Source code for pandas_ta.overlap.sma

# -*- coding: utf-8 -*-
from pandas_ta import Imports
from pandas_ta.utils import get_offset, verify_series

[docs]def sma(close, length=None, talib=None, offset=None, **kwargs): """Indicator: Simple Moving Average (SMA)""" # Validate Arguments length = int(length) if length and length > 0 else 10 min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length close = verify_series(close, max(length, min_periods)) offset = get_offset(offset) mode_tal = bool(talib) if isinstance(talib, bool) else True if close is None: return # Calculate Result if Imports["talib"] and mode_tal: from talib import SMA sma = SMA(close, length) else: sma = close.rolling(length, min_periods=min_periods).mean() # Offset if offset != 0: sma = sma.shift(offset) # Handle fills if "fillna" in kwargs: sma.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: sma.fillna(method=kwargs["fill_method"], inplace=True) # Name & Category = f"SMA_{length}" sma.category = "overlap" return sma
sma.__doc__ = \ """Simple Moving Average (SMA) The Simple Moving Average is the classic moving average that is the equally weighted average over n periods. Sources: Calculation: Default Inputs: length=10 SMA = SUM(close, length) / length 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. """