Finviz
Data extracted from Finviz includes historical data on different criteria and includes revenue breakdowns by products, services, and regions. Examples are provided below.
Important
In each result dataframe, only the first 5 rows of the DataFrame are shown at most to keep the documentation concise. The actual DataFrame returned by each function may contain more rows.
Insider Trading Data
Retrieves insider trading activity data including transaction details, relationships, and SEC form filings. Returns a DataFrame.
from stockdex import Ticker
ticker = Ticker(ticker="msft")
result = ticker.finviz_get_insider_trading()
Results:
Insider Trading |
Relationship |
Date |
Transaction |
Cost |
#Shares |
Value ($) |
#Shares Total |
SEC Form 4 |
|
|---|---|---|---|---|---|---|---|---|---|
0 |
Nadella Satya |
Chief Executive Officer |
Sep 03 ‘25 |
Sale |
504.78 |
149,205 |
75,315,121 |
790,852 |
Sep 04 06:01 PM |
1 |
SATYA NADELLA |
Director |
Sep 03 ‘25 |
Proposed Sale |
505.12 |
149,205 |
75,366,430 |
Sep 03 04:31 PM |
|
2 |
Numoto Takeshi |
EVP, Chief Marketing Officer |
Aug 12 ‘25 |
Sale |
527.32 |
4,850 |
2,557,506 |
39,111 |
Aug 13 06:03 PM |
3 |
Numoto Takeshi |
Officer |
Aug 05 ‘25 |
Proposed Sale |
527.75 |
4,870 |
2,570,142 |
Aug 05 04:55 PM |
|
4 |
Numoto Takeshi |
EVP, Chief Marketing Officer |
Jun 03 ‘25 |
Sale |
463 |
3,000 |
1,389,000 |
43,930 |
Jun 04 06:00 PM |
Earnings Revisions Data
Retrieves analyst earnings estimate revisions data including revision counts and estimate changes over time. Returns a DataFrame.
from stockdex import Ticker
ticker = Ticker(ticker="msft")
result = ticker.finviz_earnings_revisions_data()
Results:
ticker |
fiscalPeriod |
estimateType |
estimateDate |
relativeFiscalPeriod |
estimates |
upRevisions |
downRevisions |
mean |
high |
low |
price |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 |
msft |
2023FY |
E |
2023-07-06T00:00:00 |
1 |
43 |
39 |
0 |
9.6209 |
9.84 |
9.39 |
341.27 |
1 |
msft |
2023FY |
E |
2023-07-09T00:00:00 |
1 |
43 |
38 |
0 |
9.6209 |
9.84 |
9.39 |
337.22 |
2 |
msft |
2023FY |
E |
2023-07-10T00:00:00 |
1 |
43 |
5 |
0 |
9.6209 |
9.84 |
9.39 |
331.83 |
3 |
msft |
2023FY |
E |
2023-07-12T00:00:00 |
1 |
43 |
2 |
0 |
9.6196 |
9.84 |
9.39 |
337.2 |
4 |
msft |
2023FY |
E |
2023-07-13T00:00:00 |
1 |
43 |
2 |
0 |
9.6196 |
9.84 |
9.39 |
342.66 |
Earnings Annual Data
Retrieves annual earnings data including actual vs. estimated EPS, sales figures, and analyst counts. Returns a DataFrame.
