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, earnings gap scoring, or finding best recent earnings reactions.

FMP Required

Download Skill Package (.skill) View Source on GitHub

Table of Contents

1. Overview

Earnings Trade Analyzer - Post-Earnings 5-Factor Scoring

2. 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

3. 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

4. Quick Start 

# Default: 2-day lookback, top 20 results
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
  --output-dir reports/

# Custom parameters with entry quality filter
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
  --lookback-days 3 --top 10 --max-api-calls 200 \
  --apply-entry-filter --output-dir reports/

5. 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

6. Resources

References:

  • skills/earnings-trade-analyzer/references/scoring_methodology.md

Scripts:

  • skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py
  • skills/earnings-trade-analyzer/scripts/fmp_client.py
  • skills/earnings-trade-analyzer/scripts/report_generator.py
  • skills/earnings-trade-analyzer/scripts/scorer.py