Skill

earnings-trade-analyzer

Analyzes recent post-earnings stocks via 5-factor scoring (Gap Size, Pre-Earnings Trend, Volume Trend, MA200/50 Position). Scores 0-100 with A-D grades using Python CLI and FMP API for momentum trade screening.

Python

Bash

cli-tools

automation

Install

npx claudepluginhub joshuarweaver/cascade-business-ops --plugin tradermonty-claude-trading-skills

Tool Access

This skill uses the workspace's default tool permissions.

Preview

Analyze recent post-earnings stocks using a 5-factor weighted scoring system to identify the strongest earnings reactions for potential momentum trades.

Supporting Assets

references/scoring_methodology.mdscripts/analyze_earnings_trades.pyscripts/calculators/__init__.pyscripts/calculators/gap_size_calculator.pyscripts/calculators/ma200_calculator.pyscripts/calculators/ma50_calculator.pyscripts/calculators/pre_earnings_trend_calculator.pyscripts/calculators/volume_trend_calculator.pyscripts/fmp_client.pyscripts/report_generator.pyscripts/scorer.pyscripts/tests/conftest.pyscripts/tests/test_earnings_trade_analyzer.py

SKILL.md

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Stats

Stars1056

Forks235

Last CommitFeb 21, 2026

Actions

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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

Read the generated JSON and Markdown reports
Load references/scoring_methodology.md for 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