def fisher(high, low, length=None, signal=None, offset=None, **kwargs):
"""Indicator: Fisher Transform (FISHT)"""
# Validate Arguments
length = int(length) if length and length > 0 else 9
signal = int(signal) if signal and signal > 0 else 1
_length = max(length, signal)
high = verify_series(high, _length)
low = verify_series(low, _length)
offset = get_offset(offset)
if high is None or low is None: return
# Calculate Result
hl2_ = hl2(high, low)
highest_hl2 = hl2_.rolling(length).max()
lowest_hl2 = hl2_.rolling(length).min()
hlr = high_low_range(highest_hl2, lowest_hl2)
hlr[hlr < 0.001] = 0.001
position = ((hl2_ - lowest_hl2) / hlr) - 0.5
v = 0
m = high.size
result = [npNaN for _ in range(0, length - 1)] + [0]
for i in range(length, m):
v = 0.66 * position.iloc[i] + 0.67 * v
if v < -0.99: v = -0.999
if v > 0.99: v = 0.999
result.append(0.5 * (nplog((1 + v) / (1 - v)) + result[i - 1]))
fisher = Series(result, index=high.index)
signalma = fisher.shift(signal)
# Offset
if offset != 0:
fisher = fisher.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
fisher.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
fisher.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{length}_{signal}"
fisher.name = f"FISHERT{_props}"
signalma.name = f"FISHERTs{_props}"
fisher.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {fisher.name: fisher, signalma.name: signalma}
df = DataFrame(data)
df.name = f"FISHERT{_props}"
df.category = fisher.category
return df
def eom(
high,
low,
close,
volume,
length=None,
divisor=None,
drift=None,
offset=None,
**kwargs,
):
"""Indicator: Ease of Movement (EOM)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
volume = verify_series(volume)
length = int(length) if length and length > 0 else 14
divisor = divisor if divisor and divisor > 0 else 100000000
drift = get_drift(drift)
offset = get_offset(offset)
# Calculate Result
high_low_range = non_zero_range(high, low)
distance = hl2(high=high, low=low) - hl2(high=high.shift(drift),
low=low.shift(drift))
box_ratio = volume / divisor
box_ratio /= high_low_range
eom = distance / box_ratio
eom = sma(eom, length=length)
# Offset
if offset != 0:
eom = eom.shift(offset)
# Handle fills
if "fillna" in kwargs:
eom.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
eom.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
eom.name = f"EOM_{length}_{divisor}"
eom.category = "volume"
return eom
def fisher(high, low, length=None, signal=None, offset=None, **kwargs):
"""Indicator: Fisher Transform (FISHT)"""
# Validate Arguments
high = verify_series(high)
low = verify_series(low)
length = int(length) if length and length > 0 else 9
signal = int(signal) if signal and signal > 0 else 5
offset = get_offset(offset)
# Calculate Result
m = high.size
hl2_ = hl2(high, low)
max_high = hl2_.rolling(length).max()
min_low = hl2_.rolling(length).min()
hl2_range = max_high - min_low
hl2_range[hl2_range < 1e-5] = 0.001
position = (hl2_ - min_low) / hl2_range
v = 0
fish = 0
result = [npNaN for _ in range(0, length - 1)]
for i in range(length - 1, m):
v = 0.66 * (position[i] - 0.5) + 0.67 * v
if v > 0.99: v = 0.999
if v < -0.99: v = -0.999
fish = 0.5 * (fish + nplog((1 + v) / (1 - v)))
result.append(fish)
fisher = Series(result, index=high.index)
signalma = ema(fisher, length=signal)
# Offset
if offset != 0:
fisher = fisher.shift(offset)
signalma = signalma.shift(offset)
# Handle fills
if "fillna" in kwargs:
fisher.fillna(kwargs["fillna"], inplace=True)
signalma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
fisher.fillna(method=kwargs["fill_method"], inplace=True)
signalma.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{length}_{signal}"
fisher.name = f"FISHERT{_props}"
signalma.name = f"FISHERTs{_props}"
fisher.category = signalma.category = "momentum"
# Prepare DataFrame to return
data = {fisher.name: fisher, signalma.name: signalma}
df = DataFrame(data)
df.name = f"FISHERT{_props}"
df.category = fisher.category
return df
def ttm_trend(high, low, close, length=None, offset=None, **kwargs):
"""Indicator: TTM Trend (TTM_TRND)"""
# Validate arguments
length = int(length) if length and length > 0 else 6
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
if high is None or low is None or close is None: return
# Calculate Result
trend_avg = hl2(high, low)
for i in range(1, length):
trend_avg = trend_avg + hl2(high.shift(i), low.shift(i))
trend_avg = trend_avg / length
tm_trend = (close > trend_avg).astype(int)
tm_trend.replace(0, -1, inplace=True)
# Offset
if offset != 0:
tm_trend = tm_trend.shift(offset)
# Handle fills
if "fillna" in kwargs:
tm_trend.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
tm_trend.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
tm_trend.name = f"TTM_TRND_{length}"
tm_trend.category = "momentum"
# Prepare DataFrame to return
data = {tm_trend.name: tm_trend}
df = DataFrame(data)
df.name = f"TTMTREND_{length}"
df.category = tm_trend.category
return df
def supertrend(high, low, close, length=None, multiplier=None, offset=None, **kwargs):
"""Indicator: Supertrend"""
# Validate Arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
length = int(length) if length and length > 0 else 7
multiplier = float(multiplier) if multiplier and multiplier > 0 else 3.
