earnings-trade-analyzer
Evaluate recent earnings reactions by scoring stocks across gap magnitude, pre-earnings momentum, volume trends, and moving average positioning. The skill assigns composite scores and letter grades (A–D) to rank candidates for potential swing trades, with detailed breakdowns of each factor's contribution to the overall setup quality.
Earnings Trade Analyzer scores recent post-earnings stocks on a 5-factor system to identify momentum trade candidates.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-07-26
Earnings Trade Analyzer scores recent post-earnings stocks on a 5-factor system to identify momentum trade candidates. Evaluate recent earnings reactions by scoring stocks across gap magnitude, pre-earnings momentum, volume trends, and moving average positioning. The skill assigns composite scores and letter grades (A–D) to rank candidates for potential swing trades, with detailed breakdowns of each factor's contribution to the overall setup quality.
Use it when
- earnings-trade-analyzer analyzes recent post-earnings stock reactions using a 5-factor scoring methodology.
- Yes.
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Read SKILL.md below before installing (17 files). Open directory: indexed for reading, not audited.
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tradermonty/claude-trading-skills/earnings-trade-analyzer · repository language: Python
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
What is earnings-trade-analyzer and how does it work?
earnings-trade-analyzer evaluates recent earnings reactions by scoring stocks across gap magnitude, pre-earnings momentum, volume trends, and moving average positioning. The skill assigns composite scores and letter grades (A–D) to rank candidates for potential swing trades, with detailed breakdowns of each factor's contribution to the overall setup quality.
How does earnings-trade-analyzer use a 5-factor scoring system?
earnings-trade-analyzer analyzes recent post-earnings stock reactions using a 5-factor scoring methodology. This approach evaluates gap size, momentum strength, volume confirmation, technical positioning, and institutional accumulation signals to produce a composite score that helps traders identify high-probability setups.
Can earnings-trade-analyzer screen for best earnings reactions?
Yes. earnings-trade-analyzer screens for the best earnings gap trades and momentum candidates by ranking post-earnings stocks based on reaction quality and technical confirmation. It grades and ranks candidates by institutional accumulation patterns and identifies actionable setups with strong technical signals.
What post earnings stock screening does earnings-trade-analyzer provide?
earnings-trade-analyzer performs post earnings stock screening to find PEAD (post-earnings announcement drift) trading opportunities and momentum trades. It identifies recent earnings stock performance patterns and surfaces candidates showing strong accumulation activity and favorable technical setups for swing trading.
How does earnings-trade-analyzer grade post-earnings stocks?
earnings-trade-analyzer assigns letter grades (A–D) to rank post-earnings stocks by institutional accumulation and overall setup quality. Each grade reflects the composite score derived from gap magnitude, momentum, volume trends, moving average positioning, and technical confirmation factors.
What earnings trade setup finder features does this skill offer?
earnings-trade-analyzer functions as an earnings trade setup finder by identifying actionable earnings reaction setups with technical confirmation. It combines gap analysis, momentum scoring, and accumulation grading to surface the most promising post-earnings candidates for traders seeking swing trade opportunities.
SKILL.md
Rendered from the published skill. Quoted content, verbatim.
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_KEYenvironment 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
(truncated - see the full file via the links below)
File tree — 15 files
skills/earnings-trade-analyzer/SKILL.md
skills/earnings-trade-analyzer/references/scoring_methodology.md
skills/earnings-trade-analyzer/scripts/_fmp_compat.py
skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py
skills/earnings-trade-analyzer/scripts/calculators/__init__.py
skills/earnings-trade-analyzer/scripts/calculators/gap_size_calculator.py
skills/earnings-trade-analyzer/scripts/calculators/ma200_calculator.py
skills/earnings-trade-analyzer/scripts/calculators/ma50_calculator.py
skills/earnings-trade-analyzer/scripts/calculators/pre_earnings_trend_calculator.py
skills/earnings-trade-analyzer/scripts/calculators/volume_trend_calculator.py
skills/earnings-trade-analyzer/scripts/fmp_client.py
skills/earnings-trade-analyzer/scripts/report_generator.py
skills/earnings-trade-analyzer/scripts/scorer.py
skills/earnings-trade-analyzer/scripts/tests/conftest.py
skills/earnings-trade-analyzer/scripts/tests/test_earnings_trade_analyzer.py
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