A python package that scrapes data from finviz.com and utilizes the pandas module. This package uses a fixed set of parameter options so you don't have to memorize them.
pip install pyfinviz
Information from https://finviz.com/news.ashx. Uses view options (MARKET_NEWS, )
from pyfinviz.news import News
news = News() # by default scrap the Market News view
# available variables:
print(news.main_url) # scraped URL
print(news.soup) # beautiful soup object
print(news.news_df) # NEWS table information in a pd.DataFrame object
print(news.blogs_df) # BLOGS table information in a pd.DataFrame object
# all news page's views are also available by specifying the view_option value, please note that while
# all view_option will populate a news_df attribute, only News.ViewOption.MARKET_NEWS will populate
# the blogs_df attribute. Available view options parameter values are:
# - News.ViewOption.MARKET_NEWS
# - News.ViewOption.STOCKS_NEWS
# - News.ViewOption.ETF_NEWS
# - News.ViewOption.CRYPTO_NEWS
news = News(view_option = News.ViewOption.ETF_NEWS)
print(news.news_df) # NEWS table information in a pd.DataFrame objectInformation from https://finviz.com/crypto_performance.ashx. Uses relative performance options (D, W, M, MTD, Q, HY, Y, YTD)
from pyfinviz.crypto import Crypto
# with no params (SECTOR, OVERVIEW by default)
crypto = Crypto()
# with params
crypto = Crypto(relative_performance_option=Crypto.RelativePerformanceOption.ONE_YEAR)
# available variables:
print(crypto.main_url) # scraped URL
print(crypto.soup) # beautiful soup object
print(crypto.table_df) # table information in a pd.DataFrame objectInformation from https://finviz.com/groups.ashx. Uses group options (Sector, Industry..., Capitalization) and view options (Overview, Valuation, Performance, Custom)
from pyfinviz.groups import Groups
# with no params (sector overview)
groups = Groups()
# with params (View the group VALUATION of the INDUSTRY sector)
groups = Groups(group_option=Groups.GroupOption.INDUSTRY, view_option=Groups.ViewOption.VALUATION)
# with params (View the group PERFORMANCE of the TECH sector)
groups = Groups(group_option=Groups.GroupOption.INDUSTRY_TECHNOLOGY,
view_option=Groups.ViewOption.PERFORMANCE)
# available variables:
print(groups.main_url) # scraped URL
print(groups.soup) # beautiful soup object
print(groups.table_df) # table information in a pd.DataFrame objectInformation from https://finviz.com/insidertrading.ashx. Uses filter options (BUY, SELL, ALL) and view options (LATEST, TOP_INSIDER_TRADING_RECENT_WEEK, ...)
from pyfinviz.insider import Insider
# with no params (ALL the LATEST insider trades)
insider = Insider()
# with params (the LATEST BUY insider trades)
insider = Insider(filter_option=Insider.FilterOption.BUY)
# available variables:
print(insider.main_url) # scraped URL
print(insider.soup) # beautiful soup object
print(insider.table_df) # table information in a pd.DataFrame objectInformation from https://finviz.com/quote.ashx. The Quote class grabs all the information, creates an object and returns it. Variable names that end in _df are pd.DataFrame objects.
from pyfinviz.quote import Quote
quote = Quote(ticker="AMZN")
# available variables:
print(quote.exists) # check if fetch was successful (STOCK may not exist)
print(quote.ticker) # AMZN
print(quote.exchange) # NASD
print(quote.company_name) # Amazon.com, Inc.
print(quote.sectors) # ['Consumer Cyclical', 'Internet Retail', 'USA']
print(quote.fundamental_df) # Index P/E EPS (ttm) Insider Own ... SMA50 SMA200 Volume Change
print(quote.outer_ratings_df) # 0 Nov-04-20 Upgrade ... Hold → Buy $3360 → $4000
print(quote.outer_news_df) # 0 Jan-04-21 10:20PM ... Bloomberg
print(quote.income_statement_df) # 1 12/31/2019 ... 22.99206
print(quote.insider_trading_df) # 0 WILKE JEFFREY A ... http://www.sec.gov/Archives/edgar/data/1018724...
print(quote.reuters_income_statement_df) #Available only for eliteInformation from https://finviz.com/screener.ashx?ft=4. The Screener class uses ALL the options (dropdowns) in the webpage mentioned in the last sentence (over 60), and uses view options (OVERVIEW, VALUATION, ..., CUSTOM). Added signals filter too. You can also specify a range of pages to fetch.
