diff --git a/zhangfei-technical/research/task-20260417-atr-indicator/atr_indicator.py b/zhangfei-technical/research/task-20260417-atr-indicator/atr_indicator.py
new file mode 100644
index 000000000..a0ab14c29
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+++ b/zhangfei-technical/research/task-20260417-atr-indicator/atr_indicator.py
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+"""
+ATR (Average True Range) 指标计算工具
+支持SMA和EMA两种计算方式
+
+Author: 张飞 翼德
+Date: 2026-04-17
+"""
+
+import numpy as np
+import pandas as pd
+from typing import Optional, Union
+
+
+class ATRIndicator:
+    """
+    ATR (Average True Range) 指标计算类
+    
+    支持两种计算方式：
+    - SMA: 简单移动平均
+    - EMA: 指数移动平均
+    """
+    
+    def __init__(self, period: int = 14, method: str = 'sma'):
+        """
+        初始化ATR指标
+        
+        Parameters:
+            period: int, 默认14
+                ATR计算周期
+            method: str, 默认'sma'
+                计算方法，可选 'sma' 或 'ema'
+        """
+        if period <= 0:
+            raise ValueError(f"周期必须大于0，当前为 {period}")
+        
+        if method.lower() not in ['sma', 'ema']:
+            raise ValueError(f"计算方法必须是 'sma' 或 'ema'，当前为 {method}")
+        
+        self.period = period
+        self.method = method.lower()
+        self._last_atr: Optional[float] = None
+        
+    @staticmethod
+    def calculate_tr(
+        high: Union[pd.Series, np.ndarray],
+        low: Union[pd.Series, np.ndarray],
+        close: Union[pd.Series, np.ndarray]
+    ) -> Union[pd.Series, np.ndarray]:
+        """
+        计算True Range (TR)
+        
+        公式: TR = max[|high - low|, |high - prev_close|, |low - prev_close|]
+        
+        Parameters:
+            high: 最高价序列
+            low: 最低价序列
+            close: 收盘价序列
+            
+        Returns:
+            TR序列，第一个值为NaN
+        """
+        # 转换为numpy数组便于计算
+        if isinstance(high, pd.Series):
+            tr_values = ATRIndicator._calculate_tr_numpy(
+                high.values, low.values, close.values
+            )
+            return pd.Series(tr_values, index=high.index, name='TR')
+        else:
+            return ATRIndicator._calculate_tr_numpy(high, low, close)
+    
+    @staticmethod
+    def _calculate_tr_numpy(
+        high: np.ndarray,
+        low: np.ndarray,
+        close: np.ndarray
+    ) -> np.ndarray:
+        """使用numpy计算TR"""
+        if len(high) != len(low) or len(high) != len(close):
+            raise ValueError("high, low, close序列长度必须一致")
+        
+        n = len(high)
+        tr = np.full(n, np.nan)
+        
+        if n < 2:
+            return tr
+        
+        # 计算三个成分
+        high_low = high[1:] - low[1:]
+        high_prev_close = np.abs(high[1:] - close[:-1])
+        low_prev_close = np.abs(low[1:] - close[:-1])
+        
+        # 取最大值
+        tr[1:] = np.maximum(np.maximum(high_low, high_prev_close), low_prev_close)
+        
+        return tr
+    
+    def calculate(
+        self,
+        high: Union[pd.Series, np.ndarray],
+        low: Union[pd.Series, np.ndarray],
+        close: Union[pd.Series, np.ndarray]
+    ) -> Union[pd.Series, np.ndarray]:
+        """
+        计算ATR指标
+        
+        Parameters:
+            high: 最高价序列
+            low: 最低价序列
+            close: 收盘价序列
+            
+        Returns:
+            ATR序列
+        """
+        # 先计算TR
+        tr = self.calculate_tr(high, low, close)
+        
+        if self.method == 'sma':
+            return self._calculate_sma_atr(tr)
+        else:
+            return self._calculate_ema_atr(tr)
+    
+    def _calculate_sma_atr(self, tr: Union[pd.Series, np.ndarray]) -> Union[pd.Series, np.ndarray]:
+        """使用SMA计算ATR"""
+        if isinstance(tr, pd.Series):
+            atr = tr.rolling(window=self.period, min_periods=self.period).mean()
+            atr.name = f'ATR_{self.period}'
+            if len(atr) > 0:
+                self._last_atr = atr.iloc[-1]
+            return atr
+        else:
+            n = len(tr)
+            atr = np.full(n, np.nan)
+            for i in range(self.period - 1, n):
+                if i >= self.period:
+                    atr[i] = np.mean(tr[i - self.period + 1:i + 1])
