Stocks Screener

The Stocks Screener module imports the same screener found in the OpenBB Terminal. The screener utilizes presets (.ini files) to scan for stocks meeting the defined criteria. User-generated files are kept in the stocks/screener folder within the OpenBBUserData folder.

This screener can find stocks on three exchanges:

• AMEX
• NASDAQ
• NYSE

The data returned is grouped into six categories:

• Overview
• Financial
• Ownership
• Performance
• Technical
• Valuation

The default preset is, top_gainers, and the default data type is, overview. Without the use of arguments, the screener will return results using those default values.

openbb.stocks.screener.screener_data()

This preset, like some of the others, contain no parameters other than a signal - the name of the preset - and has no corresponding file which can be edited. All signals are listed in the table below.

Preset and Signal Name	Description
top_gainers	stocks with the highest % price gain today
top_losers	stocks with the highest % price loss today
new_high	stocks making 52-week high today
new_low	stocks making 52-week low today
most_volatile	stocks with the highest widest high/low trading range today
most_active	stocks with the highest trading volume today
unusual_volume	stocks with unusually high volume today - the highest relative volume ratio
overbought	stock is becoming overvalued and may experience a pullback.
oversold	oversold stocks may represent a buying opportunity for investors
downgrades	stocks downgraded by analysts today
upgrades	stocks upgraded by analysts today
earnings_before	companies reporting earnings today, before market open
earnings_after	companies reporting earnings today, after market close
recent_insider_buying	stocks with recent insider buying activity
recent_insider_selling	stocks with recent insider selling activity
major_news	stocks with the highest news coverage today
horizontal_sr	horizontal channel of price range between support and resistance trendlines
tl_resistance	once a rising trendline is broken
tl_support	once a falling trendline is broken
wedge_up	upward trendline support and upward trendline resistance (reversal)
wedge_down	downward trendline support and downward trendline resistance (reversal)
wedge	upward trendline support, downward trendline resistance (contiunation)
triangle_ascending	upward trendline support and horizontal trendline resistance
triangle_descending	horizontal trendline support and downward trendline resistance
channel_up	both support and resistance trendlines slope upward
channel_down	both support and resistance trendlines slope downward
channel	both support and resistance trendlines are horizontal
double_top	stock with 'M' shape that indicates a bearish reversal in trend
double_bottom	stock with 'W' shape that indicates a bullish reversal in trend
multiple_top	same as double_top hitting more highs
multiple_bottom	same as double_bottom hitting more lows
head_shoulders	chart formation that predicts a bullish-to-bearish trend reversal
head_shoulders_inverse	chart formation that predicts a bearish-to-bullish trend reversal

These signals offer a good starting point, and results can be narrowed by creating a custom preset with defined parameters.

How to Use

note

Refer to the template file here for all of the available parameters and accepted values.

All of the included presets can be viewed online here

Without any parameters, a screener preset file must contain the following at a minimum:

# Author of preset:
# Description:

[General]

[Descriptive]

[Fundamental]

[Technical]

Parameters should be added as required, and they all have pre-defined values; for example, Price/Free Cash Flow:

# Any, Low (<15), High (>50), Under 5, Under 10, Under 15, Under 20, Under 25, Under 30, Under 35,
# Under 40, Under 45, Under 50, Under 60, Under 70, Under 80, Under 90, Under 100, Over 5, Over 10,
# Over 15, Over 20, Over 25, Over 30, Over 35, Over 40, Over 45, Over 50, Over 60, Over 70, Over 80,
# Over 90, Over 100

Price/Free Cash Flow = Any

Examples

List All Presets

The name of each available preset and the local path to the file can be imported as a dictionary.

from openbb_terminal.stocks.screener.finviz_model import preset_choices

preset choices

Print only the name of each preset as a list:

list(preset_choices)

Creating a Preset

To make a screener with only a few filters, it should look something like this:

# Author of preset: OpenBB
# Description: SDK Demo Screener

[General]
Order = Relative Volume
Signal = Major News

[Descriptive]
Average Volume = Over 500K
Price = Over $5

[Fundamental]
Price/Free Cash Flow = Low (<15)

[Technical]
Beta = Under 1

Copy the block above to a new text file in any editor, and save the file to the OpenBBUserData folder, naming it something like, sdk_guide_preset.ini. This preset has also been included with the installation. Declaring the path to the preset file, when located in the OpenBBUserData folder, is not required. The kernel must be restarted when a file is renamed or created; however, changes to the preset itself will be reflected immediately and without restarting.

