def rsi(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
"""Indicator: Relative Strength Index (RSI)"""
# Validate arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 14
scalar = float(scalar) if scalar else 100
drift = get_drift(drift)
offset = get_offset(offset)
# Calculate Result
negative = close.diff(drift)
positive = negative.copy()
positive[positive < 0] = 0 # Make negatives 0 for the postive series
negative[negative > 0] = 0 # Make postives 0 for the negative series
positive_avg = positive.ewm(com=length, adjust=False).mean()
negative_avg = negative.ewm(com=length, adjust=False).mean().abs()
rsi = scalar * positive_avg / (positive_avg + negative_avg)
# Offset
if offset != 0:
rsi = rsi.shift(offset)
# Handle fills
if 'fillna' in kwargs:
rsi.fillna(kwargs['fillna'], inplace=True)
if 'fill_method' in kwargs:
rsi.fillna(method=kwargs['fill_method'], inplace=True)
# Name and Categorize it
rsi.name = f"RSI_{length}"
rsi.category = 'momentum'
signal_indicators = kwargs.pop('signal_indicators', False)
if signal_indicators:
signalsdf = concat(
[
DataFrame(
{rsi.name: rsi}
),
signals(
indicator=rsi,
xa=kwargs.pop('xa', 80),
xb=kwargs.pop('xb', 20),
xserie=kwargs.pop('xserie', None),
xserie_a=kwargs.pop('xserie_a', None),
xserie_b=kwargs.pop('xserie_b', None),
cross_values=kwargs.pop('cross_values', False),
cross_series=kwargs.pop('cross_series', True),
offset=offset,
),
],
axis=1
)
return signalsdf
else:
return rsi
def rsi(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
"""Indicator: Relative Strength Index (RSI)"""
# Validate arguments
length = int(length) if length and length > 0 else 14
scalar = float(scalar) if scalar else 100
close = verify_series(close, length)
drift = get_drift(drift)
offset = get_offset(offset)
if close is None: return
# Calculate Result
negative = close.diff(drift)
positive = negative.copy()
positive[positive < 0] = 0 # Make negatives 0 for the postive series
negative[negative > 0] = 0 # Make postives 0 for the negative series
positive_avg = rma(positive, length=length)
negative_avg = rma(negative, length=length)
rsi = scalar * positive_avg / (positive_avg + negative_avg.abs())
# Offset
if offset != 0:
rsi = rsi.shift(offset)
# Handle fills
if "fillna" in kwargs:
rsi.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
rsi.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
rsi.name = f"RSI_{length}"
rsi.category = "momentum"
signal_indicators = kwargs.pop("signal_indicators", False)
if signal_indicators:
signalsdf = concat(
[
DataFrame({rsi.name: rsi}),
signals(
indicator=rsi,
xa=kwargs.pop("xa", 80),
xb=kwargs.pop("xb", 20),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", False),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
],
axis=1,
)
return signalsdf
else:
return rsi
def er(close, length=None, drift=None, offset=None, **kwargs):
"""Indicator: Efficiency Ratio (ER)"""
# Validate arguments
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
drift = get_drift(drift)
if close is None: return
# Calculate Result
abs_diff = close.diff(length).abs()
abs_volatility = close.diff(drift).abs()
er = abs_diff
er /= abs_volatility.rolling(window=length).sum()
# Offset
if offset != 0:
er = er.shift(offset)
# Handle fills
if "fillna" in kwargs:
er.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
er.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
er.name = f"ER_{length}"
er.category = "momentum"
signal_indicators = kwargs.pop("signal_indicators", False)
if signal_indicators:
signalsdf = concat(
[
DataFrame({er.name: er}),
signals(
indicator=er,
xa=kwargs.pop("xa", 80),
xb=kwargs.pop("xb", 20),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", False),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
],
axis=1,
)
return signalsdf
else:
return er
def macd(close, fast=None, slow=None, signal=None, offset=None, **kwargs):
"""Indicator: Moving Average, Convergence/Divergence (MACD)"""
# Validate arguments
close = verify_series(close)
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
signal = int(signal) if signal and signal > 0 else 9
if slow < fast:
fast, slow = slow, fast
offset = get_offset(offset)
# Calculate Result
fastma = ema(close, length=fast)
slowma = ema(close, length=slow)
macd = fastma - slowma
signalma = ema(close=macd, length=signal)
histogram = macd - signalma
# Offset
if offset != 0:
macd = macd.shift(offset)
histogram = histogram.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
macd.fillna(kwargs["fillna"], inplace=True)
histogram.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
macd.fillna(method=kwargs["fill_method"], inplace=True)
histogram.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{fast}_{slow}_{signal}"
macd.name = f"MACD{_props}"
histogram.name = f"MACDh{_props}"
signalma.name = f"MACDs{_props}"
macd.category = histogram.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {macd.name: macd, histogram.name: histogram, signalma.name: signalma}
df = DataFrame(data)
df.name = f"MACD{_props}"
df.category = macd.category
signal_indicators = kwargs.pop("signal_indicators", False)
