stockbee-exhaustion-hammer-screener

Q: How do I install stockbee-exhaustion-hammer-screener?

Run npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-exhaustion-hammer-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/stockbee-exhaustion-hammer-screener/ (personal) or .claude/skills/stockbee-exhaustion-hammer-screener/ (project).

by tradermontyBusiness & Finance

Screen US stocks for Stockbee-style selling-exhaustion hammer setups using prior momentum, pullback depth, undercut/reclaim, long lower-wick geometry, close-location, volume confirmation, quality/liquidity gates, and risk-distance scoring. Use when the user asks for Stockbee, Pradeep Bonde, exhaustion setup, selling exhaustion, hammer reversal, undercut reclaim, near-close reversal candidates, or pullback entries in high-quality funds-owned stocks.

Installs: 2.6K
Last 24h: +19
Trending rank: #6041
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-exhaustion-hammer-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 stockbee-exhaustion-hammer-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.

Stockbee Exhaustion Hammer Screener

Screen US equities for Stockbee-style selling-exhaustion hammer candidates. The skill is a candidate-generation and setup-quality workflow, not a signal service or an auto-execution system.

When to Use

User asks for Stockbee / Pradeep Bonde style exhaustion setup screening
User wants near-close hammer / long lower-wick reversal candidates
User wants to scan strong, liquid stocks that pulled back and may be seeing selling exhaustion
User wants undercut/reclaim candidates before the close or after the close
User provides a symbol list, universe file, or historical / provisional OHLCV JSON for screening
User wants candidate outputs to feed into technical-analyst, position-sizer, trader-memory-core, or stockbee-setup-fluency-trainer

Prerequisites

FMP API key for live universe and historical OHLCV screening:
```
export FMP_API_KEY=your_api_key_here
```
Optional no-API path: provide --prices-json containing daily OHLCV bars by symbol. For the intended near-close use case, the latest bar should be a provisional current-day bar captured near the close.
Optional --profiles-json can add quality metadata such as marketCap, mutualFundHolders, institutionalHolders, or institutionalOwnershipPct.
Run only after the market-regime workflow allows new swing risk, or mark output as manual-review-only.

Workflow

Step 1: Choose Input Mode

Use one of three modes:

Mode A: FMP universe scan

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --fmp-universe \
  --max-symbols 300 \
  --market-gate allowed \
  --output-dir reports/

Mode B: Explicit symbols

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --symbols APP ENPH NVDA TSLA \
  --market-gate allowed \
  --output-dir reports/

Mode C: Offline / near-close OHLCV JSON

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --prices-json data/near_close_daily_ohlcv.json \
  --profiles-json data/quality_profiles.json \
  --market-gate allowed \
  --output-dir reports/

For a best-effort FMP near-close run, use quote override. This costs one additional quote call per symbol and depends on provider freshness:

python3 skills/stockbee-exhaustion-hammer-screener/scripts/screen_exhaustion_hammer.py \
  --fmp-universe \
  --use-quote-latest \
  --max-api-calls 700 \
  --market-gate allowed \
  --output-dir reports/

Step 2: Run the Screening Pass

The script detects these setup families:

Selling exhaustion hammer: long lower wick, small body, strong close-location, and recovery from the day low
Undercut/reclaim hammer: current low undercuts the prior short-term low and the near-close price reclaims that level
Prior momentum pullback: recent high formed within the configured lookback, followed by a controlled pullback rather than a long-term downtrend
High-quality / liquid context: price, volume, 20-day average dollar volume, market-cap metadata, and optional holder metadata

It then scores setup quality using:

Quality / liquidity
Prior momentum
Pullback and selling-exhaustion context
Hammer candle geometry
Risk distance to the day low plus buffer
Market gate alignment

Step 3: Review Output

Read the generated JSON and Markdown reports. For each candidate, present:

Trigger type and all matched tags
Pullback depth from recent high and days since that high
Undercut/reclaim status and short-term prior low
Hammer geometry: lower wick, body, upper wick, close location, recovery from low
Volume ratios, average dollar volume, and quality metadata
Entry reference, stop reference, and risk percentage to stop
Setup score, rating, state, and reject reasons
Suggested downstream action

Step 4: Send Survivors to Trade Planning

Use the output conservatively:

A / A- candidates: validate chart manually, check earnings/news risk, then send to position-sizer
B candidates: manual review or next-day hammer-high confirmation
Watch candidates: keep on watchlist / model book; wait for follow-through or tighter risk
Rejected candidates: retain for post-analysis, not for execution

Output

stockbee_exhaustion_hammer_YYYY-MM-DD_HHMMSS.json - Structured candidate list, metadata, thresholds, score components, and rejects
stockbee_exhaustion_hammer_YYYY-MM-DD_HHMMSS.md - Human-readable report grouped by rating/state

Resources

references/exhaustion_hammer_methodology.md - Stockbee-style method summary and implementation boundaries
references/scoring_system.md - Component weights, state thresholds, and failure filters
references/near_close_operations.md - Near-close operational checklist and scheduling notes

Shared FMP client

Live FMP access uses the bundled generated scripts/fmp_client.py, loaded only when an FMP run starts, so offline modes still work without requests. CLI key/environment precedence, the API-call budget and successful output shapes are preserved. Requests use stable endpoints only; a denied, empty or failed stable request does not fall back to retired v3 URLs. The shared client masks raw and URL-encoded key echoes, retries malformed JSON once, and retries a 429 at most once, after a 60-second wait, before stopping on a repeated rate limit.

The quote override reads stable /quote; its default bar date is the America/New_York calendar date, matching the history window.

FAQ

What does the stockbee-exhaustion-hammer-screener skill do?

How do I install stockbee-exhaustion-hammer-screener?

Run npx skills add https://github.com/tradermonty/claude-trading-skills --skill stockbee-exhaustion-hammer-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/stockbee-exhaustion-hammer-screener/ (personal) or .claude/skills/stockbee-exhaustion-hammer-screener/ (project).

Which AI agents can use stockbee-exhaustion-hammer-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 stockbee-exhaustion-hammer-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 stockbee-exhaustion-hammer-screener?

It is published by tradermonty in tradermonty/claude-trading-skills. It has 2,631 recorded installs.