Source code for pandas_ta.momentum.smi

# -*- coding: utf-8 -*-
from pandas import DataFrame
from .tsi import tsi
from pandas_ta.overlap import ema
from pandas_ta.utils import get_offset, verify_series

[docs]def smi(close, fast=None, slow=None, signal=None, scalar=None, offset=None, **kwargs): """Indicator: SMI Ergodic Indicator (SMIIO)""" # Validate arguments fast = int(fast) if fast and fast > 0 else 5 slow = int(slow) if slow and slow > 0 else 20 signal = int(signal) if signal and signal > 0 else 5 if slow < fast: fast, slow = slow, fast scalar = float(scalar) if scalar else 1 close = verify_series(close, max(fast, slow, signal)) offset = get_offset(offset) if close is None: return # Calculate Result tsi_df = tsi(close, fast=fast, slow=slow, signal=signal, scalar=scalar) smi = tsi_df.iloc[:, 0] signalma = tsi_df.iloc[:, 1] osc = smi - signalma # Offset if offset != 0: smi = smi.shift(offset) signalma = signalma.shift(offset) osc = osc.shift(offset) # Handle fills if "fillna" in kwargs: smi.fillna(kwargs["fillna"], inplace=True) signalma.fillna(kwargs["fillna"], inplace=True) osc.fillna(kwargs["fillna"], inplace=True) if "fill_method" in kwargs: smi.fillna(method=kwargs["fill_method"], inplace=True) signalma.fillna(method=kwargs["fill_method"], inplace=True) osc.fillna(method=kwargs["fill_method"], inplace=True) # Name and Categorize it _scalar = f"_{scalar}" if scalar != 1 else "" _props = f"_{fast}_{slow}_{signal}{_scalar}" = f"SMI{_props}" = f"SMIs{_props}" = f"SMIo{_props}" smi.category = signalma.category = osc.category = "momentum" # Prepare DataFrame to return data = { smi, signalma, osc} df = DataFrame(data) = f"SMI{_props}" df.category = smi.category return df
smi.__doc__ = \ """SMI Ergodic Indicator (SMI) The SMI Ergodic Indicator is the same as the True Strength Index (TSI) developed by William Blau, except the SMI includes a signal line. The SMI uses double moving averages of price minus previous price over 2 time frames. The signal line, which is an EMA of the SMI, is plotted to help trigger trading signals. The trend is bullish when crossing above zero and bearish when crossing below zero. This implementation includes both the SMI Ergodic Indicator and SMI Ergodic Oscillator. Sources: Calculation: Default Inputs: fast=5, slow=20, signal=5 TSI = True Strength Index EMA = Exponential Moving Average ERG = TSI(close, fast, slow) Signal = EMA(ERG, signal) OSC = ERG - Signal Args: close (pd.Series): Series of 'close's fast (int): The short period. Default: 5 slow (int): The long period. Default: 20 signal (int): The signal period. Default: 5 scalar (float): How much to magnify. Default: 1 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.DataFrame: smi, signal, oscillator columns. """