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ENH #246 td_seq added DOC updates
This commit is contained in:
Kevin Johnson
@@ -34,10 +34,11 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
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* [Pandas TA Strategies](#pandas-ta-strategies)
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* [Types of Strategies](#types-of-strategies)
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* [DataFrame Properties](#dataframe-properties)
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* [DataFrame Methods](#dataframe-methods)
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* [Indicators by Category](#indicators-by-category)
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* [Candles](#candles-3)
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* [Cycles](#cycles-1)
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* [Momentum](#momentum-36)
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* [Momentum](#momentum-37)
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* [Overlap](#overlap-31)
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* [Performance](#performance-4)
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* [Statistics](#statistics-9)
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@@ -84,7 +85,7 @@ $ pip install pandas_ta
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Latest Version
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--------------
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Best choice! Version: *0.2.53b*
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Best choice! Version: *0.2.62b*
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```sh
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$ pip install -U git+https://github.com/twopirllc/pandas-ta
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```
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@@ -182,7 +183,7 @@ Thanks for using **Pandas TA**!
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_Thank you for your contributions!_
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[alexonab](https://github.com/alexonab) | [allahyarzadeh](https://github.com/allahyarzadeh) | [codesutras](https://github.com/codesutras) | [daikts](https://github.com/daikts) | [DrPaprikaa](https://github.com/DrPaprikaa) | [edwardwang1](https://github.com/edwardwang1) | [ffhirata](https://github.com/ffhirata) | [FGU1](https://github.com/FGU1) | [lluissalord](https://github.com/lluissalord) | [maxdignan](https://github.com/maxdignan) | [moritzgun](https://github.com/moritzgun) | [M6stafa](https://github.com/M6stafa) | [NkosenhleDuma](https://github.com/NkosenhleDuma) | [pbrumblay](https://github.com/pbrumblay) | [RajeshDhalange](https://github.com/RajeshDhalange) | [rengel8](https://github.com/rengel8) | [rluong003](https://github.com/rluong003) | [SoftDevDanial](https://github.com/SoftDevDanial) | [tg12](https://github.com/tg12) | [twrobel](https://github.com/twrobel) | [whubsch](https://github.com/whubsch) | [witokondoria](https://github.com/witokondoria) | [wouldayajustlookatit](https://github.com/wouldayajustlookatit) | [YuvalWein](https://github.com/YuvalWein)
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[alexonab](https://github.com/alexonab) | [allahyarzadeh](https://github.com/allahyarzadeh) | [codesutras](https://github.com/codesutras) | [DrPaprikaa](https://github.com/DrPaprikaa) | [daikts](https://github.com/daikts) | [dorren](https://github.com/dorren) | [edwardwang1](https://github.com/edwardwang1) | [ffhirata](https://github.com/ffhirata) | [FGU1](https://github.com/FGU1) | [lluissalord](https://github.com/lluissalord) | [M6stafa](https://github.com/M6stafa) | [maxdignan](https://github.com/maxdignan) | [moritzgun](https://github.com/moritzgun) | [NkosenhleDuma](https://github.com/NkosenhleDuma) | [pbrumblay](https://github.com/pbrumblay) | [RajeshDhalange](https://github.com/RajeshDhalange) | [rengel8](https://github.com/rengel8) | [rluong003](https://github.com/rluong003) | [SoftDevDanial](https://github.com/SoftDevDanial) | [tg12](https://github.com/tg12) | [twrobel](https://github.com/twrobel) | [whubsch](https://github.com/whubsch) | [witokondoria](https://github.com/witokondoria) | [wouldayajustlookatit](https://github.com/wouldayajustlookatit) | [YuvalWein](https://github.com/YuvalWein)
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<br/>
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@@ -566,7 +567,7 @@ help(ta.yf)
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* _Even Better Sinewave_: **ebsw**
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<br/>
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### **Momentum** (36)
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### **Momentum** (37)
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* _Awesome Oscillator_: **ao**
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* _Absolute Price Oscillator_: **apo**
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@@ -601,7 +602,8 @@ help(ta.yf)
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* Default is John Carter's. Enable Lazybear's with ```lazybear=True```
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* _Stochastic Oscillator_: **stoch**
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* _Stochastic RSI_: **stochrsi**
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* _TD Sequential_: **td**
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* _TD Sequential_: **td_seq**
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* Excluded from ```df.ta.strategy()```.
