def wma(close, length=None, asc=None, offset=None, **kwargs):
"""Indicator: Weighted Moving Average (WMA)"""
# Validate Arguments
close = verify_series(close)
length = int(length) if length and length > 0 else 10
asc = asc if asc else True
offset = get_offset(offset)
# Calculate Result
total_weight = 0.5 * length * (length + 1)
weights_ = Series(npArange(1, length + 1))
weights = weights_ if asc else weights_[::-1]
def linear(w):
def _compute(x):
return npDot(x, w) / total_weight
return _compute
close_ = close.rolling(length, min_periods=length)
wma = close_.apply(linear(weights), raw=True)
# Offset
if offset != 0:
wma = wma.shift(offset)
# Name & Category
wma.name = f"WMA_{length}"
wma.category = "overlap"
return wma
def _plot(self,
df,
mas: bool = True,
constants: bool = False,
**kwargs) -> None:
if constants:
chart_lines = npAppend(npArange(-5, 6, 1), npArange(-100, 110, 10))
df.ta.constants(True,
chart_lines) # Adding the constants for the charts
df.ta.constants(False, npArray(
[-60, -40, 40,
60])) # Removing some constants from the DataFrame
if self.verbose: print(f"[i] {df.ticker} constants added.")
if ta.Imports["matplotlib"]:
_exchange = kwargs.pop("exchange", "NYSE")
_time = ta.get_time(_exchange, to_string=True)
_kind = kwargs.pop("plot_kind", None)
_figsize = kwargs.pop("figsize", (16, 10))
_colors = kwargs.pop("figsize",
["black", "green", "orange", "red", "maroon"])
_grid = kwargs.pop("grid", True)
_alpha = kwargs.pop("alpha", 1)
_last = kwargs.pop("last", 252)
_title = kwargs.pop("title",
f"{df.ticker} {_time} [{self.ds_name}]")
col = kwargs.pop("close", "close")
if mas:
# df.ta.strategy(self.strategy, append=True)
price = df[[col, "SMA_10", "SMA_20", "SMA_50", "SMA_200"]]
else:
price = df[col]
if _kind is None:
price.tail(_last).plot(figsize=_figsize,
color=_colors,
linewidth=2,
title=_title,
grid=_grid,
alpha=_alpha)
else:
print(f"[X] Plot kind not implemented")
return
def wma(close, length=None, asc=None, talib=None, offset=None, **kwargs):
"""Indicator: Weighted Moving Average (WMA)"""
# 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)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if close is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import WMA
wma = WMA(close, length)
else:
from numpy import arange as npArange
from numpy import dot as npDot
total_weight = 0.5 * length * (length + 1)
weights_ = Series(npArange(1, length + 1))
weights = weights_ if asc else weights_[::-1]
def linear(w):
def _compute(x):
return npDot(x, w) / total_weight
return _compute
close_ = close.rolling(length, min_periods=length)
wma = close_.apply(linear(weights), raw=True)
# Offset
if offset != 0:
wma = wma.shift(offset)
# Handle fills
if "fillna" in kwargs:
wma.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
wma.fillna(method=kwargs["fill_method"], inplace=True)
# Name & Category
wma.name = f"WMA_{length}"
wma.category = "overlap"
return wma
def dibujarFnActivacionNumpy(self, axe=None, axis=[-10.0, 10.0],
axline=[0.0, 0.0], mostrar=True):
if axe is None:
axe = plt.gca()
Xaxis = npArange(axis[0], axis[1], 0.01)
Yaxis = self(Xaxis)
axe.plot(Xaxis, Yaxis, color='red', linewidth=2.0)
# lineas horizontales y verticales
axe.axhline(axline[0], linestyle='-.', color='blue', linewidth=1.5)
axe.axvline(axline[1], linestyle='-.', color='blue', linewidth=1.5)
plt.title(self.__str__())
plt.grid(True)
plt.show() if mostrar else None
return axe
def dibujarFnActivacionTheano(self, axe=None, axis=[-10.0, 10.0],
axline=[0.0, 0.0], mostrar=True):
if axe is None:
axe = plt.gca()
Xaxis = npArange(axis[0], axis[1], 0.01)
Yaxis = self(Xaxis)
x = Tdvector('x')
s = Tcast(self(x), dtype=theanoFloat)
dibujador=Tfunction(inputs=[x], outputs=s)
axe.plot(Xaxis, dibujador(Xaxis), color='red', linewidth=2.0)
# lineas horizontales y verticales
axe.axhline(axline[0], linestyle='-.', color='blue', linewidth=1.5)
axe.axvline(axline[1], linestyle='-.', color='blue', linewidth=1.5)
plt.title(self.__str__())
plt.grid(True)
plt.show() if mostrar else None
return axe
def tos_stdevall(close,
length=None,
stds=None,
ddof=None,
offset=None,
**kwargs):
"""Indicator: TD Ameritrade's Think or Swim Standard Deviation All"""
# Validate Arguments
stds = stds if isinstance(stds, list) and len(stds) > 0 else [1, 2, 3]
if min(stds) <= 0: return
if not all(i < j for i, j in zip(stds, stds[1:])):
stds = stds[::-1]
ddof = int(ddof) if ddof and ddof >= 0 and ddof < length else 1
offset = get_offset(offset)
if length is None:
length = close.size
_props = f"STDEVALL"
else:
length = int(length) if length and length > 2 else 30
close = close.iloc[-length:]
_props = f"STDEVALL_{length}"
close = verify_series(close, length)
if close is None: return
# Calculate Result
if isinstance(close.index, DatetimeIndex):
close_ = npArray(close)
np_index = npArange(length)
m, b = npPolyfit(np_index, close_, 1)
lr_ = m * np_index + b
else:
m, b = npPolyfit(close.index, close, 1)
lr_ = m * close.index + b
lr = Series(lr_, index=close.index)
stdevall = stdev(Series(close), length=length, ddof=ddof)
# std = npStd(close, ddof=ddof)
# Name and Categorize it
df = DataFrame({f"{_props}_LR": lr}, index=close.index)
for i in stds:
df[f"{_props}_L_{i}"] = lr - i * stdevall.iloc[-1]
df[f"{_props}_U_{i}"] = lr + i * stdevall.iloc[-1]
df[f"{_props}_L_{i}"].name = df[f"{_props}_U_{i}"].name = f"{_props}"
df[f"{_props}_L_{i}"].category = df[
f"{_props}_U_{i}"].category = "statistics"
# Offset
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)
# Prepare DataFrame to return
df.name = f"{_props}"
df.category = "statistics"
return df
