财报交易分析器 - 五因子评分系统，精准识别财报后动量交易机会

earnings-trade-analyzer by tradermonty/claude-trading-skills

149 周安装量

667 GitHub Stars

GitHub

安装命令

npx skills add https://github.com/tradermonty/claude-trading-skills --skill earnings-trade-analyzer

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🇨🇳中文介绍

财报交易分析器 - 财报后五因子评分系统

使用五因子加权评分系统分析近期发布财报的股票，以识别最强劲的财报反应，寻找潜在动量交易机会。

使用时机

用户要求进行财报后交易分析或财报跳空缺口筛选
用户希望找到近期最佳的财报反应
用户请求财报动量评分或评级
用户询问财报后累积日（PEAD）候选股

先决条件

FMP API 密钥（设置 FMP_API_KEY 环境变量或传递 --api-key 参数）
免费套餐（250 次调用/天）足以满足默认筛选需求（回溯 2 天，前 20 名）
如需更大的回溯窗口或全面筛选，建议使用付费套餐

工作流程

步骤 1：运行财报交易分析器

执行分析器脚本：

# 默认：最近 2 天的财报数据，前 20 名结果
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py --output-dir reports/

# 自定义回溯期和市值过滤器
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
  --lookback-days 5 \
  --min-market-cap 1000000000 \
  --top 30 \
  --output-dir reports/

# 应用入场质量过滤器
python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py \
  --apply-entry-filter \
  --output-dir reports/

步骤 2：查看结果

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🇺🇸English

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

Weekly Installs

Repository

tradermonty/cla…g-skills

GitHub Stars

394

First Seen

Feb 23, 2026

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