"""
Momentum Indicators — Oscillators measuring speed and change of price movements.
Functions
---------
RSI — Relative Strength Index
MOM — Momentum
ROC — Rate of Change: ((price/prevPrice)-1)*100
ROCP — Rate of Change Percentage: (price-prevPrice)/prevPrice
ROCR — Rate of Change Ratio: price/prevPrice
ROCR100 — Rate of Change Ratio 100 scale: (price/prevPrice)*100
WILLR — Williams' %R
AROON — Aroon (returns aroon_down, aroon_up)
AROONOSC — Aroon Oscillator
CCI — Commodity Channel Index
MFI — Money Flow Index
BOP — Balance Of Power
STOCHF — Stochastic Fast
STOCH — Stochastic
STOCHRSI — Stochastic Relative Strength Index
APO — Absolute Price Oscillator
PPO — Percentage Price Oscillator
CMO — Chande Momentum Oscillator
PLUS_DM — Plus Directional Movement
MINUS_DM — Minus Directional Movement
PLUS_DI — Plus Directional Indicator
MINUS_DI — Minus Directional Indicator
DX — Directional Movement Index
ADX — Average Directional Movement Index
ADXR — Average Directional Movement Index Rating
DMI — Directional Movement Index (+DI, -DI, ADX)
TRIX — 1-day Rate-Of-Change of Triple Smooth EMA
ULTOSC — Ultimate Oscillator
TRANGE — True Range (also in volatility)
ELDER_RAY — Elder Ray bull / bear power
FISHER — Ehlers Fisher Transform
CRSI — Connors RSI
"""
from __future__ import annotations
import numpy as np
from numpy.typing import ArrayLike
from ferro_ta._ferro_ta import (
adx as _adx,
)
from ferro_ta._ferro_ta import (
adxr as _adxr,
)
from ferro_ta._ferro_ta import (
apo as _apo,
)
from ferro_ta._ferro_ta import (
aroon as _aroon,
)
from ferro_ta._ferro_ta import (
aroonosc as _aroonosc,
)
from ferro_ta._ferro_ta import (
bop as _bop,
)
from ferro_ta._ferro_ta import (
cci as _cci,
)
from ferro_ta._ferro_ta import (
cmo as _cmo,
)
from ferro_ta._ferro_ta import (
crsi as _crsi,
)
from ferro_ta._ferro_ta import (
dmi as _dmi,
)
from ferro_ta._ferro_ta import (
dx as _dx,
)
from ferro_ta._ferro_ta import (
elder_ray as _elder_ray,
)
from ferro_ta._ferro_ta import (
fisher as _fisher,
)
from ferro_ta._ferro_ta import (
mfi as _mfi,
)
from ferro_ta._ferro_ta import (
minus_di as _minus_di,
)
from ferro_ta._ferro_ta import (
minus_dm as _minus_dm,
)
from ferro_ta._ferro_ta import (
mom as _mom,
)
from ferro_ta._ferro_ta import (
plus_di as _plus_di,
)
from ferro_ta._ferro_ta import (
plus_dm as _plus_dm,
)
from ferro_ta._ferro_ta import (
ppo as _ppo,
)
from ferro_ta._ferro_ta import (
roc as _roc,
)
from ferro_ta._ferro_ta import (
rocp as _rocp,
)
from ferro_ta._ferro_ta import (
rocr as _rocr,
)
from ferro_ta._ferro_ta import (
rocr100 as _rocr100,
)
from ferro_ta._ferro_ta import (
rsi as _rsi,
)
from ferro_ta._ferro_ta import (
stoch as _stoch,
)
from ferro_ta._ferro_ta import (
stochf as _stochf,
)
from ferro_ta._ferro_ta import (
stochrsi as _stochrsi,
)
from ferro_ta._ferro_ta import (
trix as _trix,
)
from ferro_ta._ferro_ta import (
ultosc as _ultosc,
)
from ferro_ta._ferro_ta import (
willr as _willr,
)
from ferro_ta._utils import _to_f64, apply_nan_policy
from ferro_ta.core.exceptions import _normalize_rust_error
from ferro_ta.indicators.volatility import TRANGE
[docs]
def RSI(
close: ArrayLike, timeperiod: int = 14, *, nan_policy: str = "propagate"
) -> np.ndarray:
"""Relative Strength Index.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Number of periods (default 14).
