technical-analysis

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English

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Translation

Chinese

Technical Analysis

技术分析

Compute technical indicators using pandas-ta. Supports multi-symbol analysis and earnings data.

使用pandas-ta计算技术指标，支持多标的分析和收益数据处理。

Instructions

操作说明

Note: If
uv
is not installed or
pyproject.toml
is not found, replace
uv run python
with
python
in all commands below.

bash

uv run python scripts/technicals.py SYMBOL [--period PERIOD] [--indicators INDICATORS] [--earnings]

注意： 若未安装
uv
或未找到
pyproject.toml
，请将以下所有命令中的
uv run python
替换为
python
。

bash

uv run python scripts/technicals.py SYMBOL [--period PERIOD] [--indicators INDICATORS] [--earnings]

Arguments

参数说明

```
SYMBOL
```
- Ticker symbol or comma-separated list (e.g.,
```
AAPL
```
or
```
AAPL,MSFT,GOOGL
```
)
```
--period
```
- Historical period: 1mo, 3mo, 6mo, 1y (default: 3mo)
```
--indicators
```
- Comma-separated list: rsi,macd,bb,sma,ema,atr,adx (default: all)
```
--earnings
```
- Include earnings data (upcoming date + history)

```
SYMBOL
```
- 股票代码或逗号分隔的代码列表（例如：
```
AAPL
```
或
```
AAPL,MSFT,GOOGL
```
）
```
--period
```
- 历史数据周期：1mo、3mo、6mo、1y（默认值：3mo）
```
--indicators
```
- 逗号分隔的指标列表：rsi、macd、bb、sma、ema、atr、adx（默认值：全部）
```
--earnings
```
- 包含收益数据（即将到来的日期+历史数据）

Output

输出结果

Single symbol returns:

```
price
```
- Current price and recent change
```
indicators
```
- Computed values for each indicator
```
risk_metrics
```
- Volatility (annualized %) and Sharpe ratio
```
signals
```
- Buy/sell signals based on indicator levels
```
earnings
```
- Upcoming date and EPS history (if
```
--earnings
```
)

Multiple symbols returns:

```
results
```
- Array of individual symbol results

单标的返回内容：

```
price
```
- 当前价格及近期涨跌幅
```
indicators
```
- 各指标的计算值
```
risk_metrics
```
- 波动率（年化百分比）和夏普比率
```
signals
```
- 基于指标水平生成的买卖信号
```
earnings
```
- 即将到来的收益日期及EPS历史数据（若使用
```
--earnings
```
参数）

多标的返回内容：

```
results
```
- 单个标的结果的数组

Interpretation

指标解读

RSI > 70 = overbought, RSI < 30 = oversold
MACD crossover = momentum shift
Price near Bollinger Band = potential reversal
Golden cross (SMA20 > SMA50) = bullish
ADX > 25 = strong trend
Sharpe ratio > 1 = good risk-adjusted returns, > 2 = excellent
Volatility (annualized) = standard deviation of returns scaled to annual basis

RSI > 70 = 超买，RSI < 30 = 超卖
MACD交叉 = 动量转变
价格接近布林带 = 潜在反转信号
黄金交叉（SMA20 > SMA50）= 看涨信号
ADX > 25 = 趋势强劲
夏普比率 > 1 = 风险调整后收益良好，>2 = 收益极佳
波动率（年化）= 收益标准差按年度缩放后的数值

Examples

示例

bash

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bash

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Single symbol with all indicators

单标的计算所有指标

uv run python scripts/technicals.py AAPL

Multiple symbols

多标的分析

uv run python scripts/technicals.py AAPL,MSFT,GOOGL

With earnings data

包含收益数据

uv run python scripts/technicals.py NVDA --earnings

Specific indicators only

仅计算指定指标

uv run python scripts/technicals.py TSLA --indicators rsi,macd

---

uv run python scripts/technicals.py TSLA --indicators rsi,macd

---

Correlation Analysis

相关性分析

Compute price correlation matrix between multiple symbols for diversification analysis.

计算多个标的之间的价格相关矩阵，用于多元化投资分析。

Instructions

操作说明

bash

uv run python scripts/correlation.py SYMBOLS [--period PERIOD]

bash

uv run python scripts/correlation.py SYMBOLS [--period PERIOD]

Arguments

参数说明

```
SYMBOLS
```
- Comma-separated ticker symbols (minimum 2)
```
--period
```
- Historical period: 1mo, 3mo, 6mo, 1y (default: 3mo)

```
SYMBOLS
```
- 逗号分隔的股票代码（至少2个）
```
--period
```
- 历史数据周期：1mo、3mo、6mo、1y（默认值：3mo）

Output

输出结果

```
symbols
```
- List of symbols analyzed
```
period
```
- Time period used
```
correlation_matrix
```
- Nested dict with correlation values between all pairs

```
symbols
```
- 分析的标的列表
```
period
```
- 使用的时间周期
```
correlation_matrix
```
- 包含所有标的对相关值的嵌套字典

Interpretation

结果解读

Correlation near 1.0 = highly correlated (move together)
Correlation near -1.0 = negatively correlated (move opposite)
Correlation near 0 = uncorrelated (independent movement)
For diversification, prefer low/negative correlations

相关性接近1.0 = 高度相关（同涨同跌）
相关性接近-1.0 = 负相关（反向波动）
相关性接近0 = 不相关（独立波动）
多元化投资偏好低/负相关性的标的

Examples

示例

bash

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bash

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Portfolio correlation

投资组合相关性分析

uv run python scripts/correlation.py AAPL,MSFT,GOOGL,AMZN

Sector comparison

板块对比分析

uv run python scripts/correlation.py XLF,XLK,XLE,XLV --period 6mo

Check hedge effectiveness

对冲有效性验证

uv run python scripts/correlation.py SPY,GLD,TLT

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uv run python scripts/correlation.py SPY,GLD,TLT

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Dependencies

依赖库

```
numpy
```
```
pandas
```
```
pandas-ta
```
```
yfinance
```

```
numpy
```
```
pandas
```
```
pandas-ta
```
```
yfinance
```