finviz-screener

Q: How do I install finviz-screener?

Run npx skills add https://github.com/tradermonty/claude-trading-skills --skill finviz-screener in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/finviz-screener/ (personal) or .claude/skills/finviz-screener/ (project).

Q: Which AI agents can use finviz-screener?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Q: Is finviz-screener safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Q: Who maintains finviz-screener?

It is published by tradermonty in tradermonty/claude-trading-skills. It has 3,449 recorded installs.

by tradermontyBusiness & Finance

Build and open FinViz screener URLs from natural language requests. Use when user wants to screen stocks, find stocks matching criteria, filter by fundamentals or technicals, or asks to open FinViz with specific conditions. Supports both Japanese and English input (e.g., "高配当で成長している小型株を探したい", "Find oversold large caps with high ROE").

Installs: 3.4K
Last 24h: +20
Trending rank: #5805
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/tradermonty/claude-trading-skills --skill finviz-screener

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads finviz-screener automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

FinViz Screener

Overview

Translate natural-language stock screening requests into FinViz screener filter codes, build the URL, and open it in Chrome. No API key required for public screener; FINVIZ Elite is auto-detected from $FINVIZ_API_KEY for enhanced functionality.

Key Features:

Natural language → filter code mapping (Japanese + English)
URL construction with view type and sort order selection
Elite/Public auto-detection (environment variable or explicit flag)
Chrome-first browser opening with OS-appropriate fallbacks
Strict filter validation to prevent URL injection

When to Use This Skill

Explicit Triggers:

"高配当で成長している小型株を探したい"
"Find oversold large caps near 52-week lows"
"テクノロジーセクターの割安株をスクリーニングしたい"
"Screen for stocks with insider buying"
"FinVizでブレイクアウト候補を表示して"
"Show me high-growth small caps on FinViz"
"配当利回り5%以上でROE15%以上の銘柄を探して"

Implicit Triggers:

User describes stock screening criteria using fundamental or technical terms
User mentions FinViz screener or stock filtering
User asks to find stocks matching specific financial characteristics

When NOT to Use:

Deep fundamental analysis of a specific stock (use us-stock-analysis)
Portfolio review with holdings (use portfolio-manager)
Chart pattern analysis on images (use technical-analyst)
Earnings-based screening (use earnings-trade-analyzer or pead-screener)

Workflow

Step 1: Load Filter Reference

Read the filter knowledge base:

cat references/finviz_screener_filters.md

Step 2: Interpret User Request

Map the user's natural-language request to FinViz filter codes. Use the Common Concept Mapping table below for quick translation, and reference the full filter list for precise code selection.

Note: For range criteria (e.g., "dividend 3-8%", "P/E between 10 and 20"), use the {from}to{to} range syntax as a single filter token (e.g., fa_div_3to8, fa_pe_10to20) instead of combining separate _o and _u filters.

Common Concept Mapping:

User Concept (EN)	User Concept (JP)	Filter Codes
High dividend	高配当	`fa_div_o3` or `fa_div_o5`
Small cap	小型株	`cap_small`
Mid cap	中型株	`cap_mid`
Large cap	大型株	`cap_large`
Mega cap	超大型株	`cap_mega`
Value / cheap	割安	`fa_pe_u20,fa_pb_u2`
Growth stock	成長株	`fa_epsqoq_o25,fa_salesqoq_o15`
Oversold	売られすぎ	`ta_rsi_os30`
Overbought	買われすぎ	`ta_rsi_ob70`
Near 52W high	52週高値付近	`ta_highlow52w_b0to5h`
Near 52W low	52週安値付近	`ta_highlow52w_a0to5l`
Breakout	ブレイクアウト	`ta_highlow52w_b0to5h,sh_relvol_o1.5`
Technology	テクノロジー	`sec_technology`
Healthcare	ヘルスケア	`sec_healthcare`
Energy	エネルギー	`sec_energy`
Financial	金融	`sec_financial`
Semiconductors	半導体	`ind_semiconductors`
Biotechnology	バイオテク	`ind_biotechnology`
US stocks	米国株	`geo_usa`
Profitable	黒字	`fa_pe_profitable`
High ROE	高ROE	`fa_roe_o15` or `fa_roe_o20`
Low debt	低負債	`fa_debteq_u0.5`
Insider buying	インサイダー買い	`sh_insidertrans_verypos`
Short squeeze	ショートスクイーズ	`sh_short_o20,sh_relvol_o2`
Dividend growth	増配	`fa_divgrowth_3yo10`
Deep value	ディープバリュー	`fa_pb_u1,fa_pe_u10`
Momentum	モメンタム	`ta_perf_13wup,ta_sma50_pa,ta_sma200_pa`
Defensive	ディフェンシブ	`ta_beta_u0.5` or `sec_utilities,sec_consumerdefensive`
Liquid / high volume	高出来高	`sh_avgvol_o500` or `sh_avgvol_o1000`
Pullback from high	高値からの押し目	`ta_highlow52w_10to30-bhx`
Near 52W low reversal	安値圏リバーサル	`ta_highlow52w_10to30-alx`
Fallen angel	急落後反発	`ta_highlow52w_b20to30h,ta_rsi_os40`
AI theme	AIテーマ	`--themes "artificialintelligence"`
Cybersecurity theme	サイバーセキュリティ	`--themes "cybersecurity"`
AI + Cybersecurity	AI＆サイバーセキュリティ	`--themes "artificialintelligence,cybersecurity"`
AI Cloud sub-theme	AIクラウド	`--subthemes "aicloud"`
AI Compute sub-theme	AI半導体	`--subthemes "aicompute"`
Yield 3-8% (trap excluded)	配当3-8%（トラップ除外）	`fa_div_3to8`
Mid-range P/E	適正PER帯	`fa_pe_10to20`
EV undervalued	EV割安	`fa_evebitda_u10`
Earnings next week	来週決算	`earningsdate_nextweek`
IPO recent	直近IPO	`ipodate_thismonth`
Target price above	目標株価以上	`targetprice_a20`
Recent news	最新ニュースあり	`news_date_today`
High institutional	機関保有率高	`sh_instown_o60`
Low float	浮動株少	`sh_float_u20`
Near all-time high	史上最高値付近	`ta_alltime_b0to5h`
High ATR	高ボラティリティ	`ta_averagetruerange_o1.5`

