def efi(close,
volume,
length=None,
mamode=None,
drift=None,
offset=None,
**kwargs):
"""Indicator: Elder's Force Index (EFI)"""
# Validate arguments
length = int(length) if length and length > 0 else 13
mamode = mamode if isinstance(mamode, str) else "ema"
close = verify_series(close, length)
volume = verify_series(volume, length)
drift = get_drift(drift)
offset = get_offset(offset)
if close is None or volume is None: return
# Calculate Result
pv_diff = close.diff(drift) * volume
efi = ma(mamode, pv_diff, length=length)
# Offset
if offset != 0:
efi = efi.shift(offset)
# Handle fills
if "fillna" in kwargs:
efi.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
efi.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
efi.name = f"EFI_{length}"
efi.category = "volume"
return efi
def massi(high, low, fast=None, slow=None, offset=None, **kwargs):
"""Indicator: Mass Index (MASSI)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
high_low_range = non_zero_range(high, low)
fast = int(fast) if fast and fast > 0 else 9
slow = int(slow) if slow and slow > 0 else 25
if slow < fast:
fast, slow = slow, fast
min_periods = int(
kwargs["min_periods"]) if "min_periods" in kwargs and kwargs[
"min_periods"] is not None else fast
offset = get_offset(offset)
# Calculate Result
hl_ema1 = ema(close=high_low_range, length=fast, **kwargs)
hl_ema2 = ema(close=hl_ema1, length=fast, **kwargs)
hl_ratio = hl_ema1 / hl_ema2
massi = hl_ratio.rolling(slow, min_periods=slow).sum()
# Offset
if offset != 0:
massi = massi.shift(offset)
# Handle fills
if "fillna" in kwargs:
massi.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
massi.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
massi.name = f"MASSI_{fast}_{slow}"
massi.category = "volatility"
return massi
def bias(close, length=None, mamode=None, offset=None, **kwargs):
"""Indicator: Bias (BIAS)"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 26
mamode = mamode.lower() if mamode else None
offset = get_offset(offset)
# Calculate Result
if mamode is None or mamode == "sma":
ma = sma(close, length=length, **kwargs)
if mamode == "ema":
ma = ema(close, length=length, **kwargs)
if mamode == "hma":
ma = hma(close, length=length, **kwargs)
if mamode == "rma":
ma = rma(close, length=length, **kwargs)
if mamode == "wma":
ma = wma(close, length=length, **kwargs)
bias = (close / ma) - 1
# Offset
if offset != 0:
bias = bias.shift(offset)
# Handle fills
if "fillna" in kwargs:
bias.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
bias.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
bias.name = f"BIAS_{ma.name}"
bias.category = "momentum"
return bias
def decay(close, kind=None, length=None, mode=None, offset=None, **kwargs):
"""Indicator: Decay"""
# Validate Arguments
length = int(length) if length and length > 0 else 5
mode = mode.lower() if isinstance(mode, str) else "linear"
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
# Calculate Result
_mode = "L"
if mode == "exp" or kind == "exponential":
_mode = "EXP"
diff = close.shift(1) - npExp(-length)
else: # "linear"
diff = close.shift(1) - (1 / length)
diff[0] = close[0]
tdf = DataFrame({"close": close, "diff": diff, "0": 0})
ld = tdf.max(axis=1)
# Offset
if offset != 0:
ld = ld.shift(offset)
# Handle fills
if "fillna" in kwargs:
ld.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
ld.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
ld.name = f"{_mode}DECAY_{length}"
ld.category = "trend"
return ld
def willr(high, low, close, length=None, offset=None, **kwargs):
"""Indicator: William's Percent R (WILLR)"""
# Validate arguments
length = int(length) if length and length > 0 else 14
min_periods = int(
kwargs["min_periods"]) if "min_periods" in kwargs and kwargs[
"min_periods"] is not None else length
_length = max(length, min_periods)
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
lowest_low = low.rolling(length, min_periods=min_periods).min()
highest_high = high.rolling(length, min_periods=min_periods).max()
willr = 100 * ((close - lowest_low) / (highest_high - lowest_low) - 1)
# Offset
if offset != 0:
willr = willr.shift(offset)
# Handle fills
if "fillna" in kwargs:
willr.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
