BaggaT236
Indexado en Registry
earnings-trade-analyzer
Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening,
Resumen
Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening, earnings gap scoring, or finding best recent earnings reactions.
Leer documentación completa
Documentación de origen, no instrucciones para este sitio. Revisa los permisos antes de ejecutar comandos.
Earnings Trade Analyzer - Post-Earnings 5-Factor Scoring
Analyze recent post-earnings stocks using a 5-factor weighted scoring system to identify the strongest earnings reactions for potential momentum trades.
When to Use
- User asks for post-earnings trade analysis or earnings gap screening
- User wants to find the best recent earnings reactions
- User requests earnings momentum scoring or grading
- User asks about post-earnings accumulation day (PEAD) candidates
Prerequisites
- FMP API key (set
FMP_API_KEYenvironment variable or pass--api-key) - Free tier (250 calls/day) is sufficient for default screening (lookback 2 days, top 20)
- Paid tier recommended for larger lookback windows or full screening
Workflow
Step 1: Run the Earnings Trade Analyzer
Execute the analyzer script:
# Default: last 2 days of earnings, top 20 results
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py --output-dir reports/
# Custom lookback and market cap filter
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
--lookback-days 5 \
--min-market-cap 1000000000 \
--top 30 \
--output-dir reports/
# With entry quality filter
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
--apply-entry-filter \
--output-dir reports/
Degraded endpoint / budget fallback for scheduled reviews
If the analyzer reports a 404, an implausible empty earnings calendar, or exhausts its API-call budget before producing scored candidates during a scheduled after-close/pre-market run, do not report "no earnings reactions" immediately.
- First retry once with a narrower liquid-universe configuration so the full 5-factor scorer has a chance to complete, for example:
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
--lookback-days 2 \
--min-market-cap 5000000000 \
--top 20 \
--max-api-calls 600 \
--output-dir reports/<routine-date>
- If the scored run still returns no candidates or cannot complete, verify the same range through the stable endpoint used by the compatibility shim and clearly label the result as an ungraded fallback:
curl "https://financialmodelingprep.com/stable/earnings-calendar?from=YYYY-MM-DD&to=YYYY-MM-DD&apikey=$FMP_API_KEY"
Then optionally enrich returned US tickers through the analyzer's stable-first FMP client or per-symbol /stable/quote?symbol=<ticker> calls to rank by same-day changesPercentage, market cap, and liquidity. Use legacy /api/v3 quote calls only as a legacy-key fallback after stable has failed. Present these as preliminary / ungraded reactions because the 5-factor scorer did not run; do not assign A/B/C/D grades from the fallback alone.
No-candidate output pitfall: The analyzer may print Candidates after filtering: 0 / No candidates found matching criteria. and exit successfully without writing an earnings_trade_analyzer_*.json file. In that case, do not try to run PEAD Mode B from a nonexistent candidate file. Say explicitly that no scored analyzer JSON was produced, run the endpoint/quote enrichment fallback above if the routine needs an earnings section, and label any names as manual-review only.
Step 2: Review Results
- Read the generated JSON and Markdown reports
- Load
references/scoring_methodology.mdfor scoring interpretation context - Focus on Grade A and B stocks for actionable setups
Step 3: Present Analysis
For each top candidate, present:
- Composite score and letter grade (A/B/C/D)
- Earnings gap size and direction
- Pre-earnings 20-day trend
- Volume ratio (20-day vs 60-day average)
- Position relative to 200-day and 50-day moving averages
- Weakest and strongest scoring components
Step 4: Provide Actionable Guidance
Based on grades:
- Grade A (85+): Strong earnings reaction with institutional accumulation - consider entry
- Grade B (70-84): Good earnings reaction worth monitoring - wait for pullback or confirmation
- Grade C (55-69): Mixed signals - use caution, additional analysis needed
- Grade D (<55): Weak setup - avoid or wait for better conditions
Output
earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.json- Structured results with schema_version "1.0"earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.md- Human-readable report with tables
Resources
references/scoring_methodology.md- 5-factor scoring system, grade thresholds, and entry quality filter rules
Metadatos del archivo
name: earnings-trade-analyzer description: Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening, earnings gap scoring, or finding best recent earnings reactions.
