# pyfinviz
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.
### Install
```
pip install pyfinviz
```
## Usage
### News
Information from https://finviz.com/news.ashx.
```python
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 object
```
### Crypto
Information from https://finviz.com/crypto_performance.ashx.
Uses relative performance options (D, W, M, MTD, Q, HY, Y, YTD)
```python
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 object
```
### Groups
Information from https://finviz.com/groups.ashx. Uses group options
(Sector, Industry..., Capitalization) and view options (Overview, Valuation, Performance, Custom)
```python
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 object
```
### Insider
Information from https://finviz.com/insidertrading.ashx. Uses filter options
(BUY, SELL, ALL) and view options (LATEST, TOP_INSIDER_TRADING_RECENT_WEEK, ...)
```python
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 object
```
### Quote
Information 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.
```python
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...
```
### Screener
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). Added signals filter too. You can also specify a range of pages to fetch.
Returns an empty dataframe if there are no results.
```python
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]
```
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"description": "# pyfinviz\r\nA python package that scrapes data from finviz.com and utilizes the pandas module.\r\nThis package uses a fixed set of parameter options so you don't have to memorize them. \r\n\r\n### Install\r\n```\r\npip install pyfinviz\r\n```\r\n\r\n## Usage\r\n### News\r\nInformation from https://finviz.com/news.ashx.\r\n```python\r\nfrom pyfinviz.news import News\r\n\r\nnews = News()\r\n\r\n# available variables:\r\nprint(news.main_url) # scraped URL\r\nprint(news.soup) # beautiful soup object\r\nprint(news.news_df) # NEWS table information in a pd.DataFrame object\r\nprint(news.blogs_df) # BLOGS table information in a pd.DataFrame object\r\n```\r\n\r\n### Crypto\r\nInformation from https://finviz.com/crypto_performance.ashx. \r\nUses relative performance options (D, W, M, MTD, Q, HY, Y, YTD)\r\n```python\r\nfrom pyfinviz.crypto import Crypto\r\n\r\n# with no params (SECTOR, OVERVIEW by default)\r\ncrypto = Crypto()\r\n# with params\r\ncrypto = Crypto(relative_performance_option=Crypto.RelativePerformanceOption.ONE_YEAR)\r\n\r\n# available variables:\r\nprint(crypto.main_url) # scraped URL\r\nprint(crypto.soup) # beautiful soup object\r\nprint(crypto.table_df) # table information in a pd.DataFrame object\r\n```\r\n\r\n### Groups\r\nInformation from https://finviz.com/groups.ashx. Uses group options \r\n(Sector, Industry..., Capitalization) and view options (Overview, Valuation, Performance, Custom)\r\n```python\r\nfrom pyfinviz.groups import Groups\r\n\r\n# with no params (sector overview)\r\ngroups = Groups()\r\n# with params (View the group VALUATION of the INDUSTRY sector)\r\ngroups = Groups(group_option=Groups.GroupOption.INDUSTRY, view_option=Groups.ViewOption.VALUATION)\r\n# with params (View the group PERFORMANCE of the TECH sector)\r\ngroups = Groups(group_option=Groups.GroupOption.INDUSTRY_TECHNOLOGY,\r\n view_option=Groups.ViewOption.PERFORMANCE)\r\n\r\n# available variables:\r\nprint(groups.main_url) # scraped URL\r\nprint(groups.soup) # beautiful soup object\r\nprint(groups.table_df) # table information in a pd.DataFrame object\r\n```\r\n\r\n### Insider\r\nInformation from https://finviz.com/insidertrading.ashx. Uses filter options \r\n(BUY, SELL, ALL) and view options (LATEST, TOP_INSIDER_TRADING_RECENT_WEEK, ...)