.weekly_stocks()	Timeseries	Not that slow
eia.monthly_product_stocks()	Timeseries	Not that slow
eia.monthly_refinery_stocks()	Timeseries	Not that slow
eia.monthly_tank_and_pipeline_stocks()	Timeseries	Not that slow
<u>Consumption and sales</u>
eia.weekly_product_supplied()	Timeseries	Not that slow
eia.monthly_product_supplied()	Timeseries	Not that slow
eia.product_prices_sales_and_stock()	Timeseries	Not that slow
News data
news = NewsData( ticker, keyword_string )
news.barrons()	Timeseries	Slow
news.cnbc()	Timeseries	Very slow
news.ft()	Timeseries	Very slow
news.nyt()	Timeseries	Very slow
news.reuters()	Timeseries	Very slow
news.seeking_alpha()	Timeseries	Slow
news.wsj()	Timeseries	Very slow
Other data
nasdaq_tickers()	List of stock tickers	Fast
global_tickers()	List of stock tickers	Slow
cftc()	Timeseries	Not that slow

# 주의 (반드시 필독)

● 실제 함수명이 문서와 상이한것

fd.earnings_estimates(Ticker) → 실제로는 fd.earnings_estimate(Ticker) 로 동작함.

fd.earnings_estimates_trends() → 실제로는 fd.earnings_estimate_trends() 로 동작함

fd.ratios() → 실제로는 fd.ratio() 로 동작함

● 구글 87버전이 필요한 함수 (다른버전 안되고 87버전만 가능)

fd.income_statement()

fd.balance_sheet()

fd.cashflow_statement()

fd.ratio()

● 특히 fd.ratio() 같은 경우에는

finpie.Fundamentals('AAPL') 이렇게 해야 동작함.

티커 뒤에 다른 파라미터 넣으면 사용불가

# Method 설명 및 사용 방법

# 전체 Module 간략 요약

# [Module] Company Fundamentals
import finpie
fd = finpie.Fundamentals(ticker, source, freq)


# [Module] Price data
import finpie.price_data


# [Module] Economic
import finpie.economic_data

### [Sub Module] OECD Data
import finpie.economic_data.oecd_data
oecd = OecdData( country_code, **args )

### [Sub Module] EIA Petroleum Data : 석유 데이터
import finpie.economic_data.eia_data
eia = EiaData()


# [Module] News data
import finpie.NewsData
news = NewsData( ticker, keyword_string )


# Other data
nasdaq_tickers()
global_tickers()
cftc( report_type = 'futures_traders', year = 2000 )

# Module 세부 분석

● Company Fundamental data

# finpie.Fundamentals(ticker, source='macrotrends', freq='A')

Macrotrends는 Selenium 에서 실행되며 웹사이트가 때때로 로드되지 않을 수 있음.

import finpie # or import finpie.fundamental_data

fd = finpie.Fundamentals(ticker, source, freq)
# source : 'yahoo', 'marketwatch', 'macrotrends' (default : 'macrotrends')
# freq : 'A'(annual data) or 'Q'(quarterly data) (default : 'A')
# default key metrics for marketwatch and macrotrends come from Finviz

# Financial statements / ratios (구글 87버전이 필요)

# Financial statements
fd.income_statement()
fd.balance_sheet()
fd.cashflow_statement()

# Financial ratios and key metrics
fd.ratio()

# key_metrics

1. yahoo 에서 가져오기

fd = finpie.Fundamentals('AAPL','yahoo')

fd.key_metrics()
... (생략)
            shares_outstanding         float  %_held_by_insiders  \
date
2021-03-20        1.679000e+10  1.677000e+10              0.0007


fd.key_metrics().iloc[0]