nan_policy : {"propagate", "skip"}, optional
``"propagate"`` (default, TA-Lib semantics): a NaN input poisons every
later value. ``"skip"``: rows with a non-finite input are dropped, the
indicator runs on the remaining rows, and NaN is written back at the
dropped rows (matches the streaming classes tick for tick).
Returns
-------
numpy.ndarray
Array of RSI values (0–100); leading ``timeperiod`` entries are ``NaN``.
"""
try:
return apply_nan_policy(
lambda c: _rsi(c, timeperiod), (_to_f64(close),), nan_policy
)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def MOM(close: ArrayLike, timeperiod: int = 10) -> np.ndarray:
"""Momentum.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Number of periods (default 10).
Returns
-------
numpy.ndarray
Array of MOM values; leading ``timeperiod`` entries are ``NaN``.
"""
try:
return _mom(_to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def ROC(close: ArrayLike, timeperiod: int = 10) -> np.ndarray:
"""Rate of Change: ((price/prevPrice)-1)*100.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Number of periods (default 10).
Returns
-------
numpy.ndarray
Array of ROC values; leading ``timeperiod`` entries are ``NaN``.
"""
try:
return _roc(_to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def ROCP(close: ArrayLike, timeperiod: int = 10) -> np.ndarray:
"""Rate of Change Percentage: (price-prevPrice)/prevPrice.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Number of periods (default 10).
Returns
-------
numpy.ndarray
Array of ROCP values; leading ``timeperiod`` entries are ``NaN``.
"""
try:
return _rocp(_to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def ROCR(close: ArrayLike, timeperiod: int = 10) -> np.ndarray:
"""Rate of Change Ratio: price/prevPrice.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Number of periods (default 10).
Returns
-------
numpy.ndarray
Array of ROCR values; leading ``timeperiod`` entries are ``NaN``.
"""
try:
return _rocr(_to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def ROCR100(close: ArrayLike, timeperiod: int = 10) -> np.ndarray:
"""Rate of Change Ratio 100 scale: (price/prevPrice)*100.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Number of periods (default 10).
Returns
-------
numpy.ndarray
Array of ROCR100 values; leading ``timeperiod`` entries are ``NaN``.
"""
try:
return _rocr100(_to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def WILLR(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod: int = 14,
) -> np.ndarray:
"""Williams' %R.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Number of periods (default 14).
Returns
-------
numpy.ndarray
Array of WILLR values (-100 to 0); leading ``timeperiod - 1`` entries are ``NaN``.
"""
try:
return _willr(_to_f64(high), _to_f64(low), _to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def AROON(
high: ArrayLike,
low: ArrayLike,
timeperiod: int = 14,
) -> tuple[np.ndarray, np.ndarray]:
"""Aroon.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
timeperiod : int, optional
Number of periods (default 14).
Returns
-------
tuple[numpy.ndarray, numpy.ndarray]
``(aroondown, aroonup)`` — two arrays of equal length.
Leading ``timeperiod`` entries are ``NaN``.
"""
try:
return _aroon(_to_f64(high), _to_f64(low), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def AROONOSC(
high: ArrayLike,
low: ArrayLike,
timeperiod: int = 14,
) -> np.ndarray:
"""Aroon Oscillator.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
timeperiod : int, optional
Number of periods (default 14).
Returns
-------
numpy.ndarray
Array of AROONOSC values; leading ``timeperiod`` entries are ``NaN``.
"""
try:
return _aroonosc(_to_f64(high), _to_f64(low), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def CCI(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod: int = 14,
) -> np.ndarray:
"""Commodity Channel Index.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Number of periods (default 14).
Returns
-------
numpy.ndarray
Array of CCI values; leading ``timeperiod - 1`` entries are ``NaN``.