\r\n```python\r\nfrom pyfinviz.insider import Insider\r\n\r\n# with no params (ALL the LATEST insider trades)\r\ninsider = Insider()\r\n# with params (the LATEST BUY insider trades)\r\ninsider = Insider(filter_option=Insider.FilterOption.BUY)\r\n\r\n# available variables:\r\nprint(insider.main_url) # scraped URL\r\nprint(insider.soup) # beautiful soup object\r\nprint(insider.table_df) # table information in a pd.DataFrame object\r\n```\r\n\r\n### Quote\r\nInformation from https://finviz.com/quote.ashx. The Quote class grabs all the information, \r\ncreates an object and returns it. Variable names that end in _df are pd.DataFrame objects.\r\n```python\r\nfrom pyfinviz.quote import Quote\r\n\r\nquote = Quote(ticker=\"AMZN\")\r\n\r\n# available variables:\r\nprint(quote.exists) # check if fetch was successful (STOCK may not exist)\r\nprint(quote.ticker) # AMZN\r\nprint(quote.exchange) # NASD\r\nprint(quote.company_name) # Amazon.com, Inc.\r\nprint(quote.sectors) # ['Consumer Cyclical', 'Internet Retail', 'USA']\r\nprint(quote.fundamental_df) # Index P/E EPS (ttm) Insider Own ... SMA50 SMA200 Volume Change\r\nprint(quote.outer_ratings_df) # 0 Nov-04-20 Upgrade ... Hold \u2192 Buy $3360 \u2192 $4000\r\nprint(quote.outer_news_df) # 0 Jan-04-21 10:20PM ... Bloomberg\r\nprint(quote.income_statement_df) # 1 12/31/2019 ... 22.99206\r\nprint(quote.insider_trading_df) # 0 WILKE JEFFREY A ... http://www.sec.gov/Archives/edgar/data/1018724...\r\n```\r\n\r\n### Screener\r\nInformation from https://finviz.com/screener.ashx?ft=4. The Screener class uses \r\nALL the options (dropdowns) in the webpage mentioned in the last sentence (over 60), and uses\r\nview options (OVERVIEW, VALUATION, ..., CUSTOM). Added signals filter too. You can also specify a range of pages to fetch.\r\n\r\nReturns an empty dataframe if there are no results.\r\n\r\n```python\r\nfrom pyfinviz.screener import Screener\r\n\r\n# with no params (default screener table)\r\nscreener = Screener()\r\n# with params (The first 3 pages of \"STOCKS ONLY\" where Analyst recommend a strong buy)\r\noptions = [Screener.IndustryOption.STOCKS_ONLY_EX_FUNDS, Screener.AnalystRecomOption.STRONG_BUY_1]\r\nscreener = Screener(filter_options=options, view_option=Screener.ViewOption.VALUATION,\r\n pages=[x for x in range(1, 4)])\r\n\r\n# available variables:\r\nprint(screener.main_url) # scraped URL\r\nprint(screener.soups) # beautiful soup object per page {1: soup, 2: soup, ...}\r\nprint(screener.data_frames) # table information in a pd.DataFrame object per page {1: table_df, 2, table_df, ...}\r\n```\r\nWebpage from previous fetch:\r\n![picture alt](images/screener1.png \"Title is optional\")\r\n\r\npandas output:\r\n```\r\n No Ticker MarketCap PE ... Salespast5Y Price Change Volume\r\n0 1 ACIW 4.43B 75.21 ... 