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MAINT ENH typing validation DEV rename performance to speed_test

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Kevin Johnson
2022-02-28 12:19:11 -08:00
parent bcf47ba486
commit 70c4ced1cc
175 changed files with 4491 additions and 4204 deletions
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README.md
+8 -2
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@@ -98,7 +98,7 @@ Performance
* **TA Lib** computations are **enabled** by default. They can be disabled per indicator.
* The library includes a performance method, ```help(ta.performance)```, to check runtime indicator performance for a given _ohlcv_ DataFrame.
* Optionable **Multiprocessing** for a Pandas TA ```Study```.
* Check your Indicator Performance with the [Indicator Performance Notebook](https://github.com/twopirllc/pandas-ta/tree/main/examples/Performance_Check.ipynb).
* Check Indicator Speeds on your system with the [Indicator Speed Check Notebook](https://github.com/twopirllc/pandas-ta/tree/main/examples/Speed_Check.ipynb).
Bulk Processing
---------------
@@ -157,6 +157,12 @@ Pandas TA is used by Applications and Services like
<br/>
[Tune TA](https://github.com/jmrichardson/tuneta)
-------------------
> TuneTA optimizes technical indicators using a distance correlation measure to a user defined target feature such as next day return. Indicator parameter(s) are selected using clustering techniques to avoid "peak" or "lucky" values. The set of tuned indicators can be ...
<br/>
Back to [Contents](#contents)
<br/>
@@ -192,7 +198,7 @@ $ pip install pandas_ta[full]
Latest Version
--------------
Best choice! Version: *0.3.52b*
Best choice! Version: *0.3.53b*
* Includes all fixes and updates between **pypi** and what is covered in this README.
```sh
$ pip install -U git+https://github.com/twopirllc/pandas-ta
examples/PandasTA_Study_Examples.ipynb
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examples/Performance_Check.ipynb
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examples/Speed_Test.ipynb
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examples/TA_Analysis.ipynb
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examples/VectorBT_Backtest_with_Pandas_TA.ipynb
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examples/example.ipynb
+131 -113
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examples/ni.py
+48 -44
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@@ -1,24 +1,59 @@
# -*- coding: utf-8 -*-
from pandas_ta.overlap import sma
from pandas_ta.utils import get_offset, verify_series
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
# - Standard definition of your custom indicator function (including docs)-
def ni(
close: Series, length: Int = None,
centered: bool = False, mamode: str = None,
offset: Int = None, **kwargs: DictLike
):
"""Example indicator (NI)
def ni(close, length=None, centered=False, offset=None, **kwargs):
"""
Example indicator ni
"""
# Validate Arguments
length = int(length) if length and length > 0 else 20
close = verify_series(close, length)
offset = get_offset(offset)
Is an indicator provided solely as an example
if close is None: return
Sources:
https://github.com/twopirllc/pandas-ta/issues/264
Calculation:
Default Inputs:
length=20, centered=False
SMA = Simple Moving Average
t = int(0.5 * length) + 1
ni = close.shift(t) - SMA(close, length)
if centered:
ni = ni.shift(-t)
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 20
mamode (str): Chosen Moving Average. Default: "sma"
centered (bool): Shift the ni back by int(0.5 * length) + 1. Default: False
offset (int): How many periods to offset the result. Default: 0
Kwargs:
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.Series: New feature generated.
""" # Validate Arguments
length = v_pos_default(length, 20)
close = v_series(close, length)
if close is None:
return
mamode = v_mamode(mamode, "sma")
offset = v_offset(offset)
# Calculate Result
t = int(0.5 * length) + 1
ma = sma(close, length)
ma = ma(mamode, close, length=length, **kwargs)
t = int(0.5 * length) + 1
ni = close - ma.shift(t)
if centered:
ni = (close.shift(t) - ma).shift(-t)
@@ -39,37 +74,6 @@ def ni(close, length=None, centered=False, offset=None, **kwargs):
return ni
ni.__doc__ = \
"""Example indicator (NI)
Is an indicator provided solely as an example
Sources:
https://github.com/twopirllc/pandas-ta/issues/264
Calculation:
Default Inputs:
length=20, centered=False
SMA = Simple Moving Average
t = int(0.5 * length) + 1
ni = close.shift(t) - SMA(close, length)
if centered:
ni = ni.shift(-t)
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 20
centered (bool): Shift the ni back by int(0.5 * length) + 1. Default: False
offset (int): How many periods to offset the result. Default: 0
Kwargs:
fillna (value, optional): pd.DataFrame.fillna(value)
fill_method (value, optional): Type of fill method
Returns:
pd.Series: New feature generated.
"""
# - Define a matching class method --------------------------------------------
pandas_ta/candles/cdl_doji.py
+13 -11
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@@ -2,8 +2,9 @@
from pandas import Series
from pandas_ta.overlap import sma
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import get_offset, high_low_range, is_percent
from pandas_ta.utils import real_body, verify_series
from pandas_ta.utils import high_low_range, is_percent
from pandas_ta.utils import real_body, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series
def cdl_doji(
@@ -42,19 +43,20 @@ def cdl_doji(
pd.Series: CDL_DOJI column.
"""
# Validate
length = int(length) if length and length > 0 else 10
factor = float(factor) if is_percent(factor) else 10
scalar = float(scalar) if scalar else 100
open_ = verify_series(open_, length)
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
naive = kwargs.pop("naive", False)
length = v_pos_default(length, 10)
open_ = v_series(open_, length)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
if open_ is None or high is None or low is None or close is None:
return
factor = v_scalar(factor, 10) if is_percent(factor) else 10
scalar = v_scalar(scalar, 100)
offset = v_offset(offset)
naive = kwargs.pop("naive", False)
# Calculate
body = real_body(open_, close).abs()
hl_range = high_low_range(high, low).abs()
pandas_ta/candles/cdl_inside.py
+6 -6
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@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import candle_color, get_offset, verify_series
from pandas_ta.utils import candle_color, v_offset, v_series
def cdl_inside(
@@ -39,11 +39,11 @@ def cdl_inside(
pd.Series: New feature
"""
# Validate
open_ = verify_series(open_)
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
offset = get_offset(offset)
open_ = v_series(open_)
high = v_series(high)
low = v_series(low)
close = v_series(close)
offset = v_offset(offset)
# Calculate
inside = (high.diff() < 0) & (low.diff() > 0)
pandas_ta/candles/cdl_pattern.py
+7 -7
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@@ -2,7 +2,7 @@
from pandas import Series, DataFrame
from pandas_ta._typing import DictLike, Int, IntFloat, List, Union
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_scalar, v_series
from pandas_ta.candles import cdl_doji, cdl_inside
@@ -62,12 +62,12 @@ def cdl_pattern(
pd.DataFrame: one column for each pattern.
"""
# Validate Arguments
open_ = verify_series(open_)
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
offset = get_offset(offset)
scalar = float(scalar) if scalar else 100
open_ = v_series(open_)
high = v_series(high)
low = v_series(low)
close = v_series(close)
offset = v_offset(offset)
scalar = v_scalar(scalar, 100)
# Patterns that implemented in pandas-ta
pta_patterns = {"doji": cdl_doji, "inside": cdl_inside}
pandas_ta/candles/cdl_z.py
+10 -9
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@@ -2,7 +2,7 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.statistics import zscore
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series
def cdl_z(
@@ -36,18 +36,19 @@ def cdl_z(
pd.Series: CDL_DOJI column.
"""
# Validate
length = int(length) if length and length > 0 else 30
ddof = int(ddof) if ddof and ddof >= 0 and ddof < length else 1
open_ = verify_series(open_, length)
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
full = bool(full) if full is not None and full else False
length = v_pos_default(length, 30)
open_ = v_series(open_, length)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
if open_ is None or high is None or low is None or close is None:
return
full = v_bool(full, False) if isinstance(full, bool) else False
ddof = int(ddof) if isinstance(ddof, int) and 0 <= ddof < length else 1
offset = v_offset(offset)
# Calculate
if full:
length = close.size
pandas_ta/candles/ha.py
+6 -6
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@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_series
def ha(
@@ -37,11 +37,11 @@ def ha(
pd.DataFrame: ha_open, ha_high,ha_low, ha_close columns.
"""
# Validate
open_ = verify_series(open_)
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
offset = get_offset(offset)
open_ = v_series(open_)
high = v_series(high)
low = v_series(low)
close = v_series(close)
offset = v_offset(offset)
# Calculate
m = close.size
pandas_ta/core.py
+38 -34
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@@ -7,7 +7,6 @@ from warnings import simplefilter
from numpy import log10, ndarray
from pandas.api.extensions import register_dataframe_accessor
from pandas.core.base import PandasObject
from pandas.errors import PerformanceWarning
from pandas import DataFrame, Series
@@ -116,19 +115,15 @@ class AnalysisIndicators(object):
_time_range = "years"
def __init__(self, obj: SeriesFrame):
self._validate(obj)
v_dataframe(obj)
self._df = obj
self._last_run = get_time(self._exchange, to_string=True)
@staticmethod
def _validate(obj: SeriesFrame):
if not isinstance(obj, DataFrame) and not isinstance(obj, Series):
raise AttributeError("[X] Requires a Pandas Series or DataFrame.")
# DataFrame Behavioral Methods
def __call__(
self, kind: str = None,
timed: bool = False, version: bool = False, **kwargs: DictLike
self, kind: str = None, timed: bool = False,
version: bool = False, **kwargs: DictLike
):
if version: print(f"Pandas TA - Technical Analysis Indicators - v{self.version}")
try:
@@ -141,12 +136,12 @@ class AnalysisIndicators(object):
# Run the indicator
result = fn(**kwargs) # = getattr(self, kind)(**kwargs)
self._last_run = get_time(self.exchange, to_string=True) # Save when it completed it's run
if timed:
result.timed = final_time(stime)
print(f"[+] {kind}: {result.timed}")
self._last_run = get_time(self.exchange, to_string=True)
return result
else:
self.help()
@@ -232,10 +227,9 @@ class AnalysisIndicators(object):
@property
def datetime_ordered(self) -> bool:
"""Returns True if the index is a datetime and ordered."""
hasdf = hasattr(self, "_df")
if hasdf:
return is_datetime_ordered(self._df)
return hasdf
if hasattr(self, "_df"):
return v_datetime_ordered(self._df)
return False
@property
def reverse(self) -> DataFrame:
@@ -543,27 +537,25 @@ class AnalysisIndicators(object):
with possibly as json, yaml config file or an sqlite3 table.