"""
try:
return _cci(_to_f64(high), _to_f64(low), _to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def MFI(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
volume: ArrayLike,
timeperiod: int = 14,
) -> np.ndarray:
"""Money Flow Index.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
volume : array-like
Sequence of volume values.
timeperiod : int, optional
Number of periods (default 14).
Returns
-------
numpy.ndarray
Array of MFI values (0–100); leading ``timeperiod`` entries are ``NaN``.
"""
try:
return _mfi(
_to_f64(high), _to_f64(low), _to_f64(close), _to_f64(volume), timeperiod
)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def BOP(
open: ArrayLike,
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
) -> np.ndarray:
"""Balance Of Power.
Parameters
----------
open : array-like
Sequence of open prices.
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
Returns
-------
numpy.ndarray
Array of BOP values (-1 to 1).
"""
try:
return _bop(_to_f64(open), _to_f64(high), _to_f64(low), _to_f64(close))
except ValueError as e:
_normalize_rust_error(e)
[docs]
def STOCHF(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
fastk_period: int = 5,
fastd_period: int = 3,
fastd_matype: int = 0,
) -> tuple[np.ndarray, np.ndarray]:
"""Stochastic Fast.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
fastk_period : int, optional
%K period (default 5).
fastd_period : int, optional
%D smoothing period (default 3).
fastd_matype : int, optional
Moving average type applied to the %D leg (default 0):
* 0 = SMA (Simple)
* 1 = EMA (Exponential)
* 2 = WMA (Weighted)
* 3 = DEMA (Double EMA)
* 4 = TEMA (Triple EMA)
* 5 = TRIMA (Triangular)
* 6 = KAMA (Kaufman Adaptive)
* 7 = T3 (Tillson)
* 8 = T3 (Tillson; exact alias of 7)
Values ``0``-``6`` and ``8`` match TA-Lib's numbering, but ``7`` is T3
here where TA-Lib's ``7`` is MAMA. MAMA is not reachable through any
``matype`` value -- call :func:`ferro_ta.MAMA` directly.
Returns
-------
tuple[numpy.ndarray, numpy.ndarray]
``(fastk, fastd)`` — two arrays of equal length.
"""
try:
return _stochf(
_to_f64(high),
_to_f64(low),
_to_f64(close),
fastk_period,
fastd_period,
fastd_matype,
)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def STOCH(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
fastk_period: int = 5,
slowk_period: int = 3,
slowk_matype: int = 0,
slowd_period: int = 3,
slowd_matype: int = 0,
*,
nan_policy: str = "propagate",
) -> tuple[np.ndarray, np.ndarray]:
"""Stochastic.
Parameters follow TA-Lib's *interleaved* order: each ``*_matype`` comes
immediately after the period it types, so a mis-ordered positional call
fails the type checker instead of returning a wrong answer.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
fastk_period : int, optional
Fast %K period (default 5).
slowk_period : int, optional
Slow %K smoothing period (default 3).
slowk_matype : int, optional
Moving average type applied to the slow %K leg (default 0):
* 0 = SMA (Simple)
* 1 = EMA (Exponential)
* 2 = WMA (Weighted)
* 3 = DEMA (Double EMA)
* 4 = TEMA (Triple EMA)
* 5 = TRIMA (Triangular)
* 6 = KAMA (Kaufman Adaptive)
* 7 = T3 (Tillson)
* 8 = T3 (Tillson; exact alias of 7)
Values ``0``-``6`` and ``8`` match TA-Lib's numbering, but ``7`` is T3
here where TA-Lib's ``7`` is MAMA. MAMA is not reachable through any
``matype`` value -- call :func:`ferro_ta.MAMA` directly.
slowd_period : int, optional
Slow %D smoothing period (default 3).
