SkillsAdd

mphinance/alpha-skills

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).

First seen May 9, 2026

Installation

$ npx skills add mphinance/alpha-skills --skill earnings-trade-analyzer
View on skills.sh ↗ GitHub repository ↗

Summary

  • 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.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Also listed on

Alternate registries and mirrors of this skill.

Repository health

Stars 21
License LICENSE
Default branch main
Open issues 0
Status Active

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 2,990 B
  • docs README.md 2,990 B
  • docs SUMMARY.md 376 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 84 installs

SKILL.md

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 FMPAPIKEY 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:

# 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/

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

  • earningstradeanalyzerYYYY-MM-DDHHMMSS.json - Structured results with schema_version "1.0"
  • earningstradeanalyzerYYYY-MM-DDHHMMSS.md - Human-readable report with tables

Resources

  • references/scoring_methodology.md - 5-factor scoring system, grade thresholds, and entry quality filter rules