Kwargs:
chunksize (bool): Adjust the chunksize for the Multiprocessing Pool.
Default: Number of cores of the OS
exclude (list): List of indicator names to exclude. Some are
excluded by default for various reasons; they require additional
sources, performance (td_seq), not a time series chart (vp) etc.
chunksize (bool): Adjust the chunksize for the Multiprocessing
Pool. Default: Number of cores of the OS
exclude (list): List of indicator names to exclude.
name (str): Select all indicators or indicators by
Category such as: "candles", "cycles", "momentum", "overlap",
"performance", "statistics", "trend", "volatility", "volume", or
"all". Default: "all"
Category such as: "candles", "cycles", "momentum",
"overlap", "performance", "statistics", "trend", "volatility",
"volume", or "all". Default: "all"
ordered (bool): Whether to run "all" in order. Default: True
timed (bool): Show the process time of the study().
Default: False
verbose (bool): Provide some additional insight on the progress of
the study() execution. Default: False
verbose (bool): Provide some additional insight on the progress
of the study() execution. Default: False
warning (bool): Disables depreciation message. Automatically
disabled when using it's replacement method: df.ta.study().
Default: True
"""
_dep_warning = kwargs.pop("warning", True)
all_ordered = kwargs.pop("ordered", True)
# Ensure indicators are appended to the DataFrame
# Append indicators to the DataFrame by default
kwargs.setdefault("append", True)
# If True, it returns the resultant DataFrame. Default: False
returns = kwargs.pop("returns", False)
@@ -576,7 +568,7 @@ class AnalysisIndicators(object):
print(f"\n[!] DEPRECIATION WARNING:\n Use study() instead of strategy().\n")
# Initialize
initial_column_count = len(self._df.columns)
initial_column_count = self._df.shape[1]
excluded = ["long_run", "short_run", "tsignals", "xsignals"]
# Get the Study Name and mode
@@ -608,7 +600,8 @@ class AnalysisIndicators(object):
removal = []
for kwds in ta:
_ = False
if "length" in kwds and kwds["length"] > self._df.shape[0]: _ = True
if "length" in kwds and kwds["length"] > self._df.shape[0]:
_ = True
if _: removal.append(kwds)
if len(removal) > 0: [ta.remove(x) for x in removal]
@@ -638,16 +631,19 @@ class AnalysisIndicators(object):
use_multiprocessing = False
if Imports["tqdm"]:
# from tqdm import tqdm
from tqdm import tqdm
if use_multiprocessing:
_total_ta = len(ta)
with Pool(self.cores) as pool:
# Some magic to optimize chunksize for speed based on total ta indicators
_chunksize = mp_chunksize - 1 if mp_chunksize > _total_ta else int(log10(_total_ta)) + 1
# Some magic to optimize chunksize for speed
# based on total ta indicators
if mp_chunksize > _total_ta:
_chunksize = mp_chunksize - 1
else:
_chunksize = int(log10(_total_ta)) + 1
if verbose:
print(f"[i] Multiprocessing {_total_ta} indicators with {_chunksize} chunks and {self.cores}/{cpu_count()} cpus.")
print(f"[i] Multiprocessing {_total_ta} indicators with chunksize {_chunksize} and {self.cores}/{cpu_count()} cpus.")
results = None
if mode["custom"]:
@@ -715,15 +711,23 @@ class AnalysisIndicators(object):
# Apply prefixes/suffixes and appends indicator results to the DataFrame
[self._post_process(r, **kwargs) for r in results]
final_column_count = self._df.shape[1]
_added_columns = final_column_count - initial_column_count
if verbose:
print(f"[i] Total indicators: {len(ta)}")
print(f"[i] Columns added: {len(self._df.columns) - initial_column_count}")
print(f"[i] Columns added: {_added_columns}")
print(f"[i] Last Run: {self._last_run}")
if timed:
print(f"[i] Analysis Time: {final_time(stime)}")
if returns: return self._df
ft = final_time(stime)
if _added_columns > 0:
avgtd = (perf_counter() - stime) / _added_columns
else:
avgtd = perf_counter() - stime
print(f"[i] Analysis Time: {ft} for {_added_columns} columns (avg {avgtd * 1000:2.4f} ms / col).")
if returns:
return self._df
def study(self, *args: Args, **kwargs: DictLike) -> dataclass:
"""Study Method
pandas_ta/cycles/ebsw.py
+8 -9
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@@ -2,7 +2,7 @@
from numpy import cos, exp, mean, nan, pi, roll, sin, sqrt, zeros
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
def ebsw(
@@ -16,7 +16,7 @@ def ebsw(
remove noise. Its output is bound signal between -1 and 1 and the
maximum length of a detected trend is limited by its length input.
Written by rengel8 for Pandas TA based on a publication at
Coded by rengel8 for Pandas TA based on a publication at
'prorealcode.com' and a book by J.F.Ehlers. According to the suggestion
by Squigglez2* and major differences between the initial version's
output close to the implementation from Ehler's, the default version is
@@ -51,21 +51,20 @@ def ebsw(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if isinstance(length, int) and length > 10 else 40
bars = int(bars) if isinstance(bars, int) and bars > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 40)
close = v_series(close, length)
if close is None:
return
bars = v_pos_default(bars, 10)
offset = v_offset(offset)
# Calculate
# allow initial version to be used (more responsive/caution!)
m = close.size
initial_version = bool(initial_version) if isinstance(
initial_version, bool) else False
if initial_version:
if isinstance(initial_version, bool) and initial_version:
# not the default version that is active
alpha1 = hp = 0 # alpha and HighPass
a1 = b1 = c1 = c2 = c3 = 0
pandas_ta/cycles/reflex.py
+14 -10
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@@ -2,7 +2,7 @@
from numpy import cos, exp, nan, sqrt, zeros_like
from pandas import Series
from pandas_ta._typing import Array, DictLike, Int, IntFloat
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
try:
@@ -56,8 +56,8 @@ def reflex(
(Reflex/Trendflex) are oscillators and complement each other with the
focus for cycle and trend.
Written for Pandas TA by rengel8 (2021-08-11) based on the implementation
on ProRealCode (see Sources). Beyond the mentioned source, this
Coded by rengel8 (2021-08-11) based on the implementation on
ProRealCode (see Sources). Beyond the mentioned source, this
implementation has a separate control parameter for the internal
applied SuperSmoother.
@@ -86,13 +86,17 @@ def reflex(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if isinstance(length, int) and length > 0 else 20
smooth = int(smooth) if isinstance(smooth, int) and smooth > 0 else 20
alpha = float(alpha) if isinstance(alpha, float) and alpha > 0 else 0.04
pi = float(pi) if isinstance(pi, float) and pi > 0 else 3.14159
sqrt2 = float(sqrt2) if isinstance(sqrt2, float) and sqrt2 > 0 else 1.414
close = verify_series(close, max(length, smooth))
offset = get_offset(offset)
length = v_pos_default(length, 20)
smooth = v_pos_default(smooth, 20)
close = v_series(close, max(length, smooth))
if close is None:
return
alpha = v_pos_default(alpha, 0.04)
pi = v_pos_default(pi, 3.14159)
sqrt2 = v_pos_default(sqrt2, 1.414)
offset = v_offset(offset)
# Calculate
np_close = close.values
pandas_ta/ma.py
+1 -2
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@@ -1,6 +1,6 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta._typing import DictLike
from pandas_ta.overlap.dema import dema
from pandas_ta.overlap.ema import ema
from pandas_ta.overlap.fwma import fwma
@@ -42,7 +42,6 @@ def ma(name: str = None, source: Series = None, **kwargs: DictLike) -> Series:
Returns:
pd.Series: New feature generated.
"""
_mas = [
"dema", "ema", "fwma", "hma", "linreg", "midpoint", "pwma", "rma",
"sinwma", "sma", "ssf", "swma", "t3", "tema", "trima", "vidya", "wma"
pandas_ta/momentum/ao.py
+7 -6
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@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import sma
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
def ao(
@@ -34,18 +34,19 @@ def ao(
pd.Series: New feature generated.
"""
# Validate
fast = int(fast) if fast and fast > 0 else 5
slow = int(slow) if slow and slow > 0 else 34
fast = v_pos_default(fast, 5)
slow = v_pos_default(slow, 34)
if slow < fast:
fast, slow = slow, fast
_length = max(fast, slow)
high = verify_series(high, _length)
low = verify_series(low, _length)
offset = get_offset(offset)
high = v_series(high, _length)
low = v_series(low, _length)
if high is None or low is None:
return
offset = v_offset(offset)
# Calculate
median_price = 0.5 * (high + low)
fast_sma = sma(median_price, fast)
pandas_ta/momentum/apo.py
+9 -7
View File
@@ -3,7 +3,8 @@ from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, tal_ma, verify_series
from pandas_ta.utils import tal_ma, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_series, v_talib
def apo(
@@ -38,18 +39,19 @@ def apo(
pd.Series: New feature generated.