+            if n > 0:
+                self._last_atr = atr[-1]
+            return atr
+    
+    def _calculate_ema_atr(self, tr: Union[pd.Series, np.ndarray]) -> Union[pd.Series, np.ndarray]:
+        """使用EMA计算ATR，Wilder平滑方法"""
+        if isinstance(tr, pd.Series):
+            return self._calculate_ema_atr_pandas(tr)
+        else:
+            return self._calculate_ema_atr_numpy(tr)
+    
+    def _calculate_ema_atr_pandas(self, tr: pd.Series) -> pd.Series:
+        """Pandas版本EMA-ATR计算，使用Wilder平滑"""
+        n = len(tr)
+        atr = pd.Series(np.full(n, np.nan), index=tr.index, name=f'ATR_{self.period}')
+        
+        if n < self.period:
+            self._last_atr = None
+            return atr
+        
+        # 第一个ATR用SMA计算
+        first_atr = tr.iloc[1:self.period + 1].mean()
+        atr.iloc[self.period] = first_atr
+        self._last_atr = first_atr
+        
+        # Wilder平滑: ATR_t = (ATR_{t-1} * (period - 1) + TR_t) / period
+        alpha = 1.0 / self.period
+        
+        for i in range(self.period + 1, n):
+            if np.isnan(tr.iloc[i]):
+                atr.iloc[i] = atr.iloc[i - 1]
+            else:
+                atr.iloc[i] = atr.iloc[i - 1] * (1 - alpha) + tr.iloc[i] * alpha
+        
+        if len(atr) > 0:
+            self._last_atr = atr.iloc[-1]
+        
+        return atr
+    
+    def _calculate_ema_atr_numpy(self, tr: np.ndarray) -> np.ndarray:
+        """Numpy版本EMA-ATR计算，使用Wilder平滑"""
+        n = len(tr)
+        atr = np.full(n, np.nan)
+        
+        if n < self.period:
+            self._last_atr = None
+            return atr
+        
+        # 第一个ATR用SMA计算
+        first_atr = np.mean(tr[1:self.period + 1])
+        atr[self.period] = first_atr
+        self._last_atr = first_atr
+        
+        # Wilder平滑: ATR_t = (ATR_{t-1} * (period - 1) + TR_t) / period
+        alpha = 1.0 / self.period
+        
+        for i in range(self.period + 1, n):
+            if np.isnan(tr[i]):
+                atr[i] = atr[i - 1]
+            else:
+                atr[i] = atr[i - 1] * (1 - alpha) + tr[i] * alpha
+        
+        self._last_atr = atr[-1]
+        return atr
+    
+    def get_last_atr(self) -> Optional[float]:
+        """获取最后一个ATR值"""
+        return self._last_atr
+    
+    def update(self, high: float, low: float, prev_close: float) -> float:
+        """
+        更新单个ATR值（实盘时增量更新使用）
+        
+        Parameters:
+            high: 当前K线最高价
+            low: 当前K线最低价
+            prev_close: 前一根K线收盘价
+            
+        Returns:
+            新的ATR值
+        """
+        # 计算当前TR
+        tr1 = abs(high - low)
+        tr2 = abs(high - prev_close)
+        tr3 = abs(low - prev_close)
+        tr = max(tr1, tr2, tr3)
+        
+        if self._last_atr is None:
+            # 还没有足够数据，直接返回TR
+            return tr
+        
+        if self.method == 'sma':
+            # SMA不适合增量更新，这里简化处理，用户应该重新计算完整序列
+            raise ValueError("SMA方法不支持增量更新，请重新计算完整序列")
+        else:
+            # EMA支持增量更新
+            alpha = 1.0 / self.period
+            new_atr = self._last_atr * (1 - alpha) + tr * alpha
+            self._last_atr = new_atr
+            return new_atr
+
+
+def calculate_atr(
+    df: pd.DataFrame,
+    period: int = 14,
+    method: str = 'sma',
+    high_col: str = 'high',
+    low_col: str = 'low',
+    close_col: str = 'close',
+    drop_na: bool = False
+) -> pd.DataFrame:
+    """
+    便捷函数：直接对DataFrame计算ATR并添加到原DataFrame
+    
+    Parameters:
+        df: 输入DataFrame，必须包含high, low, close列
+        period: ATR周期，默认14
+        method: 计算方法 'sma' 或 'ema'，默认'sma'
+        high_col: 最高价列名，默认'high'
+        low_col: 最低价列名，默认'low'
+        close_col: 收盘价列名，默认'close'
+        drop_na: 是否删除NaN值，默认False
+        
+    Returns:
+        添加了TR和ATR列的DataFrame
+    """
+    atr_indicator = ATRIndicator(period, method)
+    
+    result = df.copy()
+    result['TR'] = atr_indicator.calculate_tr(
+        df[high_col], df[low_col], df[close_col]
+    )
+    result[f'ATR_{period}'] = atr_indicator.calculate(
+        df[high_col], df[low_col], df[close_col]
+    )
+    
+    if drop_na:
+        result = result.dropna()
+        
+    return result