技术指标¶

技术指标模块提供常见的技术分析指标计算功能，需要通过 akshare_one.indicators 模块调用。

导入模块¶

from akshare_one.indicators import (
    get_sma,
    get_ema,
    get_rsi,
    get_macd,
    get_bollinger_bands,
    get_stoch,
    get_atr,
    get_cci,
    get_adx,
    get_willr,
    get_ad,
    get_adosc,
    get_obv,
    get_mom,
    get_sar,
    get_tsf,
    get_apo,
    get_aroon,
    get_aroonosc,
    get_bop,
    get_cmo,
    get_dx,
    get_mfi,
    get_minus_di,
    get_minus_dm,
    get_plus_di,
    get_plus_dm,
    get_ppo,
    get_roc,
    get_rocp,
    get_rocr,
    get_rocr100,
    get_trix,
    get_ultosc,
)

注意事项

大部分指标函数支持 calculator_type 参数，可以是 talib 或 simple，默认值为 simple。
simple 使用 Python 实现，不需要额外依赖。
talib 需要额外安装 TA-Lib 依赖并使用 pip install akshare-one[talib] 安装。

趋势指标¶

简单移动平均线 (SMA)¶

def get_sma(df: pd.DataFrame, window: int = 20) -> pd.DataFrame

指数移动平均线 (EMA)¶

def get_ema(df: pd.DataFrame, window: int = 20) -> pd.DataFrame

移动平均收敛发散指标 (MACD)¶

def get_macd(df: pd.DataFrame, fast: int = 12, slow: int = 26, signal: int = 9) -> pd.DataFrame

抛物线转向指标 (Parabolic SAR)¶

def get_sar(df: pd.DataFrame, acceleration: float = 0.02, maximum: float = 0.2) -> pd.DataFrame

时间序列预测 (Time Series Forecast)¶

def get_tsf(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

平均方向性指标 (ADX)¶

def get_adx(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

动向指标 (DMI/DX)¶

def get_dx(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

正/负方向指标 (+DI/-DI)¶

def get_plus_di(df: pd.DataFrame, window: int = 14) -> pd.DataFrame
def get_minus_di(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

正/负方向运动 (+DM/-DM)¶

def get_plus_dm(df: pd.DataFrame, window: int = 14) -> pd.DataFrame
def get_minus_dm(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

阿隆指标 (Aroon)¶

def get_aroon(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

动量指标¶

相对强弱指数 (RSI)¶

def get_rsi(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

随机指标 (Stochastic Oscillator)¶

def get_stoch(df: pd.DataFrame, window: int = 14, smooth_d: int = 3, smooth_k: int = 3) -> pd.DataFrame

商品通道指数 (CCI)¶

def get_cci(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

威廉指标 (Williams' %R)¶

def get_willr(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

动量指标 (Momentum)¶

def get_mom(df: pd.DataFrame, window: int = 10) -> pd.DataFrame

变动率 (ROC)¶

def get_roc(df: pd.DataFrame, window: int = 10) -> pd.DataFrame
def get_rocp(df: pd.DataFrame, window: int = 10) -> pd.DataFrame
def get_rocr(df: pd.DataFrame, window: int = 10) -> pd.DataFrame
def get_rocr100(df: pd.DataFrame, window: int = 10) -> pd.DataFrame

终极振荡器 (Ultimate Oscillator)¶

def get_ultosc(df: pd.DataFrame, window1: int = 7, window2: int = 14, window3: int = 28) -> pd.DataFrame

钱德动量振荡器 (CMO)¶

def get_cmo(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

均势指标 (BOP)¶

def get_bop(df: pd.DataFrame) -> pd.DataFrame

三重指数平滑平均线的1日变动率 (TRIX)¶

def get_trix(df: pd.DataFrame, window: int = 30) -> pd.DataFrame

绝对价格振荡器 (APO)¶

def get_apo(df: pd.DataFrame, fast_period: int = 12, slow_period: int = 26, ma_type: int = 0) -> pd.DataFrame

价格振荡器百分比 (PPO)¶

def get_ppo(df: pd.DataFrame, fast_period: int = 12, slow_period: int = 26, ma_type: int = 0) -> pd.DataFrame

阿隆振荡器 (Aroon Oscillator)¶

def get_aroonosc(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

波动率指标¶

布林带 (Bollinger Bands)¶

def get_bollinger_bands(df: pd.DataFrame, window: int = 20, std: int = 2) -> pd.DataFrame

平均真实波幅 (ATR)¶

def get_atr(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

成交量指标¶

能量潮 (OBV)¶

def get_obv(df: pd.DataFrame) -> pd.DataFrame

蔡金 A/D 线 (Chaikin A/D Line)¶

def get_ad(df: pd.DataFrame) -> pd.DataFrame

蔡金 A/D 振荡器 (Chaikin A/D Oscillator)¶

def get_adosc(df: pd.DataFrame, fast_period: int = 3, slow_period: int = 10) -> pd.DataFrame

资金流量指标 (MFI)¶

def get_mfi(df: pd.DataFrame, window: int = 14) -> pd.DataFrame

使用示例¶

from akshare_one import get_hist_data
from akshare_one.indicators import get_sma, get_macd

# 获取历史数据
df = get_hist_data(symbol="600000", interval="day")

# 计算20日简单移动平均
df_sma = get_sma(df, window=20)
print(df_sma.tail())

# 计算MACD指标
df_macd = get_macd(df)
print(df_macd.tail())