4.40% 38.43 -0.16% 608,554\r\n1 2 ACRS 276.59M - ... - 6.47 -2.27% 373,915\r\n2 3 ACU 97.02M 14.92 ... 5.80% 30.13 -2.43% 13,524\r\n3 4 ADC 3.67B 36.03 ... 28.50% 66.58 1.49% 315,917\r\n4 5 ADUS 1.85B 53.79 ... 15.70% 117.09 0.92% 61,737\r\n5 6 AESE 48.74M - ... - 1.58 0.64% 1,009,212\r\n6 7 AEYE 259.33M - ... 83.10% 25.83 -5.00% 41,683\r\n7 8 AFT 224.25M - ... - 14.40 0.49% 43,953\r\n8 9 AGEN 620.70M - ... 84.70% 3.18 -3.34% 1,340,472\r\n9 10 AGM 785.57M 9.02 ... 21.80% 74.25 0.16% 30,179\r\n10 11 AHCO 3.39B - ... - 37.56 -0.82% 450,352\r\n11 12 AKUS 735.30M - ... - 19.83 4.04% 85,960\r\n12 13 ALBO 710.06M - ... - 37.51 -1.81% 258,926\r\n13 14 ALG 1.64B 28.10 ... 5.90% 137.95 1.27% 25,093\r\n14 15 ALPN 299.00M - ... - 12.60 0.32% 166,333\r\n15 16 ALRN 43.44M - ... - 1.04 -4.59% 1,071,395\r\n16 17 AMRK 182.88M 3.48 ... -2.10% 25.65 0.31% 119,102\r\n17 18 AMSWA 559.23M 85.85 ... 2.30% 17.17 0.94% 67,980\r\n18 19 AMTI 1.07B - ... - 30.77 -8.31% 70,411\r\n19 20 ANIK 656.72M - ... 1.70% 45.26 1.05% 79,476\r\n0 21 APT 155.99M 7.69 ... -0.40% 11.15 -1.24% 1,148,691\r\n1 22 AQMS 172.56M - ... - 3.00 -1.64% 2,168,579\r\n2 23 ARAY 378.01M 27.80 ... 0.20% 4.17 -0.48% 621,424\r\n3 24 ARDC 327.45M - ... - 14.29 0.07% 70,648\r\n4 25 ARDX 588.96M - ... -30.10% 6.47 -3.86% 323,062\r\n5 26 ASND 9.02B - ... -0.90% 166.78 -2.00% 74,233\r\n6 27 ASX 12.11B 14.67 ... - 5.84 -0.85% 439,892\r\n7 28 ATEN 776.87M 78.88 ... 3.40% 9.86 0.41% 357,503\r\n8 29 ATHA 1.21B - ... - 34.25 4.90% 129,947\r\n9 30 ATNI 657.72M - ... 5.50% 41.76 -0.33% 25,380\r\n10 31 ATRC 2.50B - ... 16.50% 55.67 1.51% 244,269\r\n11 32 ATRS 663.26M 70.00 ... 36.10% 3.99 -0.99% 812,128\r\n12 33 AUVI 36.63M - ... - 4.59 -6.52% 181,841\r\n13 34 AVDL 395.06M - ... 31.60% 6.68 -0.15% 692,233\r\n14 35 AVEO 169.35M - ... 9.70% 5.77 0.87% 218,677\r\n15 36 AVO 1.03B 29.74 ... - 15.05 0.07% 129,926\r\n16 37 AWH 687.64M - ... 12.50% 6.71 5.84% 601,774\r\n17 38 AYTU 104.65M - ... 153.90% 5.98 -0.99% 611,093\r\n18 39 BASI 141.08M - ... 21.60% 12.30 10.91% 184,761\r\n19 40 BBGI 43.09M - ... 34.80% 1.49 -3.87% 192,009\r\n0 41 BBI 38.58M - ... -12.30% 0.78 -3.21% 1,150,725\r\n1 42 BBL 150.24B 16.89 ... -0.80% 53.03 -0.77% 673,974\r\n2 43 BBSI 531.36M 13.73 ... 8.20% 68.21 3.33% 53,629\r\n3 44 BCOR 740.93M - ... 47.20% 15.91 -0.81% 501,047\r\n4 45 BCS 34.69B 12.99 ... 1.70% 7.99 -0.50% 2,017,726\r\n5 46 BDSX 583.83M - ... - 20.16 6.84% 97,245\r\n6 47 BEAM 4.88B - ... - 81.64 -1.07% 936,147\r\n7 48 BIO 17.42B 4.99 ... 1.20% 582.94 1.41% 139,476\r\n8 49 BIOX 229.59M 69.66 ... - 6.20 8.87% 95,378\r\n9 50 BLCT 366.63M - ... - 10.10 -0.79% 131,826\r\n10 51 BLX 625.60M 8.62 ... 5.20% 15.83 1.41% 91,844\r\n11 52 BTG 5.88B 9.15 ... - 5.60 -2.10% 5,698,582\r\n12 53 BWAY 83.88M - ... - 7.54 10.23% 86,655\r\n13 54 BWMX 1.18B - ... - 34.15 -2.15% 21,649\r\n14 55 BYSI 491.78M - ... - 12.20 -7.99% 389,083\r\n15 56 CALA 359.41M - ... - 4.91 -3.91% 1,257,056\r\n16 57 CALT 839.52M - ... - 33.62 -1.03% 999\r\n17 58 CASI 378.07M - ... 180.60% 2.95 -0.34% 347,045\r\n18 59 CBAY 399.04M - ... - 5.74 -2.38% 4,248,910\r\n19 60 CBZ 1.44B 19.13 ... 5.70% 26.61 -0.11% 212,684\r\n\r\n[60 rows x 18 columns]\r\n```\r\n",
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