willr.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
willr.name = f"WILLR_{length}"
willr.category = "momentum"
return willr
def ui(close, length=None, scalar=None, offset=None, **kwargs):
"""Indicator: Ulcer Index (UI)"""
# Validate arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 14
scalar = float(scalar) if scalar and scalar > 0 else 100
offset = get_offset(offset)
# Calculate Result
highest_close = close.rolling(length).max()
downside = scalar * (close - highest_close)
downside /= highest_close
d2 = downside * downside
everget = kwargs.pop("everget", False)
if everget:
# Everget uses SMA instead of SUM for calculation
ui = (sma(d2, length) / length).apply(npsqrt)
else:
ui = (d2.rolling(length).sum() / length).apply(npsqrt)
# Offset
if offset != 0:
ui = ui.shift(offset)
# Handle fills
if "fillna" in kwargs:
ui.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
ui.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
ui.name = f"UI{'' if not everget else 'e'}_{length}"
ui.category = "volatility"
return ui
def variance(close, length=None, ddof=1, offset=None, **kwargs):
"""Indicator: Variance"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 1 else 30
ddof = int(ddof) if ddof >= 0 and ddof < length else 1
min_periods = int(
kwargs["min_periods"]) if "min_periods" in kwargs and kwargs[
"min_periods"] is not None else length
offset = get_offset(offset)
# Calculate Result
variance = close.rolling(length, min_periods=min_periods).var(ddof)
# Offset
if offset != 0:
variance = variance.shift(offset)
# Name & Category
variance.name = f"VAR_{length}"
variance.category = "statistics"
return variance
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)
distance -= 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 midprice(high, low, length=None, offset=None, **kwargs):
"""Indicator: Midprice"""
# Validate arguments
length = int(length) if length and length > 0 else 2
min_periods = int(kwargs["min_periods"]) if "min_periods" in kwargs and kwargs["min_periods"] is not None else length
_length = max(length, min_periods)
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
if Imports["talib"]:
from talib import MIDPRICE
midprice = MIDPRICE(high, low, length)
else:
lowest_low = low.rolling(length, min_periods=min_periods).min()
highest_high = high.rolling(length, min_periods=min_periods).max()
midprice = 0.5 * (lowest_low + highest_high)
# Offset
if offset != 0:
midprice = midprice.shift(offset)
# Handle fills
if "fillna" in kwargs:
midprice.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
midprice.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
midprice.name = f"MIDPRICE_{length}"
midprice.category = "overlap"
return midprice
def sinwma(close, length=None, offset=None, **kwargs):
"""Indicator: Sine Weighted Moving Average (SINWMA) by Everget of TradingView"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 14
offset = get_offset(offset)
# Calculate Result
sines = Series(
[sin((i + 1) * pi / (length + 1)) for i in range(0, length)])
w = sines / sines.sum()
sinwma = close.rolling(length, min_periods=length).apply(weights(w),
raw=True)
# Offset
if offset != 0:
sinwma = sinwma.shift(offset)
# Name & Category
sinwma.name = f"SINWMA_{length}"
sinwma.category = "overlap"
return sinwma
def hma(close, length=None, offset=None, **kwargs):
"""Indicator: Hull Moving Average (HMA)"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 10
offset = get_offset(offset)
# Calculate Result
half_length = int(length / 2)
sqrt_length = int(sqrt(length))
wmaf = wma(close=close, length=half_length)
wmas = wma(close=close, length=length)
hma = wma(close=2 * wmaf - wmas, length=sqrt_length)
# Offset
if offset != 0:
hma = hma.shift(offset)
# Name & Category
hma.name = f"HMA_{length}"
hma.category = "overlap"
return hma
def pwma(close, length=None, asc=None, offset=None, **kwargs):
"""Indicator: Pascals Weighted Moving Average (PWMA)"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
asc = asc if asc else True
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
# Calculate Result
triangle = pascals_triangle(n=length - 1, weighted=True)
pwma = close.rolling(length, min_periods=length).apply(weights(triangle),
raw=True)
# Offset
if offset != 0:
pwma = pwma.shift(offset)
# Name & Category
pwma.name = f"PWMA_{length}"
pwma.category = "overlap"
return pwma
def apo(close, fast=None, slow=None, offset=None, **kwargs):
"""Indicator: Absolute Price Oscillator (APO)"""
# Validate Arguments
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
if slow < fast:
fast, slow = slow, fast
close = verify_series(close, max(fast, slow))
offset = get_offset(offset)
if close is None: return
# Calculate Result
if Imports["talib"]:
from talib import APO
apo = APO(close, fast, slow)
else:
fastma = sma(close, length=fast)
slowma = sma(close, length=slow)
apo = fastma - slowma
# Offset
if offset != 0:
apo = apo.shift(offset)
# Handle fills
if "fillna" in kwargs:
apo.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
apo.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
apo.name = f"APO_{fast}_{slow}"
apo.category = "momentum"
return apo
def kurtosis(close, length=None, offset=None, **kwargs):
"""Indicator: Kurtosis"""
# Validate Arguments
length = int(length) if length and length > 0 else 30
min_periods = int(
kwargs["min_periods"]) if "min_periods" in kwargs and kwargs[
"min_periods"] is not None else length
close = verify_series(close, max(length, min_periods))
offset = get_offset(offset)
if close is None: return
# Calculate Result
kurtosis = close.rolling(length, min_periods=min_periods).kurt()
# Offset
if offset != 0:
kurtosis = kurtosis.shift(offset)
# Name & Category
kurtosis.name = f"KURT_{length}"
kurtosis.category = "statistics"
return kurtosis
def mcgd(close, length=None, offset=None, c=None, **kwargs):
"""Indicator: McGinley Dynamic Indicator"""
# Validate arguments
length = int(length) if length and length > 0 else 10
c = float(c) if c and 0 < c <= 1 else 1
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
# Calculate Result
close = close.copy()
def mcg_(series):
denom = (c * length * (series.iloc[1] / series.iloc[0]) ** 4)
series.iloc[1] = (series.iloc[0] + ((series.iloc[1] - series.iloc[0]) / denom))
return series.iloc[1]
mcg_cell = close[0:].rolling(2, min_periods=2).apply(mcg_, raw=False)
mcg_ds = close[:1].append(mcg_cell[1:])
# Offset
if offset != 0:
mcg_ds = mcg_ds.shift(offset)
# Handle fills
if "fillna" in kwargs:
mcg_ds.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
mcg_ds.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
mcg_ds.name = f"MCGD_{length}"
mcg_ds.category = "overlap"
return mcg_ds
def percent_return(close, length=None, cumulative=False, offset=None, **kwargs):
"""Indicator: Percent Return"""
# Validate Arguments
length = int(length) if length and length > 0 else 1
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
# Calculate Result
pct_return = close.pct_change(length)
if cumulative:
pct_return = pct_return.cumsum()
# Offset
if offset != 0:
pct_return = pct_return.shift(offset)
# Name & Category
pct_return.name = f"{'CUM' if cumulative else ''}PCTRET_{length}"
pct_return.category = "performance"
return pct_return
def true_range(high, low, close, drift=None, offset=None, **kwargs):
"""Indicator: True Range"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
drift = get_drift(drift)
offset = get_offset(offset)
# Calculate Result
if Imports["talib"]:
from talib import TRANGE
true_range = TRANGE(high, low, close)
else:
high_low_range = non_zero_range(high, low)
prev_close = close.shift(drift)
ranges = [high_low_range, high - prev_close, prev_close - low]
true_range = concat(ranges, axis=1)
true_range = true_range.abs().max(axis=1)
true_range.iloc[:drift] = npNaN
# Offset
if offset != 0:
true_range = true_range.shift(offset)
# Handle fills
if "fillna" in kwargs:
true_range.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
true_range.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
true_range.name = f"TRUERANGE_{drift}"
true_range.category = "volatility"
return true_range
def zscore(close, length=None, std=None, offset=None, **kwargs):
"""Indicator: Z Score"""
# Validate Arguments
length = int(length) if length and length > 1 else 30
std = float(std) if std and std > 1 else 1
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
# Calculate Result
std *= stdev(close=close, length=length, **kwargs)
mean = sma(close=close, length=length, **kwargs)
