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/

# Deterministic anchor date (America/New_York); the window is anchored on the
# ET calendar date, not the runner's local clock. For reproducible runs/tests.
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
  --as-of 2026-09-15 \
  --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