Pandas TA - A Technical Analysis Library in Python 3 ================= [](https://pypi.org/project/pandas_ta/) [](https://pypi.org/project/pandas_ta/) [](https://pypi.org/project/pandas_ta/) [](https://pypistats.org/packages/pandas_ta) [](#contributors-)  _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that leverages the Pandas library with more than 130 Indicators and Utility functions. Many commonly used indicators are included, such as: _Simple Moving Average_ (**sma**) _Moving Average Convergence Divergence_ (**macd**), _Hull Exponential Moving Average_ (**hma**), _Bollinger Bands_ (**bbands**), _On-Balance Volume_ (**obv**), _Aroon & Aroon Oscillator_ (**aroon**), _Squeeze_ (**squeeze**) and **_many more_**.
# **Table of contents** * [Features](#features) * [Installation](#installation) * [Stable](#stable) * [Latest Version](#latest-version) * [Cutting Edge](#cutting-edge) * [Quick Start](#quick-start) * [Help](#help) * [Issues and Contributions](#issues-and-contributions) * [Programming Conventions](#programming-conventions) * [Pandas TA Strategies](#pandas-ta-strategies) * [Types of Strategies](#types-of-strategies) * [DataFrame Properties](#dataframe-properties) * [Indicators by Category](#indicators-by-category) * [Candles](#candles-3) * [Cycles](#cycles-1) * [Momentum](#momentum-36) * [Overlap](#overlap-31) * [Performance](#performance-4) * [Statistics](#statistics-9) * [Trend](#trend-15) * [Utility](#utility-5) * [Volatility](#volatility-13) * [Volume](#volume-14) * [Performance Metrics](#performance-metrics) * [Changes](#changes) * [General](#general) * [Breaking Indicators](#breaking-indicators) * [New Indicators](#new-indicators) * [Updated Indicators](#updated-indicators)
# **Features** * Has 130+ indicators and utility functions. * Indicators are tightly correlated with the de facto [TA Lib](https://mrjbq7.github.io/ta-lib/) if they share common indicators. * Have the need for speed? By using the DataFrame _strategy_ method, you get **multiprocessing** for free! * Easily add _prefixes_ or _suffixes_ or both to columns names. Useful for Custom Chained Strategies. * Example Jupyter Notebooks under the [examples](https://github.com/twopirllc/pandas-ta/tree/master/examples) directory, including how to create Custom Strategies using the new [__Strategy__ Class](https://github.com/twopirllc/pandas-ta/tree/master/examples/PandaTA_Strategy_Examples.ipynb) * Potential Data Leaks: **ichimoku** and **dpo**. See indicator list below for details. * **UNDER DEVELOPMENT:** Performance Metrics
**Installation** =================== Stable ------ The ```pip``` version is the last most stable release. Version: *0.2.45b* ```sh $ pip install pandas_ta ``` Latest Version -------------- Best choice! Version: *0.2.45b* ```sh $ pip install -U git+https://github.com/twopirllc/pandas-ta ``` Cutting Edge ------------ This is the _Development Version_ which could have bugs and other undesireable side effects. Use at own risk! ```sh $ pip install -U git+https://github.com/twopirllc/pandas-ta.git@development ```
# **Quick Start** ```python import pandas as pd import pandas_ta as ta # Load data df = pd.read_csv("path/to/symbol.csv", sep=",") # VWAP requires the DataFrame index to be a DatetimeIndex. # Replace "datetime" with the appropriate column from your DataFrame df.set_index(pd.DatetimeIndex(df["datetime"]), inplace=True) # Calculate Returns and append to the df DataFrame df.ta.log_return(cumulative=True, append=True) df.ta.percent_return(cumulative=True, append=True) # New Columns with results df.columns # Take a peek df.tail() # vv Continue Post Processing vv ```
# **Help** ```python import pandas as pd import pandas_ta as ta # Create a DataFrame so 'ta' can be used. df = pd.DataFrame() # Help about this, 'ta', extension help(df.ta) # List of all indicators df.ta.indicators() # Help about the log_return indicator help(ta.log_return) ```
# **Issues and Contributions** Thanks for using **Pandas TA**!
* ### [Comments and Feedback](https://github.com/twopirllc/pandas-ta/issues) * Have you read **_this_** document? * Are you running the latest version? * ```$ pip install -U git+https://github.com/twopirllc/pandas-ta``` * Have you tried the [Examples](https://github.com/twopirllc/pandas-ta/tree/master/examples/)? * Did they help? * What is missing? * Could you help improve them? * Did you know you can easily build _Custom Strategies_ with the **[Strategy](https://github.com/twopirllc/pandas-ta/blob/master/examples/PandasTA_Strategy_Examples.ipynb) Class**? * Documentation could _always_ be improved. Can you help contribute? * ### [Bugs, Indicators or Feature Requests](https://github.com/twopirllc/pandas-ta/issues) * First, search the _Closed_ Issues **before** you _Open_ a new Issue; it may have already been solved. * Please be as **detailed** as possible _with_ reproducible code, links if any, applicable screenshots, errors, logs, and data samples. You **will** be asked again if you provide nothing. * You want a new indicator not currently listed. * You want an alternate version of an existing indicator. * The indicator does not match another website, library, broker platform, language, et al. * Do you have correlation analysis to back your claim? * Can you contribute? * You will be asked to fill out an Issue even if you email my personal email address.
