def MFI(df, n, high='High', low='Low', close='Close', volume='Volume'):
"""
Money Flow Index
"""
mfi_list = []
typprice_list = jhta.TYPPRICE(df, high, low, close)
mf_pos_list = []
mf_neg_list = []
i = 0
while i < len(df[low]):
mf = typprice_list[i] * df[volume][i]
if i + 1 < n:
mfi = float('NaN')
mf_pos_list.append(float('NaN'))
mf_neg_list.append(float('NaN'))
else:
start = i + 1 - n
end = i + 1
if typprice_list[i] > typprice_list[i - 1]:
mf_pos_list.append(mf)
mf_neg_list.append(.0)
else:
mf_pos_list.append(.0)
mf_neg_list.append(mf)
x = sum(mf_pos_list[start:end])
# FIX ZeroDivisionError: float division by zero:
y = sum(mf_neg_list[start:end]) or .00000001
mr = x / y
mfi = 100 - (100 / (1 + mr))
mfi_list.append(mfi)
i += 1
return mfi_list
def MFI(df, n, high='High', low='Low', close='Close', volume='Volume'):
"""
Money Flow Index
Returns: list of floats = jhta.MFI(df, n, high='High', low='Low', close='Close', volume='Volume')
Source: https://www.fmlabs.com/reference/default.htm?url=MoneyFlowIndex.htm
"""
mfi_list = []
typprice_list = jhta.TYPPRICE(df, high, low, close)
mf_pos_list = []
mf_neg_list = []
for i in range(len(df[low])):
mf = typprice_list[i] * df[volume][i]
if i + 1 < n:
mfi = float('NaN')
mf_pos_list.append(float('NaN'))
mf_neg_list.append(float('NaN'))
else:
start = i + 1 - n
end = i + 1
if typprice_list[i] > typprice_list[i - 1]:
mf_pos_list.append(mf)
mf_neg_list.append(.0)
else:
mf_pos_list.append(.0)
mf_neg_list.append(mf)
x = sum(mf_pos_list[start:end])
# FIX ZeroDivisionError: float division by zero:
y = sum(mf_neg_list[start:end]) or .00000001
mr = x / y
mfi = 100 - (100 / (1 + mr))
mfi_list.append(mfi)
return mfi_list
def STYPP(df, high='High', low='Low', close='Close'):
"""
Swing Typical Price - previous Typical Price
Returns: list of floats = jhta.STYPP(df, high='High', low='Low', close='Close')
"""
stypp_list = []
typp_list = jhta.TYPPRICE(df, high, low, close)
for i in range(len(df[close])):
if i < 1:
stypp = float('NaN')
else:
stypp = typp_list[i] - typp_list[i - 1]
stypp_list.append(stypp)
return stypp_list
def JH_STYPP(df):
"""
Swing Typical Price - previous Typical Price
"""
stypp_list = []
typp_list = jhta.TYPPRICE(df)
i = 0
while i < len(df['Close']):
if i < 1:
stypp = float('NaN')
else:
stypp = typp_list[i] - typp_list[i - 1]
stypp_list.append(stypp)
i += 1
return stypp_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 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