slowd_matype : int, optional
Moving average type applied to the slow %D leg (default 0):
* 0 = SMA (Simple)
* 1 = EMA (Exponential)
* 2 = WMA (Weighted)
* 3 = DEMA (Double EMA)
* 4 = TEMA (Triple EMA)
* 5 = TRIMA (Triangular)
* 6 = KAMA (Kaufman Adaptive)
* 7 = T3 (Tillson)
* 8 = T3 (Tillson; exact alias of 7)
Values ``0``-``6`` and ``8`` match TA-Lib's numbering, but ``7`` is T3
here where TA-Lib's ``7`` is MAMA. MAMA is not reachable through any
``matype`` value -- call :func:`ferro_ta.MAMA` directly.
nan_policy : {"propagate", "skip"}, optional
``"propagate"`` (default, TA-Lib semantics): a NaN input poisons every
later value. ``"skip"``: rows with a non-finite input are dropped, the
indicator runs on the remaining rows, and NaN is written back at the
dropped rows (matches the streaming classes tick for tick).
Returns
-------
tuple[numpy.ndarray, numpy.ndarray]
``(slowk, slowd)`` — two arrays of equal length.
"""
try:
return apply_nan_policy(
lambda hi, lo, cl: _stoch(
hi,
lo,
cl,
fastk_period,
slowk_period,
slowk_matype,
slowd_period,
slowd_matype,
),
(_to_f64(high), _to_f64(low), _to_f64(close)),
nan_policy,
)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def STOCHRSI(
close: ArrayLike,
timeperiod: int = 14,
fastk_period: int = 5,
fastd_period: int = 3,
fastd_matype: int = 0,
) -> tuple[np.ndarray, np.ndarray]:
"""Stochastic Relative Strength Index.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
RSI period (default 14).
fastk_period : int, optional
Stochastic %K period (default 5).
fastd_period : int, optional
Stochastic %D smoothing period (default 3).
fastd_matype : int, optional
Moving average type applied to the %D leg (default 0):
* 0 = SMA (Simple)
* 1 = EMA (Exponential)
* 2 = WMA (Weighted)
* 3 = DEMA (Double EMA)
* 4 = TEMA (Triple EMA)
* 5 = TRIMA (Triangular)
* 6 = KAMA (Kaufman Adaptive)
* 7 = T3 (Tillson)
* 8 = T3 (Tillson; exact alias of 7)
Values ``0``-``6`` and ``8`` match TA-Lib's numbering, but ``7`` is T3
here where TA-Lib's ``7`` is MAMA. MAMA is not reachable through any
``matype`` value -- call :func:`ferro_ta.MAMA` directly.
Returns
-------
tuple[numpy.ndarray, numpy.ndarray]
``(fastk, fastd)`` — two arrays of equal length.
"""
try:
return _stochrsi(
_to_f64(close), timeperiod, fastk_period, fastd_period, fastd_matype
)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def APO(
close: ArrayLike,
fastperiod: int = 12,
slowperiod: int = 26,
matype: int = 1,
) -> np.ndarray:
"""Absolute Price Oscillator.
Parameters
----------
close : array-like
Sequence of closing prices.
fastperiod : int, optional
Fast MA period (default 12).
slowperiod : int, optional
Slow MA period (default 26).
matype : int, optional
Moving average type for both legs. Defaults to ``1`` (EMA) rather
than TA-Lib's ``0`` (SMA), because this function has always computed
the EMA form; pass ``0`` for TA-Lib's SMA form (default 1):
* 0 = SMA (Simple)
* 1 = EMA (Exponential)
* 2 = WMA (Weighted)
* 3 = DEMA (Double EMA)
* 4 = TEMA (Triple EMA)
* 5 = TRIMA (Triangular)
* 6 = KAMA (Kaufman Adaptive)
* 7 = T3 (Tillson)
* 8 = T3 (Tillson; exact alias of 7)
Values ``0``-``6`` and ``8`` match TA-Lib's numbering, but ``7`` is T3
here where TA-Lib's ``7`` is MAMA. MAMA is not reachable through any
``matype`` value -- call :func:`ferro_ta.MAMA` directly.
Returns
-------
numpy.ndarray
Array of APO values; leading entries are ``NaN`` for the slow leg's
lookback (which depends on *matype*).