beta_(5y_monthly)                                        1.25
52-week_change                                         1.1031
sandp500_52-week_change                                0.6526
52_week_high                                           145.09
52_week_low                                             53.15
50-day_moving_average                                   128.0
200-day_moving_average                                 123.07
avg_vol_(3_month)                                 110580000.0
avg_vol_(10_day)                                  108930000.0
shares_outstanding                              16790000000.0
float                                           16770000000.0
%_held_by_insiders                                     0.0007
%_held_by_institutions                                 0.5966
shares_short_(feb_25_2021)                        100800000.0
short_ratio_(feb_25_2021)                                0.97
short_%_of_float_(feb_25_2021)                          0.006
short_%_of_shares_outstanding_(feb_25_2021)             0.006
shares_short_(prior_month_jan_28_2021)             91860000.0
forward_annual_dividend_rate                             0.82
forward_annual_dividend_yield                          0.0068
trailing_annual_dividend_rate                            0.81
trailing_annual_dividend_yield                         0.0067
5_year_average_dividend_yield                            1.42
payout_ratio                                           0.2177
dividend_date                                     Feb 10 2021
ex-dividend_date                                  Feb 04 2021
last_split_factor                                         4:1
last_split_date                                   Aug 30 2020
fiscal_year_ends                                  Sep 25 2020
most_recent_quarter_(mrq)                         Dec 25 2020
profit_margin                                          0.2174
operating_margin_(ttm)                                 0.2525
return_on_assets_(ttm)                                 0.1336
return_on_equity_(ttm)                                 0.8209
revenue_(ttm)                                  294140000000.0
revenue_per_share_(ttm)                                 17.13
quarterly_revenue_growth_(yoy)                          0.214
gross_profit_(ttm)                             104960000000.0
ebitda                                          85160000000.0
net_income_avi_to_common_(ttm)                  63930000000.0
diluted_eps_(ttm)                                        3.69
quarterly_earnings_growth_(yoy)                         0.293
total_cash_(mrq)                                76830000000.0
total_cash_per_share_(mrq)                               4.58
total_debt_(mrq)                               112040000000.0
total_debtequity_(mrq)                                 169.19
current_ratio_(mrq)                                      1.16
book_value_per_share_(mrq)                               3.94
operating_cash_flow_(ttm)                       88920000000.0
levered_free_cash_flow_(ttm)                    66890000000.0
ticker                                                   AAPL

fd.revenue_estimates()
                      reference_date  no_of_analysts  avg_estimate  low_estimate  high_estimate  year_ago_sales  sales_growth_(yearest)
date
2021-03-19  Current Qtr. (E6ar 2021)            27.0  7.692000e+10  7.079000e+10   8.319000e+10    5.831000e+10                   0.319
2021-03-19      Next Qtr. (Jun 2021)            25.0  6.883000e+10  6.171000e+10   7.704000e+10    5.969000e+10                   0.153
2021-03-19       Current Year (2021)            35.0  3.334200e+11  3.162700e+11   3.435500e+11    2.745100e+11                   0.215
2021-03-19          Next Year (2022)            34.0  3.477100e+11  2.915600e+11   3.865000e+11    3.334200e+11                   0.043

fd.earnings_estimate()
                      reference_date  no_of_analysts  avg_estimate  low_estimate  high_estimate  year_ago_eps
date
2021-03-19  Current Qtr. (E6ar 2021)            30.0          0.98          0.85           1.09          0.64
2021-03-19      Next Qtr. (Jun 2021)            28.0          0.82          0.70           0.98          0.64
2021-03-19       Current Year (2021)            39.0          4.45          4.10           4.89          3.28
2021-03-19          Next Year (2022)            38.0          4.68          3.64           5.50          4.45

2. Finbiz 에서 가져오기

아래 3개는 모두 다 동일 데이터 들고옴
fd = finpie.Fundamentals('AAPL')
fd = finpie.Fundamentals('AAPL', 'macrotrends')
fd = finpie.Fundamentals('AAPL', 'marketwatch')

stock_datas = fd.key_metrics().iloc[0]
print(stock_datas)

revenue_ttm = stock_datas['sales']
shares_outstanding(ttm) = stock_datas['shs_outstand']
p_s_ratio = stock_datas['p_to_s']
p_e_ratio = stock_datas['p_to_e']