"""
# Validate
fast = int(fast) if fast and fast > 0 else 12
slow = int(slow) if slow and slow > 0 else 26
fast = v_pos_default(fast, 12)
slow = v_pos_default(slow, 26)
if slow < fast:
fast, slow = slow, fast
close = verify_series(close, max(fast, slow))
mamode = mamode if isinstance(mamode, str) else "sma"
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
close = v_series(close, max(fast, slow))
if close is None:
return
mamode = v_mamode(mamode, "sma")
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import APO
pandas_ta/momentum/bias.py
+6 -5
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
def bias(
@@ -31,14 +31,15 @@ def bias(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 26
mamode = mamode if isinstance(mamode, str) else "sma"
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 26)
close = v_series(close, length)
if close is None:
return
mamode = v_mamode(mamode, "sma")
offset = v_offset(offset)
# Calculate
bma = ma(mamode, close, length=length, **kwargs)
bias = (close / bma) - 1
pandas_ta/momentum/bop.py
+8 -8
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, non_zero_range, verify_series
from pandas_ta.utils import non_zero_range, v_offset, v_scalar, v_series, v_talib
def bop(
@@ -35,13 +35,13 @@ def bop(
pd.Series: New feature generated.
"""
# Validate
open_ = verify_series(open_)
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
scalar = float(scalar) if scalar else 1
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
open_ = v_series(open_)
high = v_series(high)
low = v_series(low)
close = v_series(close)
scalar = v_scalar(scalar, 1)
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
pandas_ta/momentum/brar.py
+13 -11
View File
@@ -1,7 +1,8 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
from pandas_ta.utils import non_zero_range, v_drift, v_offset
from pandas_ta.utils import v_pos_default, v_scalar, v_series
def brar(
@@ -35,21 +36,22 @@ def brar(
pd.DataFrame: ar, br columns.
"""
# Validate
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)
length = v_pos_default(length, 26)
open_ = v_series(open_, length)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
if open_ is None or high is None or low is None or close is None:
return
scalar = v_scalar(scalar, 100)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
high_open_range = non_zero_range(high, open_)
open_low_range = non_zero_range(open_, low)
hcy = non_zero_range(high, close.shift(drift))
cyl = non_zero_range(close.shift(drift), low)
pandas_ta/momentum/cci.py
+9 -8
View File
@@ -4,7 +4,7 @@ from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.overlap import hlc3, sma
from pandas_ta.statistics import mad
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
def cci(
@@ -38,17 +38,18 @@ def cci(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 14
c = float(c) if c and c > 0 else 0.015
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 14)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
if high is None or low is None or close is None:
return
c = v_pos_default(c, 0.015)
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import CCI
pandas_ta/momentum/cfo.py
+7 -6
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import linreg
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_scalar, v_series
def cfo(
@@ -34,15 +34,16 @@ def cfo(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 9
scalar = float(scalar) if scalar else 100
close = verify_series(close, length)
drift = get_drift(drift)
offset = get_offset(offset)
length = v_pos_default(length, 9)
close = v_series(close, length)
if close is None:
return
scalar = v_scalar(scalar, 100)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
# Finding linear regression of Series
cfo = scalar * (close - linreg(close, length=length, tsf=True))
pandas_ta/momentum/cg.py
+5 -4
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series, weights
from pandas_ta.utils import v_offset, v_pos_default, v_series, weights
def cg(
@@ -29,13 +29,14 @@ def cg(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
offset = v_offset(offset)
# Calculate
coefficients = [length - i for i in range(0, length)]
numerator = -close.rolling(length).apply(weights(coefficients), raw=True)
pandas_ta/momentum/cmo.py
+10 -7
View File
@@ -3,7 +3,9 @@ from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.overlap import rma
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_drift, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series, v_talib
def cmo(
@@ -39,16 +41,17 @@ def cmo(
pd.Series: New feature generated.
"""
# Validate
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)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 14)
close = v_series(close, length)
if close is None:
return
scalar = v_scalar(scalar, 100)
mode_tal = v_talib(talib)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import CMO
pandas_ta/momentum/coppock.py
+7 -6
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import wma
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
from .roc import roc
@@ -36,15 +36,16 @@ def coppock(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
fast = int(fast) if fast and fast > 0 else 11
slow = int(slow) if slow and slow > 0 else 14
close = verify_series(close, max(length, fast, slow))
offset = get_offset(offset)
length = v_pos_default(length, 10)
fast = v_pos_default(fast, 11)
slow = v_pos_default(slow, 14)
close = v_series(close, max(length, fast, slow))
if close is None:
return
offset = v_offset(offset)
# Calculate
total_roc = roc(close, fast) + roc(close, slow)
coppock = wma(total_roc, length)
pandas_ta/momentum/cti.py
+5 -4
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import linreg
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
def cti(
@@ -30,13 +30,14 @@ def cti(
pd.Series: Series of the CTI values for the given period.
"""
# Validate
length = int(length) if length and length > 0 else 12
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 12)
close = v_series(close, length)
if close is None:
return
offset = v_offset(offset)
# Calculate
cti = linreg(close, length=length, r=True)
pandas_ta/momentum/dm.py
+10 -8
View File
@@ -3,7 +3,8 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series, get_drift, zero
from pandas_ta.utils import v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_series, v_talib, zero
def dm(
@@ -38,17 +39,18 @@ def dm(
pd.DataFrame: DMP (+DM) and DMN (-DM) columns.
"""
# Validate
length = int(length) if length and length > 0 else 14
mamode = mamode.lower() if mamode and isinstance(mamode, str) else "rma"
high = verify_series(high)
low = verify_series(low)
drift = get_drift(drift)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 14)
high = v_series(high)
low = v_series(low)
if high is None or low is None:
return
mamode = v_mamode(mamode, "rma")
mode_tal = v_talib(talib)
drift = v_drift(drift)
offset = v_offset(offset)
if Imports["talib"] and mode_tal:
from talib import MINUS_DM, PLUS_DM
pos = PLUS_DM(high, low, length)
pandas_ta/momentum/er.py
+6 -5
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, concat, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_drift, get_offset, verify_series, signals
from pandas_ta.utils import signals, v_drift, v_offset, v_pos_default, v_series
def er(
@@ -33,14 +33,15 @@ def er(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
drift = get_drift(drift)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
abs_diff = close.diff(length).abs()
abs_volatility = close.diff(drift).abs()
pandas_ta/momentum/eri.py
+7 -6
View File
@@ -2,7 +2,7 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import ema
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
def eri(
@@ -38,15 +38,16 @@ def eri(
pd.DataFrame: bull power and bear power columns.
"""
# Validate
length = int(length) if length and length > 0 else 13
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 13)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
if high is None or low is None or close is None:
return
offset = v_offset(offset)
# Calculate
ema_ = ema(close, length)
bull = high - ema_
pandas_ta/momentum/fisher.py
+7 -6
View File
@@ -3,7 +3,7 @@ from numpy import log, nan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import hl2
from pandas_ta.utils import get_offset, high_low_range, verify_series
from pandas_ta.utils import high_low_range, v_offset, v_pos_default, v_series
def fisher(
@@ -34,16 +34,17 @@ def fisher(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 9
signal = int(signal) if signal and signal > 0 else 1
length = v_pos_default(length, 9)
signal = v_pos_default(signal, 1)
_length = max(length, signal)
high = verify_series(high, _length)
low = verify_series(low, _length)
offset = get_offset(offset)
high = v_series(high, _length)
low = v_series(low, _length)
if high is None or low is None:
return
offset = v_offset(offset)
# Calculate
hl2_ = hl2(high, low)
highest_hl2 = hl2_.rolling(length).max()
pandas_ta/momentum/inertia.py
+27 -18
View File
@@ -2,7 +2,8 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import linreg
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_bool, v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_scalar, v_series
from pandas_ta.volatility import rvi
@@ -44,36 +45,44 @@ def inertia(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 20
rvi_length = int(rvi_length) if rvi_length and rvi_length > 0 else 14
scalar = float(scalar) if scalar and scalar > 0 else 100
refined = False if refined is None else True
thirds = False if thirds is None else True
mamode = mamode if isinstance(mamode, str) else "ema"
length = v_pos_default(length, 20)
rvi_length = v_pos_default(rvi_length, 14)
_length = max(length, rvi_length)
close = verify_series(close, _length)
drift = get_drift(drift)
offset = get_offset(offset)
close = v_series(close, _length)
if close is None:
return
refined = v_bool(refined, False)
thirds = v_bool(thirds, False)
if refined or thirds:
high = verify_series(high, _length)
low = verify_series(low, _length)
high = v_series(high, _length)
low = v_series(low, _length)
if high is None or low is None:
return
scalar = v_scalar(scalar, 100)
mamode = v_mamode(mamode, "ema")
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
if refined:
_mode, rvi_ = "r", rvi(close, high=high, low=low, length=rvi_length,
scalar=scalar, refined=refined, mamode=mamode)
_mode = "r"
rvi_ = rvi(
close, high=high, low=low, length=rvi_length,
scalar=scalar, refined=refined, mamode=mamode
)
elif thirds:
_mode, rvi_ = "t", rvi(close, high=high, low=low, length=rvi_length,
scalar=scalar, thirds=thirds, mamode=mamode)
_mode = "t"
rvi_ = rvi(
close, high=high, low=low, length=rvi_length,
scalar=scalar, thirds=thirds, mamode=mamode
)
else:
_mode, rvi_ = "", rvi(close, length=rvi_length,
scalar=scalar, mamode=mamode)
_mode = ""
rvi_ = rvi(close, length=rvi_length, scalar=scalar, mamode=mamode)
inertia = linreg(rvi_, length=length)
pandas_ta/momentum/kdj.py
+9 -7
View File
@@ -1,7 +1,8 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, non_zero_range, rma_pandas, verify_series
from pandas_ta.utils import non_zero_range, rma_pandas, v_offset
from pandas_ta.utils import v_pos_default, v_series
def kdj(
@@ -37,17 +38,18 @@ def kdj(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 9
signal = int(signal) if signal and signal > 0 else 3
length = v_pos_default(length, 9)
signal = v_pos_default(signal, 3)
_length = max(length, signal)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
offset = v_offset(offset)
# Calculate
highest_high = high.rolling(length).max()
lowest_low = low.rolling(length).min()
pandas_ta/momentum/kst.py
+6 -5
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series
from .roc import roc
@@ -52,15 +52,16 @@ def kst(
sma3 = int(sma3) if sma3 and sma3 > 0 else 10
sma4 = int(sma4) if sma4 and sma4 > 0 else 15
signal = int(signal) if signal and signal > 0 else 9
signal = v_pos_default(signal, 9)
_length = max(roc1, roc2, roc3, roc4, sma1, sma2, sma3, sma4, signal)
close = verify_series(close, _length)
drift = get_drift(drift)
offset = get_offset(offset)
close = v_series(close, _length)
if close is None:
return
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
rocma1 = roc(close, roc1).rolling(sma1).mean()
rocma2 = roc(close, roc2).rolling(sma2).mean()
pandas_ta/momentum/macd.py
+11 -9
View File
@@ -3,12 +3,14 @@ from pandas import concat, DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.overlap import ema
from pandas_ta.utils import get_offset, verify_series, signals
from pandas_ta.utils import signals, v_offset, v_mamode
from pandas_ta.utils import v_pos_default, v_series, v_talib
def macd(
close: Series, fast: Int = None, slow: Int = None, signal: Int = None,
talib: bool = None, offset: Int = None, **kwargs: DictLike
close: Series, fast: Int = None, slow: Int = None,
signal: Int = None, talib: bool = None,
offset: Int = None, **kwargs: DictLike
) -> DataFrame:
"""Moving Average Convergence Divergence (MACD)
@@ -40,18 +42,18 @@ def macd(
pd.DataFrame: macd, histogram, signal columns.
