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docstring google format compliance

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This commit is contained in:
ryan russell
2022-01-30 23:29:29 -06:00
parent 4af8056a43
commit 140ad834b3
32 changed files with 62 additions and 51 deletions
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pandas_ta/candles/cdl_pattern.py
+9 -6
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@@ -33,14 +33,17 @@ def cdl_pattern(
Examples:
Get all candle patterns (This is the default behaviour)
>>> df = df.ta.cdl_pattern(name="all")
Get all candle patterns (This is the default behaviour)::
Get only one pattern
>>> df = df.ta.cdl_pattern(name="doji")
df = df.ta.cdl_pattern(name="all")
Get some patterns
>>> df = df.ta.cdl_pattern(name=["doji", "inside"])
Get only one pattern::
df = df.ta.cdl_pattern(name="doji")
Get some patterns::
df = df.ta.cdl_pattern(name=["doji", "inside"])
Args:
open_ (pd.Series): Series of 'open's
pandas_ta/momentum/apo.py
+1 -1
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@@ -23,7 +23,7 @@ def apo(
close (pd.Series): Series of 'close's
fast (int): The short period. Default: 12
slow (int): The long period. Default: 26
mamode (str): See ```help(ta.ma)```. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
offset (int): How many periods to offset the result. Default: 0
pandas_ta/momentum/bias.py
+1 -1
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@@ -19,7 +19,7 @@ def bias(
Args:
close (pd.Series): Series of 'close's
length (int): The period. Default: 26
mamode (str): See ```help(ta.ma)```. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
pandas_ta/momentum/dm.py
+1 -1
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@@ -23,7 +23,7 @@ def dm(
Args:
high (pd.Series): Series of 'high's
low (pd.Series): Series of 'low's
mamode (str): See ```help(ta.ma)```. Default: 'rma'
mamode (str): See ``help(ta.ma)``. Default: 'rma'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
drift (int): The difference period. Default: 1
pandas_ta/momentum/inertia.py
+1 -1
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@@ -31,7 +31,7 @@ def inertia(
rvi_length (int): RVI period. Default: 14
refined (bool): Use 'refined' calculation. Default: False
thirds (bool): Use 'thirds' calculation. Default: False
mamode (str): See ```help(ta.ma)```. Default: 'ema'
mamode (str): See ``help(ta.ma)``. Default: 'ema'
drift (int): The difference period. Default: 1
offset (int): How many periods to offset the result. Default: 0
pandas_ta/momentum/ppo.py
+1 -1
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@@ -23,7 +23,7 @@ def ppo(
slow(int): The long period. Default: 26
signal(int): The signal period. Default: 9
scalar (float): How much to magnify. Default: 100
mamode (str): See ```help(ta.ma)```. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
offset(int): How many periods to offset the result. Default: 0
pandas_ta/momentum/qqe.py
+1 -1
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@@ -28,7 +28,7 @@ def qqe(
length (int): RSI period. Default: 14
smooth (int): RSI smoothing period. Default: 5
factor (float): QQE Factor. Default: 4.236
mamode (str): See ```help(ta.ma)```. 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
pandas_ta/momentum/stoch.py
+1 -1
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@@ -33,7 +33,7 @@ def stoch(
k (int): The Fast %K period. Default: 14
d (int): The Slow %D period. Default: 3
smooth_k (int): The Slow %K period. Default: 3
mamode (str): See ```help(ta.ma)```. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
offset (int): How many periods to offset the result. Default: 0
pandas_ta/momentum/stochf.py
+1 -1
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@@ -26,7 +26,7 @@ def stochf(
close (pd.Series): Series of 'close's
k (int): The Fast %K period. Default: 14
d (int): The Slow %D period. Default: 3
mamode (str): See ```help(ta.ma)```. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
offset (int): How many periods to offset the result. Default: 0
pandas_ta/momentum/stochrsi.py
+1 -1
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@@ -30,7 +30,7 @@ def stochrsi(
rsi_length (int): RSI period. Default: 14
k (int): The Fast %K period. Default: 3
d (int): The Slow %K period. Default: 3
mamode (str): See ```help(ta.ma)```. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
pandas_ta/momentum/tsi.py
+1 -1
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@@ -26,7 +26,7 @@ def tsi(
signal (int): The signal period. Default: 13
scalar (float): How much to magnify. Default: 100
mamode (str): Moving Average of TSI Signal Line.