fd.key_metrics().iloc[0]
index                 DJIA S&P500
market_cap        2029880000000.0
income              63930000000.0
sales              294130000000.0
book_to_sh                   3.91
cash_to_sh                   4.54
dividend                     0.82
dividend_%                 0.0068
employees                147000.0
optionable                    Yes
shortable                     Yes
recom                         2.0
p_to_e                      32.44
forward_p_to_e              25.63
peg                          2.21
p_to_s                        6.9
p_to_b                      30.69
p_to_c                      26.42
p_to_fcf                    30.73
quick_ratio                   1.1
current_ratio                 1.2
debt_to_eq                   1.69
lt_debt_to_eq                 1.5
sma20                     -0.0249
eps_(ttm)                     3.7
eps_next_y                   4.68
eps_next_q                   0.98
eps_this_y                  0.102
eps_next_y                 0.0521
eps_next_5y                0.1469
eps_past_5y                 0.073
sales_past_5y               0.033
sales_q_to_q                0.214
eps_q_to_q                  0.346
earnings              Jan 27 AE6C
sma50                     -0.0739
insider_own                0.0007
insider_trans             -0.0505
inst_own                    0.598
inst_trans                      -
roa                         0.194
roe                         0.906
roi                         0.317
gross_margin                0.388
oper_margin                 0.252
profit_margin               0.217
payout                      0.216
sma200                     0.0362
shs_outstand        16940000000.0
shs_float           16780000000.0
short_float                 0.006
short_ratio                  0.91
target_price               151.75
52w_range          53.15 - 145.09
52w_high                   -0.173
52w_low                    1.2575
rsi_(14)                    41.49
rel_volume                   1.67
avg_volume            110690000.0
volume                184768356.0
perf_week                 -0.0086
perf_month                -0.0749
perf_quarter              -0.0612
perf_half_y                0.0875
perf_year                  0.9608
perf_ytd                  -0.0957
beta                         1.23
atr                          3.72
volatility        2.34E-2 3.09E-2
prev_close                 120.53
price                      119.99
change                    -0.0045


analyst들의 평가 (Finviz)
# fd.analyst_ratings()
                action      rating_institution      rating    price_target
date
2021-03-20  Reiterated                 Needham         Buy     $140 → $170
2021-03-20  Reiterated  Monness Crespi & Hardt         Buy     $144 → $170
2021-03-20  Reiterated       Canaccord Genuity         Buy     $150 → $155
2021-03-20  Reiterated                 Wedbush  Outperform     $160 → $175
2021-03-20  Reiterated                   Cowen  Outperform     $133 → $153
2021-03-20  Reiterated          Morgan Stanley  Overweight     $144 → $152
2021-03-20  Reiterated       Canaccord Genuity         Buy     $145 → $150
2021-03-20  Reiterated          Morgan Stanley  Overweight     $136 → $144
2021-03-20  Reiterated                 Wedbush  Outperform     $150 → $160
2021-03-20     Upgrade            Loop Capital  Hold → Buy     $110 → $131
2021-03-20     Resumed       Atlantic Equities  Overweight            $150
2021-03-20  Reiterated               Citigroup         Buy  $112.50 → $125
2021-03-20  Reiterated               Jefferies         Buy  $116.25 → $135
2021-03-20  Reiterated                 Needham         Buy  $112.50 → $140
2021-03-20  Reiterated             Oppenheimer  Outperform     $105 → $125
2021-03-20  Reiterated               JP Morgan  Overweight     $115 → $150
2021-03-20  Reiterated                   Cowen  Outperform     $530 → $133
2021-03-20  Reiterated  Monness Crespi & Hardt         Buy  $117.50 → $144
2021-03-20  Reiterated                 Wedbush  Outperform     $515 → $600
2021-03-20  Reiterated                   Cowen  Outperform     $470 → $530

● Price Data

finpie.price_data.historical_prices(Ticker)
                  open        high         low       close   adj_close       volume
date
1980-12-12    0.128348    0.128906    0.128348    0.128348    0.100922  469033600.0
1980-12-15    0.122210    0.122210    0.121652    0.121652    0.095657  175884800.0
1980-12-16    0.113281    0.113281    0.112723    0.112723    0.088636  105728000.0
1980-12-17    0.115513    0.116071    0.115513    0.115513    0.090830   86441600.0
1980-12-18    0.118862    0.119420    0.118862    0.118862    0.093463   73449600.0
...                ...         ...         ...         ...         ...          ...
2021-03-15  121.410004  124.000000  120.419998  123.989998  123.989998   92403800.0
2021-03-16  125.699997  127.220001  124.720001  125.570000  125.570000  114740000.0
2021-03-17  124.050003  125.860001  122.339996  124.760002  124.760002  111437500.0
2021-03-18  122.879997  123.180000  120.320000  120.529999  120.529999  121229700.0
2021-03-19  119.900002  121.430000  119.675003  120.214996  120.214996   50705960.0

저작자표시 동일조건 (새창열림)

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