offset = get_offset(offset)
# Calculate Results
m = close.size
dir_, trend = [1] * m, [0] * m
long, short = [npNaN] * m, [npNaN] * m
hl2_ = hl2(high, low)
matr = multiplier * atr(high, low, close, length)
upperband = hl2_ + matr
lowerband = hl2_ - matr
for i in range(1, m):
if close.iloc[i] > upperband.iloc[i - 1]:
dir_[i] = 1
elif close.iloc[i] < lowerband.iloc[i - 1]:
dir_[i] = -1
else:
dir_[i] = dir_[i - 1]
if dir_[i] > 0 and lowerband.iloc[i] < lowerband.iloc[i - 1]:
lowerband.iloc[i] = lowerband.iloc[i - 1]
if dir_[i] < 0 and upperband.iloc[i] > upperband.iloc[i - 1]:
upperband.iloc[i] = upperband.iloc[i - 1]
if dir_[i] > 0:
trend[i] = long[i] = lowerband.iloc[i]
else:
trend[i] = short[i] = upperband.iloc[i]
# Prepare DataFrame to return
_props = f"_{length}_{multiplier}"
df = DataFrame({
f"SUPERT{_props}": trend,
f"SUPERTd{_props}": dir_,
f"SUPERTl{_props}": long,
f"SUPERTs{_props}": short
}, index=close.index)
df.name = f"SUPERT{_props}"
df.category = "overlap"
# Apply offset if needed
if offset != 0:
df = df.shift(offset)
# Handle fills
if "fillna" in kwargs:
df.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
df.fillna(method=kwargs["fill_method"], inplace=True)
return df
def pmax(high,
low,
close,
length=None,
multiplier=None,
mamode=None,
offset=None,
**kwargs):
"""Indicator: PMAX"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
mamode = mamode.lower() if mamode and isinstance(mamode, str) else "ema"
multiplier = float(multiplier) if multiplier and multiplier > 0 else 3.0
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
if high is None or low is None or close is None: return
# Calculate Results
m = close.size
dir_, trend = [1] * m, [0] * m
long, short = [npNaN] * m, [npNaN] * m
hl2_ = hl2(high, low)
mavg = ma(mamode, hl2_, length=length)
matr = multiplier * atr(high, low, close, length)
upperband = mavg + matr
lowerband = mavg - matr
for i in range(1, m):
if mavg.iloc[i] > upperband.iloc[i - 1]:
dir_[i] = 1
elif mavg.iloc[i] < lowerband.iloc[i - 1]:
dir_[i] = -1
else:
dir_[i] = dir_[i - 1]
if dir_[i] > 0 and lowerband.iloc[i] < lowerband.iloc[i - 1]:
lowerband.iloc[i] = lowerband.iloc[i - 1]
if dir_[i] < 0 and upperband.iloc[i] > upperband.iloc[i - 1]:
upperband.iloc[i] = upperband.iloc[i - 1]
if dir_[i] > 0:
trend[i] = long[i] = lowerband.iloc[i]
else:
trend[i] = short[i] = upperband.iloc[i]
# Prepare DataFrame to return
_props = f"_{length}_{multiplier}"
df = DataFrame(
{
f"PMAX{_props}": trend,
f"PMAXd{_props}": dir_,
f"PMAXSL{_props}": mavg,
f"PMAXlong{_props}": long,
f"PMAXshort{_props}": short
},
index=close.index)
df.name = f"PMAX{_props}"
df.category = "overlap"
# Apply offset if needed
if offset != 0:
df = df.shift(offset)
# Handle fills
if "fillna" in kwargs:
df.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
df.fillna(method=kwargs["fill_method"], inplace=True)
return df