Returns an empty dataframe if there are no results.
from pyfinviz.screener import Screener
# with no params (default screener table)
screener = Screener()
# with params (The first 3 pages of "STOCKS ONLY" where Analyst recommend a strong buy)
options = [Screener.IndustryOption.STOCKS_ONLY_EX_FUNDS, Screener.AnalystRecomOption.STRONG_BUY_1]
screener = Screener(filter_options=options, view_option=Screener.ViewOption.VALUATION,
pages=[x for x in range(1, 4)])
# available variables:
print(screener.main_url) # scraped URL
print(screener.soups) # beautiful soup object per page {1: soup, 2: soup, ...}
print(screener.data_frames) # table information in a pd.DataFrame object per page {1: table_df, 2, table_df, ...}Webpage from previous fetch:
pandas output:
No Ticker MarketCap PE ... Salespast5Y Price Change Volume
0 1 ACIW 4.43B 75.21 ... 4.40% 38.43 -0.16% 608,554
1 2 ACRS 276.59M - ... - 6.47 -2.27% 373,915
2 3 ACU 97.02M 14.92 ... 5.80% 30.13 -2.43% 13,524
3 4 ADC 3.67B 36.03 ... 28.50% 66.58 1.49% 315,917
4 5 ADUS 1.85B 53.79 ... 15.70% 117.09 0.92% 61,737
5 6 AESE 48.74M - ... - 1.58 0.64% 1,009,212
6 7 AEYE 259.33M - ... 83.10% 25.83 -5.00% 41,683
7 8 AFT 224.25M - ... - 14.40 0.49% 43,953
8 9 AGEN 620.70M - ... 84.70% 3.18 -3.34% 1,340,472
9 10 AGM 785.57M 9.02 ... 21.80% 74.25 0.16% 30,179
10 11 AHCO 3.39B - ... - 37.56 -0.82% 450,352
11 12 AKUS 735.30M - ... - 19.83 4.04% 85,960
12 13 ALBO 710.06M - ... - 37.51 -1.81% 258,926
13 14 ALG 1.64B 28.10 ... 5.90% 137.95 1.27% 25,093
14 15 ALPN 299.00M - ... - 12.60 0.32% 166,333
15 16 ALRN 43.44M - ... - 1.04 -4.59% 1,071,395
16 17 AMRK 182.88M 3.48 ... -2.10% 25.65 0.31% 119,102
17 18 AMSWA 559.23M 85.85 ... 2.30% 17.17 0.94% 67,980
18 19 AMTI 1.07B - ... - 30.77 -8.31% 70,411
19 20 ANIK 656.72M - ... 1.70% 45.26 1.05% 79,476
0 21 APT 155.99M 7.69 ... -0.40% 11.15 -1.24% 1,148,691
1 22 AQMS 172.56M - ... - 3.00 -1.64% 2,168,579
2 23 ARAY 378.01M 27.80 ... 0.20% 4.17 -0.48% 621,424
3 24 ARDC 327.45M - ... - 14.29 0.07% 70,648
4 25 ARDX 588.96M - ... -30.10% 6.47 -3.86% 323,062
5 26 ASND 9.02B - ... -0.90% 166.78 -2.00% 74,233
6 27 ASX 12.11B 14.67 ... - 5.84 -0.85% 439,892
7 28 ATEN 776.87M 78.88 ... 3.40% 9.86 0.41% 357,503
8 29 ATHA 1.21B - ... - 34.25 4.90% 129,947
9 30 ATNI 657.72M - ... 5.50% 41.76 -0.33% 25,380
10 31 ATRC 2.50B - ... 16.50% 55.67 1.51% 244,269
11 32 ATRS 663.26M 70.00 ... 36.10% 3.99 -0.99% 812,128
12 33 AUVI 36.63M - ... - 4.59 -6.52% 181,841