if signal_indicators:
signalsdf = concat(
[
df,
signals(
indicator=histogram,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", True),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
signals(
indicator=macd,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", False),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
],
axis=1,
)
return signalsdf
else:
return df
def rsx(close, length=None, drift=None, offset=None, **kwargs):
"""Indicator: Relative Strength Xtra (inspired by Jurik RSX)"""
# Validate arguments
length = int(length) if length and length > 0 else 14
close = verify_series(close, length)
drift = get_drift(drift)
offset = get_offset(offset)
if close is None: return
# variables
vC, v1C = 0, 0
v4, v8, v10, v14, v18, v20 = 0, 0, 0, 0, 0, 0
f0, f8, f10, f18, f20, f28, f30, f38 = 0, 0, 0, 0, 0, 0, 0, 0
f40, f48, f50, f58, f60, f68, f70, f78 = 0, 0, 0, 0, 0, 0, 0, 0
f80, f88, f90 = 0, 0, 0
# Calculate Result
m = close.size
result = [npNaN for _ in range(0, length - 1)] + [0]
for i in range(length, m):
if f90 == 0:
f90 = 1.0
f0 = 0.0
if length - 1.0 >= 5:
f88 = length - 1.0
else:
f88 = 5.0
f8 = 100.0 * close.iloc[i]
f18 = 3.0 / (length + 2.0)
f20 = 1.0 - f18
else:
if f88 <= f90:
f90 = f88 + 1
else:
f90 = f90 + 1
f10 = f8
f8 = 100 * close.iloc[i]
v8 = f8 - f10
f28 = f20 * f28 + f18 * v8
f30 = f18 * f28 + f20 * f30
vC = 1.5 * f28 - 0.5 * f30
f38 = f20 * f38 + f18 * vC
f40 = f18 * f38 + f20 * f40
v10 = 1.5 * f38 - 0.5 * f40
f48 = f20 * f48 + f18 * v10
f50 = f18 * f48 + f20 * f50
v14 = 1.5 * f48 - 0.5 * f50
f58 = f20 * f58 + f18 * abs(v8)
f60 = f18 * f58 + f20 * f60
v18 = 1.5 * f58 - 0.5 * f60
f68 = f20 * f68 + f18 * v18
f70 = f18 * f68 + f20 * f70
v1C = 1.5 * f68 - 0.5 * f70
f78 = f20 * f78 + f18 * v1C
f80 = f18 * f78 + f20 * f80
v20 = 1.5 * f78 - 0.5 * f80
if f88 >= f90 and f8 != f10:
f0 = 1.0
if f88 == f90 and f0 == 0.0:
f90 = 0.0
if f88 < f90 and v20 > 0.0000000001:
v4 = (v14 / v20 + 1.0) * 50.0
if v4 > 100.0:
v4 = 100.0
if v4 < 0.0:
v4 = 0.0
else:
v4 = 50.0
result.append(v4)
rsx = Series(result, index=close.index)
# Offset
if offset != 0:
rsx = rsx.shift(offset)
# Handle fills
if "fillna" in kwargs:
rsx.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
rsx.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
rsx.name = f"RSX_{length}"
rsx.category = "momentum"
signal_indicators = kwargs.pop("signal_indicators", False)
if signal_indicators:
signalsdf = concat([
DataFrame({rsx.name: rsx}),
signals(
indicator=rsx,
xa=kwargs.pop("xa", 80),
xb=kwargs.pop("xb", 20),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", False),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
],
axis=1)
return signalsdf
else:
return rsx
def macd(close,
fast=None,
slow=None,
signal=None,
talib=None,
offset=None,
**kwargs):
"""Indicator: Moving Average, Convergence/Divergence (MACD)"""
# Validate arguments
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
signal = int(signal) if signal and signal > 0 else 9
if slow < fast:
fast, slow = slow, fast
close = verify_series(close, max(fast, slow, signal))
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if close is None: return
as_mode = kwargs.setdefault("asmode", False)
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import MACD
macd, signalma, histogram = MACD(close, fast, slow, signal)
else:
fastma = ema(close, length=fast)
slowma = ema(close, length=slow)
macd = fastma - slowma
signalma = ema(close=macd.loc[macd.first_valid_index():, ],
length=signal)
histogram = macd - signalma
if as_mode:
macd = macd - signalma
signalma = ema(close=macd.loc[macd.first_valid_index():, ],
length=signal)
histogram = macd - signalma
# Offset
if offset != 0:
macd = macd.shift(offset)
histogram = histogram.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
macd.fillna(kwargs["fillna"], inplace=True)
histogram.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
macd.fillna(method=kwargs["fill_method"], inplace=True)
histogram.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_asmode = "AS" if as_mode else ""
_props = f"_{fast}_{slow}_{signal}"
macd.name = f"MACD{_asmode}{_props}"
histogram.name = f"MACD{_asmode}h{_props}"
signalma.name = f"MACD{_asmode}s{_props}"
macd.category = histogram.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {
macd.name: macd,
histogram.name: histogram,
signalma.name: signalma
}
df = DataFrame(data)
df.name = f"MACD{_asmode}{_props}"
df.category = macd.category
signal_indicators = kwargs.pop("signal_indicators", False)
if signal_indicators:
signalsdf = concat(
[
df,
signals(
indicator=histogram,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", True),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
signals(
indicator=macd,
xa=kwargs.pop("xa", 0),
xb=kwargs.pop("xb", None),
xserie=kwargs.pop("xserie", None),
xserie_a=kwargs.pop("xserie_a", None),
xserie_b=kwargs.pop("xserie_b", None),
cross_values=kwargs.pop("cross_values", False),
cross_series=kwargs.pop("cross_series", True),
offset=offset,
),
],
axis=1,
)
return signalsdf
else:
return df