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* _Trix_: **trix**
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* _True strength index_: **tsi**
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* _Ultimate Oscillator_: **uo**
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@@ -799,27 +801,12 @@ result = ta.cagr(df.close)
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<br />
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## **Breaking Indicators**
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* _Bollinger Bands_ (**bbands**): New column 'bandwidth' appended to the returning DataFrame. See: ```help(ta.bbands)```
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* _Volume Weighted Average Price_ (**vwap**): **Requires** the DataFrame index to be a DatetimeIndex.
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## **New Indicators**
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* _Arnaud Legoux Moving Average_ (**alma**) uses the curve of the Normal (Gauss) distribution to allow regulating the smoothness and high sensitivity of the indicator. See: ```help(ta.alma)```
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* _Drawdown_ (**drawdown**) shows the peak-to-trough decline during a specific period for an investment,
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trading account, or fund. See: ```help(ta.drawdown)```
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* _Even Better Sinewave_ (**ebsw**) measures market cycles and uses a low pass filter to remove noise. See: ```help(ta.ebsw)```
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* _Gann High-Low Activator_ (**hilo**) was created by Robert Krausz in a 1998. See: ```help(ta.hilo)```
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* _Holt-Winter Moving Average_ (**hwma**) is a three-parameter moving average by the Holt-Winter method.
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* _McGinley Dynamic_ (**mcgd**) is an overlap indicator developed by John R. McGinley, a Certified Market Technician. See: ```help(ta.mcgd)```
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* _Price Volume Rank_ (**pvr**) was created by Anthony J. Macek. See: ```help(ta.pvr)```
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* _Quantitative Qualitative Estimation_ (**qqe**) is like SuperTrend for a Smoothed RSI. See: ```help(ta.qqe)```
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article in the June, 1994 issue of Technical Analysis of Stocks & Commodities Magazine. See: ```help(ta.pvr)```
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* _Relative Strength Xtra_ (**rsx**) is based on the popular RSI indicator and inspired by the work Jurik Research. See: ```help(ta.rsx)```
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* _Ehler's Super Smoother Filter_ (**ssf**). Ehler's solution to reduce lag and remove aliasing noise compared to other common moving average indicators. See: ```help(ta.ssf)```
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* _Elder's Thermometer_ (**thermo**) measures price volatility. See: ```help(ta.thermo)```
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* _TTM Trend_ (**ttm_trend**) is a trend indicator inspired from John Carter's book "Mastering the Trade" issue of Stocks & Commodities Magazine. It is a moving average based trend indicator consisting of two different simple moving averages. See: ```help(ta.ttm_trend)```
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* _Variable Index Dynamic Average_ (**vidya**) is a popular Dynamic Moving Average created by Tushar Chande. See: ```help(ta.vidya)```
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* _Tom DeMark's Sequential_ (**td_seq**) attempts to identify a price point where an uptrend or a downtrend exhausts itself and reverses. Currently exlcuded from ```df.ta.strategy()``` for performance reasons. See: ```help(ta.td_seq)```
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<br/>
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## **Updated Indicators**
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* _ADX_ (**adx**): Added ```mamode``` with default "**RMA**" and with the same ```mamode``` options as TradingView. See ```help(ta.adx)```.
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@@ -47,7 +47,7 @@ Category = {
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"ao", "apo", "bias", "bop", "brar", "cci", "cfo", "cg", "cmo",
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"coppock", "er", "eri", "fisher", "inertia", "kdj", "kst", "macd",
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"mom", "pgo", "ppo", "psl", "pvo", "qqe", "roc", "rsi", "rsx", "rvgi",
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"slope", "smi", "squeeze", "stoch", "stochrsi", "trix", "tsi", "uo",
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"slope", "smi", "squeeze", "stoch", "stochrsi", "td_seq", "trix", "tsi", "uo",
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"willr"
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],
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# Overlap
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+27
-14
@@ -607,21 +607,30 @@ class AnalysisIndicators(BasePandasObject):
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"""Strategy Method
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An experimental method that by default runs all applicable indicators.
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Future implementations will allow more specific indicator generation through
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a json config file.
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Future implementations will allow more specific indicator generation
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with possibly as json, yaml config file or an sqlite3 table.
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Args:
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name (str, optional): Default: "all"
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exclude (list, optional): Default: []. List of indicator names to exclude.
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kwargs:
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(optional) Default: {}. Any indicator argument you want to modify.
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For example, length=20 or offset=-1 or high=df["high"] ...
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Kwargs:
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chunksize (bool): Adjust the chunksize for the Multiprocessing Pool.