Ver texto original
--- name: earnings-trade-analyzer description: Analyze recent post-earnings stocks using a 5-factor scoring system (Gap Size, Pre-Earnings Trend, Volume Trend, MA200 Position, MA50 Position). Scores each stock 0-100 and assigns A/B/C/D grades. Use when user asks about earnings trade analysis, post-earnings momentum screening, earnings gap scoring, or finding best recent earnings reactions. --- # Earnings Trade Analyzer - Post-Earnings 5-Factor Scoring Analyze recent post-earnings stocks using a 5-factor weighted scoring system to identify the strongest earnings reactions for potential momentum trades. ## When to Use - User asks for post-earnings trade analysis or earnings gap screening - User wants to find the best recent earnings reactions - User requests earnings momentum scoring or grading - User asks about post-earnings accumulation day (PEAD) candidates ## Prerequisites - FMP API key (set `FMP_API_KEY` environment variable or pass `--api-key`) - Free tier (250 calls/day) is sufficient for default screening (lookback 2 days, top 20) - Paid tier recommended for larger lookback windows or full screening ## Workflow ### Step 1: Run the Earnings Trade Analyzer Execute the analyzer script: ```bash # Default: last 2 days of earnings, top 20 results python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py --output-dir reports/ # Custom lookback and market cap filter python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \ --lookback-days 5 \ --min-market-cap 1000000000 \ --top 30 \ --output-dir reports/ # With entry quality filter python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \ --apply-entry-filter \ --output-dir reports/ ``` #### Degraded endpoint / budget fallback for scheduled reviews If the analyzer reports a 404, an implausible empty earnings calendar, or exhausts its API-call budget before producing scored candidates during a scheduled after-close/pre-market run, do not report "no earnings reactions" immediately. 1. First retry once with a narrower liquid-universe configuration so the full 5-factor scorer has a chance to complete, for example: ```bash python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \ --lookback-days 2 \ --min-market-cap 5000000000 \ --top 20 \ --max-api-calls 600 \ --output-dir reports/<routine-date> ``` 2. If the scored run still returns no candidates or cannot complete, verify the same range through the stable endpoint used by the compatibility shim and clearly label the result as an ungraded fallback: ```bash curl "https://financialmodelingprep.com/stable/earnings-calendar?from=YYYY-MM-DD&to=YYYY-MM-DD&apikey=$FMP_API_KEY" ``` Then optionally enrich returned US tickers through the analyzer's stable-first FMP client or per-symbol `/stable/quote?symbol=<ticker>` calls to rank by same-day `changesPercentage`, market cap, and liquidity. Use legacy `/api/v3` quote calls only as a legacy-key fallback after stable has failed. Present these as **preliminary / ungraded reactions** because the 5-factor scorer did not run; do not assign A/B/C/D grades from the fallback alone. **No-candidate output pitfall:** The analyzer may print `Candidates after filtering: 0` / `No candidates found matching criteria.` and exit successfully without writing an `earnings_trade_analyzer_*.json` file. In that case, do not try to run PEAD Mode B from a nonexistent candidate file. Say explicitly that no scored analyzer JSON was produced, run the endpoint/quote enrichment fallback above if the routine needs an earnings section, and label any names as manual-review only. ### Step 2: Review Results 1. Read the generated JSON and Markdown reports 2. Load `references/scoring_methodology.md` for scoring interpretation context 3. Focus on Grade A and B stocks for actionable setups ### Step 3: Present Analysis For each top candidate, present: - Composite score and letter grade (A/B/C/D) - Earnings gap size and direction - Pre-earnings 20-day trend - Volume ratio (20-day vs 60-day average) - Position relative to 200-day and 50-day moving averages - Weakest and strongest scoring components ### Step 4: Provide Actionable Guidance Based on grades: - **Grade A (85+):** Strong earnings reaction with institutional accumulation - consider entry - **Grade B (70-84):** Good earnings reaction worth monitoring - wait for pullback or confirmation - **Grade C (55-69):** Mixed signals - use caution, additional analysis needed - **Grade D (<55):** Weak setup - avoid or wait for better conditions ## Output - `earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.json` - Structured results with schema_version "1.0" - `earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.md` - Human-readable report with tables ## Resources - `references/scoring_methodology.md` - 5-factor scoring system, grade thresholds, and entry quality filter rules
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- Precio sin confirmar
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- Requisitos sin confirmar. Consulta los costes del agente, API y servicios en la fuente.
- Licencia
- MIT
- Precio sin confirmar
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Fuente del skill registrada
La ruta de instrucciones está registrada. No implica pruebas de ejecución, seguridad ni compatibilidad.