zscore = (close - mean) / std
# Offset
if offset != 0:
zscore = zscore.shift(offset)
# Name & Category
zscore.name = f"Z_{length}"
zscore.category = "statistics"
return zscore
def pdist(open_, high, low, close, drift=None, offset=None, **kwargs):
"""Indicator: Price Distance (PDIST)"""
# Validate Arguments
open_ = verify_series(open_)
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
drift = get_drift(drift)
offset = get_offset(offset)
# Calculate Result
pdist = 2 * non_zero_range(high, low)
pdist += non_zero_range(open_, close.shift(drift)).abs()
pdist -= non_zero_range(close, open_).abs()
# Offset
if offset != 0:
pdist = pdist.shift(offset)
# Name & Category
pdist.name = "PDIST"
pdist.category = "volatility"
return pdist
def adosc(high, low, close, volume, open_=None, fast=None, slow=None, offset=None, **kwargs):
"""Indicator: Accumulation/Distribution Oscillator"""
# Validate Arguments
fast = int(fast) if fast and fast > 0 else 3
slow = int(slow) if slow and slow > 0 else 10
_length = max(fast, slow)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
volume = verify_series(volume, _length)
offset = get_offset(offset)
if "length" in kwargs: kwargs.pop("length")
if high is None or low is None or close is None or volume is None: return
# Calculate Result
ad_ = ad(high=high, low=low, close=close, volume=volume, open_=open_)
fast_ad = ema(close=ad_, length=fast, **kwargs)
slow_ad = ema(close=ad_, length=slow, **kwargs)
adosc = fast_ad - slow_ad
# Offset
if offset != 0:
adosc = adosc.shift(offset)
# Handle fills
if "fillna" in kwargs:
adosc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
adosc.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
adosc.name = f"ADOSC_{fast}_{slow}"
adosc.category = "volume"
return adosc
def tema(close, length=None, offset=None, **kwargs):
"""Indicator: Triple Exponential Moving Average (TEMA)"""
# Validate Arguments
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
# Calculate Result
ema1 = ema(close=close, length=length, **kwargs)
ema2 = ema(close=ema1, length=length, **kwargs)
ema3 = ema(close=ema2, length=length, **kwargs)
tema = 3 * (ema1 - ema2) + ema3
# Offset
if offset != 0:
tema = tema.shift(offset)
# Name & Category
tema.name = f"TEMA_{length}"
tema.category = "overlap"
return tema
def vwap(high, low, close, volume, anchor=None, offset=None, **kwargs):
"""Indicator: Volume Weighted Average Price (VWAP)"""
# Validate Arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
volume = verify_series(volume)
anchor = anchor.upper() if anchor and isinstance(anchor, str) and len(anchor) >= 1 else "D"
offset = get_offset(offset)
typical_price = hlc3(high=high, low=low, close=close)
if not is_datetime_ordered(volume):
print(f"[!] VWAP volume series is not datetime ordered. Results may not be as expected.")
if not is_datetime_ordered(typical_price):
print(f"[!] VWAP price series is not datetime ordered. Results may not be as expected.")
# Calculate Result
wp = typical_price * volume
vwap = wp.groupby(wp.index.to_period(anchor)).cumsum()
vwap /= volume.groupby(volume.index.to_period(anchor)).cumsum()
# Offset
if offset != 0:
vwap = vwap.shift(offset)
# Handle fills
if "fillna" in kwargs:
vwap.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
vwap.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
vwap.name = f"VWAP_{anchor}"
vwap.category = "overlap"
return vwap
def hlc3(high, low, close, offset=None, **kwargs):
"""Indicator: HLC3"""
# Validate Arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
offset = get_offset(offset)
# Calculate Result
if Imports["talib"]:
from talib import TYPPRICE
hlc3 = TYPPRICE(high, low, close)
else:
hlc3 = (high + low + close) / 3.0
# Offset
if offset != 0:
hlc3 = hlc3.shift(offset)
# Name & Category
hlc3.name = "HLC3"
hlc3.category = "overlap"
return hlc3
def squeeze(high,
low,
close,
bb_length=None,
bb_std=None,
kc_length=None,
kc_scalar=None,
mom_length=None,
mom_smooth=None,
use_tr=None,
offset=None,
**kwargs):
"""Indicator: Squeeze Momentum (SQZ)"""
# Validate arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
offset = get_offset(offset)
bb_length = int(bb_length) if bb_length and bb_length > 0 else 20
bb_std = float(bb_std) if bb_std and bb_std > 0 else 2.
