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.
npx skills add BaggaT236/AI-Trading-Skills --skill earnings-trade-analyzer --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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: ```bash # 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
- When to Use
- Prerequisites
- Workflow
- Step 1: Run the Earnings Trade Analyzer
- Step 2: Review Results
- Step 3: Present Analysis
- Step 4: Provide Actionable Guidance
- Output
- Resources
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 \ With entry quality filter curl "https://financialmodelingprep.com/stable/earnings-calendar?from=YYYY-MM-DD&to=YYYY-MM-DD&apikey=$FMP_API_KEY"
What does the earnings-trade-analyzer skill do?
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 do I install it?
Run `npx skills add BaggaT236/AI-Trading-Skills --skill earnings-trade-analyzer --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BaggaT236/AI-Trading-Skills, a repository with 118 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.