PandasTA 源码解析（十）（2）

# -*- coding: utf-8 -*-
# 从 pandas_ta.utils 模块中导入 get_offset, symmetric_triangle, verify_series, weights 函数
from pandas_ta.utils import get_offset, symmetric_triangle, verify_series, weights
# 定义 Symmetric Weighted Moving Average (SWMA) 函数
def swma(close, length=None, asc=None, offset=None, **kwargs):
    """Indicator: Symmetric Weighted Moving Average (SWMA)"""
    
    # 验证参数
    length = int(length) if length and length > 0 else 10
    asc = asc if asc else True
    close = verify_series(close, length)
    offset = get_offset(offset)
    if close is None: return
    # 计算结果
    triangle = symmetric_triangle(length, weighted=True)
    swma = close.rolling(length, min_periods=length).apply(weights(triangle), raw=True)
    # 偏移
    if offset != 0:
        swma = swma.shift(offset)
    # 处理填充
    if "fillna" in kwargs:
        swma.fillna(kwargs["fillna"], inplace=True)
    if "fill_method" in kwargs:
        swma.fillna(method=kwargs["fill_method"], inplace=True)
    # 设置名称和类别
    swma.name = f"SWMA_{length}"
    swma.category = "overlap"
    return swma
# 设置 SWMA 函数的文档字符串
swma.__doc__ = \
"""Symmetric Weighted Moving Average (SWMA)
Symmetric Weighted Moving Average where weights are based on a symmetric
triangle.  For example: n=3 -> [1, 2, 1], n=4 -> [1, 2, 2, 1], etc...
This moving average has variable length in contrast to TradingView's fixed
length of 4.
Source:
    https://www.tradingview.com/study-script-reference/#fun_swma
Calculation:
    Default Inputs:
        length=10
    def weights(w):
        def _compute(x):
            return np.dot(w * x)
        return _compute
    triangle = utils.symmetric_triangle(length - 1)
    SWMA = close.rolling(length)_.apply(weights(triangle), raw=True)
Args:
    close (pd.Series): Series of 'close's
    length (int): It's period. Default: 10
    asc (bool): Recent values weigh more. Default: True
    offset (int): How many periods to offset the result. Default: 0
Kwargs:
    fillna (value, optional): pd.DataFrame.fillna(value)
    fill_method (value, optional): Type of fill method
Returns:
    pd.Series: New feature generated.
"""

# -*- coding: utf-8 -*-
# 导入 ema 函数
from .ema import ema
# 导入 Imports 模块
from pandas_ta import Imports
# 导入 get_offset 和 verify_series 函数
from pandas_ta.utils import get_offset, verify_series
# 定义 T3 函数，计算 T3 指标
def t3(close, length=None, a=None, talib=None, offset=None, **kwargs):
    """Indicator: T3"""
    # 验证参数
   # 将 length 转换为整数，如果 length 大于 0 则使用 length，否则使用默认值 10
    length = int(length) if length and length > 0 else 10
    # 将 a 转换为浮点数，如果 a 在 0 和 1 之间则使用 a，否则使用默认值 0.7
    a = float(a) if a and a > 0 and a < 1 else 0.7
    # 验证 close 数据类型为 Series，长度为 length
    close = verify_series(close, length)
    # 获取 offset
    offset = get_offset(offset)
    # 判断是否使用 talib，默认为 True
    mode_tal = bool(talib) if isinstance(talib, bool) else True
    # 如果 close 为 None，则返回
    if close is None: return
    # 计算结果
    if Imports["talib"] and mode_tal:
        # 如果导入了 talib 并且 mode_tal 为 True，则使用 talib 中的 T3 函数
        from talib import T3
        t3 = T3(close, length, a)
    else:
        # 计算 T3 指标的系数
        c1 = -a * a**2
        c2 = 3 * a**2 + 3 * a**3
        c3 = -6 * a**2 - 3 * a - 3 * a**3
        c4 = a**3 + 3 * a**2 + 3 * a + 1
        # 计算 ema1 到 ema6
        e1 = ema(close=close, length=length, **kwargs)
        e2 = ema(close=e1, length=length, **kwargs)
        e3 = ema(close=e2, length=length, **kwargs)
        e4 = ema(close=e3, length=length, **kwargs)
        e5 = ema(close=e4, length=length, **kwargs)
        e6 = ema(close=e5, length=length, **kwargs)
        # 计算 T3
        t3 = c1 * e6 + c2 * e5 + c3 * e4 + c4 * e3
    # 偏移结果
    if offset != 0:
        t3 = t3.shift(offset)
    # 处理填充
    if "fillna" in kwargs:
        t3.fillna(kwargs["fillna"], inplace=True)
    if "fill_method" in kwargs:
        t3.fillna(method=kwargs["fill_method"], inplace=True)
    # 设置指标名称和类别
    t3.name = f"T3_{length}_{a}"
    t3.category = "overlap"
    return t3
# 设置 T3 函数的文档字符串
t3.__doc__ = """Tim Tillson's T3 Moving Average (T3)
Tim Tillson's T3 Moving Average is considered a smoother and more responsive
moving average relative to other moving averages.
Sources:
    http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/
Calculation:
    Default Inputs:
        length=10, a=0.7
    c1 = -a^3
    c2 = 3a^2 + 3a^3 = 3a^2 * (1 + a)
    c3 = -6a^2 - 3a - 3a^3
    c4 = a^3 + 3a^2 + 3a + 1
    ema1 = EMA(close, length)
    ema2 = EMA(ema1, length)
    ema3 = EMA(ema2, length)
    ema4 = EMA(ema3, length)
    ema5 = EMA(ema4, length)
    ema6 = EMA(ema5, length)
    T3 = c1 * ema6 + c2 * ema5 + c3 * ema4 + c4 * ema3
Args:
    close (pd.Series): Series of 'close's
    length (int): It's period. Default: 10
    a (float): 0 < a < 1. Default: 0.7
    talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
        version. Default: True
    offset (int): How many periods to offset the result. Default: 0
Kwargs:
    adjust (bool): Default: True
    presma (bool, optional): If True, uses SMA for initial value.
    fillna (value, optional): pd.DataFrame.fillna(value)
    fill_method (value, optional): Type of fill method
Returns:
    pd.Series: New feature generated.
"""