"""
# Validate
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
fast = v_pos_default(fast, 12)
slow = v_pos_default(slow, 26)
signal = v_pos_default(signal, 9)
if slow < fast:
fast, slow = slow, fast
close = verify_series(close, slow + signal)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
close = v_series(close, slow + signal)
if close is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
as_mode = kwargs.setdefault("asmode", False)
# Calculate
pandas_ta/momentum/mom.py
+6 -5
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
def mom(
@@ -32,14 +32,15 @@ def mom(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import MOM
pandas_ta/momentum/pgo.py
+7 -6
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import ema, sma
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
from pandas_ta.volatility import atr
@@ -36,15 +36,16 @@ def pgo(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 14
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 14)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
if high is None or low is None or close is None:
return
offset = v_offset(offset)
# Calculate
pgo = close - sma(close, length)
pgo /= ema(atr(high, low, close, length), length)
pandas_ta/momentum/ppo.py
+11 -9
View File
@@ -3,7 +3,8 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, tal_ma, verify_series
from pandas_ta.utils import tal_ma, v_mamode, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series, v_talib
def ppo(
@@ -37,20 +38,21 @@ def ppo(
pd.DataFrame: ppo, histogram, signal columns
"""
# Validate
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
scalar = float(scalar) if scalar else 100
mamode = mamode if isinstance(mamode, str) else "sma"
fast = v_pos_default(fast, 12)
slow = v_pos_default(slow, 26)
signal = v_pos_default(signal, 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
close = v_series(close, max(fast, slow, signal))
if close is None:
return
scalar = v_scalar(scalar, 100)
mamode = v_mamode(mamode, "sma")
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import PPO
pandas_ta/momentum/psl.py
+8 -7
View File
@@ -2,7 +2,7 @@
from numpy import sign
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_scalar, v_series
def psl(
@@ -36,18 +36,19 @@ def psl(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 12
scalar = float(scalar) if scalar and scalar > 0 else 100
close = verify_series(close, length)
drift = get_drift(drift)
offset = get_offset(offset)
length = v_pos_default(length, 12)
close = v_series(close, length)
if close is None:
return
scalar = v_scalar(scalar, 100)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
if open_ is not None:
open_ = verify_series(open_)
open_ = v_series(open_)
diff = sign(close - open_)
else:
diff = sign(close.diff(drift))
pandas_ta/momentum/pvo.py
+8 -7
View File
@@ -2,7 +2,7 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import ema
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_scalar, v_series
def pvo(
@@ -33,18 +33,19 @@ def pvo(
pd.DataFrame: pvo, histogram, signal columns.
"""
# Validate
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
scalar = float(scalar) if scalar else 100
fast = v_pos_default(fast, 12)
slow = v_pos_default(slow, 26)
signal = v_pos_default(signal, 9)
if slow < fast:
fast, slow = slow, fast
volume = verify_series(volume, max(fast, slow, signal))
offset = get_offset(offset)
volume = v_series(volume, max(fast, slow, signal))
if volume is None:
return
scalar = v_scalar(scalar, 100)
offset = v_offset(offset)
# Calculate
fastma = ema(volume, length=fast)
slowma = ema(volume, length=slow)
pandas_ta/momentum/qqe.py
+10 -8
View File
@@ -3,7 +3,8 @@ from numpy import isnan, maximum, minimum, nan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_scalar, v_series
from .rsi import rsi
@@ -49,18 +50,19 @@ def qqe(
pd.DataFrame: QQE, RSI_MA (basis), QQEl (long), QQEs (short) columns.
"""
# Validate
length = int(length) if isinstance(length, int) and length > 0 else 14
smooth = int(smooth) if isinstance(smooth, int) and smooth > 0 else 5
factor = float(factor) if isinstance(factor, float) and factor else 4.236
length = v_pos_default(length, 14)
smooth = v_pos_default(smooth, 5)
wilders_length = 2 * length - 1
mamode = mamode if isinstance(mamode, str) else "ema"
close = verify_series(close, smooth + wilders_length)
drift = get_drift(drift)
offset = get_offset(offset)
close = v_series(close, smooth + wilders_length)
if close is None:
return
factor = v_scalar(factor, 4.236)
mamode = v_mamode(mamode, "ema")
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
rsi_ = rsi(close, length)
_mode = mamode.lower()[0] if mamode != "ema" else ""
pandas_ta/momentum/roc.py
+8 -6
View File
@@ -2,7 +2,8 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_scalar
from pandas_ta.utils import v_series, v_talib
from .mom import mom
@@ -37,15 +38,16 @@ def roc(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
scalar = float(scalar) if scalar and scalar > 0 else 100
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
scalar = v_scalar(scalar, 100)
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import ROC
pandas_ta/momentum/rsi.py
+9 -7
View File
@@ -3,7 +3,8 @@ from pandas import DataFrame, concat, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.overlap import rma
from pandas_ta.utils import get_drift, get_offset, verify_series, signals
from pandas_ta.utils import signals, v_drift, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series, v_talib
def rsi(
@@ -37,16 +38,17 @@ def rsi(
pd.Series: New feature generated.
"""
# Validate
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)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 14)
close = v_series(close, length)
if close is None:
return
scalar = v_scalar(scalar, 100)
mode_tal = v_talib(talib)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import RSI
pandas_ta/momentum/rsx.py
+6 -5
View File
@@ -2,7 +2,7 @@
from numpy import nan
from pandas_ta._typing import DictLike, Int
from pandas import concat, DataFrame, Series
from pandas_ta.utils import get_drift, get_offset, verify_series, signals
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series, signals
def rsx(
@@ -35,14 +35,15 @@ def rsx(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 14
close = verify_series(close, length)
drift = get_drift(drift)
offset = get_offset(offset)
length = v_pos_default(length, 14)
close = v_series(close, length)
if close is None:
return
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
m = close.size
vC, v1C = 0, 0
pandas_ta/momentum/rvgi.py
+12 -10
View File
@@ -2,7 +2,7 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import swma
from pandas_ta.utils import get_offset, non_zero_range, verify_series
from pandas_ta.utils import non_zero_range, v_offset, v_pos_default, v_series
def rvgi(
@@ -37,21 +37,23 @@ def rvgi(
pd.Series: New feature generated.
"""
# Validate
high_low_range = non_zero_range(high, low)
close_open_range = non_zero_range(close, open_)
length = int(length) if length and length > 0 else 14
swma_length = int(swma_length) if swma_length and swma_length > 0 else 4
length = v_pos_default(length, 14)
swma_length = v_pos_default(swma_length, 4)
_length = max(length, swma_length)
open_ = verify_series(open_, _length)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
open_ = v_series(open_, _length)
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if open_ is None or high is None or low is None or close is None:
return
offset = v_offset(offset)
# Calculate
high_low_range = non_zero_range(high, low)
close_open_range = non_zero_range(close, open_)
numerator = swma(
close_open_range, length=swma_length
).rolling(length).sum()
pandas_ta/momentum/slope.py
+7 -6
View File
@@ -2,7 +2,7 @@
from numpy import arctan, pi
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series
def slope(
@@ -43,15 +43,16 @@ def slope(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 1
as_angle = True if isinstance(as_angle, bool) else False
to_degrees = True if isinstance(to_degrees, bool) else False
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 1)
close = v_series(close, length)
if close is None:
return
as_angle = v_bool(as_angle, False)
to_degrees = v_bool(to_degrees, False)
offset = v_offset(offset)
# Calculate
slope = close.diff(length) / length
if as_angle:
pandas_ta/momentum/smi.py
+10 -9
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_scalar, v_series
from .tsi import tsi
@@ -42,18 +42,19 @@ def smi(
pd.DataFrame: smi, signal, oscillator columns.
"""
# Validate
fast = int(fast) if fast and fast > 0 else 5
slow = int(slow) if slow and slow > 0 else 20
signal = int(signal) if signal and signal > 0 else 5
fast = v_pos_default(fast, 5)
slow = v_pos_default(slow, 20)
signal = v_pos_default(signal, 5)
if slow < fast:
fast, slow = slow, fast
scalar = float(scalar) if scalar else 1
close = verify_series(close, max(fast, slow, signal))
offset = get_offset(offset)
close = v_series(close, max(fast, slow, signal))
if close is None:
return
scalar = v_scalar(scalar, 1)
offset = v_offset(offset)
# Calculate
tsi_df = tsi(close, fast=fast, slow=slow, signal=signal, scalar=scalar)
smi = tsi_df.iloc[:, 0]
@@ -77,8 +78,8 @@ def smi(
osc.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Category
_scalar = f"_{scalar}" if scalar != 1 else ""
_props = f"_{fast}_{slow}_{signal}{_scalar}"
# _scalar = f"_{scalar}" if scalar != 1 else ""
_props = f"_{fast}_{slow}_{signal}_{scalar}"
smi.name = f"SMI{_props}"
signalma.name = f"SMIs{_props}"
osc.name = f"SMIo{_props}"
pandas_ta/momentum/squeeze.py
+15 -13
View File
@@ -4,7 +4,8 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import ema, linreg, sma
from pandas_ta.trend import decreasing, increasing
from pandas_ta.utils import get_offset, simplify_columns, unsigned_differences, verify_series
from pandas_ta.utils import simplify_columns, unsigned_differences, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series
from pandas_ta.volatility import bbands, kc
from .mom import mom
@@ -62,26 +63,27 @@ def squeeze(
detailed columns if 'detailed' kwarg is True.
