kavi-lin/earnings-trade-analyzer
skills/earnings-trade-analyzer/SKILL.md
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.
development
Updated Apr 27, 2026
$ install --global
skillsauthnpx skillsauth add kavi-lin/stock earnings-trade-analyzerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- 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_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/
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
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