earnings-trade-analyzer

Files (16)

• README.md
• SKILL.md
• scoring_methodology.md · references/
• analyze_earnings_trades.py · scripts/
• __init__.py · scripts/calculators/
• gap_size_calculator.py · scripts/calculators/
• ma200_calculator.py · scripts/calculators/
• ma50_calculator.py · scripts/calculators/
• pre_earnings_trend_calculator.py · scripts/calculators/
• volume_trend_calculator.py · scripts/calculators/
• fmp_client.py · scripts/
• report_generator.py · scripts/
• scorer.py · scripts/
• conftest.py · scripts/tests/
• test_earnings_trade_analyzer.py · scripts/tests/
• test_fmp_client_historical.py · scripts/tests/

SKILL.md (first lines)

# 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 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
  --apply-entry-filter \
  --output-dir reports/
```

### Step 2: Review Results

1. Read the generated JSON and Markdown reports
2. Load `references/scoring_methodology.md` for scoring interpretation context
3. 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

From mphinance/alpha-skills, MIT.

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