from stockdex import Ticker
ticker = Ticker(ticker="GOOG")
result = ticker.finviz_earnings_annual_data()
results:
ticker |
fiscalPeriod |
earningsDate |
fiscalEndDate |
epsActual |
epsEstimate |
epsReportedActual |
epsReportedEstimate |
salesActual |
salesEstimate |
epsAnalysts |
epsReportedAnalysts |
salesAnalysts |
peRatio |
peRatioGaap |
psRatio |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 |
GOOG |
2010FY |
2010-12-31 |
0.7423 |
0.7252 |
0.6584 |
0.638 |
22006 |
21691.3 |
35 |
30 |
38 |
332.009 |
374.317 |
nan |
|
1 |
GOOG |
2011FY |
2011-12-31 |
0.9026 |
0.9251 |
0.7447 |
0.7862 |
29095 |
29390.9 |
37 |
28 |
33 |
273.045 |
330.939 |
nan |
|
2 |
GOOG |
2012FY |
2012-12-31 |
0.9817 |
0.9952 |
0.8085 |
0.8143 |
40419 |
41472.8 |
38 |
28 |
32 |
251.044 |
304.824 |
nan |
|
3 |
GOOG |
2013FY |
2013-12-31 |
1.111 |
1.1025 |
0.9542 |
0.9457 |
47575 |
47592.7 |
39 |
28 |
31 |
221.827 |
258.279 |
nan |
|
4 |
GOOG |
2014FY |
2014-12-31 |
1.2873 |
1.2892 |
1.051 |
1.0052 |
52511 |
52749.9 |
47 |
34 |
39 |
191.447 |
234.491 |
nan |
Earnings Data
Retrieves quarterly earnings data including actual vs. estimated EPS and sales figures with analyst coverage information. Returns a DataFrame.
from stockdex import Ticker
ticker = Ticker(ticker="UNH")
result = ticker.finviz_earnings_data()
results:
ticker |
fiscalPeriod |
earningsDate |
fiscalEndDate |
epsActual |
epsEstimate |
epsReportedActual |
epsReportedEstimate |
salesActual |
salesEstimate |
epsAnalysts |
epsReportedAnalysts |
salesAnalysts |
|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 |
UNH |
2025Q2 |
2025-07-29T08:30:00 |
2025-06-30 |
4.08 |
4.4502 |
3.74 |
4.3225 |
111616 |
111523 |
23 |
14 |
18 |
1 |
UNH |
2025Q1 |
2025-04-17T08:30:00 |
2025-03-31 |
7.2 |
7.2917 |
6.85 |
6.9024 |
109575 |
111579 |
23 |
13 |
19 |
2 |
UNH |
2024Q4 |
2025-01-16T08:30:00 |
2024-12-31 |
6.81 |
6.7379 |
5.98 |
6.2509 |
100807 |
101595 |
24 |
15 |
18 |
3 |
UNH |
2024Q3 |
2024-10-15T08:30:00 |
2024-09-30 |
7.15 |
7.0052 |
6.51 |
6.6621 |
100820 |
99139.4 |
24 |
15 |
18 |
4 |
UNH |
2024Q2 |
2024-07-16T08:30:00 |
2024-06-30 |
6.8 |
6.6483 |
4.54 |
6.3021 |
98855 |
98785.6 |
23 |
13 |
18 |
Overall Dividend
Retrieves overall dividend information including ex-date, estimates, and trailing twelve months data. Returns a DataFrame.
from stockdex import Ticker
ticker = Ticker(ticker="UNH")
result = ticker.finviz_overall_dividend()
results:
lastClose |
dividendExDate |
dividendEstimate |
dividendTTM |
|
|---|---|---|---|---|
0 |
497.85 |
2025-07-21T00:00:00 |
5.819 |
5.74 |
Dividend Date
Retrieves historical dividend payment dates and amounts including ordinary and special dividends. Returns a DataFrame.
from stockdex import Ticker
ticker = Ticker(ticker="CAT")
result = ticker.finviz_dividends_date_data()
results:
Ticker |
Exdate |
Ordinary |
Special |
|
|---|---|---|---|---|
0 |
CAT |
2025-07-21 |
1.51 |
0 |
1 |
CAT |
2025-04-21 |
1.41 |
0 |
2 |
CAT |
2025-01-21 |
1.41 |
0 |
3 |
CAT |
2024-10-21 |
1.41 |
0 |
4 |
CAT |
2024-07-22 |
1.41 |
0 |
Dividends Annual Data
Retrieves annual dividend data including amounts, yields, payout ratios, and estimate indicators. Returns a DataFrame.
from stockdex import Ticker
ticker = Ticker(ticker="AAPL")
result = ticker.finviz_dividends_annual_data()
results:
Ticker |
FiscalPeriod |
Amount |
Yield |
Payout |
Estimate |
|
|---|---|---|---|---|---|---|
0 |
AAPL |
2015FY |
0.49 |
1.73 |
21.48 |
False |
1 |
AAPL |
2016FY |
0.55 |
1.93 |
26.23 |
False |
2 |
AAPL |
2017FY |
0.6 |
1.56 |
26.06 |
False |
3 |
AAPL |
2018FY |
0.68 |
1.2 |
22.84 |
False |
4 |
AAPL |
2019FY |
0.75 |
1.37 |
25.24 |
False |
Revenue by Products and Services
Retrieves revenue breakdown by different products and services over fiscal years. Returns a dictionary where each key is a product or service name, and the value is another dictionary containing two lists: fiscal_year and value.