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Default: Number of cores of the OS
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exclude (list): List of indicator names to exclude. Some are
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excluded by default for various reasons; they require additional
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sources, performance (td_seq), not a ohlcv chart (vp) etc.
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name (str): Select all indicators or indicators by
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Category such as: "candles", "cycles", "momentum", "overlap",
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"performance", "statistics", "trend", "volatility", "volume", or
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"all". Default: "all"
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ordered (bool): Whether to run "all" in order. Default: True
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timed (bool): Show the process time of the strategy().
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Default: False
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verbose (bool): Provide some additional insight on the progress of
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the strategy() execution. Default: False
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"""
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# cpus = cpu_count()
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# Ensure indicators are appended to the DataFrame
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kwargs["append"] = True
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all_ordered = kwargs.pop("ordered", False)
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all_ordered = kwargs.pop("ordered", True)
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mp_chunksize = kwargs.pop("chunksize", self.cores)
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# Initialize
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@@ -637,6 +646,7 @@ class AnalysisIndicators(BasePandasObject):
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"long_run",
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"short_run",
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"trend_return",
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"td_seq", # Performance exclusion
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"vp",
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]
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@@ -702,7 +712,7 @@ class AnalysisIndicators(BasePandasObject):
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# Some magic to optimize chunksize for speed based on total ta indicators
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_chunksize = mp_chunksize - 1 if mp_chunksize > _total_ta else int(npLog10(_total_ta)) + 1
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if verbose:
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print(f"[i] Multiprocessing {_total_ta} indicators with {_chunksize} chunks over {self.cores}/{cpu_count()} cpus.")
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print(f"[i] Multiprocessing {_total_ta} indicators with {_chunksize} chunks and {self.cores}/{cpu_count()} cpus.")
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results = None
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if mode["custom"]:
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@@ -810,7 +820,11 @@ class AnalysisIndicators(BasePandasObject):
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elif df.empty:
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print(f"[X] DataFrame is empty: {df.shape}")
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return
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else: self._df = df
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else:
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if kwargs.pop("lc_input", False):
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df.index.name = df.index.name.lower()
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df.columns = df.columns.str.lower()
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self._df = df
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if strategy is not None: self.strategy(strategy, **kwargs)
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return df
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@@ -1043,10 +1057,9 @@ class AnalysisIndicators(BasePandasObject):
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result = stochrsi(high=high, low=low, close=close, length=length, rsi_length=rsi_length, k=k, d=d, offset=offset, **kwargs)
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return self._post_process(result, **kwargs)
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def td(self, offset=None, show_all=True, **kwargs):
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def td_seq(self, asint=None, offset=None, show_all=None, **kwargs):
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close = self._get_column(kwargs.pop("close", "close"))
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result = td(close=close, offset=offset, show_all=show_all, **kwargs)
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result = td_seq(close=close, asint=asint, offset=offset, show_all=show_all, **kwargs)
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return self._post_process(result, **kwargs)
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def trix(self, length=None, signal=None, scalar=None, drift=None, offset=None, **kwargs):
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@@ -31,7 +31,7 @@ from .smi import smi
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from .squeeze import squeeze
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from .stoch import stoch
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from .stochrsi import stochrsi
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from .td import td
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from .td_seq import td_seq
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from .trix import trix
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from .tsi import tsi
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from .uo import uo
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@@ -1,67 +0,0 @@
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# -*- coding: utf-8 -*-
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import numpy as np
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from pandas import DataFrame, Series
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from pandas_ta.utils import get_offset, verify_series
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def true_sequence_count(s):
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index = s.where(s == False).last_valid_index()
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if index is None:
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return s.count()
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else:
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s = s[s.index > index]
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return s.count()
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def calc_td(close, direction, show_all):
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td_bool = close.diff(4) > 0 if direction=='up' else close.diff(4) < 0
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td_num = np.where(td_bool, td_bool.rolling(13, min_periods=0).apply(true_sequence_count), 0)
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td_num = Series(td_num)
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if show_all:
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td_num = td_num.mask(td_num == 0)
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else:
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td_num = td_num.mask(~td_num.between(6,9))
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return td_num
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def td(close, offset=None, show_all=True, **kwargs):
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up = calc_td(close, 'up', show_all)
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down = calc_td(close, 'down', show_all)
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df = DataFrame({'TD_up': up, 'TD_down': down})
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# Offset
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if offset and offset != 0:
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df = df.shift(offset)
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if "fillna" in kwargs:
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df.fillna(kwargs["fillna"], inplace=True)
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# Name & Category
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df.name = "TD"
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df.category = "momentum"
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return df
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td.__doc__ = \
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"""TD Sequential (TD)
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TD Sequential indicator.
