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volatility_indicators.py
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volatility_indicators.py
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""""""
# Import Built-Ins:
import math
import statistics
# Import Third-Party:
# Import Homebrew:
import jhtalib as jhta
def AEM(df, high='High', low='Low', volume='Volume'):
"""
Arms Ease of Movement
Returns: list of floats = jhta.AEM(df, high='High', low='Low', volume='Volume')
Source: https://www.fmlabs.com/reference/default.htm?url=ArmsEMV.htm
"""
aem_list = []
for i in range(len(df[low])):
if i < 1:
aem = float('NaN')
else:
midpoint_move = ((df[high][i] - df[low][i]) / 2) - ((df[high][i - 1] - df[low][i - 1]) / 2)
boxratio = (df[volume][i] / 10000) / (df[high][i] - df[low][i])
aem = midpoint_move / boxratio
aem_list.append(aem)
return aem_list
def ATR(df, n, high='High', low='Low', close='Close'):
"""
Average True Range
Returns: list of floats = jhta.ATR(df, n, high='High', low='Low', close='Close')
Source: https://www.fmlabs.com/reference/default.htm?url=ATR.htm
"""
tr_list = jhta.TRANGE(df, high, low, close)
atr_list = []
for i in range(len(df[close])):
if i + 1 < n:
atr = float('NaN')
else:
atr = ((tr_list[i - 1] * (n - 1)) + tr_list[i]) / n
atr_list.append(atr)
return atr_list
def AVOLA(df, n=30, na=252, price='Close'):
"""
Annual Volatility
Returns: list of floats = jhta.AVOLA(df, n=30, na=252, price='Close')
Source: https://www.wallstreetmojo.com/volatility-formula/
"""
dvola_list = jhta.DVOLA(df, n, price)
avola_list = []
for i in range(len(df[price])):
if i + 1 < n:
avola = float('NaN')
else:
avola = math.sqrt(na) * dvola_list[i]
avola_list.append(avola)
return avola_list
def DVOLA(df, n=30, price='Close'):
"""
Daily Volatility
Returns: list of floats = jhta.DVOLA(df, n=30, price='Close')
Source: https://www.wallstreetmojo.com/volatility-formula/
"""
dvola_list = []
for i in range(len(df[price])):
if i + 1 < n:
dvola = float('NaN')
else:
start = i + 1 - n
end = i + 1
pvariance = statistics.pvariance(df[price][start:end])
dvola = math.sqrt(pvariance)
dvola_list.append(dvola)
return dvola_list
def INERTIA(df, n, price='Close'):
"""
Inertia
Returns: list of floats = jhta.INERTIA(df, n, price='Close')
Source: https://www.fmlabs.com/reference/default.htm?url=Inertia.htm
"""
rvioc = jhta.RVIOC(df, n, price)
return jhta.LSMA({'rvioc': rvioc}, n, 'rvioc')
def NATR(df, n):
"""
Normalized Average True Range
"""
def PRANGE(df, n, max_price='High', min_price='Low'):
"""
%Range
Returns: list of floats = jhta.PRANGE(df, n, max_price='High', min_price='Low')
Source: book: An Introduction to Algorithmic Trading
"""
max_list = jhta.MAX(df, n, max_price)
min_list = jhta.MIN(df, n, min_price)
prange_list = []
for i in range(len(df[max_price])):
if i + 1 < n:
prange = float('NaN')
else:
prange = (max_list[i] - min_list[i]) / ((max_list[i] + min_list[i]) / 2) * 100
prange_list.append(prange)
return prange_list
def RVI(df, n, high='High', low='Low'):
"""
Relative Volatility Index
Returns: list of floats = jhta.RVI(df, n, high='High', low='Low')
Source: https://www.fmlabs.com/reference/default.htm?url=RVI.htm
"""
rvi_list = []
h_upavg = .0
h_dnavg = .0
l_upavg = .0
l_dnavg = .0
for i in range(len(df[low])):
if i + 1 < n or i < 9:
h_rvi = float('NaN')
l_rvi = float('NaN')
else:
start = i + 1 - n
end = i + 1
h = df[high][start:end]
h_stdev = jhta.STDEV({'h': h}, 9, 'h')[-1]
if df[high][i] > df[high][i - 1]:
h_up = h_stdev
h_dn = 0
else:
h_up = 0
h_dn = h_stdev
h_upavg = (h_upavg * (n - 1) + h_up) / n
h_dnavg = (h_dnavg * (n - 1) + h_dn) / n
h_rvi = 100 * h_upavg / (h_upavg + h_dnavg)
l = df[low][start:end]
l_stdev = jhta.STDEV({'l': l}, 9, 'l')[-1]
if df[low][i] > df[low][i - 1]:
l_up = l_stdev
l_dn = 0
else:
l_up = 0
l_dn = l_stdev
l_upavg = (l_upavg * (n - 1) + l_up) / n
l_dnavg = (l_dnavg * (n - 1) + l_dn) / n
l_rvi = 100 * l_upavg / (l_upavg + l_dnavg)
rvi = (h_rvi + l_rvi) / 2
rvi_list.append(rvi)
return rvi_list
def RVIOC(df, n, price='Close'):
"""
Relative Volatility Index Original Calculation
Returns: list of floats = jhta.RVIOC(df, n, price='Close')
Source: https://www.fmlabs.com/reference/default.htm?url=RVIoriginal.htm
"""
rvioc_list = []
upavg = .0
dnavg = .0
for i in range(len(df[price])):
if i + 1 < n or i < 9:
rvioc = float('NaN')
else:
start = i + 1 - n
end = i + 1
y_list = df[price][start:end]
stdev = jhta.STDEV({'y': y_list}, 9, 'y')[-1]
if df[price][i] > df[price][i - 1]:
up = stdev
dn = 0
else:
up = 0
dn = stdev
upavg = (upavg * (n - 1) + up) / n
dnavg = (dnavg * (n - 1) + dn) / n
rvioc = 100 * upavg / (upavg + dnavg)
rvioc_list.append(rvioc)
return rvioc_list
def TRANGE(df, high='High', low='Low', close='Close'):
"""
True Range
Returns: list of floats = jhta.TRANGE(df, high='High', low='Low', close='Close')
Source: https://www.fmlabs.com/reference/default.htm?url=TR.htm
"""
tr_list = []
for i in range(len(df[close])):
if i < 1:
tr = float('NaN')
else:
true_high = df[high][i]
if df[close][i - 1] > df[high][i]:
true_high = df[close][i - 1]
true_low = df[low][i]
if df[close][i - 1] < df[low][i]:
true_low = df[close][i - 1]
tr = true_high - true_low
tr_list.append(tr)
return tr_list