"""
try:
return _apo(_to_f64(close), fastperiod, slowperiod, matype)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def PPO(
close: ArrayLike,
fastperiod: int = 12,
slowperiod: int = 26,
signalperiod: int = 9,
matype: int = 1,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Percentage Price Oscillator.
Parameters
----------
close : array-like
Sequence of closing prices.
fastperiod : int, optional
Fast MA period (default 12).
slowperiod : int, optional
Slow MA period (default 26).
signalperiod : int, optional
Signal EMA period (default 9).
matype : int, optional
Moving average type for the fast and slow legs. Defaults to ``1``
(EMA) rather than TA-Lib's ``0`` (SMA), because this function has
always computed the EMA form; pass ``0`` for TA-Lib's SMA form (default 1):
* 0 = SMA (Simple)
* 1 = EMA (Exponential)
* 2 = WMA (Weighted)
* 3 = DEMA (Double EMA)
* 4 = TEMA (Triple EMA)
* 5 = TRIMA (Triangular)
* 6 = KAMA (Kaufman Adaptive)
* 7 = T3 (Tillson)
* 8 = T3 (Tillson; exact alias of 7)
Values ``0``-``6`` and ``8`` match TA-Lib's numbering, but ``7`` is T3
here where TA-Lib's ``7`` is MAMA. MAMA is not reachable through any
``matype`` value -- call :func:`ferro_ta.MAMA` directly.
Returns
-------
tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]
``(ppo, signal, histogram)`` — three arrays of equal length.
"""
try:
return _ppo(_to_f64(close), fastperiod, slowperiod, signalperiod, matype)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def CMO(close: ArrayLike, timeperiod: int = 14) -> np.ndarray:
"""Chande Momentum Oscillator.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Number of periods (default 14).
Returns
-------
numpy.ndarray
Array of CMO values (-100 to 100); leading ``timeperiod`` entries are ``NaN``.
"""
try:
return _cmo(_to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def PLUS_DM(high: ArrayLike, low: ArrayLike, timeperiod: int = 14) -> np.ndarray:
"""Plus Directional Movement.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
timeperiod : int, optional
Smoothing period (default 14).
Returns
-------
numpy.ndarray
Array of +DM values.
"""
try:
return _plus_dm(_to_f64(high), _to_f64(low), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def MINUS_DM(high: ArrayLike, low: ArrayLike, timeperiod: int = 14) -> np.ndarray:
"""Minus Directional Movement.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
timeperiod : int, optional
Smoothing period (default 14).
Returns
-------
numpy.ndarray
Array of -DM values.
"""
try:
return _minus_dm(_to_f64(high), _to_f64(low), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def PLUS_DI(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod: int = 14,
) -> np.ndarray:
"""Plus Directional Indicator.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Smoothing period (default 14).
Returns
-------
numpy.ndarray
Array of +DI values.
"""
try:
return _plus_di(_to_f64(high), _to_f64(low), _to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def MINUS_DI(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod: int = 14,
) -> np.ndarray:
"""Minus Directional Indicator.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Smoothing period (default 14).
Returns
-------
numpy.ndarray
Array of -DI values.
"""
try:
return _minus_di(_to_f64(high), _to_f64(low), _to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def DX(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod: int = 14,
) -> np.ndarray:
"""Directional Movement Index.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Smoothing period (default 14).
Returns
-------
numpy.ndarray
Array of DX values (0–100).
"""
try:
return _dx(_to_f64(high), _to_f64(low), _to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def ADX(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod: int = 14,
) -> np.ndarray:
"""Average Directional Movement Index.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Smoothing period (default 14).
Returns
-------
numpy.ndarray
Array of ADX values (0–100).
"""
try:
return _adx(_to_f64(high), _to_f64(low), _to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def DMI(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod: int = 14,
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Directional Movement Index — ``(PLUS_DI, MINUS_DI, ADX)`` in one pass.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Wilder smoothing period (default 14).
Returns
-------
plus_di, minus_di, adx : numpy.ndarray
The three ADX-family series, identical to calling ``PLUS_DI``,
``MINUS_DI``, and ``ADX`` separately.