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Sources:
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https://tradetrekker.wordpress.com/tdsequential/
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Calculation:
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compare current close price with 4 days ago price, up to 13 days.
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for the consecutive ascending or descending price sequence, display 6th to 9th day value.
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Args:
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close (pd.Series): Series of 'close's
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offset (int): How many periods to offset the result. Default: 0
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show_all (bool): default True, show 1 - 13. If set to false, only show 6 - 9
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Kwargs:
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fillna (value, optional): pd.DataFrame.fillna(value)
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Returns:
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pd.DataFrame: New feature generated.
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"""
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@@ -0,0 +1,105 @@
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# -*- coding: utf-8 -*-
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# import numpy as np
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from numpy import where as npWhere
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from pandas import DataFrame, Series
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from pandas_ta.utils import get_offset, verify_series
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def td_seq(close, asint=None, offset=None, **kwargs):
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"""Indicator: Tom Demark Sequential (TD_SEQ)"""
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# Validate arguments
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close = verify_series(close)
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offset = get_offset(offset)
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asint = asint if isinstance(asint, bool) else False
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show_all = kwargs.setdefault("show_all", True)
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def true_sequence_count(series: Series):
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index = series.where(series == False).last_valid_index()
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if index is None:
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return series.count()
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else:
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s = series[series.index > index]
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return s.count()
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def calc_td(series: Series, direction: str, show_all: bool):
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td_bool = series.diff(4) > 0 if direction=="up" else series.diff(4) < 0
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td_num = npWhere(
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td_bool, td_bool.rolling(13, min_periods=0).apply(true_sequence_count), 0
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)
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td_num = Series(td_num)
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if show_all:
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td_num = td_num.mask(td_num == 0)
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else:
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td_num = td_num.mask(~td_num.between(6,9))
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return td_num
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up_seq = calc_td(close, "up", show_all)
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down_seq = calc_td(close, "down", show_all)
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if asint:
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if up_seq.hasnans and down_seq.hasnans:
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up_seq.fillna(0, inplace=True)
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down_seq.fillna(0, inplace=True)
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up_seq = up_seq.astype(int)
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down_seq = down_seq.astype(int)
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# Offset
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if offset != 0:
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up_seq = up_seq.shift(offset)
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down_seq = down_seq.shift(offset)
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# Handle fills
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if "fillna" in kwargs:
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up_seq.fillna(kwargs["fillna"], inplace=True)
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down_seq.fillna(kwargs["fillna"], inplace=True)
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if "fill_method" in kwargs:
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up_seq.fillna(method=kwargs["fill_method"], inplace=True)
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down_seq.fillna(method=kwargs["fill_method"], inplace=True)
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# Name & Category
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up_seq.name = f"TD_SEQ_UPa" if show_all else f"TD_SEQ_UP"
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down_seq.name = f"TD_SEQ_DNa" if show_all else f"TD_SEQ_DN"
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up_seq.category = down_seq.category = "momentum"
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# Prepare Dataframe to return
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data = {
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up_seq.name: up_seq,
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down_seq.name: down_seq
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}
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df = DataFrame(data)
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df.name = "TD_SEQ"
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df.category = up_seq.category
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return df
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td_seq.__doc__ = \
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"""TD Sequential (TD_SEQ)
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Tom DeMark's Sequential indicator attempts to identify a price point where an
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uptrend or a downtrend exhausts itself and reverses.
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Sources:
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https://tradetrekker.wordpress.com/tdsequential/
|
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Calculation:
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Compare current close price with 4 days ago price, up to 13 days. For the
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consecutive ascending or descending price sequence, display 6th to 9th day
|
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value.
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Args:
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close (pd.Series): Series of 'close's
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asint (bool): If True, fillnas with 0 and change type to int. Default: False
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offset (int): How many periods to offset the result. Default: 0
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Kwargs:
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show_all (bool): Show 1 - 13. If set to False, show 6 - 9. Default: True
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fillna (value, optional): pd.DataFrame.fillna(value)
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Returns:
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pd.DataFrame: New feature generated.