kc_length = int(kc_length) if kc_length and kc_length > 0 else 20
kc_scalar = float(kc_scalar) if kc_scalar and kc_scalar > 0 else 1.5
mom_length = int(mom_length) if mom_length and mom_length > 0 else 12
mom_smooth = int(mom_smooth) if mom_smooth and mom_smooth > 0 else 6
use_tr = kwargs.setdefault("tr", True)
asint = kwargs.pop("asint", True)
mamode = kwargs.pop("mamode", "sma").lower()
lazybear = kwargs.pop("lazybear", False)
detailed = kwargs.pop("detailed", False)
def simplify_columns(df, n=3):
df.columns = df.columns.str.lower()
return [c.split('_')[0][n - 1:n] for c in df.columns]
# Calculate Result
bbd = bbands(close, length=bb_length, std=bb_std, mamode=mamode)
kch = kc(high,
low,
close,
length=kc_length,
scalar=kc_scalar,
mamode=mamode,
tr=use_tr)
# Simplify KC and BBAND column names for dynamic access
bbd.columns = simplify_columns(bbd)
kch.columns = simplify_columns(kch)
if lazybear:
highest_high = high.rolling(kc_length).max()
lowest_low = low.rolling(kc_length).min()
avg_ = 0.25 * (highest_high + lowest_low) + 0.5 * kch.b
squeeze = linreg(close - avg_, length=kc_length)
else:
momo = mom(close, length=mom_length)
if mamode == "ema":
squeeze = ema(momo, length=mom_smooth)
else:
squeeze = sma(momo, length=mom_smooth)
# Classify Squeezes
squeeze_on = (bbd.l > kch.l) & (bbd.u < kch.u)
squeeze_off = (bbd.l < kch.l) & (bbd.u > kch.u)
no_squeeze = ~squeeze_on & ~squeeze_off
# Offset
if offset != 0:
squeeze = squeeze.shift(offset)
squeeze_on = squeeze_on.shift(offset)
squeeze_off = squeeze_off.shift(offset)
no_squeeze = no_squeeze.shift(offset)
# Handle fills
if "fillna" in kwargs:
squeeze.fillna(kwargs["fillna"], inplace=True)
squeeze_on.fillna(kwargs["fillna"], inplace=True)
squeeze_off.fillna(kwargs["fillna"], inplace=True)
no_squeeze.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
squeeze.fillna(method=kwargs["fill_method"], inplace=True)
squeeze_on.fillna(method=kwargs["fill_method"], inplace=True)
squeeze_off.fillna(method=kwargs["fill_method"], inplace=True)
no_squeeze.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = "" if use_tr else "hlr"
_props += f"_{bb_length}_{bb_std}_{kc_length}_{kc_scalar}"
_props += "_LB" if lazybear else ""
squeeze.name = f"SQZ{_props}"
data = {
squeeze.name: squeeze,
f"SQZ_ON": squeeze_on.astype(int) if asint else squeeze_on,
f"SQZ_OFF": squeeze_off.astype(int) if asint else squeeze_off,
f"SQZ_NO": no_squeeze.astype(int) if asint else no_squeeze
}
df = DataFrame(data)
df.name = squeeze.name
df.category = squeeze.category = "momentum"
# Detailed Squeeze Series
if detailed:
pos_squeeze = squeeze[squeeze >= 0]
neg_squeeze = squeeze[squeeze < 0]
pos_inc, pos_dec = unsigned_differences(pos_squeeze, asint=True)
neg_inc, neg_dec = unsigned_differences(neg_squeeze, asint=True)
pos_inc *= squeeze
pos_dec *= squeeze
neg_dec *= squeeze
neg_inc *= squeeze
pos_inc.replace(0, npNaN, inplace=True)
pos_dec.replace(0, npNaN, inplace=True)
neg_dec.replace(0, npNaN, inplace=True)
neg_inc.replace(0, npNaN, inplace=True)
sqz_inc = squeeze * increasing(squeeze)
sqz_dec = squeeze * decreasing(squeeze)
sqz_inc.replace(0, npNaN, inplace=True)
sqz_dec.replace(0, npNaN, inplace=True)
# Handle fills
if "fillna" in kwargs:
sqz_inc.fillna(kwargs["fillna"], inplace=True)
sqz_dec.fillna(kwargs["fillna"], inplace=True)
pos_inc.fillna(kwargs["fillna"], inplace=True)
