https://github.com/bukosabino/ta
Tip revision: 8b926c5a0ef0397c1da080978a56453babd2de35 authored by Dario Lopez Padial on 10 April 2019, 10:36:20 UTC
code style max line length 119
code style max line length 119
Tip revision: 8b926c5
trend.py
# -*- coding: utf-8 -*-
"""
.. module:: trend
:synopsis: Trend Indicators.
.. moduleauthor:: Dario Lopez Padial (Bukosabino)
"""
import numpy as np
import pandas as pd
from .utils import *
def macd(close, n_fast=12, n_slow=26, fillna=False):
"""Moving Average Convergence Divergence (MACD)
Is a trend-following momentum indicator that shows the relationship between
two moving averages of prices.
https://en.wikipedia.org/wiki/MACD
Args:
close(pandas.Series): dataset 'Close' column.
n_fast(int): n period short-term.
n_slow(int): n period long-term.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
emafast = ema(close, n_fast, fillna)
emaslow = ema(close, n_slow, fillna)
macd = emafast - emaslow
if fillna:
macd = macd.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(macd, name='MACD_%d_%d' % (n_fast, n_slow))
def macd_signal(close, n_fast=12, n_slow=26, n_sign=9, fillna=False):
"""Moving Average Convergence Divergence (MACD Signal)
Shows EMA of MACD.
https://en.wikipedia.org/wiki/MACD
Args:
close(pandas.Series): dataset 'Close' column.
n_fast(int): n period short-term.
n_slow(int): n period long-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
emafast = ema(close, n_fast, fillna)
emaslow = ema(close, n_slow, fillna)
macd = emafast - emaslow
macd_signal = ema(macd, n_sign, fillna)
if fillna:
macd_signal = macd_signal.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(macd_signal, name='MACD_sign')
def macd_diff(close, n_fast=12, n_slow=26, n_sign=9, fillna=False):
"""Moving Average Convergence Divergence (MACD Diff)
Shows the relationship between MACD and MACD Signal.
https://en.wikipedia.org/wiki/MACD
Args:
close(pandas.Series): dataset 'Close' column.
n_fast(int): n period short-term.
n_slow(int): n period long-term.
n_sign(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
emafast = ema(close, n_fast, fillna)
emaslow = ema(close, n_slow, fillna)
macd = emafast - emaslow
macdsign = ema(macd, n_sign, fillna)
macd_diff = macd - macdsign
if fillna:
macd_diff = macd_diff.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(macd_diff, name='MACD_diff')
def ema_indicator(close, n=12, fillna=False):
"""EMA
Exponential Moving Average via Pandas
Args:
close(pandas.Series): dataset 'Close' column.
n_fast(int): n period short-term.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
ema_ = ema(close, n, fillna)
return pd.Series(ema_, name='ema')
def adx(high, low, close, n=14, fillna=False):
"""Average Directional Movement Index (ADX)
The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI)
are derived from smoothed averages of these differences, and measure trend
direction over time. These two indicators are often referred to
collectively as the Directional Movement Indicator (DMI).
The Average Directional Index (ADX) is in turn derived from the smoothed
averages of the difference between +DI and -DI, and measures the strength
of the trend (regardless of direction) over time.
Using these three indicators together, chartists can determine both the
direction and strength of the trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
cs = close.shift(1)
pdm = high.combine(cs, lambda x1, x2: get_min_max(x1, x2, 'max'))
pdn = low.combine(cs, lambda x1, x2: get_min_max(x1, x2, 'min'))
tr = pdm - pdn
trs_initial = np.zeros(n-1)
trs = np.zeros(len(close) - (n - 1))
trs[0] = tr.dropna()[0:n].sum()
tr = tr.reset_index(drop=True)
for i in range(1, len(trs)-1):
trs[i] = trs[i-1] - (trs[i-1]/float(n)) + tr[n+i]
up = high - high.shift(1)
dn = low.shift(1) - low
pos = abs(((up > dn) & (up > 0)) * up)
neg = abs(((dn > up) & (dn > 0)) * dn)
dip_mio = np.zeros(len(close) - (n - 1))
dip_mio[0] = pos.dropna()[0:n].sum()
pos = pos.reset_index(drop=True)
for i in range(1, len(dip_mio)-1):
dip_mio[i] = dip_mio[i-1] - (dip_mio[i-1]/float(n)) + pos[n+i]
din_mio = np.zeros(len(close) - (n - 1))
din_mio[0] = neg.dropna()[0:n].sum()
neg = neg.reset_index(drop=True)
for i in range(1, len(din_mio)-1):
din_mio[i] = din_mio[i-1] - (din_mio[i-1]/float(n)) + neg[n+i]
dip = np.zeros(len(trs))
for i in range(len(trs)):
dip[i] = 100 * (dip_mio[i]/trs[i])
din = np.zeros(len(trs))
for i in range(len(trs)):
din[i] = 100 * (din_mio[i]/trs[i])
dx = 100 * np.abs((dip - din) / (dip + din))
adx = np.zeros(len(trs))
adx[n] = dx[0:n].mean()
for i in range(n+1, len(adx)):
adx[i] = ((adx[i-1] * (n - 1)) + dx[i-1]) / float(n)
adx = np.concatenate((trs_initial, adx), axis=0)
adx = pd.Series(data=adx, index=close.index)
if fillna:
adx = adx.replace([np.inf, -np.inf], np.nan).fillna(20)
return pd.Series(adx, name='adx')
def adx_pos(high, low, close, n=14, fillna=False):
"""Average Directional Movement Index Positive (ADX)
The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI)
are derived from smoothed averages of these differences, and measure trend
direction over time. These two indicators are often referred to
collectively as the Directional Movement Indicator (DMI).