Revisar antes de instalar: Evitar instalación automática
Licencia: MIT
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- No critical security issues found. The skill uses an external API key (FMP_API_KEY) but does not expose it insecurely; it is passed via environment variable or CLI argument.
- The skill relies on an external service (Financial Modeling Prep) which may have rate limits, but the documentation clearly states prerequisites and fallback procedures.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
Las herramientas son indicios de metadatos, no compatibilidad probada. Los prompts son sugerencias.
Empieza con una tarea pequeña
- 1Lee la fuente y confirma entradas, resultados, dependencias y permisos.
- 2Pide un plan al agente. Aprueba la configuración y los costes antes de probar en un entorno aislado.
- 3Comprueba resultados y archivos modificados. Informa solo de lo ejecutado y conserva la revisión de la fuente.
Consulta dependencias, claves API y costes externos en la fuente. Un repositorio público no implica servicios gratuitos.
Fuente y notas de uso
Indexado
Los metadatos y revisiones son orientativos. Popularidad, descubrimiento y ejecución correcta son hechos distintos.
- Repositorio fuente
- BaggaT236/AI-Trading-Skills
- Licencia
- MIT
- Versión
- 1.0.0
- Último push de GitHub
- 14 sept 2026
- Registro actualizado
- 4 oct 2026
- Ruta de instrucciones
- skills/earnings-trade-analyzer/SKILL.md @ 8d77f8949c76
Versión declarada en el registro; consulta las versiones de la fuente.
Calidad
68/100
Prometedor
Confianza
59/100
Do not auto-install
Auditoría
75/100
Requiere revisión
- Dependency or permission surface needs review
- Permission surface may require sandboxing
- Financial research output is not financial advice; require human review before any live investment decision
- No critical security issues found. The skill uses an external API key (FMP_API_KEY) but does not expose it insecurely; it is passed via environment variable or CLI argument.
- The skill relies on an external service (Financial Modeling Prep) which may have rate limits, but the documentation clearly states prerequisites and fallback procedures.
- Financial research output is not financial advice; require human review before any live investment decision.
- Quality score needs review
- Permission surface needs review: secrets or environment access, shell or command execution
- Dependency/runtime risk: command execution surface, credential or environment access
- Permission surface: secrets or environment access, shell or command execution
- Verified installs
- —
- Resultados
- —
Copiar no es instalar. Los recuentos requieren un informe de instalación correcta, no garantizan calidad general.
Acceso para agentes
La API Registry expone señales de decisión, confianza, auditoría, casos de uso e instalación sin raspar la interfaz.
Más detalles
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"payload_template": {
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"skill_slug": "baggat236-earnings-trade-analyzer",
"task": "Use earnings-trade-analyzer in an agent workflow",
"agent": "codex",
"outcome": "success",
"install_used": true,
"risk_blocked": false,
"setup_required": false,
"task_success": true,
"output_quality": 4,
"error_type": null,
"human_review_required": false,
"workspace": "sandbox",
"time_to_useful_ms": 120000,
"notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
}
},
"endpoints": {
"web": "https://www.openagentskill.com/skills/baggat236-earnings-trade-analyzer",
"api": "https://www.openagentskill.com/api/agent/skills/baggat236-earnings-trade-analyzer",
"audit": "https://www.openagentskill.com/skills/baggat236-earnings-trade-analyzer/audit",
"eval": "https://www.openagentskill.com/api/agent/evals?slug=baggat236-earnings-trade-analyzer&task=Use%20earnings-trade-analyzer%20in%20an%20agent%20workflow&max_risk=medium",
"resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20earnings-trade-analyzer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
"receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20earnings-trade-analyzer%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
"install": "https://www.openagentskill.com/api/skills/baggat236-earnings-trade-analyzer/install",
"manifest": "https://www.openagentskill.com/api/registry/manifest/baggat236-earnings-trade-analyzer"
}
}Para el creador
Fuente de la ficha
Indexado por Registry
Esta ficha se indexó desde fuentes públicas y no está marcada como oficial hasta que se apruebe una reclamación de mantenedor.
- Creador
- BaggaT236
- Indexado por
- Índice comunitario de OpenAgentSkill
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[](https://www.openagentskill.com/skills/baggat236-earnings-trade-analyzer?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[](https://www.openagentskill.com/skills/baggat236-earnings-trade-analyzer/audit)
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