Step 3: Present Filter Selection

Before executing, present the selected filters in a table for user confirmation:

| Type | Value | Meaning |
|---|---|---|
| Theme | artificialintelligence | Artificial Intelligence |
| Sub-theme | aicloud | AI - Cloud & Infrastructure |
| Filter | cap_small | Small Cap ($300M–$2B) |
| Filter | fa_div_o3 | Dividend Yield > 3% |
| Filter | fa_pe_u20 | P/E < 20 |
| Filter | geo_usa | USA |

View: Overview (v=111)
Mode: Public / Elite (auto-detected)

Ask the user to confirm or adjust before proceeding.

Step 4: Execute Script

Run the screener script to build the URL and open Chrome:

python3 scripts/open_finviz_screener.py \
  --filters "cap_small,fa_div_o3,fa_pe_u20,geo_usa" \
  --view overview

# Theme-only screening (no --filters required)
python3 scripts/open_finviz_screener.py \
  --themes "artificialintelligence,cybersecurity" \
  --url-only

# Theme + sub-theme + filters combined
python3 scripts/open_finviz_screener.py \
  --themes "artificialintelligence" \
  --subthemes "aicloud,aicompute" \
  --filters "cap_midover" \
  --url-only

Script arguments:

--filters (optional): Comma-separated filter codes. Note: theme_* and subtheme_* tokens are not allowed here — use --themes / --subthemes instead.
--themes (optional): Comma-separated theme slugs (e.g., artificialintelligence,cybersecurity). Accepts bare slugs or theme_-prefixed values.
--subthemes (optional): Comma-separated sub-theme slugs (e.g., aicloud,aicompute). Accepts bare slugs or subtheme_-prefixed values.
--elite: Force Elite mode (auto-detected from $FINVIZ_API_KEY if not set)
--view: View type — overview, valuation, financial, technical, ownership, performance, custom
--order: Sort order (e.g., -marketcap, dividendyield, -change)
--url-only: Print URL without opening browser

At least one of --filters, --themes, or --subthemes must be provided.

Step 5: Report Results

After opening the screener, report:

The constructed URL
Elite or Public mode used
Summary of applied filters
Suggested next steps (e.g., "Sort by dividend yield", "Switch to Financial view for detailed ratios")

Usage Recipes

Real-world screening patterns distilled from repeated use. Each recipe includes a starter filter set, recommended view, and tips for iterative refinement.

Recipe 1: High-Dividend Growth Stocks (Kanchi-Style)

Goal: High yield + dividend growth + earnings growth, excluding yield traps.

--filters "fa_div_3to8,fa_sales5years_pos,fa_eps5years_pos,fa_divgrowth_5ypos,fa_payoutratio_u60,geo_usa"
--view financial

Filter Code	Purpose
`fa_div_3to8`	Yield 3-8% (caps high-yield traps)
`fa_sales5years_pos`	Positive 5Y revenue growth
`fa_eps5years_pos`	Positive 5Y EPS growth
`fa_divgrowth_5ypos`	Positive 5Y dividend growth
`fa_payoutratio_u60`	Payout ratio < 60% (sustainability)
`geo_usa`	US-listed stocks

Iterative refinement: Start broad with fa_div_o3 → review results → add fa_div_3to8 to cap yield → add fa_payoutratio_u60 to exclude traps → switch to financial view for payout and growth columns.

Recipe 2: Minervini Trend Template + VCP

Goal: Stocks in a Stage 2 uptrend with volatility contraction (VCP setup).