The Average Directional Index (ADX) is in turn derived from the smoothed
averages of the difference between +DI and -DI, and measures the strength
of the trend (regardless of direction) over time.
Using these three indicators together, chartists can determine both the
direction and strength of the trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
cs = close.shift(1)
pdm = high.combine(cs, lambda x1, x2: get_min_max(x1, x2, 'max'))
pdn = low.combine(cs, lambda x1, x2: get_min_max(x1, x2, 'min'))
tr = pdm - pdn
trs_initial = np.zeros(n-1)
trs = np.zeros(len(close) - (n - 1))
trs[0] = tr.dropna()[0:n].sum()
tr = tr.reset_index(drop=True)
for i in range(1, len(trs)-1):
trs[i] = trs[i-1] - (trs[i-1]/float(n)) + tr[n+i]
up = high - high.shift(1)
dn = low.shift(1) - low
pos = abs(((up > dn) & (up > 0)) * up)
neg = abs(((dn > up) & (dn > 0)) * dn)
dip_mio = np.zeros(len(close) - (n - 1))
dip_mio[0] = pos.dropna()[0:n].sum()
pos = pos.reset_index(drop=True)
for i in range(1, len(dip_mio)-1):
dip_mio[i] = dip_mio[i-1] - (dip_mio[i-1]/float(n)) + pos[n+i]
dip = np.zeros(len(close))
for i in range(1, len(trs)-1):
dip[i+n] = 100 * (dip_mio[i]/trs[i])
dip = pd.Series(data=dip, index=close.index)
if fillna:
dip = dip.replace([np.inf, -np.inf], np.nan).fillna(20)
return pd.Series(dip, name='adx_pos')
def adx_neg(high, low, close, n=14, fillna=False):
"""Average Directional Movement Index Negative (ADX)
The Plus Directional Indicator (+DI) and Minus Directional Indicator (-DI)
are derived from smoothed averages of these differences, and measure trend
direction over time. These two indicators are often referred to
collectively as the Directional Movement Indicator (DMI).
The Average Directional Index (ADX) is in turn derived from the smoothed
averages of the difference between +DI and -DI, and measures the strength
of the trend (regardless of direction) over time.
Using these three indicators together, chartists can determine both the
direction and strength of the trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:average_directional_index_adx
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
cs = close.shift(1)
pdm = high.combine(cs, lambda x1, x2: get_min_max(x1, x2, 'max'))
pdn = low.combine(cs, lambda x1, x2: get_min_max(x1, x2, 'min'))
tr = pdm - pdn
trs_initial = np.zeros(n-1)
trs = np.zeros(len(close) - (n - 1))
trs[0] = tr.dropna()[0:n].sum()
tr = tr.reset_index(drop=True)
for i in range(1, len(trs)-1):
trs[i] = trs[i-1] - (trs[i-1]/float(n)) + tr[n+i]
up = high - high.shift(1)
dn = low.shift(1) - low
pos = abs(((up > dn) & (up > 0)) * up)
neg = abs(((dn > up) & (dn > 0)) * dn)
din_mio = np.zeros(len(close) - (n - 1))
din_mio[0] = neg.dropna()[0:n].sum()
neg = neg.reset_index(drop=True)
for i in range(1, len(din_mio)-1):
din_mio[i] = din_mio[i-1] - (din_mio[i-1]/float(n)) + neg[n+i]
din = np.zeros(len(close))
for i in range(1, len(trs)-1):
din[i+n] = 100 * (din_mio[i]/float(trs[i]))
din = pd.Series(data=din, index=close.index)
if fillna:
din = din.replace([np.inf, -np.inf], np.nan).fillna(20)
return pd.Series(din, name='adx_neg')
def vortex_indicator_pos(high, low, close, n=14, fillna=False):
"""Vortex Indicator (VI)
It consists of two oscillators that capture positive and negative trend
movement. A bullish signal triggers when the positive trend indicator
crosses above the negative trend indicator or a key level.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
tr = (high.combine(close.shift(1, fill_value=close.mean()), max)
- low.combine(close.shift(1, fill_value=close.mean()), min))
trn = tr.rolling(n).sum()
vmp = np.abs(high - low.shift(1, fill_value=low.mean()))
vmm = np.abs(low - high.shift(1, fill_value=high.mean()))
vip = vmp.rolling(n, min_periods=0).sum() / trn
if fillna:
vip = vip.replace([np.inf, -np.inf], np.nan).fillna(1)
return pd.Series(vip, name='vip')
def vortex_indicator_neg(high, low, close, n=14, fillna=False):
"""Vortex Indicator (VI)
It consists of two oscillators that capture positive and negative trend
movement. A bearish signal triggers when the negative trend indicator
crosses above the positive trend indicator or a key level.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
tr = high.combine(close.shift(1), max) - low.combine(close.shift(1), min)
trn = tr.rolling(n).sum()
vmp = np.abs(high - low.shift(1))
vmm = np.abs(low - high.shift(1))
vin = vmm.rolling(n).sum() / trn
if fillna:
vin = vin.replace([np.inf, -np.inf], np.nan).fillna(1)
return pd.Series(vin, name='vin')
def trix(close, n=15, fillna=False):
"""Trix (TRIX)
Shows the percent rate of change of a triple exponentially smoothed moving
average.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:trix
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
ema1 = ema(close, n, fillna)
ema2 = ema(ema1, n, fillna)
ema3 = ema(ema2, n, fillna)
trix = (ema3 - ema3.shift(1, fill_value=ema3.mean())) / ema3.shift(1, fill_value=ema3.mean())
trix *= 100
if fillna:
trix = trix.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(trix, name='trix_'+str(n))
def mass_index(high, low, n=9, n2=25, fillna=False):
"""Mass Index (MI)
It uses the high-low range to identify trend reversals based on range
expansions. It identifies range bulges that can foreshadow a reversal of
the current trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
n(int): n low period.
n2(int): n high period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
amplitude = high - low
ema1 = ema(amplitude, n, fillna)
ema2 = ema(ema1, n, fillna)
mass = ema1 / ema2
mass = mass.rolling(n2, min_periods=0).sum()
if fillna:
mass = mass.replace([np.inf, -np.inf], np.nan).fillna(n2)
return pd.Series(mass, name='mass_index_'+str(n))
def cci(high, low, close, n=20, c=0.015, fillna=False):
"""Commodity Channel Index (CCI)
CCI measures the difference between a security's price change and its
average price change. High positive readings indicate that prices are well
above their average, which is a show of strength. Low negative readings
indicate that prices are well below their average, which is a show of
weakness.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:commodity_channel_index_cci
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
c(int): constant.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
pp = (high + low + close) / 3.0
cci = (pp - pp.rolling(n, min_periods=0).mean()) / (c * pp.rolling(n, min_periods=0).std())
if fillna:
cci = cci.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(cci, name='cci')
def dpo(close, n=20, fillna=False):
"""Detrended Price Oscillator (DPO)
Is an indicator designed to remove trend from price and make it easier to
identify cycles.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
dpo = close.shift(int((0.5 * n) + 1), fill_value=close.mean()) - close.rolling(n, min_periods=0).mean()
if fillna:
dpo = dpo.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(dpo, name='dpo_'+str(n))
def kst(close, r1=10, r2=15, r3=20, r4=30, n1=10, n2=10, n3=10, n4=15, fillna=False):
"""KST Oscillator (KST)
It is useful to identify major stock market cycle junctures because its
formula is weighed to be more greatly influenced by the longer and more
dominant time spans, in order to better reflect the primary swings of stock
market cycle.
https://en.wikipedia.org/wiki/KST_oscillator
Args:
close(pandas.Series): dataset 'Close' column.
r1(int): r1 period.
r2(int): r2 period.
r3(int): r3 period.
r4(int): r4 period.
n1(int): n1 smoothed period.
n2(int): n2 smoothed period.
n3(int): n3 smoothed period.