It is a good idea to test choices made before making it too complicated. Start with a handful of filters and modify, or add, them one-at-a-time. Let's pass what we have so far through the screener with performance selected as the data_type.

Performance

openbb.stocks.screener.screener_data(preset_loaded='sdk_guide_preset', data_type = 'performance')

	Ticker	Perf Week	Perf Month	Perf Quart	Perf Half	Perf Year	Perf YTD	Volatility W	Volatility M	Recom	Avg Volume	Rel Volume	Price	Change	Volume
0	GEO	0.1389	0.4005	0.4589	0.6723	0.3955	0.5342	0.0574	0.0558	2.5	2.09e+06	4.23	11.89	0.1623	8.86013e+06
1	EBS	-0.0059	-0.4513	-0.5136	-0.6429	-0.7204	-0.7292	0.0452	0.0827	2.7	753610	2.91	11.77	0.0138	2.19107e+06
2	TME	0.1871	0.7984	0.3721	0.5789	-0.0531	-0.0365	0.0681	0.0756	2.3	9.95e+06	2.2	6.6	0.0611	2.18486e+07
3	QFIN	0.0874	0.4331	-0.0738	-0.0744	-0.389	-0.365	0.0472	0.0787	1.4	997070	1.62	14.56	0.0849	1.61386e+06
4	ATHM	0.0229	0.0391	-0.1772	-0.2256	-0.1971	-0.0455	0.0492	0.0579	2.2	577050	1.6	28.14	0.0484	922812
93	TGNA	0.0093	-0.0514	-0.0882	-0.1073	-0.0528	0.0533	0.0133	0.0286	2.3	1.38e+06	0.45	19.55	0.0046	620767
94	AMN	0.0096	-0.0365	0.1685	0.2591	0.0558	-0.0026	0.0243	0.0464	1.7	607420	0.43	122.01	-0.0078	264138
95	MCY	-0.0028	0.2416	0.1165	-0.2619	-0.3014	-0.3191	0.0206	0.0386	2	565130	0.43	36.13	0.0036	241950
96	FIBK	-0.0138	-0.0355	0.079	0.1411	0.0616	0.0681	0.0168	0.0206	2.2	596280	0.41	43.44	-0.0044	243947
97	BPOP	0.0217	0.0126	-0.0777	-0.1245	-0.1108	-0.128	0.0158	0.0236	2	596460	0.4	71.54	0.0007	238605

It found just under 100 tickers meeting the criteria. Let's dial down the beta value to see how many remain at less than 0.5.

Change the one argument:

[Technical]
Beta = Under 0.5

Running the exact same command as before trims down the list to only eleven.

openbb.stocks.screener.screener_data(preset_loaded='sdk_guide_preset', data_type = 'performance')

	Ticker	Perf Week	Perf Month	Perf Quart	Perf Half	Perf Year	Perf YTD	Volatility W	Volatility M	Recom	Avg Volume	Rel Volume	Price	Change	Volume
0	QFIN	0.0874	0.4331	-0.0738	-0.0744	-0.389	-0.365	0.0472	0.0787	1.4	997070	1.62	14.56	0.0849	1.61386e+06
1	ATHM	0.0229	0.0391	-0.1772	-0.2256	-0.1971	-0.0455	0.0492	0.0579	2.2	577050	1.6	28.14	0.0484	922812
2	JD	0.0143	0.4065	-0.1343	-0.0511	-0.379	-0.2209	0.029	0.0454	1.8	9.14e+06	1.47	53.25	0.0669	1.34395e+07
3	NI	0.0256	0.0518	-0.0844	-0.1342	0.0784	-0.0138	0.0151	0.0257	1.9	4.61e+06	1.36	27.23	0.0004	6.2667e+06
4	ZTO	0.1176	0.3862	-0.0572	-0.1073	-0.216	-0.1393	0.0447	0.0437	1.7	3.21e+06	0.96	24.05	0.0217	3.06905e+06
5	QDEL	-0.058	-0.0614	0.077	-0.1232	-0.4438	-0.3828	0.0335	0.0408	2.3	642680	0.79	83.32	-0.018	508575
6	ED	0.0228	0.0888	-0.0203	-0.0298	0.1985	0.1287	0.0149	0.0202	3.5	1.73e+06	0.77	96.3	-0.0025	1.3419e+06
7	SFM	0.027	0.1279	0.1656	0.237	0.237	0.129	0.0249	0.034	3.2	1.54e+06	0.74	33.51	-0.0095	1.14442e+06
8	PINC	0.0244	-0.0549	-0.0635	-0.1125	-0.1377	-0.1936	0.0158	0.0217	2.6	519290	0.72	33.2	0.0097	373171
9	AEP	0.0161	0.0587	-0.066	-0.0723	0.1361	0.0638	0.018	0.022	2.2	3.19e+06	0.64	94.65	-0.006	2.04444e+06
10	LRN	-0.0025	0.0796	-0.0594	-0.0846	0.0398	0.0741	0.0207	0.0288	1.4	587770	0.46	35.8	-0.0047	270842
11	AMN	0.0096	-0.0365	0.1685	0.2591	0.0558	-0.0026	0.0243	0.0464	1.7	607420	0.43	122.01	-0.0078	264138