See ```help(ta.ma)```. Default: 'ema'
See ``help(ta.ma)``. Default: 'ema'
drift (int): The difference period. Default: 1
offset (int): How many periods to offset the result. Default: 0
pandas_ta/overlap/hilo.py
+1 -1
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@@ -33,7 +33,7 @@ def hilo(
close (pd.Series): Series of 'close's
high_length (int): It's period. Default: 13
low_length (int): It's period. Default: 21
mamode (str): See ```help(ta.ma)```. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
pandas_ta/overlap/kama.py
+1 -1
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@@ -27,7 +27,7 @@ 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
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'
drift (int): The difference period. Default: 1
pandas_ta/overlap/smma.py
+1 -1
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@@ -28,7 +28,7 @@ def smma(
Args:
close (pd.Series): Series of 'close's
length (int): It's period. Default: 10
mamode (str): See ```help(ta.ma)```. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
offset (int): How many periods to offset the result. Default: 0
pandas_ta/trend/adx.py
+1 -1
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@@ -27,7 +27,7 @@ def adx(
length (int): It's period. Default: 14
lensig (int): Signal Length. Like TradingView's default ADX. Default: length
scalar (float): How much to magnify. Default: 100
mamode (str): See ```help(ta.ma)```. Default: 'rma'
mamode (str): See ``help(ta.ma)``. Default: 'rma'
drift (int): The difference period. Default: 1
offset (int): How many periods to offset the result. Default: 0
pandas_ta/trend/amat.py
+3 -3
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@@ -14,8 +14,8 @@ def amat(
"""Archer Moving Averages Trends (AMAT)
Archer Moving Averages Trends (AMAT) developed by Kevin Johnson provides
creates both long run ```help(ta.long_run)``` and short run
```help(ta.short_run)``` trend signals given two moving average speeds,
creates both long run ``help(ta.long_run)`` and short run
``help(ta.short_run)`` trend signals given two moving average speeds,
fast and slow. The long runs and short runs are binary Series where '1' is
a trend and '0' is not a trend.
@@ -27,7 +27,7 @@ def amat(
fast (int): The period of the fast moving average. Default: 8
slow (int): The period of the slow moving average. Default: 21
lookback (int): Lookback period for long_run and short_run. Default: 2
mamode (str): See ```help(ta.ma)```. Default: 'ema'
mamode (str): See ``help(ta.ma)``. Default: 'ema'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
pandas_ta/trend/long_run.py
+1 -1
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@@ -22,7 +22,7 @@ def long_run(
signals.
Sources:
It is part of the Converging and Diverging Conditional logic in:
It is part of the Converging and Diverging Conditional logic in:
https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/
Args:
pandas_ta/trend/short_run.py
+3 -3
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@@ -17,12 +17,12 @@ def short_run(
It is recommended to use 'smooth' signals for 'fast' and 'slow' for the
comparison to reduce unnecessary noise. For indicators using long_run, see
Archer Moving Average Trend (```help(ta.amat)```) and Archer On Balance
Volume (```help(ta.aobv)```). Both use Moving Averages for 'fast' and 'slow'
Archer Moving Average Trend (``help(ta.amat)``) and Archer On Balance
Volume (``help(ta.aobv)``). Both use Moving Averages for 'fast' and 'slow'
signals.
Sources:
It is part of the Converging and Diverging Conditional logic in:
It is part of the Converging and Diverging Conditional logic in:
https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/
Args:
pandas_ta/trend/tsignals.py
+5 -3
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@@ -19,10 +19,12 @@ def tsignals(
whose values are boolean or integers (0 or 1).
Examples:
ta.tsignals(close > ta.sma(close, 50), asbool=False)
ta.tsignals(ta.ema(close, 8) > ta.ema(close, 21), asbool=True)
ta.tsignals(close > ta.sma(close, 50), asbool=False)
Source: Kevin Johnson
ta.tsignals(ta.ema(close, 8) > ta.ema(close, 21), asbool=True)
Source:
Kevin Johnson
Args:
trend (pd.Series): Series of 'trend's. The trend can be either a boolean or
pandas_ta/trend/xsignals.py
+14 -8
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@@ -29,15 +29,21 @@ def xsignals(
False, it does the same but for the short side.
Example:
# These are two different outcomes and depends on the indicator and it's
# characteristics. Please check BOTH outcomes BEFORE making an Issue.
rsi = df.ta.rsi()
# Returns tsignal DataFrame when RSI crosses above 20 and then below 80
ta.xsignals(rsi, 20, 80, above=True)
# Returns tsignal DataFrame when RSI crosses below 20 and then above 80
ta.xsignals(rsi, 20, 80, above=False)
These are two different outcomes and depends on the indicator and it's
characteristics. Please check BOTH outcomes BEFORE making an Issue::
rsi = df.ta.rsi()
Returns tsignal DataFrame when RSI crosses above 20 and then below 80::
Source: Kevin Johnson
ta.xsignals(rsi, 20, 80, above=True)
Returns tsignal DataFrame when RSI crosses below 20 and then above 80::
ta.xsignals(rsi, 20, 80, above=False)
Source:
Kevin Johnson
Args:
signal (pd.Series): The Signal to compare from. Commonly the 'close'.
pandas_ta/utils/data/polygon_api.py
+1 -1
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@@ -12,7 +12,7 @@ def polygon_api(ticker: str, **kwargs) -> DataFrame:
It returns OCHLV data from polygon (requires a valid subscription of course). To install the
`polygon library <https://github.com/pssolanki111/polygon>`__ , use
``pip install polygon``.
