def PRANGE(df, n, max_price='High', min_price='Low'):
"""
%Range
"""
max_list = jhta.MAX(df, n, max_price)
min_list = jhta.MIN(df, n, min_price)
prange_list = []
i = 0
while i < 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)
i += 1
return prange_list
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 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 NORMALIZE(df, price_max='High', price_min='Low', price='Close'):
"""
Normalize
Returns: list of floats = jhta.NORMALIZE(df, price_max='High', price_min='Low', price='Close')
Source: https://machinelearningmastery.com/normalize-standardize-time-series-data-python/
"""
normalize_list = []
start = None
for i in range(len(df[price])):
if df[price_max][i] != df[price_max][i] or df[price_min][i] != df[price_min][i] or df[price][i] != df[price][i] or i < 1:
normalize = float('NaN')
else:
if start is None:
start = i
x_max = df[price_max][start:]
norm_max = jhta.MAX({'x': x_max}, len(x_max), 'x')[-1]
x_min = df[price_min][start:]
norm_min = jhta.MIN({'x': x_min}, len(x_min), 'x')[-1]
normalize = (df[price][i] - norm_min) / (norm_max - norm_min)
normalize_list.append(normalize)
return normalize_list
def NORMALIZE(df, price_max='High', price_min='Low', price='Close'):
"""
Normalize
"""
normalize_list = []
i = 0
start = None
while i < len(df[price]):
if df[price_max][i] != df[price_max][i] or df[price_min][i] != df[
price_min][i] or df[price][i] != df[price][i] or i < 1:
normalize = float('NaN')
else:
if start is None:
start = i
x_max = df[price_max][start:]
norm_max = jhta.MAX({'x': x_max}, len(x_max), 'x')[-1]
x_min = df[price_min][start:]
norm_min = jhta.MIN({'x': x_min}, len(x_min), 'x')[-1]
normalize = (df[price][i] - norm_min) / (norm_max - norm_min)
normalize_list.append(normalize)
i += 1
return normalize_list