"""
# Validate
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 2.0
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
bb_length = v_pos_default(bb_length, 20)
kc_length = v_pos_default(kc_length, 20)
mom_length = v_pos_default(mom_length, 12)
mom_smooth = v_pos_default(mom_smooth, 6)
_length = max(bb_length, kc_length, mom_length, mom_smooth)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
use_tr = kwargs.setdefault("tr", True)
bb_std = v_pos_default(bb_std, 2.0)
kc_scalar = v_pos_default(kc_scalar, 1.5)
mamode = v_mamode(mamode, "sma")
offset = v_offset(offset)
use_tr = kwargs.pop("tr", True)
asint = kwargs.pop("asint", True)
detailed = kwargs.pop("detailed", False)
lazybear = kwargs.pop("lazybear", False)
mamode = mamode if isinstance(mamode, str) else "sma"
# Calculate
bbd = bbands(close, length=bb_length, std=bb_std, mamode=mamode)
pandas_ta/momentum/squeeze_pro.py
+22 -37
View File
@@ -2,12 +2,13 @@
from numpy import nan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.momentum import mom
from pandas_ta.overlap import ema, sma
# from pandas_ta.overlap import ema, sma
from pandas_ta.trend import decreasing, increasing
from pandas_ta.volatility import bbands, kc
from pandas_ta.utils import get_offset, simplify_columns, unsigned_differences, verify_series
from pandas_ta.utils import simplify_columns, unsigned_differences, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_scalar, v_series
def squeeze_pro(
high: Series, low: Series, close: Series,
@@ -67,46 +68,33 @@ def squeeze_pro(
More detailed columns if 'detailed' kwarg is True.
"""
# Validate
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 2.0
kc_length = int(kc_length) if kc_length and kc_length > 0 else 20
if kc_scalar_wide and kc_scalar_wide > 0:
kc_scalar_wide = float(kc_scalar_wide)
else:
kc_scalar_wide = 2
if kc_scalar_normal and kc_scalar_normal > 0:
kc_scalar_normal = float(kc_scalar_normal)
else:
kc_scalar_normal = 1.5
if kc_scalar_narrow and kc_scalar_narrow > 0:
kc_scalar_narrow = float(kc_scalar_narrow)
else:
kc_scalar_narrow = 1
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
bb_length = v_pos_default(bb_length, 20)
kc_length = v_pos_default(kc_length, 20)
mom_length = v_pos_default(mom_length, 12)
mom_smooth = v_pos_default(mom_smooth, 6)
_length = max(bb_length, kc_length, mom_length, mom_smooth)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
kc_scalar_narrow = v_scalar(kc_scalar_narrow, 1)
kc_scalar_normal = v_scalar(kc_scalar_normal, 1.5)
kc_scalar_wide = v_scalar(kc_scalar_wide, 2)
valid_kc_scaler = kc_scalar_wide > kc_scalar_normal \
and kc_scalar_normal > kc_scalar_narrow
if not valid_kc_scaler:
return
if high is None or low is None or close is None:
return
use_tr = kwargs.setdefault("tr", True)
bb_std = v_pos_default(bb_std, 2.0)
mamode = v_mamode(mamode, "sma")
offset = v_offset(offset)
use_tr = kwargs.pop("tr", True)
asint = kwargs.pop("asint", True)
detailed = kwargs.pop("detailed", False)
mamode = mamode if isinstance(mamode, str) else "sma"
# Calculate
bbd = bbands(close, length=bb_length, std=bb_std, mamode=mamode)
@@ -130,10 +118,7 @@ def squeeze_pro(
kch_narrow.columns = simplify_columns(kch_narrow)
momo = mom(close, length=mom_length)
if mamode.lower() == "ema":
squeeze = ema(momo, length=mom_smooth)
else: # "sma"
squeeze = sma(momo, length=mom_smooth)
squeeze = ma(mamode, momo, length=mom_smooth)
# Classify Squeezes
squeeze_on_wide = (bbd.l > kch_wide.l) & (bbd.u < kch_wide.u)
pandas_ta/momentum/stc.py
+14 -14
View File
@@ -2,7 +2,8 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import ema
from pandas_ta.utils import get_offset, non_zero_range, verify_series
from pandas_ta.utils import non_zero_range, v_offset
from pandas_ta.utils import v_pos_default, v_series
def stc(
@@ -33,8 +34,9 @@ def stc(
The same goes for osc=, which allows the input of an externally
calculated oscillator, overriding ma1 & ma2.
Coded by rengel8
Sources:
Implemented by rengel8 based on work found here:
https://www.prorealcode.com/prorealtime-indicators/schaff-trend-cycle2/
Args:
@@ -58,22 +60,20 @@ def stc(
pd.DataFrame: stc, macd, stoch
"""
# Validate
if isinstance(tclength, int) and tclength > 0:
tclength = int(tclength)
else:
tclength = 10
fast = int(fast) if isinstance(fast, int) and fast > 0 else 12
slow = int(slow) if isinstance(slow, int) and slow > 0 else 26
factor = float(factor) if isinstance(factor, int) and factor > 0 else 0.5
fast = v_pos_default(fast, 12)
slow = v_pos_default(slow, 26)
tclength = v_pos_default(tclength, 10)
if slow < fast: # mandatory condition, but might be confusing
fast, slow = slow, fast
_length = max(tclength, fast, slow)
close = verify_series(close, _length)
offset = get_offset(offset)
close = v_series(close, _length)
if close is None:
return
factor = v_pos_default(factor, 0.5)
offset = v_offset(offset)
# Calculate
# kwargs allows for three more series (ma1, ma2 and osc) which can be passed
# here ma1 and ma2 input negate internal ema calculations, osc substitutes
@@ -84,8 +84,8 @@ def stc(
# 3 different modes of calculation..
if isinstance(ma1, Series) and isinstance(ma2, Series) and not osc:
ma1 = verify_series(ma1, _length)
ma2 = verify_series(ma2, _length)
ma1 = v_series(ma1, _length)
ma2 = v_series(ma2, _length)
if ma1 is None or ma2 is None:
return
@@ -93,7 +93,7 @@ def stc(
xmacd = ma1 - ma2
pff, pf = schaff_tc(close, xmacd, tclength, factor)
elif isinstance(osc, Series):
osc = verify_series(osc, _length)
osc = v_series(osc, _length)
if osc is None:
return
# According to feeded oscillator (should be ranging around 0 x-axis)
pandas_ta/momentum/stoch.py
+12 -10
View File
@@ -3,7 +3,8 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
from pandas_ta.utils import non_zero_range, tal_ma, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
def stoch(
@@ -48,20 +49,21 @@ def stoch(
pd.DataFrame: %K, %D columns.
"""
# Validate
k = k if k and k > 0 else 14
d = d if d and d > 0 else 3
smooth_k = smooth_k if smooth_k and smooth_k > 0 else 3
k = v_pos_default(k, 14)
d = v_pos_default(d, 3)
smooth_k = v_pos_default(smooth_k, 3)
_length = k + d + smooth_k
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
mamode = mamode if isinstance(mamode, str) else "sma"
mode_tal = bool(talib) if isinstance(talib, bool) else True
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
mode_tal = v_talib(talib)
mamode = v_mamode(mamode, "sma")
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import STOCH
pandas_ta/momentum/stochf.py
+11 -9
View File
@@ -3,7 +3,8 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
from pandas_ta.utils import non_zero_range, tal_ma, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
def stochf(
@@ -41,19 +42,20 @@ def stochf(
pd.DataFrame: Fast %K, %D columns.
"""
# Validate
k = k if k and k > 0 else 14
d = d if d and d > 0 else 3
k = v_pos_default(k, 14)
d = v_pos_default(d, 3)
_length = max(k, d)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
mamode = mamode if isinstance(mamode, str) else "sma"
mode_tal = bool(talib) if isinstance(talib, bool) else True
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
mamode = v_mamode(mamode, "sma")
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import STOCHF
pandas_ta/momentum/stochrsi.py
+10 -8
View File
@@ -3,7 +3,8 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.momentum import rsi
from pandas_ta.utils import get_offset, non_zero_range, verify_series
from pandas_ta.utils import non_zero_range, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series
def stochrsi(
@@ -44,17 +45,18 @@ def stochrsi(
pd.DataFrame: RSI %K, RSI %D columns.
"""
# Validate
length = length if length and length > 0 else 14
rsi_length = rsi_length if rsi_length and rsi_length > 0 else 14
k = k if k and k > 0 else 3
d = d if d and d > 0 else 3
close = verify_series(close, length + rsi_length + k + d)
offset = get_offset(offset)
mamode = mamode if isinstance(mamode, str) else "sma"
length = v_pos_default(length, 14)
rsi_length = v_pos_default(rsi_length, 14)
k = v_pos_default(k, 3)
d = v_pos_default(d, 3)
close = v_series(close, length + rsi_length + k + d)
if close is None:
return
mamode = v_mamode(mamode, "sma")
offset = v_offset(offset)
# Calculate
rsi_ = rsi(close, length=rsi_length)
lowest_rsi = rsi_.rolling(length).min()
pandas_ta/momentum/td_seq.py
+6 -5
View File
@@ -2,7 +2,7 @@
from numpy import where
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_bool, v_offset, v_series
def td_seq(
@@ -32,14 +32,15 @@ def td_seq(
pd.DataFrame: New feature generated.
