stock-performance

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Stock Performance

股票绩效分析

Retrieve daily closing prices, trading volume, and performance metrics for public companies using the Octagon MCP server.

使用Octagon MCP服务器获取上市公司的每日收盘价、交易量和绩效指标。

Prerequisites

前提条件

Ensure Octagon MCP is configured in your AI agent (Cursor, Claude Desktop, Windsurf, etc.). See references/mcp-setup.md for installation instructions.

确保你的AI Agent（Cursor、Claude Desktop、Windsurf等）中已配置Octagon MCP。安装说明请参考references/mcp-setup.md。

Workflow

工作流程

1. Identify Analysis Parameters

1. 确定分析参数

Determine the following before querying:

Ticker: Stock symbol (e.g., AAPL, MSFT, GOOGL)
Time Period: Number of days or date range
Metrics (optional): Price, volume, returns

查询前请确定以下信息：

Ticker：股票代码（例如：AAPL、MSFT、GOOGL）
时间段：天数或日期范围
指标（可选）：价格、交易量、回报率

2. Execute Query via Octagon MCP

2. 通过Octagon MCP执行查询

Use the

octagon-agent

tool with a natural language prompt:

Retrieve the daily closing prices for <TICKER> over the last <N> days.

MCP Call Format:

json

{
  "server": "octagon-mcp",
  "toolName": "octagon-agent",
  "arguments": {
    "prompt": "Retrieve the daily closing prices for AAPL over the last 30 days."
  }
}

使用

octagon-agent

工具，配合自然语言提示词：

Retrieve the daily closing prices for <TICKER> over the last <N> days.

MCP调用格式：

json

{
  "server": "octagon-mcp",
  "toolName": "octagon-agent",
  "arguments": {
    "prompt": "Retrieve the daily closing prices for AAPL over the last 30 days."
  }
}

3. Expected Output

3. 预期输出

The agent returns structured price data including:

Date	Closing Price	Volume
2026-02-02	$270.01	73,677,607
2026-01-30	$259.48	92,443,408
2026-01-29	$258.28	67,253,009
...	...	...

Data Sources: octagon-stock-data-agent, octagon-web-search-agent

Agent会返回结构化的价格数据，包括：

日期	收盘价	交易量
2026-02-02	$270.01	73,677,607
2026-01-30	$259.48	92,443,408
2026-01-29	$258.28	67,253,009
...	...	...

数据来源：octagon-stock-data-agent、octagon-web-search-agent

4. Interpret Results

4. 结果解读

See references/interpreting-results.md for guidance on:

Analyzing price trends
Evaluating volume patterns
Calculating returns
Identifying support/resistance levels

关于以下内容的指导，请参考references/interpreting-results.md：

分析价格趋势
评估交易量模式
计算回报率
识别支撑/阻力位

Example Queries

查询示例

Daily Closing Prices:

Retrieve the daily closing prices for AAPL over the last 30 days.

Extended Historical Data:

Get historical stock prices for MSFT for the past 90 days.

Volume Analysis:

Retrieve daily trading volume for TSLA over the last 2 weeks.

Price Range:

What are the high and low prices for NVDA over the past month?

Multi-Stock Comparison:

Compare the stock performance of AAPL, MSFT, and GOOGL over the last 30 days.

52-Week Analysis:

What is the 52-week high and low for AMZN?

每日收盘价：

Retrieve the daily closing prices for AAPL over the last 30 days.

扩展历史数据：

Get historical stock prices for MSFT for the past 90 days.

交易量分析：

Retrieve daily trading volume for TSLA over the last 2 weeks.

价格区间：

What are the high and low prices for NVDA over the past month?

多股票对比：

Compare the stock performance of AAPL, MSFT, and GOOGL over the last 30 days.

52周分析：

What is the 52-week high and low for AMZN?

Key Metrics

核心指标

Price Metrics

价格指标

Metric	Description
Closing Price	End-of-day price
Opening Price	Start-of-day price
High	Intraday high
Low	Intraday low
Adjusted Close	Dividend/split adjusted

指标	描述
Closing Price	当日收盘价
Opening Price	当日开盘价
High	盘中最高价
Low	盘中最低价
Adjusted Close	经股息/拆股调整后的价格

Volume Metrics

交易量指标

Metric	Description
Daily Volume	Shares traded per day
Average Volume	Typical daily volume
Relative Volume	Current vs. average
Volume Trend	Direction over time

指标	描述
Daily Volume	每日成交量
Average Volume	日均成交量
Relative Volume	当前成交量与日均成交量的比值
Volume Trend	交易量趋势变化

Return Metrics

回报率指标

Metric	Calculation
Daily Return	(Close - Prior Close) / Prior Close
Period Return	(End - Start) / Start
Cumulative Return	Running return over period
Annualized Return	Period return scaled to 1 year

指标	计算方式
Daily Return	(当日收盘价 - 前一日收盘价) / 前一日收盘价
Period Return	(期末价格 - 期初价格) / 期初价格
Cumulative Return	时间段内的累计回报率
Annualized Return	时间段回报率按一年周期换算后的数值