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"""
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@@ -57,10 +57,9 @@ def cksp(high, low, close, p=None, x=None, q=None, offset=None, **kwargs):
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cksp.__doc__ = \
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"""Chande Kroll Stop (CKSP)
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The Tushar Chande and Stanley Kroll in their book
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“The New Technical Trader”. It is a trend-following indicator,
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identifying your stop by calculating the average true range of
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the recent market volatility.
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||||
The Tushar Chande and Stanley Kroll in their book “The New Technical Trader”.
|
||||
It is a trend-following indicator, identifying your stop by calculating the
|
||||
average true range of the recent market volatility.
|
||||
|
||||
Sources:
|
||||
https://www.multicharts.com/discussion/viewtopic.php?t=48914
|
||||
|
||||
@@ -57,7 +57,7 @@ def fibonacci(n: int = 2, **kwargs: dict) -> npNdArray:
|
||||
a, b = 1, 1
|
||||
|
||||
result = npArray([a])
|
||||
for i in range(0, n):
|
||||
for _ in range(0, n):
|
||||
a, b = b, a + b
|
||||
result = npAppend(result, a)
|
||||
|
||||
|
||||
@@ -18,7 +18,7 @@ setup(
|
||||
"pandas_ta.volatility",
|
||||
"pandas_ta.volume"
|
||||
],
|
||||
version=".".join(("0", "2", "61b")),
|
||||
version=".".join(("0", "2", "62b")),
|
||||
description=long_description,
|
||||
long_description=long_description,
|
||||
author="Kevin Johnson",
|
||||
|
||||
@@ -207,10 +207,14 @@ class TestMomentumExtension(TestCase):
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(list(self.data.columns[-2:]), ["STOCHRSIk_14_14_3_3", "STOCHRSId_14_14_3_3"])
|
||||
|
||||
def test_td_ext(self):
|
||||
self.data.ta.td(append=True)
|
||||
def test_td_seq_ext(self):
|
||||
self.data.ta.td_seq(show_all=False, append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(list(self.data.columns[-2:]), ["TD_up", "TD_down"])
|
||||
self.assertEqual(list(self.data.columns[-2:]), ["TD_SEQ_UP", "TD_SEQ_DN"])
|
||||
|
||||
self.data.ta.td_seq(show_all=True, append=True)
|
||||
self.assertIsInstance(self.data, DataFrame)
|
||||
self.assertEqual(list(self.data.columns[-2:]), ["TD_SEQ_UPa", "TD_SEQ_DNa"])
|
||||
|
||||
def test_trix_ext(self):
|
||||
self.data.ta.trix(append=True)
|
||||
|
||||
@@ -362,11 +362,10 @@ class TestMomentum(TestCase):
|
||||
self.assertIsInstance(result, DataFrame)
|
||||
self.assertEqual(result.name, "STOCHRSI_14_14_3_3")
|
||||
|
||||
def test_td(self):
|
||||
# TD Sequential
|
||||
result = pandas_ta.td(self.close)
|
||||
def test_td_seq(self):
|
||||
result = pandas_ta.td_seq(self.close)
|
||||
self.assertIsInstance(result, DataFrame)
|
||||
self.assertEqual(result.name, "TD")
|
||||
self.assertEqual(result.name, "TD_SEQ")
|
||||
|
||||
def test_trix(self):
|
||||
result = pandas_ta.trix(self.close)
|
||||
|
||||
@@ -165,6 +165,25 @@ class TestStrategyMethods(TestCase):
|
||||
)
|
||||
self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed)
|
||||
|
||||
# @skip
|
||||
def test_custom_a(self):
|
||||
self.category = "Custom E"
|
||||
|
||||
amat_logret_ta = [
|
||||
{"kind": "amat", "fast": 20, "slow": 50 }, # 2
|
||||
{"kind": "log_return", "cumulative": True}, # 1
|
||||
{"kind": "ema", "close": "CUMLOGRET_1", "length": 5} # 1
|
||||
]
|
||||
|
||||
custom = pandas_ta.Strategy(
|
||||
"AMAT Log Returns", # name
|
||||
amat_logret_ta, # ta
|
||||
"AMAT Log Returns", # description
|
||||
)
|
||||
self.data.ta.strategy(custom, verbose=verbose, timed=strategy_timed, ordered=True)
|
||||
self.data.ta.trend_return(trend=self.data["AMATe_LR_2"], cumulative=True, append=True)
|
||||
self.assertEqual(len(self.data.columns), 13)
|
||||
|
||||
# @skip
|
||||
def test_momentum_category(self):
|
||||
self.category = "Momentum"
|
||||
|
||||
Reference in New Issue
Block a user