n4(int): n4 smoothed period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
rocma1 = ((close - close.shift(r1, fill_value=close.mean()))
/ close.shift(r1, fill_value=close.mean())).rolling(n1, min_periods=0).mean()
rocma2 = ((close - close.shift(r2, fill_value=close.mean()))
/ close.shift(r2, fill_value=close.mean())).rolling(n2, min_periods=0).mean()
rocma3 = ((close - close.shift(r3, fill_value=close.mean()))
/ close.shift(r3, fill_value=close.mean())).rolling(n3, min_periods=0).mean()
rocma4 = ((close - close.shift(r4, fill_value=close.mean()))
/ close.shift(r4, fill_value=close.mean())).rolling(n4, min_periods=0).mean()
kst = 100 * (rocma1 + 2 * rocma2 + 3 * rocma3 + 4 * rocma4)
if fillna:
kst = kst.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(kst, name='kst')
def kst_sig(close, r1=10, r2=15, r3=20, r4=30, n1=10, n2=10, n3=10, n4=15, nsig=9, fillna=False):
"""KST Oscillator (KST Signal)
It is useful to identify major stock market cycle junctures because its
formula is weighed to be more greatly influenced by the longer and more
dominant time spans, in order to better reflect the primary swings of stock
market cycle.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:know_sure_thing_kst
Args:
close(pandas.Series): dataset 'Close' column.
r1(int): r1 period.
r2(int): r2 period.
r3(int): r3 period.
r4(int): r4 period.
n1(int): n1 smoothed period.
n2(int): n2 smoothed period.
n3(int): n3 smoothed period.
n4(int): n4 smoothed period.
nsig(int): n period to signal.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
rocma1 = ((close - close.shift(r1, fill_value=close.mean()))
/ close.shift(r1, fill_value=close.mean())).rolling(n1, min_periods=0).mean()
rocma2 = ((close - close.shift(r2, fill_value=close.mean()))
/ close.shift(r2, fill_value=close.mean())).rolling(n2, min_periods=0).mean()
rocma3 = ((close - close.shift(r3, fill_value=close.mean()))
/ close.shift(r3, fill_value=close.mean())).rolling(n3, min_periods=0).mean()
rocma4 = ((close - close.shift(r4, fill_value=close.mean()))
/ close.shift(r4, fill_value=close.mean())).rolling(n4, min_periods=0).mean()
kst = 100 * (rocma1 + 2 * rocma2 + 3 * rocma3 + 4 * rocma4)
kst_sig = kst.rolling(nsig, min_periods=0).mean()
if fillna:
kst_sig = kst_sig.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(kst_sig, name='kst_sig')
def ichimoku_a(high, low, n1=9, n2=26, visual=False, fillna=False):
"""Ichimoku Kinkō Hyō (Ichimoku)
It identifies the trend and look for potential signals within that trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
n1(int): n1 low period.
n2(int): n2 medium period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
conv = 0.5 * (high.rolling(n1, min_periods=0).max() + low.rolling(n1, min_periods=0).min())
base = 0.5 * (high.rolling(n2, min_periods=0).max() + low.rolling(n2, min_periods=0).min())
spana = 0.5 * (conv + base)
if visual:
spana = spana.shift(n2, fill_value=spana.mean())
if fillna:
spana = spana.replace([np.inf, -np.inf], np.nan).fillna(method='backfill')
return pd.Series(spana, name='ichimoku_a_'+str(n2))
def ichimoku_b(high, low, n2=26, n3=52, visual=False, fillna=False):
"""Ichimoku Kinkō Hyō (Ichimoku)
It identifies the trend and look for potential signals within that trend.
http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ichimoku_cloud
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
n2(int): n2 medium period.
n3(int): n3 high period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
spanb = 0.5 * (high.rolling(n3, min_periods=0).max() + low.rolling(n3, min_periods=0).min())
if visual:
spanb = spanb.shift(n2, fill_value=spanb.mean())
if fillna:
spanb = spanb.replace([np.inf, -np.inf], np.nan).fillna(method='backfill')
return pd.Series(spanb, name='ichimoku_b_'+str(n2))
def aroon_up(close, n=25, fillna=False):
"""Aroon Indicator (AI)
Identify when trends are likely to change direction (uptrend).
Aroon Up - ((N - Days Since N-day High) / N) x 100
https://www.investopedia.com/terms/a/aroon.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
aroon_up = close.rolling(n, min_periods=0).apply(lambda x: float(np.argmax(x) + 1) / n * 100, raw=True)
if fillna:
aroon_up = aroon_up.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(aroon_up, name='aroon_up'+str(n))
def aroon_down(close, n=25, fillna=False):
"""Aroon Indicator (AI)
Identify when trends are likely to change direction (downtrend).
Aroon Down - ((N - Days Since N-day Low) / N) x 100
https://www.investopedia.com/terms/a/aroon.asp
Args:
close(pandas.Series): dataset 'Close' column.
n(int): n period.
fillna(bool): if True, fill nan values.
Returns:
pandas.Series: New feature generated.
"""
aroon_down = close.rolling(n, min_periods=0).apply(lambda x: float(np.argmin(x) + 1) / n * 100, raw=True)
if fillna:
aroon_down = aroon_down.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(aroon_down, name='aroon_down'+str(n))