# -*- coding: utf-8 -*-
# 从 ema 模块中导入 ema 函数
from .ema import ema
# 从 pandas_ta 模块中导入 Imports 类
from pandas_ta import Imports
# 从 pandas_ta.utils 模块中导入 get_offset 和 verify_series 函数
from pandas_ta.utils import get_offset, verify_series
# 定义 Triple Exponential Moving Average (TEMA) 指标函数
def tema(close, length=None, talib=None, offset=None, **kwargs):
    """Indicator: Triple Exponential Moving Average (TEMA)"""
    # 验证参数
    # 如果 length 存在且大于 0，则将其转换为整数，否则设为默认值 10
    length = int(length) if length and length > 0 else 10
    # 验证 close 数据，并设定长度
    close = verify_series(close, length)
    # 获取偏移量
    offset = get_offset(offset)
    # 判断是否使用 talib 模式
    mode_tal = bool(talib) if isinstance(talib, bool) else True
    # 如果 close 为空，则返回空值
    if close is None: return
    # 计算结果
    if Imports["talib"] and mode_tal:
        # 如果导入了 talib 模块并且使用 talib 模式，则调用 talib 中的 TEMA 函数
        from talib import TEMA
        tema = TEMA(close, length)
    else:
        # 否则，使用 ema 函数计算三次指数移动平均
        ema1 = ema(close=close, length=length, **kwargs)
        ema2 = ema(close=ema1, length=length, **kwargs)
        ema3 = ema(close=ema2, length=length, **kwargs)
        tema = 3 * (ema1 - ema2) + ema3
    # 偏移结果
    if offset != 0:
        tema = tema.shift(offset)
    # 处理填充值
    if "fillna" in kwargs:
        tema.fillna(kwargs["fillna"], inplace=True)
    if "fill_method" in kwargs:
        tema.fillna(method=kwargs["fill_method"], inplace=True)
    # 设置指标名称和类别
    tema.name = f"TEMA_{length}"
    tema.category = "overlap"
    return tema
# 设置 TEMA 函数的文档字符串
tema.__doc__ = \
"""Triple Exponential Moving Average (TEMA)
A less laggy Exponential Moving Average.
Sources:
    https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/
Calculation:
    Default Inputs:
        length=10
    EMA = Exponential Moving Average
    ema1 = EMA(close, length)
    ema2 = EMA(ema1, length)
    ema3 = EMA(ema2, length)
    TEMA = 3 * (ema1 - ema2) + ema3
Args:
    close (pd.Series): Series of 'close's
    length (int): It's period. Default: 10
    talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
        version. Default: True
    offset (int): How many periods to offset the result. Default: 0
Kwargs:
    adjust (bool): Default: True
    presma (bool, optional): If True, uses SMA for initial value.
    fillna (value, optional): pd.DataFrame.fillna(value)
    fill_method (value, optional): Type of fill method
Returns:
    pd.Series: New feature generated.
"""