Earnings Trade Analyzer

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:

# 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

• 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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: xonevn-ai
• Source: xonevn-ai/xone-trading-skills
• License: MIT
• Homepage: https://xone.vn

Install and usage instructions live in the source repository linked above.