"""
try:
return _dmi(_to_f64(high), _to_f64(low), _to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def ADXR(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod: int = 14,
) -> np.ndarray:
"""Average Directional Movement Index Rating.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod : int, optional
Smoothing period (default 14).
Returns
-------
numpy.ndarray
Array of ADXR values (0–100).
"""
try:
return _adxr(_to_f64(high), _to_f64(low), _to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def TRIX(close: ArrayLike, timeperiod: int = 30) -> np.ndarray:
"""1-day Rate-Of-Change of a Triple Smooth EMA.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
EMA period (default 30).
Returns
-------
numpy.ndarray
Array of TRIX values.
"""
try:
return _trix(_to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def ULTOSC(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod1: int = 7,
timeperiod2: int = 14,
timeperiod3: int = 28,
) -> np.ndarray:
"""Ultimate Oscillator.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod1 : int, optional
First period (default 7).
timeperiod2 : int, optional
Second period (default 14).
timeperiod3 : int, optional
Third period (default 28).
Returns
-------
numpy.ndarray
Array of ULTOSC values (0–100).
"""
try:
return _ultosc(
_to_f64(high),
_to_f64(low),
_to_f64(close),
timeperiod1,
timeperiod2,
timeperiod3,
)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def ELDER_RAY(
high: ArrayLike,
low: ArrayLike,
close: ArrayLike,
timeperiod: int = 13,
) -> tuple[np.ndarray, np.ndarray]:
"""Elder Ray Index — bull power and bear power versus EMA(close).
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
close : array-like
Sequence of closing prices.
timeperiod : int, optional
EMA period (default 13).
Returns
-------
tuple[numpy.ndarray, numpy.ndarray]
``(bull_power, bear_power)``. Leading ``timeperiod - 1`` entries
are ``NaN``.
"""
try:
return _elder_ray(_to_f64(high), _to_f64(low), _to_f64(close), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def FISHER(
high: ArrayLike,
low: ArrayLike,
timeperiod: int = 9,
) -> tuple[np.ndarray, np.ndarray]:
"""Ehlers Fisher Transform of median price.
Parameters
----------
high : array-like
Sequence of high prices.
low : array-like
Sequence of low prices.
timeperiod : int, optional
Highest / lowest lookback (default 9).
Returns
-------
tuple[numpy.ndarray, numpy.ndarray]
``(fisher, signal)`` where ``signal`` is the previous Fisher value.
Leading ``timeperiod - 1`` Fisher entries are ``NaN``.
"""
try:
return _fisher(_to_f64(high), _to_f64(low), timeperiod)
except ValueError as e:
_normalize_rust_error(e)
[docs]
def CRSI(
close: ArrayLike,
timeperiod: int = 3,
streakperiod: int = 2,
rankperiod: int = 100,
) -> np.ndarray:
"""Connors RSI — average of price RSI, streak RSI, and ROC percent rank.
Parameters
----------
close : array-like
Sequence of closing prices.
timeperiod : int, optional
RSI period of close (default 3).
streakperiod : int, optional
RSI period of the up/down streak (default 2).
rankperiod : int, optional
Percent-rank lookback of 1-bar ROC (default 100).
Returns
-------
numpy.ndarray
Array of Connors RSI values (0–100).
"""
try:
return _crsi(_to_f64(close), timeperiod, streakperiod, rankperiod)
except ValueError as e:
_normalize_rust_error(e)
__all__ = [
"RSI",
"MOM",
"ROC",
"ROCP",
"ROCR",
"ROCR100",
"WILLR",
"AROON",
"AROONOSC",
"CCI",
"MFI",
"BOP",
"STOCHF",
"STOCH",
"STOCHRSI",
"APO",
"PPO",
"CMO",
"PLUS_DM",
"MINUS_DM",
"PLUS_DI",
"MINUS_DI",
"DX",
"ADX",
"DMI",
"ADXR",
"TRIX",
"ULTOSC",
"TRANGE",
"ELDER_RAY",
"FISHER",
"CRSI",
]