Note
Only first 2 entries are shown for each product/service for brevity. Full data can be obtained by running the code.
from stockdex import Ticker
ticker = Ticker(ticker="AAPL")
result = ticker.finviz_revenue_by_products_and_services()
Mac
Fiscal Year |
Value |
|---|---|
2016 |
22831000000.0 |
2017 |
25569000000.0 |
Manufactured Product, Other
Fiscal Year |
Value |
|---|---|
2016 |
11132000000.0 |
2017 |
0.0 |
Service
Fiscal Year |
Value |
|---|---|
2016 |
24348000000.0 |
2017 |
32700000000.0 |
Wearables, Home and Accessories
Fiscal Year |
Value |
|---|---|
2017 |
12826000000.0 |
2018 |
17381000000.0 |
iPad
Fiscal Year |
Value |
|---|---|
2016 |
20628000000.0 |
2017 |
18802000000.0 |
iPhone
Fiscal Year |
Value |
|---|---|
2016 |
136700000000.0 |
2017 |
139337000000.0 |
Revenue by Segment
Retrieves revenue breakdown by business segments over fiscal years. Returns a dictionary where each key is a segment name, and the value is another dictionary containing two lists: fiscal_year and value.
from stockdex import Ticker
ticker = Ticker(ticker="AAPL")
result = ticker.finviz_revenue_by_segment()
The output is a dictionary where each key is a segment name, and the value is another dictionary containing two lists: fiscal_year and value. The fiscal_year list contains the fiscal years for which revenue data is available, and the value list contains the corresponding revenue figures.
Note
Only first 2 entries are shown for each segment for brevity. Full data can be obtained by running the code.
Americas
Fiscal Year |
Value |
|---|---|
2016 |
86613000000.0 |
2017 |
96600000000.0 |
Europe
Fiscal Year |
Value |
|---|---|
2016 |
49952000000.0 |
2017 |
54938000000.0 |
Greater China
Fiscal Year |
Value |
|---|---|
2016 |
48492000000.0 |
2017 |
44764000000.0 |
Japan
Fiscal Year |
Value |
|---|---|
2016 |
16928000000.0 |
2017 |
17733000000.0 |
Rest of Asia Pacific
Fiscal Year |
Value |
|---|---|
2016 |
13654000000.0 |
2017 |
15199000000.0 |
Revenue by Regions
Retrieves revenue breakdown by geographic regions over fiscal years. Returns a dictionary where each key is a region name, and the value is another dictionary containing two lists: fiscal_year and value.
from stockdex import Ticker
ticker = Ticker(ticker="AAPL")
result = ticker.finviz_revenue_by_regions()
Note
Only first 2 entries are shown for each region for brevity. Full data can be obtained by running the code.
The output is a dictionary where each key is a region name, and the value is another dictionary containing two lists: fiscal_year and value. The fiscal_year list contains the fiscal years for which revenue data is available, and the value list contains the corresponding revenue figures.
China
Fiscal Year |
Value |
|---|---|
2016 |
48492000000.0 |
2017 |
44764000000.0 |
2018 |
51942000000.0 |
Other countries
Fiscal Year |
Value |
|---|---|
2016 |
91480000000.0 |
2017 |
100131000000.0 |
United States
Fiscal Year |
Value |
|---|---|
2016 |
75667000000.0 |
2017 |
84339000000.0 |
Price Reaction to Earnings Report
… code-block:: python
from stockdex import Ticker
ticker = Ticker(ticker=”PLTR”) result = ticker.finviz_price_reaction_to_earnings_report()
Note
The output is a dictionary.