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.
How this skill is triggered — by the user, by Claude, or both
Slash command
/trading-earnings-timing:earnings-trade-analyzerThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
Analyze recent post-earnings stocks using a 5-factor weighted scoring system to identify the strongest earnings reactions for potential momentum trades.
Analyze recent post-earnings stocks using a 5-factor weighted scoring system to identify the strongest earnings reactions for potential momentum trades.
FMP_API_KEY environment variable or pass --api-key)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/
references/scoring_methodology.md for scoring interpretation contextFor each top candidate, present:
Based on grades:
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 tablesreferences/scoring_methodology.md - 5-factor scoring system, grade thresholds, and entry quality filter rulesnpx claudepluginhub pasie15/claude-trading-skills-marketplace --plugin trading-earnings-timingCreates, edits, and optimizes skills for Claude Code, including drafting, evaluating with test prompts, iterating on performance, and improving skill descriptions for better triggering accuracy.