--filters "ta_sma50_pa,ta_sma200_pa,ta_sma200_sb50,ta_highlow52w_0to25-bhx,ta_perf_26wup,sh_avgvol_o300,cap_midover"
--view technical

Filter Code	Purpose
`ta_sma50_pa`	Price above 50-day SMA
`ta_sma200_pa`	Price above 200-day SMA
`ta_sma200_sb50`	200 SMA below 50 SMA (uptrend)
`ta_highlow52w_0to25-bhx`	Within 25% of 52W high
`ta_perf_26wup`	Positive 26-week performance
`sh_avgvol_o300`	Avg volume > 300K
`cap_midover`	Mid cap and above

VCP tightening filters (add to narrow): ta_volatility_wo3,ta_highlow20d_b0to5h,sh_relvol_u1 — low weekly volatility, near 20-day high, below-average relative volume (contraction signal).

Recipe 3: Unfairly Sold-Off Growth Stocks

Goal: Fundamentally strong companies with recent sharp declines — potential mean reversion candidates.

--filters "fa_sales5years_o5,fa_eps5years_o10,fa_roe_o15,fa_salesqoq_pos,fa_epsqoq_pos,ta_perf_13wdown,ta_highlow52w_10to30-bhx,cap_large,sh_avgvol_o200"
--view overview

Filter Code	Purpose
`fa_sales5years_o5`	5Y sales growth > 5%
`fa_eps5years_o10`	5Y EPS growth > 10%
`fa_roe_o15`	ROE > 15%
`fa_salesqoq_pos`	Positive QoQ sales growth
`fa_epsqoq_pos`	Positive QoQ EPS growth
`ta_perf_13wdown`	Negative 13-week performance
`ta_highlow52w_10to30-bhx`	10-30% below 52W high
`cap_large`	Large cap
`sh_avgvol_o200`	Avg volume > 200K

After review: Switch to valuation view to check P/E and P/S for entry attractiveness.

Recipe 4: Turnaround Stocks

Goal: Companies with previously declining earnings now showing recovery — bottom-fishing with fundamental confirmation.

--filters "fa_eps5years_neg,fa_epsqoq_pos,fa_salesqoq_pos,ta_highlow52w_b30h,ta_perf_13wup,cap_smallover,sh_avgvol_o200"
--view performance

Filter Code	Purpose
`fa_eps5years_neg`	Negative 5Y EPS growth (prior decline)
`fa_epsqoq_pos`	Positive QoQ EPS growth (recovery)
`fa_salesqoq_pos`	Positive QoQ sales growth (recovery)
`ta_highlow52w_b30h`	Within 30% of 52W high (not at bottom)
`ta_perf_13wup`	Positive 13-week performance
`cap_smallover`	Small cap and above
`sh_avgvol_o200`	Avg volume > 200K

Recipe 5: Momentum Trade Candidates

Goal: Short-term momentum leaders near 52W highs with increasing volume.

--filters "ta_sma50_pa,ta_sma200_pa,ta_highlow52w_b0to3h,ta_perf_4wup,sh_relvol_o1.5,sh_avgvol_o1000,cap_midover"
--view technical

Filter Code	Purpose
`ta_sma50_pa`	Price above 50-day SMA
`ta_sma200_pa`	Price above 200-day SMA
`ta_highlow52w_b0to3h`	Within 3% of 52W high
`ta_perf_4wup`	Positive 4-week performance
`sh_relvol_o1.5`	Relative volume > 1.5x
`sh_avgvol_o1000`	Avg volume > 1M
`cap_midover`	Mid cap and above

Recipe 6: Theme Screening (AI + Sub-theme Drill-Down)

Goal: Find mid-cap+ AI stocks focused on cloud infrastructure and compute acceleration.

--themes "artificialintelligence"
--subthemes "aicloud,aicompute"
--filters "cap_midover"
--view overview

Type	Value	Purpose
Theme	`artificialintelligence`	AI theme universe
Sub-theme	`aicloud`	Cloud & Infrastructure vertical
Sub-theme	`aicompute`	Compute & Acceleration vertical
Filter	`cap_midover`	Mid cap and above

Multi-theme example: --themes "artificialintelligence,cybersecurity" selects stocks tagged with either theme (OR logic via | grouping).

Tips: Iterative Refinement Pattern

Screening works best as a dialogue, not a one-shot query:

Start broad — use 3-4 core filters to get an initial result set
Review count — if too many results (>100), add tightening filters; if too few (<5), relax constraints
Switch views — start with overview for a quick scan, then switch to financial or valuation for deeper inspection
Layer in technicals — after confirming fundamental quality, add ta_ filters to time entries
Save and iterate — bookmark the URL, then adjust one filter at a time to understand its impact

Resources

references/finviz_screener_filters.md — Complete filter code reference with natural language keywords (includes industry code examples; full 142-code list is in the Industry Codes section)
scripts/open_finviz_screener.py — URL builder and Chrome opener

FAQ

What does the finviz-screener skill do?

How do I install finviz-screener?

Run npx skills add https://github.com/tradermonty/claude-trading-skills --skill finviz-screener in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/finviz-screener/ (personal) or .claude/skills/finviz-screener/ (project).

Which AI agents can use finviz-screener?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is finviz-screener safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains finviz-screener?

It is published by tradermonty in tradermonty/claude-trading-skills. It has 3,449 recorded installs.