"""
# Validate
close = verify_series(close)
offset = get_offset(offset)
asint = asint if isinstance(asint, bool) else False
show_all = show_all if isinstance(show_all, bool) else True
close = v_series(close)
if close is None:
return
asint = v_bool(asint, False)
show_all = v_bool(show_all, True)
offset = v_offset(offset)
# Calculate
up_seq = calc_td(close, "up", show_all)
down_seq = calc_td(close, "down", show_all)
pandas_ta/momentum/trix.py
+9 -7
View File
@@ -2,7 +2,8 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap.ema import ema
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_drift, v_offset, v_pos_default
from pandas_ta.utils import v_scalar, v_series
def trix(
@@ -33,17 +34,18 @@ def trix(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 30
signal = int(signal) if signal and signal > 0 else 9
scalar = float(scalar) if scalar else 100
length = v_pos_default(length, 30)
_length = 3 * length - 2
close = verify_series(close, _length)
drift = get_drift(drift)
offset = get_offset(offset)
close = v_series(close, _length)
if close is None:
return
signal = v_pos_default(signal, 9)
scalar = v_scalar(scalar, 100)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
ema1 = ema(close=close, length=length, **kwargs)
# if all(isnan(ema1)): return # Emergency Break
pandas_ta/momentum/tsi.py
+11 -11
View File
@@ -3,7 +3,8 @@ from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.ma import ma
from pandas_ta.overlap import ema
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_drift, v_mamode, v_offset
from pandas_ta.utils import v_pos_default, v_scalar, v_series
def tsi(
@@ -40,22 +41,21 @@ def tsi(
pd.DataFrame: tsi, signal.
"""
# Validate
fast = int(fast) if fast and fast > 0 else 13
slow = int(slow) if slow and slow > 0 else 25
signal = int(signal) if signal and signal > 0 else 13
# if slow < fast:
# fast, slow = slow, fast
scalar = float(scalar) if scalar else 100
close = verify_series(close, max(fast, slow))
drift = get_drift(drift)
offset = get_offset(offset)
mamode = mamode if isinstance(mamode, str) else "ema"
fast = v_pos_default(fast, 13)
slow = v_pos_default(slow, 25)
close = v_series(close, max(fast, slow))
if "length" in kwargs:
kwargs.pop("length")
if close is None:
return
signal = v_pos_default(signal, 13)
scalar = v_scalar(scalar, 100)
mamode = v_mamode(mamode, "ema")
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
diff = close.diff(drift)
slow_ema = ema(close=diff, length=slow, **kwargs)
pandas_ta/momentum/uo.py
+14 -13
View File
@@ -2,7 +2,7 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series, v_talib
def uo(
@@ -43,23 +43,24 @@ def uo(
pd.Series: New feature generated.
"""
# Validate
fast = int(fast) if fast and fast > 0 else 7
fast_w = float(fast_w) if fast_w and fast_w > 0 else 4.0
medium = int(medium) if medium and medium > 0 else 14
medium_w = float(medium_w) if medium_w and medium_w > 0 else 2.0
slow = int(slow) if slow and slow > 0 else 28
slow_w = float(slow_w) if slow_w and slow_w > 0 else 1.0
fast = v_pos_default(fast, 7)
medium = v_pos_default(medium, 14)
slow = v_pos_default(slow, 28)
_length = max(fast, medium, slow)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
drift = get_drift(drift)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
fast_w = v_pos_default(fast_w, 4.0)
medium_w = v_pos_default(medium_w, 2.0)
slow_w = v_pos_default(slow_w, 1.0)
mode_tal = v_talib(talib)
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import ULTOSC
pandas_ta/momentum/willr.py
+8 -7
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
def willr(
@@ -35,21 +35,22 @@ def willr(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 14
length = v_pos_default(length, 14)
if "min_periods" in kwargs and kwargs["min_periods"] is not None:
min_periods = int(kwargs["min_periods"])
else:
min_periods = 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)
mode_tal = bool(talib) if isinstance(talib, bool) else True
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import WILLR
pandas_ta/overlap/alligator.py
+8 -7
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
from .smma import smma
@@ -42,16 +42,17 @@ def alligator(
pd.DataFrame: JAW, TEETH, LIPS columns.
"""
# Validate
jaw = int(jaw) if jaw and jaw > 0 else 13
teeth = int(teeth) if teeth and teeth > 0 else 8
lips = int(lips) if lips and lips > 0 else 5
close = verify_series(close, max(jaw, teeth, lips))
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
jaw = v_pos_default(jaw, 13)
teeth = v_pos_default(teeth, 8)
lips = v_pos_default(lips, 5)
close = v_series(close, max(jaw, teeth, lips))
if close is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
gator_jaw = smma(close, length=jaw, talib=mode_tal)
gator_teeth = smma(close, length=teeth, talib=mode_tal)
pandas_ta/overlap/alma.py
+9 -7
View File
@@ -3,7 +3,7 @@ from numpy import append, arange, array, exp, floor, nan, tensordot
from numpy.version import version as npVersion
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas import Series
from pandas_ta.utils import get_offset, strided_window, verify_series
from pandas_ta.utils import strided_window, v_offset, v_pos_default, v_series
def alma(
@@ -39,17 +39,19 @@ def alma(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if isinstance(length, int) and length > 0 else 9
sigma = float(sigma) if isinstance(sigma, float) and sigma > 0 else 6.0
length = v_pos_default(length, 9)
close = v_series(close, length)
if close is None:
return
sigma = v_pos_default(sigma, 6.0)
if isinstance(dist_offset, float) and 0 <= dist_offset <= 1:
offset_ = float(dist_offset)
else:
offset_ = 0.85
close = verify_series(close, length)
offset = get_offset(offset)
if close is None:
return
offset = v_offset(offset)
# Calculate
np_close = close.values
pandas_ta/overlap/dema.py
+6 -5
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
from .ema import ema
@@ -33,14 +33,15 @@ def dema(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import DEMA
pandas_ta/overlap/ema.py
+8 -7
View File
@@ -3,7 +3,7 @@ from numpy import nan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series, v_talib
try:
from numba import njit
@@ -59,16 +59,17 @@ def ema(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
presma = bool(presma) if isinstance(presma, bool) else True
mode_tal = bool(talib) if isinstance(talib, bool) else True
close = verify_series(close, length)
offset = get_offset(offset)
adjust = kwargs.pop("adjust", False)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
mode_tal = v_talib(talib)
presma = v_bool(presma, True)
offset = v_offset(offset)
adjust = kwargs.setdefault("adjust", False)
# Calculate
if Imports["talib"] and mode_tal:
from talib import EMA
pandas_ta/overlap/fwma.py
+7 -5
View File
@@ -1,7 +1,8 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import fibonacci, get_offset, verify_series, weights
from pandas_ta.utils import fibonacci, v_ascending, v_offset
from pandas_ta.utils import v_pos_default, v_series, weights
def fwma(
@@ -29,14 +30,15 @@ def fwma(
pd.Series: New feature generated.
"""
# Validate
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)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
asc = v_ascending(asc)
offset = v_offset(offset)
# Calculate
fibs = fibonacci(n=length, weighted=True)
fwma = close.rolling(length, min_periods=length) \
pandas_ta/overlap/hilo.py
+9 -8
View File
@@ -3,7 +3,7 @@ from numpy import nan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
def hilo(
@@ -47,18 +47,19 @@ def hilo(
pd.DataFrame: HILO (line), HILOl (long), HILOs (short) columns.
"""
# Validate
high_length = int(high_length) if high_length and high_length > 0 else 13
low_length = int(low_length) if low_length and low_length > 0 else 21
mamode = mamode.lower() if isinstance(mamode, str) else "sma"
high_length = v_pos_default(high_length, 13)
low_length = v_pos_default(low_length, 21)
_length = max(high_length, low_length)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
offset = get_offset(offset)
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return
mamode = v_mamode(mamode, "sma")
offset = v_offset(offset)
# Calculate
m = close.size
hilo = Series(nan, index=close.index)
pandas_ta/overlap/hl2.py
+4 -4
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_series
def hl2(
@@ -27,9 +27,9 @@ def hl2(
pd.Series: New feature generated.
"""
# Validate
high = verify_series(high)
low = verify_series(low)
offset = get_offset(offset)
high = v_series(high)
low = v_series(low)
offset = v_offset(offset)
# Calculate
avg = 0.5 * (high.values + low.values)
pandas_ta/overlap/hlc3.py
+6 -6
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_series, v_talib
def hlc3(
@@ -29,11 +29,11 @@ def hlc3(
pd.Series: New feature generated.
"""
# Validate
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
high = v_series(high)
low = v_series(low)
close = v_series(close)
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
pandas_ta/overlap/hma.py
+5 -4
View File
@@ -2,7 +2,7 @@
from numpy import sqrt
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
from .wma import wma
@@ -31,13 +31,14 @@ def hma(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
offset = v_offset(offset)
# Calculate
half_length = int(length / 2)
sqrt_length = int(sqrt(length))
pandas_ta/overlap/hwma.py
+7 -8
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_series
def hwma(
@@ -15,8 +15,7 @@ def hwma(
moving average by the Holt-Winter method; the three parameters should
be selected to obtain a forecast.
This version has been implemented for Pandas TA by rengel8 based
on a publication for MetaTrader 5.
Coded by rengel8 based on a publication for MetaTrader 5.