**Contributors** ================ _Thank you for your contributions!_ [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) | [FGU1](https://github.com/FGU1) | [lluissalord](https://github.com/lluissalord) | [maxdignan](https://github.com/maxdignan) | [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) | [YuvalWein](https://github.com/YuvalWein)
**Programming Conventions** =========================== **Pandas TA** has three primary "styles" of processing Technical Indicators for your use case and/or requirements. They are: _Standard_, _DataFrame Extension_, and the _Pandas TA Strategy_. Each with increasing levels of abstraction for ease of use. As you become more familiar with **Pandas TA**, the simplicity and speed of using a _Pandas TA Strategy_ may become more apparent. Furthermore, you can create your own indicators through Chaining or Composition. Lastly, each indicator either returns a _Series_ or a _DataFrame_ in Uppercase Underscore format regardless of style.
_Standard_ ==================== You explicitly define the input columns and take care of the output. * ```sma10 = ta.sma(df["Close"], length=10)``` * Returns a Series with name: ```SMA_10``` * ```donchiandf = ta.donchian(df["HIGH"], df["low"], lower_length=10, upper_length=15)``` * Returns a DataFrame named ```DC_10_15``` and column names: ```DCL_10_15, DCM_10_15, DCU_10_15``` * ```ema10_ohlc4 = ta.ema(ta.ohlc4(df["Open"], df["High"], df["Low"], df["Close"]), length=10)``` * Chaining indicators is possible but you have to be explicit. * Since it returns a Series named ```EMA_10```. If needed, you may need to uniquely name it.
_Pandas TA DataFrame Extension_ ==================== Calling ```df.ta``` will automatically lowercase _OHLCVA_ to _ohlcva_: _open, high, low, close, volume_, _adj_close_. By default, ```df.ta``` will use the _ohlcva_ for the indicator arguments removing the need to specify input columns directly. * ```sma10 = df.ta.sma(length=10)``` * Returns a Series with name: ```SMA_10``` * ```ema10_ohlc4 = df.ta.ema(close=df.ta.ohlc4(), length=10, suffix="OHLC4")``` * Returns a Series with name: ```EMA_10_OHLC4``` * Chaining Indicators _require_ specifying the input like: ```close=df.ta.ohlc4()```. * ```donchiandf = df.ta.donchian(lower_length=10, upper_length=15)``` * Returns a DataFrame named ```DC_10_15``` and column names: ```DCL_10_15, DCM_10_15, DCU_10_15``` Same as the last three examples, but appending the results directly to the DataFrame ```df```. * ```df.ta.sma(length=10, append=True)``` * Appends to ```df``` column name: ```SMA_10```. * ```df.ta.ema(close=df.ta.ohlc4(append=True), length=10, suffix="OHLC4", append=True)``` * Chaining Indicators _require_ specifying the input like: ```close=df.ta.ohlc4()```. * ```df.ta.donchian(lower_length=10, upper_length=15, append=True)``` * Appends to ```df``` with column names: ```DCL_10_15, DCM_10_15, DCU_10_15```.
_Pandas TA Strategy_ ==================== A **Pandas TA** Strategy is a named group of indicators to be run by the _strategy_ method. All Strategies use **mulitprocessing** _except_ when using the ```col_names``` parameter (see [below](#multiprocessing)). There are different types of _Strategies_ listed in the following section.
### Here are the previous _Styles_ implemented using a Strategy Class: ```python # (1) Create the Strategy MyStrategy = ta.Strategy( name="DCSMA10", ta=[ {"kind": "ohlc4"}, {"kind": "sma", "length": 10}, {"kind": "donchian", "lower_length": 10, "upper_length": 15}, {"kind": "ema", "close": "OHLC4", "length": 10, "suffix": "OHLC4"}, ] ) # (2) Run the Strategy df.ta.strategy(MyStrategy, **kwargs) ```

# **Pandas TA** _Strategies_ The _Strategy_ Class is a simple way to name and group your favorite TA Indicators by using a _Data Class_. **Pandas TA** comes with two prebuilt basic Strategies to help you get started: __AllStrategy__ and __CommonStrategy__. A _Strategy_ can be as simple as the __CommonStrategy__ or as complex as needed using Composition/Chaining. * When using the _strategy_ method, **all** indicators will be automatically appended to the DataFrame ```df```. * You are using a Chained Strategy when you have the output of one indicator as input into one or more indicators in the same _Strategy_. * **Note:** Use the 'prefix' and/or 'suffix' keywords to distinguish the composed indicator from it's default Series. See the [Pandas TA Strategy Examples Notebook](https://github.com/twopirllc/pandas-ta/tree/master/examples/PandasTA_Strategy_Examples.ipynb) for examples including _Indicator Composition/Chaining_. Strategy Requirements --------------------- - _name_: Some short memorable string. _Note_: Case-insensitive "All" is reserved. - _ta_: A list of dicts containing keyword arguments to identify the indicator and the indicator's arguments - **Note:** A Strategy will fail when consumed by Pandas TA if there is no ```{"kind": "indicator name"}``` attribute. _Remember_ to check your spelling. Optional Parameters ------------------- - _description_: A more detailed description of what the Strategy tries to capture. Default: None - _created_: At datetime string of when it was created. Default: Automatically generated.