pos_dec.fillna(kwargs["fillna"], inplace=True)
neg_dec.fillna(kwargs["fillna"], inplace=True)
neg_inc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
sqz_inc.fillna(method=kwargs["fill_method"], inplace=True)
sqz_dec.fillna(method=kwargs["fill_method"], inplace=True)
pos_inc.fillna(method=kwargs["fill_method"], inplace=True)
pos_dec.fillna(method=kwargs["fill_method"], inplace=True)
neg_dec.fillna(method=kwargs["fill_method"], inplace=True)
neg_inc.fillna(method=kwargs["fill_method"], inplace=True)
df[f"SQZ_INC"] = sqz_inc
df[f"SQZ_DEC"] = sqz_dec
df[f"SQZ_PINC"] = pos_inc
df[f"SQZ_PDEC"] = pos_dec
df[f"SQZ_NDEC"] = neg_dec
df[f"SQZ_NINC"] = neg_inc
return df
def ebsw(close, length=None, bars=None, offset=None, **kwargs):
"""Indicator: Even Better SineWave (EBSW)"""
# Validate arguments
length = int(length) if length and length > 38 else 40
bars = int(bars) if bars and bars > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
if close is None: return
# variables
alpha1 = HP = 0 # alpha and HighPass
a1 = b1 = c1 = c2 = c3 = 0
Filt = Pwr = Wave = 0
lastClose = lastHP = 0
FilterHist = [0, 0] # Filter history
# Calculate Result
m = close.size
result = [npNaN for _ in range(0, length - 1)] + [0]
for i in range(length, m):
# HighPass filter cyclic components whose periods are shorter than Duration input
alpha1 = (1 - npSin(360 / length)) / npCos(360 / length)
HP = 0.5 * (1 + alpha1) * (close[i] - lastClose) + alpha1 * lastHP
# Smooth with a Super Smoother Filter from equation 3-3
a1 = npExp(-npSqrt(2) * npPi / bars)
b1 = 2 * a1 * npCos(npSqrt(2) * 180 / bars)
c2 = b1
c3 = -1 * a1 * a1
c1 = 1 - c2 - c3
Filt = c1 * (HP + lastHP) / 2 + c2 * FilterHist[1] + c3 * FilterHist[0]
# Filt = float("{:.8f}".format(float(Filt))) # to fix for small scientific notations, the big ones fail
# 3 Bar average of Wave amplitude and power
Wave = (Filt + FilterHist[1] + FilterHist[0]) / 3
Pwr = (Filt * Filt + FilterHist[1] * FilterHist[1] +
FilterHist[0] * FilterHist[0]) / 3
# Normalize the Average Wave to Square Root of the Average Power
Wave = Wave / npSqrt(Pwr)
# update storage, result
FilterHist.append(Filt) # append new Filt value
FilterHist.pop(
0) # remove first element of list (left) -> updating/trim
lastHP = HP
lastClose = close[i]
result.append(Wave)
ebsw = Series(result, index=close.index)
# Offset
if offset != 0:
ebsw = ebsw.shift(offset)
# Handle fills
if "fillna" in kwargs:
ebsw.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
ebsw.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
ebsw.name = f"EBSW_{length}_{bars}"
ebsw.category = "cycles"
return ebsw
def brar(open_,
high,
low,
close,
length=None,
scalar=None,
drift=None,
offset=None,
**kwargs):
"""Indicator: BRAR (BRAR)"""
# Validate Arguments
length = int(length) if length and length > 0 else 26
scalar = float(scalar) if scalar else 100
high_open_range = non_zero_range(high, open_)
open_low_range = non_zero_range(open_, low)
open_ = verify_series(open_, length)
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
drift = get_drift(drift)
offset = get_offset(offset)
if open_ is None or high is None or low is None or close is None: return
# Calculate Result
hcy = non_zero_range(high, close.shift(drift))
cyl = non_zero_range(close.shift(drift), low)
hcy[hcy < 0] = 0 # Zero negative values
cyl[cyl < 0] = 0 # ""
ar = scalar * high_open_range.rolling(length).sum()