You can customize the range of data using kwargs ``from_date``, ``to_date````timespan`` and ``multiplier``. For a
You can customize the range of data using kwargs ``from_date``, ``to_date``, ``timespan`` and ``multiplier``. For a
description of these arguments, see
`Here <https://polygon.readthedocs.io/en/latest/Stocks.html#get-aggregate-bars-candles>`__
pandas_ta/volatility/accbands.py
+1 -1
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@@ -23,7 +23,7 @@ def accbands(
close (pd.Series): Series of 'close's
length (int): It's period. Default: 10
c (int): Multiplier. Default: 4
mamode (str): See ```help(ta.ma)```. 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
pandas_ta/volatility/atr.py
+1 -1
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@@ -24,7 +24,7 @@ def atr(
low (pd.Series): Series of 'low's
close (pd.Series): Series of 'close's
length (int): It's period. Default: 14
mamode (str): See ```help(ta.ma)```. Default: 'rma'
mamode (str): See ``help(ta.ma)``. Default: 'rma'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
drift (int): The difference period. Default: 1
pandas_ta/volatility/bbands.py
+1 -1
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@@ -23,7 +23,7 @@ def bbands(
length (int): The short period. Default: 5
std (int): The long period. Default: 2
ddof (int): Degrees of Freedom to use. Default: 0
mamode (str): See ```help(ta.ma)```. Default: 'sma'
mamode (str): See ``help(ta.ma)``. Default: 'sma'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
ddof (int): Delta Degrees of Freedom.
pandas_ta/volatility/kc.py
+1 -1
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@@ -24,7 +24,7 @@ def kc(
close (pd.Series): Series of 'close's
length (int): The short period. Default: 20
scalar (float): A positive float to scale the bands. Default: 2
mamode (str): See ```help(ta.ma)```. Default: 'ema'
mamode (str): See ``help(ta.ma)``. Default: 'ema'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
pandas_ta/volatility/natr.py
+1 -1
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@@ -24,7 +24,7 @@ def natr(
close (pd.Series): Series of 'close's
length (int): The short period. Default: 20
scalar (float): How much to magnify. Default: 100
mamode (str): See ```help(ta.ma)```. Default: 'ema'
mamode (str): See ``help(ta.ma)``. Default: 'ema'
talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
version. Default: True
offset (int): How many periods to offset the result. Default: 0
pandas_ta/volatility/rvi.py
+1 -1
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@@ -31,7 +31,7 @@ def rvi(
refined (bool): Use 'refined' calculation which is the average of
RVI(high) and RVI(low) instead of RVI(close). Default: False
thirds (bool): Average of high, low and close. Default: False
mamode (str): See ```help(ta.ma)```. Default: 'ema'
mamode (str): See ``help(ta.ma)``. Default: 'ema'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
pandas_ta/volatility/thermo.py
+1 -1
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@@ -24,7 +24,7 @@ def thermo(
length (int): The period. Default: 20
long(int): The buy factor
short(float): The sell factor
mamode (str): See ```help(ta.ma)```. Default: 'ema'
mamode (str): See ``help(ta.ma)``. Default: 'ema'
drift (int): The diff period. Default: 1
offset (int): How many periods to offset the result. Default: 0
pandas_ta/volume/aobv.py
+2 -2
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@@ -16,7 +16,7 @@ def aobv(
Archer On Balance Volume (AOBV) developed by Kevin Johnson provides
additional indicator analysis on OBV. It calculates moving averages, default
'ema', of OBV as well as the moving average Long and Short Run Trends, see
```help(ta.long_run)```. Lastly, the indicator also calculates the rolling
``help(ta.long_run)``. Lastly, the indicator also calculates the rolling
Maximum and Minimum OBV.
Sources:
@@ -29,7 +29,7 @@ def aobv(
slow (int): The period of the slow moving average. Default: 12
max_lookback (int): Maximum OBV bars back. Default: 2
min_lookback (int): Minimum OBV bars back. Default: 2
mamode (str): See ```help(ta.ma)```. Default: 'ema'
mamode (str): See ``help(ta.ma)``. Default: 'ema'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
pandas_ta/volume/efi.py
+1 -1
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@@ -23,7 +23,7 @@ def efi(
volume (pd.Series): Series of 'volume's
length (int): The short period. Default: 13
drift (int): The diff period. Default: 1
mamode (str): See ```help(ta.ma)```. Default: 'ema'
mamode (str): See ``help(ta.ma)``. Default: 'ema'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
pandas_ta/volume/kvo.py
+1 -1
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@@ -29,7 +29,7 @@ def kvo(
fast (int): The fast period. Default: 34
slow (int): The slow period. Default: 55
signal (int): The signal period. Default: 13
mamode (str): See ```help(ta.ma)```. Default: 'ema'
mamode (str): See ``help(ta.ma)``. Default: 'ema'
offset (int): How many periods to offset the result. Default: 0
Kwargs:
pandas_ta/volume/wb_tsv.py
+1 -1
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@@ -29,7 +29,7 @@ def wb_tsv(
volume (pd.Series): Series of 'volume's
length (int): It's period. Default: 18
signal (int): It's avg period. Default: 10
mamode (str): See ```help(ta.ma)```. 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