Sources:
https://www.mql5.com/en/code/20856
@@ -36,11 +35,11 @@ def hwma(
pd.Series: hwma
"""
# Validate
na = float(na) if na and na > 0 and na < 1 else 0.2
nb = float(nb) if nb and nb > 0 and nb < 1 else 0.1
nc = float(nc) if nc and nc > 0 and nc < 1 else 0.1
close = verify_series(close)
offset = get_offset(offset)
close = v_series(close)
na = float(na) if isinstance(na, float) and 0 < na < 1 else 0.2
nb = float(nb) if isinstance(nb, float) and 0 < nb < 1 else 0.1
nc = float(nc) if isinstance(nc, float) and 0 < nc < 1 else 0.1
offset = v_offset(offset)
# Calculate
last_a = last_v = 0
pandas_ta/overlap/ichimoku.py
+11 -10
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import date_range, DataFrame, RangeIndex, Timedelta, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
from .midprice import midprice
@@ -40,20 +40,21 @@ def ichimoku(
For the forward looking period: spanA and spanB columns
"""
# Validate
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
tenkan = v_pos_default(tenkan, 9)
kijun = v_pos_default(kijun, 26)
senkou = v_pos_default(senkou, 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
high = v_series(high, _length)
low = v_series(low, _length)
close = v_series(close, _length)
if high is None or low is None or close is None:
return None, None
offset = v_offset(offset)
if not kwargs.get("lookahead", True):
include_chikou = False
# Calculate
tenkan_sen = midprice(high=high, low=low, length=tenkan)
kijun_sen = midprice(high=high, low=low, length=kijun)
pandas_ta/overlap/jma.py
+7 -5
View File
@@ -4,7 +4,7 @@ from numpy import average, log, nan, sqrt, zeros_like
from numpy import power as np_power
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_float, v_offset, v_pos_default, v_series
def jma(
@@ -35,13 +35,15 @@ def jma(
pd.Series: New feature generated.
"""
# Validate
_length = int(length) if length and length > 0 else 7
phase = float(phase) if phase and phase != 0 else 0
close = verify_series(close, _length)
offset = get_offset(offset)
_length = v_pos_default(length, 7)
close = v_series(close, _length)
if close is None:
return
phase = v_float(phase, 0.0)
offset = v_offset(offset)
# Calculate
jma = zeros_like(close)
volty = zeros_like(close)
pandas_ta/overlap/kama.py
+11 -18
View File
@@ -3,7 +3,8 @@ from numpy import nan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import get_drift, get_offset, non_zero_range, verify_series
from pandas_ta.utils import non_zero_range, v_drift, v_mamode
from pandas_ta.utils import v_offset, v_pos_default, v_series
def kama(
@@ -30,9 +31,7 @@ def kama(
length (int): It's period. Default: 10
fast (int): Fast MA period. Default: 2
slow (int): Slow MA period. Default: 30
mamode (str): See ``help(ta.ma)``. Valid MAs that support initialize
the first value: 'ema', 'fwma', 'linreg', 'midpoint', 'pwma',
'rma', 'sinwma', 'sma', 'swma', 'trima', 'wma'. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
drift (int): The difference period. Default: 1
offset (int): How many periods to offset the result. Default: 0
@@ -44,24 +43,18 @@ def kama(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
fast = int(fast) if fast and fast > 0 else 2
slow = int(slow) if slow and slow > 0 else 30
close = verify_series(close, max(fast, slow, length))
valid_ma = [
"ema", "fwma", "linreg", "midpoint", "pwma", "rma",
"sinwma", "sma", "swma", "trima", "wma"
]
if isinstance(mamode, str) and mamode.lower() in valid_ma:
mamode = mamode.lower()
else:
mamode = "sma"
drift = get_drift(drift)
offset = get_offset(offset)
length = v_pos_default(length, 10)
fast = v_pos_default(fast, 2)
slow = v_pos_default(slow, 30)
close = v_series(close, max(fast, slow, length))
if close is None:
return
mamode = v_mamode(mamode, "sma")
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
def weight(length: int) -> float:
return 2 / (length + 1)
pandas_ta/overlap/linreg.py
+11 -8
View File
@@ -4,7 +4,8 @@ from numpy.version import version
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, strided_window, verify_series
from pandas_ta.utils import strided_window, v_offset, v_pos_default
from pandas_ta.utils import v_series, v_talib
def linreg(
@@ -45,19 +46,21 @@ def linreg(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 14
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 14)
close = v_series(close, length)
if close is None:
return
mode_tal = v_talib(talib)
offset = v_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)
mode_tal = bool(talib) if isinstance(talib, bool) else True
if close is None:
return
# Calculate
np_close = close.values
pandas_ta/overlap/mcgd.py
+6 -5
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
def mcgd(
@@ -37,14 +37,15 @@ def mcgd(
pd.Series: New feature generated.
"""
# Validate
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)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
c = float(c) if isinstance(c, float) and 0 < c <= 1 else 1
offset = v_offset(offset)
# Calculate
close = close.copy()
pandas_ta/overlap/midpoint.py
+6 -5
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
def midpoint(
@@ -28,18 +28,19 @@ def midpoint(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 2
length = v_pos_default(length, 2)
if "min_periods" in kwargs and kwargs["min_periods"] is not None:
min_periods = int(kwargs["min_periods"])
else:
min_periods = length
close = verify_series(close, max(length, min_periods))
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
close = v_series(close, max(length, min_periods))
if close is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import MIDPOINT
pandas_ta/overlap/midprice.py
+7 -6
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
def midprice(
@@ -29,20 +29,21 @@ def midprice(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 2
length = v_pos_default(length, 2)
if "min_periods" in kwargs and kwargs["min_periods"] is not None:
min_periods = int(kwargs["min_periods"])
else:
min_periods = length
_length = max(length, min_periods)
high = verify_series(high, _length)
low = verify_series(low, _length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
high = v_series(high, _length)
low = v_series(low, _length)
if high is None or low is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import MIDPRICE
pandas_ta/overlap/ohlc4.py
+6 -6
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_series
def ohlc4(
@@ -29,11 +29,11 @@ def ohlc4(
pd.Series: New feature generated.
"""
# Validate
open_ = verify_series(open_)
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
offset = get_offset(offset)
open_ = v_series(open_)
high = v_series(high)
low = v_series(low)
close = v_series(close)
offset = v_offset(offset)
# Calculate
avg = 0.25 * (open_.values + high.values + low.values + close.values)
pandas_ta/overlap/pwma.py
+7 -5
View File
@@ -1,7 +1,8 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, pascals_triangle, verify_series, weights
from pandas_ta.utils import pascals_triangle, v_offset
from pandas_ta.utils import v_ascending, v_pos_default, v_series, weights
def pwma(
@@ -29,14 +30,15 @@ def pwma(
pd.Series: New feature generated.
"""
# Validate
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)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
asc = v_ascending(asc)
offset = v_offset(offset)
# Calculate
triangle = pascals_triangle(n=length - 1, weighted=True)
pwma = close.rolling(length, min_periods=length) \
pandas_ta/overlap/rma.py
+6 -5
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
def rma(
@@ -30,14 +30,15 @@ def rma(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
alpha = (1.0 / length) if length > 0 else 0.5
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
alpha = (1.0 / length) if length > 0 else 0.5
offset = v_offset(offset)
# Calculate
rma = close.ewm(alpha=alpha, min_periods=length).mean()
pandas_ta/overlap/sinwma.py
+5 -4
View File
@@ -2,7 +2,7 @@
from numpy import pi, sin
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series, weights
from pandas_ta.utils import v_offset, v_pos_default, v_series, weights
def sinwma(
@@ -31,13 +31,14 @@ def sinwma(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 14
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 14)
close = v_series(close, length)
if close is None:
return
offset = v_offset(offset)
# Calculate
sines = Series([sin((i + 1) * pi / (length + 1))
for i in range(0, length)])
pandas_ta/overlap/sma.py
+7 -5
View File
@@ -3,7 +3,8 @@ from numpy import convolve, ndarray, ones
from pandas import Series
from pandas_ta._typing import Array, DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, np_prepend, verify_series
from pandas_ta.utils import np_prepend, v_offset, v_pos_default
from pandas_ta.utils import v_series, v_talib
try:
@@ -61,18 +62,19 @@ def sma(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
length = v_pos_default(length, 10)
if "min_periods" in kwargs and kwargs["min_periods"] is not None:
min_periods = int(kwargs["min_periods"])
else:
min_periods = length
close = verify_series(close, max(length, min_periods))
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
close = v_series(close, max(length, min_periods))
if close is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import SMA
pandas_ta/overlap/smma.py
+8 -6
View File
@@ -3,7 +3,8 @@ from numpy import nan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.ma import ma
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_mamode, v_offset, v_pos_default
from pandas_ta.utils import v_series, v_talib
def smma(
@@ -43,19 +44,20 @@ def smma(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 7
length = v_pos_default(length, 7)
if "min_periods" in kwargs and kwargs["min_periods"] is not None:
min_periods = int(kwargs["min_periods"])
else:
min_periods = length
close = verify_series(close, max(length, min_periods))
offset = get_offset(offset)
mamode = mamode.lower() if isinstance(mamode, str) else "sma"
mode_tal = bool(talib) if isinstance(talib, bool) else True
close = v_series(close, max(length, min_periods))
if close is None:
return
mamode = v_mamode(mamode, "sma")
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
m = close.size
smma = close.copy()
pandas_ta/overlap/ssf.py
+8 -7
View File
@@ -2,7 +2,7 @@
from numpy import copy, cos, exp
from pandas import Series
from pandas_ta._typing import Array, DictLike, Int, IntFloat
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series
try:
@@ -86,16 +86,17 @@ def ssf(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if isinstance(length, int) and length > 0 else 20
everget = bool(everget) if isinstance(everget, bool) else False
pi = float(pi) if isinstance(pi, float) and pi > 0 else 3.14159
sqrt2 = float(sqrt2) if isinstance(sqrt2, float) and sqrt2 > 0 else 1.414
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 20)
close = v_series(close, length)
if close is None:
return
pi = v_pos_default(pi, 3.14159)
sqrt2 = v_pos_default(sqrt2, 1.414)
everget = v_bool(everget, False)
offset = v_offset(offset)
# Calculate
np_close = close.values
if everget:
pandas_ta/overlap/ssf3.py
+7 -6
View File
@@ -2,7 +2,7 @@
from numpy import copy, cos, exp
from pandas import Series
from pandas_ta._typing import Array, DictLike, Int, IntFloat
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
try:
from numba import njit
@@ -70,15 +70,16 @@ def ssf3(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if isinstance(length, int) and length > 0 else 20
pi = float(pi) if isinstance(pi, float) and pi > 0 else 3.14159
sqrt3 = float(sqrt3) if isinstance(sqrt3, float) and sqrt3 > 0 else 1.732
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 20)
close = v_series(close, length)
if close is None:
return
pi = v_pos_default(pi, 3.14159)
sqrt3 = v_pos_default(sqrt3, 1.732)
offset = v_offset(offset)
# Calculate
np_close = close.values
ssf = np_ssf3(np_close, length, pi, sqrt3)
pandas_ta/overlap/supertrend.py
+9 -10
View File
@@ -3,7 +3,7 @@ from numpy import nan
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.overlap import hl2
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
from pandas_ta.volatility import atr
@@ -39,19 +39,18 @@ def supertrend(
SUPERTl (long), SUPERTs (short) columns.