Overview

We know know that these eleven companies have a beta relative to the S&P of under 0.5, and that they all have a price-to-free-cashflow ratio under 15. The signal argument has also been set as Major News, so we know at least this much about the companies and their relative performance over the last year. Setting the data_type to 'overview', will fetch the data which helps us understand who these companies are.

openbb.stocks.screener.screener_data(preset_loaded='sdk_guide_preset', data_type = 'overview')

	Ticker	Company	Sector	Industry	Country	Market Cap	P/E	Price	Change	Volume
0	QFIN	360 DigiTech, Inc.	Financial	Credit Services	China	2.19e+09	3.73	14.56	0.0849	1.61386e+06
1	ATHM	Autohome Inc.	Communication Services	Internet Content & Information	China	3.49e+09	16.96	28.14	0.0484	922812
2	JD	JD.com, Inc.	Consumer Cyclical	Internet Retail	China	8.152e+10	300.85	53.25	0.0669	1.34395e+07
3	NI	NiSource Inc.	Utilities	Utilities - Regulated Gas	USA	1.09e+10	17.69	27.23	0.0004	6.2667e+06
4	ZTO	ZTO Express (Cayman) Inc.	Industrials	Integrated Freight & Logistics	China	1.947e+10	24.67	24.05	0.0217	3.06905e+06
5	QDEL	QuidelOrtho Corporation	Healthcare	Diagnostics & Research	USA	5.53e+09	4.96	83.32	-0.018	508575
6	ED	Consolidated Edison, Inc.	Utilities	Utilities - Regulated Electric	USA	3.366e+10	20.21	96.3	-0.0025	1.3419e+06
7	SFM	Sprouts Farmers Market, Inc.	Consumer Defensive	Grocery Stores	USA	3.57e+09	14.67	33.51	-0.0095	1.14442e+06
8	PINC	Premier, Inc.	Healthcare	Health Information Services	USA	3.92e+09	21.52	33.2	0.0097	373171
9	AEP	American Electric Power Company, Inc.	Utilities	Utilities - Regulated Electric	USA	4.791e+10	19.63	94.65	-0.006	2.04444e+06
10	LRN	Stride, Inc.	Consumer Defensive	Education & Training Services	USA	1.54e+09	16.74	35.8	-0.0047	270842
11	AMN	AMN Healthcare Services, Inc.	Healthcare	Medical Care Facilities	USA	5.4e+09	11.76	122.01	-0.0078	264138

Ownership

When data_type = 'ownership', data presented are statistics for the general float, insider, institutional, and the short ratio.

openbb.stocks.screener.screener_data(preset_loaded='sdk_guide_preset', data_type = 'ownership')

	Ticker	Market Cap	Outstanding	Float	Insider Own	Insider Trans	Inst Own	Inst Trans	Float Short	Short Ratio	Avg Volume	Price	Change	Volume
0	QFIN	2.19e+09	1.5624e+08	1.2743e+08	0.0654	0	0.691	0.0034	0.0226	2.89	997070	14.56	0.0849	1.61386e+06
1	ATHM	3.49e+09	1.246e+08	6.846e+07	nan	nan	0.513	-0.0397	0.0213	2.53	577050	28.14	0.0484	922812
2	JD	8.152e+10	1.56e+09	1.26e+09	0.049	0	0.161	-0.1149	0.0136	1.88	9.14e+06	53.25	0.0669	1.34395e+07
3	NI	1.09e+10	4.065e+08	4.0459e+08	0.001	0	0.948	-0.0254	0.0345	3.04	4.61e+06	27.23	0.0004	6.2667e+06
4	ZTO	1.947e+10	8.0973e+08	6.4491e+08	0.0071	0	0.315	0.0345	0.0241	4.83	3.21e+06	24.05	0.0217	3.06905e+06
5	QDEL	5.53e+09	6.69e+07	6.092e+07	0.011	0	0.973	0.0058	0.0515	4.88	642680	83.32	-0.018	508575
6	ED	3.366e+10	3.546e+08	3.5443e+08	0.001	0.0056	0.684	0.0082	0.0206	4.22	1.73e+06	96.3	-0.0025	1.3419e+06
7	SFM	3.57e+09	1.0723e+08	1.0362e+08	0.008	-0.1276	nan	-0.0287	0.1386	9.31	1.54e+06	33.51	-0.0095	1.14442e+06
8	PINC	3.92e+09	1.1835e+08	1.1817e+08	0.008	0	0.707	-0.0044	0.0136	3.1	519290	33.2	0.0097	373171
9	AEP	4.791e+10	5.1373e+08	5.137e+08	0.0003	-0.0336	0.758	0.0033	0.0129	2.07	3.19e+06	94.65	-0.006	2.04444e+06
10	LRN	1.54e+09	4.208e+07	4.082e+07	0.04	0	nan	0.0158	0.0808	5.61	587770	35.8	-0.0047	270842
11	AMN	5.4e+09	4.378e+07	4.299e+07	0.003	-0.0868	nan	0.0148	0.0983	6.96	607420	122.01	-0.0078	264138