ar /= open_low_range.rolling(length).sum()
br = scalar * hcy.rolling(length).sum()
br /= cyl.rolling(length).sum()
# Offset
if offset != 0:
ar = ar.shift(offset)
br = ar.shift(offset)
# Handle fills
if "fillna" in kwargs:
ar.fillna(kwargs["fillna"], inplace=True)
br.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
ar.fillna(method=kwargs["fill_method"], inplace=True)
br.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
_props = f"_{length}"
ar.name = f"AR{_props}"
br.name = f"BR{_props}"
ar.category = br.category = "momentum"
# Prepare DataFrame to return
brardf = DataFrame({ar.name: ar, br.name: br})
brardf.name = f"BRAR{_props}"
brardf.category = "momentum"
return brardf
def linreg(close, length=None, offset=None, **kwargs):
"""Indicator: Linear Regression"""
# Validate arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 14
min_periods = int(
kwargs["min_periods"]) if "min_periods" in kwargs and kwargs[
"min_periods"] is not None else length
offset = get_offset(offset)
angle = kwargs.pop("angle", False)
intercept = kwargs.pop("intercept", False)
degrees = kwargs.pop("degrees", False)
r = kwargs.pop("r", False)
slope = kwargs.pop("slope", False)
tsf = kwargs.pop("tsf", False)
# Calculate Result
x = range(1, length + 1) # [1, 2, ..., n] from 1 to n keeps Sum(xy) low
x_sum = 0.5 * length * (length + 1)
x2_sum = x_sum * (2 * length + 1) / 3
divisor = length * x2_sum - x_sum * x_sum
def linear_regression(series):
y_sum = series.sum()
xy_sum = (x * series).sum()
m = (length * xy_sum - x_sum * y_sum) / divisor
if slope:
return m
b = (y_sum * x2_sum - x_sum * xy_sum) / divisor
if intercept:
return b
if angle:
theta = math.atan(m)
if degrees:
theta *= 180 / math.pi
return theta
if r:
y2_sum = (series * series).sum()
rn = length * xy_sum - x_sum * y_sum
rd = math.sqrt(divisor * (length * y2_sum - y_sum * y_sum))
return rn / rd
return m * length + b if tsf else m * (length - 1) + b
linreg = close.rolling(length, min_periods=length).apply(linear_regression,
raw=False)
# Offset
if offset != 0:
linreg = linreg.shift(offset)
# Handle fills
if "fillna" in kwargs:
linreg.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
linreg.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
linreg.name = f"LR"
if slope: linreg.name += "m"
if intercept: linreg.name += "b"
if angle: linreg.name += "a"
if r: linreg.name += "r"
linreg.name += f"_{length}"
linreg.category = "overlap"
return linreg
def ichimoku(high,
low,
close,
tenkan=None,
kijun=None,
senkou=None,
include_chikou=True,
offset=None,
**kwargs):
"""Indicator: Ichimoku Kinkō Hyō (Ichimoku)"""
tenkan = int(tenkan) if tenkan and tenkan > 0 else 9
kijun = int(kijun) if kijun and kijun > 0 else 26
senkou = int(senkou) if senkou and senkou > 0 else 52
_length = max(tenkan, kijun, senkou)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
if not kwargs.get("lookahead", True):
include_chikou = False
if high is None or low is None or close is None: return None, None
# Calculate Result
tenkan_sen = midprice(high=high, low=low, length=tenkan)
kijun_sen = midprice(high=high, low=low, length=kijun)
span_a = 0.5 * (tenkan_sen + kijun_sen)
span_b = midprice(high=high, low=low, length=senkou)
# Copy Span A and B values before their shift
_span_a = span_a[-kijun:].copy()
_span_b = span_b[-kijun:].copy()
span_a = span_a.shift(kijun)
span_b = span_b.shift(kijun)
chikou_span = close.shift(-kijun)
