earnings-trade-analyzer

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Original

English

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Translation

Chinese

Earnings Trade Analyzer - Post-Earnings 5-Factor Scoring

财报交易分析器 - 财报后5因子评分

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

使用5因子加权评分系统分析近期财报后的股票，以识别最强劲的财报反应，为潜在的动量交易提供参考。

When to Use

使用场景

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

用户询问财报后交易分析或财报缺口筛选
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
用户询问财报后积累日（PEAD）候选股票

Prerequisites

前提条件

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

FMP API密钥（设置
```
FMP_API_KEY
```
环境变量或传递
```
--api-key
```
参数）
免费层级（每日250次调用）足以满足默认筛选需求（回溯2天，前20名结果）
若需更大的回溯窗口或全面筛选，推荐使用付费层级

Workflow

工作流程

Step 1: Run the Earnings Trade Analyzer

步骤1：运行财报交易分析器

Execute the analyzer script:

bash

undefined

执行分析器脚本：

bash

undefined

Default: last 2 days of earnings, top 20 results

默认：过去2天的财报，前20名结果

python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py --output-dir reports/

Custom lookback and market cap filter

自定义回溯天数和市值筛选

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

应用入场质量筛选

python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py
--apply-entry-filter
--output-dir reports/

undefined

python3 skills/earnings-trade-analyzer/scripts/analyze_earnings_trades.py
--apply-entry-filter
--output-dir reports/

undefined

Step 2: Review Results

步骤2：查看结果

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

阅读生成的JSON和Markdown报告
查看
```
references/scoring_methodology.md
```
以了解评分解读背景
重点关注A和B级股票，寻找可操作的交易机会

Step 3: Present Analysis

步骤3：呈现分析结果

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

针对每只优质候选股票，需呈现：

综合得分和字母等级（A/B/C/D）
财报缺口大小和方向
财报前20天趋势
成交量比率（20日均值 vs 60日均值）
相对于200日均线和50日均线的位置
评分表现最弱和最强的组件

Step 4: Provide Actionable Guidance

步骤4：提供可操作的指导

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

根据等级：

A级（85分及以上）： 强劲的财报反应，伴随机构资金流入 - 考虑入场
B级（70-84分）： 良好的财报反应，值得关注 - 等待回调或确认信号
C级（55-69分）： 信号混杂 - 谨慎操作，需额外分析
D级（低于55分）： 弱势交易机会 - 避免或等待更佳条件

Output

输出内容

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

earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.json

- 结构化结果，schema_version为"1.0"

earnings_trade_analyzer_YYYY-MM-DD_HHMMSS.md

- 带表格的易读性报告

Resources

资源

```
references/scoring_methodology.md
```
- 5-factor scoring system, grade thresholds, and entry quality filter rules

```
references/scoring_methodology.md
```
- 5因子评分系统、等级阈值及入场质量筛选规则