Technical

With data_type set to technical, aspects of technical analysis is returned.

openbb.stocks.screener.screener_data(preset_loaded='sdk_guide_preset', data_type = 'technical')

	Ticker	Beta	ATR	SMA20	SMA50	SMA200	52W High	52W Low	RSI	Price	Change	from Open	Gap	Volume
0	QFIN	0.37	1.11	0.1061	0.1107	-0.0206	-0.3953	0.5375	56.6	14.56	0.0849	0.0341	0.0492	1.61386e+06
1	ATHM	0.19	1.89	-0.0324	-0.0405	-0.1047	-0.3118	0.3734	47.13	28.14	0.0484	0.0057	0.0425	922812
2	JD	0.39	3.3	0.1141	0.1183	-0.0584	-0.386	0.6054	58.07	53.25	0.0669	-0.0039	0.0711	1.34395e+07
3	NI	0.44	0.62	0.049	0.0498	-0.0598	-0.1643	0.1451	61.62	27.23	0.0004	0.0052	-0.0048	6.2667e+06
4	ZTO	-0.09	1.14	0.1724	0.0968	-0.0412	-0.2565	0.4782	65.44	24.05	0.0217	0.0021	0.0195	3.06905e+06
5	QDEL	0.29	3.46	-0.0666	0.0262	-0.1185	-0.5373	0.2458	42.18	83.32	-0.018	-0.018	0	508575
6	ED	0.32	1.83	0.0574	0.0843	0.0365	-0.0578	0.2426	66.88	96.3	-0.0025	0.002	-0.0045	1.3419e+06
7	SFM	0.41	1.12	0.0584	0.1446	0.1605	-0.0518	0.4854	63.99	33.51	-0.0095	-0.0053	-0.0041	1.14442e+06
8	PINC	0.36	0.7	0.0322	-0.0004	-0.0711	-0.2042	0.0773	55.19	33.2	0.0097	0.0125	-0.0027	373171
9	AEP	0.41	2.07	0.0456	0.0559	-0.0117	-0.1037	0.1799	61.62	94.65	-0.006	0.0005	-0.0065	2.04444e+06
10	LRN	0.25	1.36	0.0302	-0.0923	-0.0562	-0.2439	0.3957	44.85	35.8	-0.0047	-0.0014	-0.0033	270842
11	AMN	0.3	4.85	0.011	0.0599	0.1431	-0.0551	0.4744	54.13	122.01	-0.0078	-0.0059	-0.0019	264138

Valuation

Lastly, valuation gets the basic fundamental ratios.

	Ticker	Price	Change	Volume	Market Cap	P/E	Fwd P/E	PEG	P/S	P/B	P/C	P/FCF	EPS this Y	EPS next Y	EPS past 5Y	EPS next 5Y	Sales past 5Y
0	AAPL	141.17	-0.0211	83578852	2.23252e+12	23.14	20.74	2.6	5.66	44.67	46.22	23.11	0.089	0.0898	0.216	0.0889	0.115
1	MSFT	240.33	-0.0059	17929705	1.77308e+12	25.9	21.49	1.99	8.73	10.32	16.53	39.59	0.198	0.1709	0.243	0.1301	0.155
2	GOOG	95.44	-0.0084	20145081	1.23434e+12	18.68	18.04	2.09	4.38	4.88	N/A	N/A	-0.159	0.121	0.2848	0.0895	N/A
3	GOOGL	95.19	-0.009	20047848	1.21605e+12	19.51	18.12	2.18	4.31	4.89	10.46	19.44	0.914	0.1139	0.321	0.0895	0.233
4	AMZN	92.42	-0.0163	65245723	9.3352e+11	85.26	55.08	3.28	1.86	6.85	15.91	N/A	0.549	0.8239	0.676	0.26	0.281
...continued

Combined, the five DataFrames provide an outline of who they are, which segment of the market they belong to, and how they are currently trading.