# Offset
if offset != 0:
tenkan_sen = tenkan_sen.shift(offset)
kijun_sen = kijun_sen.shift(offset)
span_a = span_a.shift(offset)
span_b = span_b.shift(offset)
chikou_span = chikou_span.shift(offset)
# Handle fills
if "fillna" in kwargs:
span_a.fillna(kwargs["fillna"], inplace=True)
span_b.fillna(kwargs["fillna"], inplace=True)
chikou_span.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
span_a.fillna(method=kwargs["fill_method"], inplace=True)
span_b.fillna(method=kwargs["fill_method"], inplace=True)
chikou_span.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
span_a.name = f"ISA_{tenkan}"
span_b.name = f"ISB_{kijun}"
tenkan_sen.name = f"ITS_{tenkan}"
kijun_sen.name = f"IKS_{kijun}"
chikou_span.name = f"ICS_{kijun}"
chikou_span.category = kijun_sen.category = tenkan_sen.category = "trend"
span_b.category = span_a.category = chikou_span
# Prepare Ichimoku DataFrame
data = {
span_a.name: span_a,
span_b.name: span_b,
tenkan_sen.name: tenkan_sen,
kijun_sen.name: kijun_sen,
}
if include_chikou:
data[chikou_span.name] = chikou_span
ichimokudf = DataFrame(data)
ichimokudf.name = f"ICHIMOKU_{tenkan}_{kijun}_{senkou}"
ichimokudf.category = "overlap"
# Prepare Span DataFrame
last = close.index[-1]
if close.index.dtype == "int64":
ext_index = RangeIndex(start=last + 1, stop=last + kijun + 1)
spandf = DataFrame(index=ext_index, columns=[span_a.name, span_b.name])
_span_a.index = _span_b.index = ext_index
else:
df_freq = close.index.value_counts().mode()[0]
tdelta = Timedelta(df_freq, unit="d")
new_dt = date_range(start=last + tdelta, periods=kijun, freq="B")
spandf = DataFrame(index=new_dt, columns=[span_a.name, span_b.name])
_span_a.index = _span_b.index = new_dt
spandf[span_a.name] = _span_a
spandf[span_b.name] = _span_b
spandf.name = f"ICHISPAN_{tenkan}_{kijun}"
spandf.category = "overlap"
return ichimokudf, spandf
def adx(high,
low,
close,
length=None,
scalar=None,
drift=None,
offset=None,
**kwargs):
"""Indicator: ADX"""
# Validate Arguments
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
length = 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
atr_ = atr(high=high, low=low, close=close, length=length)
up = high - high.shift(drift) # high.diff(drift)
dn = low.shift(drift) - low # low.diff(-drift).shift(drift)
pos = ((up > dn) & (up > 0)) * up
neg = ((dn > up) & (dn > 0)) * dn
pos = pos.apply(zero)
neg = neg.apply(zero)
k = scalar / atr_
dmp = k * rma(close=pos, length=length)
dmn = k * rma(close=neg, length=length)
dx = scalar * (dmp - dmn).abs() / (dmp + dmn)
adx = rma(close=dx, length=length)
# Offset
if offset != 0:
dmp = dmp.shift(offset)
dmn = dmn.shift(offset)
adx = adx.shift(offset)
# Handle fills
if "fillna" in kwargs:
adx.fillna(kwargs["fillna"], inplace=True)
dmp.fillna(kwargs["fillna"], inplace=True)
dmn.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
adx.fillna(method=kwargs["fill_method"], inplace=True)
dmp.fillna(method=kwargs["fill_method"], inplace=True)
dmn.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
adx.name = f"ADX_{length}"
dmp.name = f"DMP_{length}"
dmn.name = f"DMN_{length}"
adx.category = dmp.category = dmn.category = "trend"
# Prepare DataFrame to return
data = {adx.name: adx, dmp.name: dmp, dmn.name: dmn}
adxdf = DataFrame(data)
adxdf.name = f"ADX_{length}"
adxdf.category = "trend"
return adxdf
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