def COR(x_list, y_list):
"""
Correlation
Returns: float = jhta.COR(x_list, y_list)
"""
x_stdev = jhta.STDEV({'x_list': x_list}, len(x_list), 'x_list')[-1]
y_stdev = jhta.STDEV({'y_list': y_list}, len(y_list), 'y_list')[-1]
return jhta.COV(x_list, y_list) / (x_stdev * y_stdev)
def RVIOC(df, n, price='Close'):
"""
Relative Volatility Index Original Calculation
"""
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 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 RVI(df, n, high='High', low='Low'):
"""
Relative Volatility Index
"""
rvi_list = []
h_upavg = .0
h_dnavg = .0
l_upavg = .0
l_dnavg = .0
i = 0
while i < 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)
i += 1
return rvi_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 BBANDW(df, n, f=2, high='High', low='Low', close='Close'):
"""
Bollinger Band Width
Returns: list of floats = jhta.BBANDW(df, n, f=2, high='High', low='Low', close='Close')
Source: https://www.fmlabs.com/reference/default.htm?url=BollingerWidth.htm
"""
bbandw_list = []
tp_dict = {'tp': jhta.TYPPRICE(df, high, low, close)}
stdev_list = jhta.STDEV(tp_dict, n, 'tp')
for i in range(len(df[close])):
if i + 1 < n:
bbandw = float('NaN')
else:
bbandw = 2 * f * stdev_list[i]
bbandw_list.append(bbandw)
return bbandw_list
def BBANDW(df, n, f=2, high='High', low='Low', close='Close'):
"""
Bollinger Band Width
"""
bbandw_list = []
tp_dict = {'tp': jhta.TYPPRICE(df, high, low, close)}
stdev_list = jhta.STDEV(tp_dict, n, 'tp')
i = 0
while i < len(df[close]):
if i + 1 < n:
bbandw = float('NaN')
else:
bbandw = 2 * f * stdev_list[i]
bbandw_list.append(bbandw)
i += 1
return bbandw_list
def BBANDW(df, n, f=2):
"""
Bollinger Band Width
"""
bbandw_list = []
tp_dict = {'tp': jhta.TYPPRICE(df)}
stdev_list = jhta.STDEV(tp_dict, n, 'tp')
i = 0
while i < len(df['Close']):
if i + 1 < n:
bbandw = float('NaN')
else:
bbandw = 2 * f * stdev_list[i]
bbandw_list.append(bbandw)
i += 1
return bbandw_list
def STANDARDIZE(df, price='Close'):
"""
Standardize
Returns: list of floats = jhta.STANDARDIZE(df, price='Close')
Source: https://machinelearningmastery.com/normalize-standardize-time-series-data-python/
"""
standardize_list = []
start = None
for i in range(len(df[price])):
if df[price][i] != df[price][i] or i < 1:
standardize = float('NaN')
else:
if start is None:
start = i
x = df[price][start:]
mean = jhta.MEAN({'x': x}, len(x), 'x')[-1]
standard_deviation = jhta.STDEV({'x': x}, len(x), 'x')[-1]
standardize = (df[price][i] - mean) / standard_deviation
standardize_list.append(standardize)
return standardize_list
def STANDARDIZE(df, price='Close'):
"""
Standardize
"""
standardize_list = []
i = 0
start = None
while i < len(df[price]):
if df[price][i] != df[price][i] or i < 1:
standardize = float('NaN')
else:
if start is None:
start = i
x = df[price][start:]
mean = jhta.MEAN({'x': x}, len(x), 'x')[-1]
standard_deviation = jhta.STDEV({'x': x}, len(x), 'x')[-1]
standardize = (df[price][i] - mean) / standard_deviation
standardize_list.append(standardize)
i += 1
return standardize_list
def INFO(df, price='Close'):
"""
Print df Information
"""
print('{:_<28}:{:_>22}'.format('DF PRICE COLUMN', price))
print('{:_<28}:{:_>22d}'.format('LEN', len(df[price])))
print('{:_<28}:{:_>28.5f}'.format('MIN',
jhta.MIN(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format('MAX',
jhta.MAX(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format('SUM',
jhta.SUM(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format('MEAN',
jhta.MEAN(df, len(df[price]),
price)[-1]))
# print ('{:_<28}:{:_>28.5f}'.format('HARMONIC_MEAN', jhta.HARMONIC_MEAN(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format(
'MEDIAN',
jhta.MEDIAN(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format(
'MEDIAN_LOW',
jhta.MEDIAN_LOW(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format(
'MEDIAN_HIGH',
jhta.MEDIAN_HIGH(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format(
'MEDIAN_GROUPED',
jhta.MEDIAN_GROUPED(df, len(df[price]), price)[-1]))
# print ('{:_<28}:{:_>28.5f}'.format('MODE', jhta.MODE(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format(
'PSTDEV',
jhta.PSTDEV(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format(
'PVARIANCE',
jhta.PVARIANCE(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format(
'STDEV',
jhta.STDEV(df, len(df[price]), price)[-1]))
print('{:_<28}:{:_>28.5f}'.format(
'VARIANCE',
jhta.VARIANCE(df, len(df[price]), price)[-1]))
def BBANDS(df, n, f=2, high='High', low='Low', close='Close'):
"""
Bollinger Bands
Returns: dict of lists of floats = jhta.BBANDS(df, n, f=2, high='High', low='Low', close='Close')
Source: https://www.fmlabs.com/reference/default.htm?url=Bollinger.htm
"""
bbands_dict = {'midband': [], 'upperband': [], 'lowerband': []}
tp_dict = {'tp': jhta.TYPPRICE(df, high, low, close)}
sma_list = jhta.SMA(tp_dict, n, 'tp')
stdev_list = jhta.STDEV(tp_dict, n, 'tp')
for i in range(len(df[close])):
if i + 1 < n:
midband = float('NaN')
upperband = float('NaN')
lowerband = float('NaN')
else:
midband = sma_list[i]
upperband = midband + f * stdev_list[i]
lowerband = midband - f * stdev_list[i]
bbands_dict['midband'].append(midband)
bbands_dict['upperband'].append(upperband)
bbands_dict['lowerband'].append(lowerband)
return bbands_dict
def BBANDS(df, n, f=2, high='High', low='Low', close='Close'):
"""
Bollinger Bands
"""
bbands_dict = {'midband': [], 'upperband': [], 'lowerband': []}
tp_dict = {'tp': jhta.TYPPRICE(df, high, low, close)}
sma_list = SMA(tp_dict, n, 'tp')
stdev_list = jhta.STDEV(tp_dict, n, 'tp')
i = 0
while i < len(df[close]):
if i + 1 < n:
midband = float('NaN')
upperband = float('NaN')
lowerband = float('NaN')
else:
midband = sma_list[i]
upperband = midband + f * stdev_list[i]
lowerband = midband - f * stdev_list[i]
bbands_dict['midband'].append(midband)
bbands_dict['upperband'].append(upperband)
bbands_dict['lowerband'].append(lowerband)
i += 1
return bbands_dict