Unconventional Applications

One way to use the screener is to feed it the least amount of variables possible. In this next example, the preset file contains only one filter; requesting data for all constituents of the S&P 500 Index.

# Author of preset: OpenBB
# Description: S&P Index

[General]
Order = Market Cap.

[Descriptive]
Index = S&P 500

[Fundamental]

[Technical]

Copy and past the block above into any text editor; then save the file, as sp500_filter.ini, to the ~/OpenBBUserData/presets/stocks/screener folder. The sample code below combines all five DataFrames for the entire S&P 500 into one, fifty-column, DataFrame. It will likely take over two-minutes to collect the data.

from openbb_terminal.sdk import openbb
import pandas as pd

sp500_overview = openbb.stocks.screener.screener_data(preset_loaded='sp500_filter', data_type = 'overview')
sp500_ownership = openbb.stocks.screener.screener_data(preset_loaded='sp500_filter', data_type = 'ownership')
sp500_performance = openbb.stocks.screener.screener_data(preset_loaded='sp500_filter', data_type = 'performance')
sp500_technical = openbb.stocks.screener.screener_data(preset_loaded='sp500_filter', data_type = 'technical')
sp500_valuation = openbb.stocks.screener.screener_data(preset_loaded='sp500_filter', data_type = 'valuation')

sp500_overview = sp500_overview.convert_dtypes()
sp500_ownership = sp500_ownership.convert_dtypes()
sp500_performance = sp500_performance.convert_dtypes()
sp500_technical = sp500_technical.convert_dtypes()
sp500_valuation = sp500_valuation.convert_dtypes()

sp500_overview.drop(columns = ['P/E'], inplace = True)
sp500_overview.set_index(keys = ['Ticker', 'Price', 'Change', 'Volume'], inplace = True)
sp500_performance.drop(columns = ['Avg Volume', 'Price', 'Change', 'Volume'], inplace = True)
sp500_performance.set_index(keys = ['Ticker'], inplace = True)
sp500_ownership.drop(columns = ['Price', 'Change', 'Volume', 'Market Cap'], inplace = True)
sp500_ownership.set_index(keys = ['Ticker'], inplace = True)
sp500_technical.drop(columns = ['Price', 'Change', 'Volume'], inplace = True)
sp500_technical.set_index(keys = ['Ticker'], inplace = True)
sp500_valuation.drop(columns = ['Price', 'Change', 'Volume', 'Market Cap'], inplace = True)
sp500_valuation.set_index(keys = ['Ticker'], inplace = True)

sp500_df = sp500_overview.join(sp500_valuation)
sp500_df = sp500_df.join(sp500_ownership)
sp500_df = sp500_df.join(sp500_performance)
sp500_df = sp500_df.join(sp500_technical)

sp500_df.reset_index(inplace = True)

sp500_df

A summary of the output:

    Ticker   Price  Change    Volume  ... 52W Low    RSI from Open     Gap
0     AAPL  141.17 -0.0211  83578852  ...   0.094  42.47   -0.0211     0.0
1     MSFT  240.33 -0.0059  17929705  ...   0.126  50.94   -0.0044 -0.0015
2     GOOG   95.44 -0.0084  20145081  ...  0.1437  48.17   -0.0052 -0.0032
3    GOOGL   95.19  -0.009  20047848  ...  0.1422  48.24   -0.0063 -0.0027
4     AMZN   92.42 -0.0163  65245723  ...  0.0763  38.54   -0.0167  0.0004
..     ...     ...     ...       ...  ...     ...    ...       ...     ...
498   LUMN    5.47 -0.0091  31885469  ...  0.0018  30.19   -0.0036 -0.0054
499   PENN   34.07  0.0119   1689121  ...  0.3366   50.3    0.0068   0.005
500    NWL   12.87 -0.0191   5725896  ...   0.051  40.29     -0.01 -0.0091
501    VNO   25.31  0.0218   2560781  ...  0.2636  59.59    0.0181  0.0036
502    PVH   64.88  0.0399   1100532  ...  0.4918  66.83    0.0328  0.0069

[503 rows x 50 columns]