Claude-trading-skills 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.

install

source · Clone the upstream repo

git clone https://github.com/tradermonty/claude-trading-skills

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/tradermonty/claude-trading-skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/earnings-trade-analyzer" ~/.claude/skills/tradermonty-claude-trading-skills-earnings-trade-analyzer && rm -rf "$T"

manifest: skills/earnings-trade-analyzer/SKILL.md

source content

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