"""
# Validate
length = int(length) if isinstance(length, int) and length > 0 else 7
if isinstance(multiplier, float) and multiplier > 0:
multiplier = float(multiplier)
else:
multiplier = 3.0
high = verify_series(high, length)
low = verify_series(low, length)
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 7)
high = v_series(high, length)
low = v_series(low, length)
close = v_series(close, length)
if high is None or low is None or close is None:
return
multiplier = v_pos_default(multiplier, 3.0)
offset = v_offset(offset)
# Calculate
m = close.size
dir_, trend = [1] * m, [0] * m
pandas_ta/overlap/swma.py
+6 -4
View File
@@ -1,7 +1,8 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, symmetric_triangle, verify_series, weights
from pandas_ta.utils import symmetric_triangle, v_offset, v_pos_default
from pandas_ta.utils import v_series, weights
def swma(
@@ -31,13 +32,14 @@ def swma(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
offset = v_offset(offset)
# Calculate
triangle = symmetric_triangle(length, weighted=True)
swma = close.rolling(length, min_periods=length) \
pandas_ta/overlap/t3.py
+7 -6
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
from .ema import ema
@@ -36,15 +36,16 @@ def t3(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
a = float(a) if a and a > 0 and a < 1 else 0.7
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
a = float(a) if isinstance(a, float) and 0 < a < 1 else 0.7
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import T3
pandas_ta/overlap/tema.py
+6 -5
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
from .ema import ema
@@ -34,14 +34,15 @@ def tema(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import TEMA
pandas_ta/overlap/trima.py
+6 -5
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series, v_talib
from .sma import sma
@@ -36,14 +36,15 @@ def trima(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import TRIMA
pandas_ta/overlap/vidya.py
+6 -5
View File
@@ -2,7 +2,7 @@
from numpy import nan
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_drift, get_offset, verify_series
from pandas_ta.utils import v_drift, v_offset, v_pos_default, v_series
def vidya(
@@ -40,14 +40,15 @@ def vidya(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 14
close = verify_series(close, length)
drift = get_drift(drift)
offset = get_offset(offset)
length = v_pos_default(length, 14)
close = v_series(close, length)
if close is None:
return
drift = v_drift(drift)
offset = v_offset(offset)
# Calculate
m = close.size
alpha = 2 / (length + 1)
pandas_ta/overlap/vwap.py
+10 -9
View File
@@ -2,7 +2,7 @@
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int, List
from pandas_ta.overlap import hlc3
from pandas_ta.utils import get_offset, is_datetime_ordered, verify_series
from pandas_ta.utils import v_datetime_ordered, v_list, v_offset, v_series
def vwap(
@@ -47,22 +47,23 @@ def vwap(
pd.DataFrame: New feature generated.
"""
# Validate
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
volume = verify_series(volume)
high = v_series(high)
low = v_series(low)
close = v_series(close)
volume = v_series(volume)
bands = v_list(bands)
offset = v_offset(offset)
if anchor and isinstance(anchor, str) and len(anchor) >= 1:
anchor = anchor.upper()
else:
anchor = "D"
bands = bands if isinstance(bands, list) and len(bands) else []
offset = get_offset(offset)
typical_price = hlc3(high=high, low=low, close=close)
if not is_datetime_ordered(volume):
if not v_datetime_ordered(volume):
_s = "[!] VWAP volume series is not datetime ordered."
print(f"{_s} Results may not be as expected.")
if not is_datetime_ordered(typical_price):
if not v_datetime_ordered(typical_price):
_s = "[!] VWAP price series is not datetime ordered."
print(f"{_s} Results may not be as expected.")
pandas_ta/overlap/vwma.py
+6 -5
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.overlap import sma
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
def vwma(
@@ -30,14 +30,15 @@ def vwma(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
close = verify_series(close, length)
volume = verify_series(volume, length)
offset = get_offset(offset)
length = v_pos_default(length, 10)
close = v_series(close, length)
volume = v_series(volume, length)
if close is None or volume is None:
return
offset = v_offset(offset)
# Calculate
pv = close * volume
vwma = sma(close=pv, length=length) / sma(close=volume, length=length)
pandas_ta/overlap/wcp.py
+6 -6
View File
@@ -2,7 +2,7 @@
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_series, v_talib
def wcp(
@@ -33,11 +33,11 @@ def wcp(
pd.Series: New feature generated.
"""
# Validate
high = verify_series(high)
low = verify_series(low)
close = verify_series(close)
offset = get_offset(offset)
mode_tal = bool(talib) if isinstance(talib, bool) else True
high = v_series(high)
low = v_series(low)
close = v_series(close)
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
pandas_ta/overlap/wma.py
+8 -6
View File
@@ -3,7 +3,8 @@ from numpy import arange, dot
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.maps import Imports
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_ascending, v_offset, v_pos_default
from pandas_ta.utils import v_series, v_talib
def wma(
@@ -35,15 +36,16 @@ def wma(
pd.Series: New feature generated.
"""
# Validate
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
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
asc = v_ascending(asc)
mode_tal = v_talib(talib)
offset = v_offset(offset)
# Calculate
if Imports["talib"] and mode_tal:
from talib import WMA
pandas_ta/overlap/zlma.py
+6 -7
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_mamode, v_offset, v_pos_default, v_series
from .dema import dema
from .ema import ema
from .fwma import fwma
@@ -87,14 +87,15 @@ def zlma(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
mamode = mamode.lower() if isinstance(mamode, str) else "ema"
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
mamode = v_mamode(mamode, "ema")
offset = v_offset(offset)
# Calculate
lag = int(0.5 * (length - 1))
close_ = 2 * close - close.shift(lag)
@@ -102,8 +103,6 @@ def zlma(
kwargs.update({"close": close_})
kwargs.update({"length": length})
zlma = _ma(mamode, **kwargs)
# Offset
pandas_ta/performance/drawdown.py
+3 -3
View File
@@ -2,7 +2,7 @@
from numpy import log, seterr
from pandas import DataFrame, Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_series
def drawdown(
@@ -29,8 +29,8 @@ def drawdown(
pd.DataFrame: drawdown, drawdown percent, drawdown log columns
"""
# Validate
close = verify_series(close)
offset = get_offset(offset)
close = v_series(close)
offset = v_offset(offset)
# Calculate
max_close = close.cummax()
pandas_ta/performance/log_return.py
+6 -8
View File
@@ -2,7 +2,7 @@
from pandas import Series
from numpy import log, nan, roll
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series
def log_return(
@@ -32,17 +32,15 @@ def log_return(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 1
if cumulative is not None and cumulative:
cumulative = bool(cumulative)
else:
cumulative = False
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 1)
close = v_series(close, length)
if close is None:
return
cumulative = v_bool(cumulative, False)
offset = v_offset(offset)
# Calculate
np_close = close.values
if cumulative:
pandas_ta/performance/percent_return.py
+6 -8
View File
@@ -2,7 +2,7 @@
from numpy import nan, roll
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_bool, v_offset, v_pos_default, v_series
def percent_return(
@@ -31,17 +31,15 @@ def percent_return(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 1
if cumulative is not None and cumulative:
cumulative = bool(cumulative)
else:
cumulative = False
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 1)
close = v_series(close, length)
if close is None:
return
cumulative = v_bool(cumulative, False)
offset = v_offset(offset)
# Calculate
np_close = close.values
if cumulative:
pandas_ta/statistics/entropy.py
+6 -5
View File
@@ -2,7 +2,7 @@
from numpy import log
from pandas import Series
from pandas_ta._typing import DictLike, Int, IntFloat
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
def entropy(
@@ -32,14 +32,15 @@ def entropy(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 10
base = float(base) if base and base > 0 else 2.0
close = verify_series(close, length)
offset = get_offset(offset)
length = v_pos_default(length, 10)
close = v_series(close, length)
if close is None:
return
base = v_pos_default(base, 2.0)
offset = v_offset(offset)
# Calculate
p = close / close.rolling(length).sum()
entropy = (-p * log(p) / log(base)).rolling(length).sum()
pandas_ta/statistics/kurtosis.py
+7 -5
View File
@@ -1,7 +1,7 @@
# -*- coding: utf-8 -*-
from pandas import Series
from pandas_ta._typing import DictLike, Int
from pandas_ta.utils import get_offset, verify_series
from pandas_ta.utils import v_offset, v_pos_default, v_series
def kurtosis(
@@ -25,15 +25,17 @@ def kurtosis(
pd.Series: New feature generated.
"""
# Validate
length = int(length) if length and length > 0 else 30
length = v_pos_default(length, 30)
if "min_periods" in kwargs and kwargs["min_periods"] is not None:
min_periods = int(kwargs["min_periods"])
else:
min_periods = length
close = verify_series(close, max(length, min_periods))
offset = get_offset(offset)
close = v_series(close, max(length, min_periods))
if close is None: return
if close is None:
return
offset = v_offset(offset)
# Calculate
kurtosis = close.rolling(length, min_periods=min_periods).kurt()

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