Price Analysis Framework

价格分析框架

Trend Analysis

趋势分析

Pattern	Characteristics
Uptrend	Higher highs, higher lows
Downtrend	Lower highs, lower lows
Sideways	Range-bound movement
Breakout	Move beyond range

形态	特征
上升趋势	更高的高点，更高的低点
下降趋势	更低的高点，更低的低点
横盘整理	区间内波动
突破	突破区间范围

Volatility Assessment

波动性评估

Measure	Description
Price Range	High - Low over period
Daily Range	Average daily high-low
Standard Deviation	Price dispersion
Beta	Relative to market

衡量指标	描述
价格区间	时间段内最高价与最低价的差值
日均波幅	平均每日最高价与最低价的差值
标准差	价格离散程度
Beta系数	相对于市场的波动幅度

Support/Resistance

支撑/阻力位

Level	Description
Support	Price floor, buying interest
Resistance	Price ceiling, selling pressure
Moving Averages	Dynamic support/resistance
Round Numbers	Psychological levels

水平	描述
支撑位	价格底部，存在买入需求
阻力位	价格顶部，存在卖出压力
移动平均线	动态支撑/阻力位
整数价位	心理关口位

Volume Analysis

交易量分析

Volume Patterns

交易量形态

Pattern	Interpretation
High Volume + Price Up	Strong buying conviction
High Volume + Price Down	Strong selling pressure
Low Volume + Price Up	Weak rally, may reverse
Low Volume + Price Down	Lack of selling interest

形态	解读
高交易量 + 价格上涨	买入信心强劲
高交易量 + 价格下跌	抛售压力较大
低交易量 + 价格上涨	反弹力度较弱，可能反转
低交易量 + 价格下跌	抛售意愿不足

Volume Indicators

交易量指标

Indicator	Usage
Volume Spike	Unusual activity, potential catalyst
Volume Dry-up	Consolidation, waiting mode
Volume Trend	Confirms price trend
On-Balance Volume	Cumulative volume direction

指标	用途
交易量激增	异常交易活动，可能存在催化剂
交易量枯竭	盘整阶段，等待方向选择
交易量趋势	确认价格趋势的有效性
能量潮指标(On-Balance Volume)	累计交易量的方向变化

Time Period Analysis

时间段分析

Short-Term (1-30 Days)

短期（1-30天）

Focus	Use Case
Recent Performance	Current momentum
Trading Signals	Entry/exit timing
News Impact	Event analysis
Volatility	Risk assessment

重点	使用场景
近期表现	当前走势动量
交易信号	买卖时机选择
事件影响	新闻事件分析
波动性	风险评估

Medium-Term (1-6 Months)

中期（1-6个月）

Focus	Use Case
Trend Identification	Direction confirmation
Seasonality	Cyclical patterns
Earnings Impact	Quarterly effects
Sector Rotation	Relative performance

重点	使用场景
趋势识别	方向确认
季节性	周期性形态
财报影响	季度业绩影响
板块轮动	相对表现对比

Long-Term (1+ Years)

长期（1年以上）

Focus	Use Case
Major Trends	Secular moves
52-Week Range	Valuation context
Recovery/Decline	Major shifts
Dividend Yield	Income analysis

重点	使用场景
主要趋势	长期走势
52周区间	估值参考
复苏/下跌	重大趋势转变
股息率	收益分析

Comparative Analysis

对比分析

Peer Comparison

同行对比

Metric	What to Compare
Return	Relative performance
Volatility	Risk comparison
Correlation	Movement similarity
Volume	Liquidity comparison

指标	对比内容
回报率	相对表现
波动性	风险对比
相关性	走势相似度
交易量	流动性对比

Benchmark Comparison

基准对比

Benchmark	Usage
S&P 500	Large cap reference
Sector ETF	Industry context
Nasdaq	Tech comparison
Russell 2000	Small cap reference

基准	用途
S&P 500	大盘股参考基准
行业ETF	行业背景参考
Nasdaq	科技股对比基准
Russell 2000	小盘股参考基准

Analysis Tips

分析技巧

Consider context: Market conditions affect individual stocks.
Adjust for events: Earnings, dividends, splits affect prices.
Use volume confirmation: Price moves need volume support.
Multiple timeframes: Longer and shorter perspectives.
Compare to peers: Relative performance matters.
Watch key levels: Round numbers, 52-week highs/lows.

考虑市场环境：整体市场行情会影响个股表现。
调整事件影响：财报、股息、拆股等事件会影响价格。
用交易量确认趋势：价格变动需要交易量的支撑。
结合多时间段分析：兼顾长期和短期视角。
与同行对比：相对表现至关重要。
关注关键价位：整数关口、52周高点/低点。

Use Cases

使用场景

Trading analysis: Entry and exit timing
Performance tracking: Portfolio monitoring
Event analysis: Earnings, news impact
Volatility assessment: Risk evaluation
Peer comparison: Relative performance

交易分析：买卖时机选择
绩效跟踪：投资组合监控
事件分析：财报、新闻的影响评估
波动性评估：风险分析
同行对比：相对表现分析