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.
from pyfinviz.news import News
news = News()
# 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 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...Information 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). You can also specify a range of pages to fetch.
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]