13 34 AVDL 395.06M - ... 31.60% 6.68 -0.15% 692,233
14 35 AVEO 169.35M - ... 9.70% 5.77 0.87% 218,677
15 36 AVO 1.03B 29.74 ... - 15.05 0.07% 129,926
16 37 AWH 687.64M - ... 12.50% 6.71 5.84% 601,774
17 38 AYTU 104.65M - ... 153.90% 5.98 -0.99% 611,093
18 39 BASI 141.08M - ... 21.60% 12.30 10.91% 184,761
19 40 BBGI 43.09M - ... 34.80% 1.49 -3.87% 192,009
0 41 BBI 38.58M - ... -12.30% 0.78 -3.21% 1,150,725
1 42 BBL 150.24B 16.89 ... -0.80% 53.03 -0.77% 673,974
2 43 BBSI 531.36M 13.73 ... 8.20% 68.21 3.33% 53,629
3 44 BCOR 740.93M - ... 47.20% 15.91 -0.81% 501,047
4 45 BCS 34.69B 12.99 ... 1.70% 7.99 -0.50% 2,017,726
5 46 BDSX 583.83M - ... - 20.16 6.84% 97,245
6 47 BEAM 4.88B - ... - 81.64 -1.07% 936,147
7 48 BIO 17.42B 4.99 ... 1.20% 582.94 1.41% 139,476
8 49 BIOX 229.59M 69.66 ... - 6.20 8.87% 95,378
9 50 BLCT 366.63M - ... - 10.10 -0.79% 131,826
10 51 BLX 625.60M 8.62 ... 5.20% 15.83 1.41% 91,844
11 52 BTG 5.88B 9.15 ... - 5.60 -2.10% 5,698,582
12 53 BWAY 83.88M - ... - 7.54 10.23% 86,655
13 54 BWMX 1.18B - ... - 34.15 -2.15% 21,649
14 55 BYSI 491.78M - ... - 12.20 -7.99% 389,083
15 56 CALA 359.41M - ... - 4.91 -3.91% 1,257,056
16 57 CALT 839.52M - ... - 33.62 -1.03% 999
17 58 CASI 378.07M - ... 180.60% 2.95 -0.34% 347,045
18 59 CBAY 399.04M - ... - 5.74 -2.38% 4,248,910
19 60 CBZ 1.44B 19.13 ... 5.70% 26.61 -0.11% 212,684
[60 rows x 18 columns]
from pyfinviz.screener import Screener
from pyfinviz.quote import Quote
from pyfinviz.converter.industry import industry_by_display_name
from pyfinviz.converter.country import country_by_display_name
quote = Quote(ticker="AMZN")
#industry and country converters from prettified display names (useful for using the industry value from Quote and using it for further screening. Also useful for displaying on UI, for example a dropdown)
country = quote.sectors[2]
industry = quote.sectors[1]
options = [industry_by_display_name[industry], country_by_display_name[country]]
screener = Screener(filter_options=options, view_option=Screener.ViewOption.OVERVIEW,
pages=[x for x in range(1, 4)])
Just add an extra argument 'api_key' while creating the classes. Example:
quote = Quote(ticker="AAPL", api_key="YOUR_API_KEY")
screener = Screener(api_key="YOUR_API_KEY")
screener = Screener(filter_options=options, view_option=Screener.ViewOption.OVERVIEW,
pages=[x for x in range(1, 4)], api_key="YOUR_API_KEY")It works for any supported feature (Quote, Screener, News, Insider, Groups, Crypto).