Types of Strategies ======================= ## _Builtin_ ```python # Running the Builtin CommonStrategy as mentioned above df.ta.strategy(ta.CommonStrategy) # The Default Strategy is the ta.AllStrategy. The following are equivalent: df.ta.strategy() df.ta.strategy("All") df.ta.strategy(ta.AllStrategy) ``` ## _Categorical_ ```python # List of indicator categories df.ta.categories # Running a Categorical Strategy only requires the Category name df.ta.strategy("Momentum") # Default values for all Momentum indicators df.ta.strategy("overlap", length=42) # Override all Overlap 'length' attributes ``` ## _Custom_ ```python # Create your own Custom Strategy CustomStrategy = ta.Strategy( name="Momo and Volatility", description="SMA 50,200, BBANDS, RSI, MACD and Volume SMA 20", ta=[ {"kind": "sma", "length": 50}, {"kind": "sma", "length": 200}, {"kind": "bbands", "length": 20}, {"kind": "rsi"}, {"kind": "macd", "fast": 8, "slow": 21}, {"kind": "sma", "close": "volume", "length": 20, "prefix": "VOLUME"}, ] ) # To run your "Custom Strategy" df.ta.strategy(CustomStrategy) ```
**Multiprocessing** ======================= The **Pandas TA** _strategy_ method utilizes **multiprocessing** for bulk indicator processing of all Strategy types with **ONE EXCEPTION!** When using the ```col_names``` parameter to rename resultant column(s), the indicators in ```ta``` array will be ran in order. ```python # VWAP requires the DataFrame index to be a DatetimeIndex. # * Replace "datetime" with the appropriate column from your DataFrame df.set_index(pd.DatetimeIndex(df["datetime"]), inplace=True) # Runs and appends all indicators to the current DataFrame by default # The resultant DataFrame will be large. df.ta.strategy() # Or the string "all" df.ta.strategy("all") # Or the ta.AllStrategy df.ta.strategy(ta.AllStrategy) # Use verbose if you want to make sure it is running. df.ta.strategy(verbose=True) # Use timed if you want to see how long it takes to run. df.ta.strategy(timed=True) # Choose the number of cores to use. Default is all available cores. # For no multiprocessing, set this value to 0. df.ta.cores = 4 # Maybe you do not want certain indicators. # Just exclude (a list of) them. df.ta.strategy(exclude=["bop", "mom", "percent_return", "wcp", "pvi"], verbose=True) # Perhaps you want to use different values for indicators. # This will run ALL indicators that have fast or slow as parameters. # Check your results and exclude as necessary. df.ta.strategy(fast=10, slow=50, verbose=True) # Sanity check. Make sure all the columns are there df.columns ```
## Custom Strategy without Multiprocessing **Remember** These will not be utilizing **multiprocessing** ```python NonMPStrategy = ta.Strategy( name="EMAs, BBs, and MACD", description="Non Multiprocessing Strategy by rename Columns", ta=[ {"kind": "ema", "length": 8}, {"kind": "ema", "length": 21}, {"kind": "bbands", "length": 20, "col_names": ("BBL", "BBM", "BBU")}, {"kind": "macd", "fast": 8, "slow": 21, "col_names": ("MACD", "MACD_H", "MACD_S")} ] ) # Run it df.ta.strategy(NonMPStrategy) ```

# **DataFrame Properties** ## **adjusted** ```python # Set ta to default to an adjusted column, 'adj_close', overriding default 'close'. df.ta.adjusted = "adj_close" df.ta.sma(length=10, append=True) # To reset back to 'close', set adjusted back to None. df.ta.adjusted = None ``` ## **categories** ```python # List of Pandas TA categories. df.ta.categories ``` ## **cores** ```python # Set the number of cores to use for strategy multiprocessing # Defaults to the number of cpus you have. df.ta.cores = 4 # Set the number of cores to 0 for no multiprocessing. df.ta.cores = 0 # Returns the number of cores you set or your default number of cpus. df.ta.cores ``` ## **datetime_ordered** ```python # The 'datetime_ordered' property returns True if the DataFrame # index is of Pandas datetime64 and df.index[0] < df.index[-1]. # Otherwise it returns False. df.ta.datetime_ordered ``` ## **reverse** ```python # The 'reverse' is a helper property that returns the DataFrame # in reverse order. df.ta.reverse ``` ## **prefix & suffix** ```python # Applying a prefix to the name of an indicator. prehl2 = df.ta.hl2(prefix="pre") print(prehl2.name) # "pre_HL2" # Applying a suffix to the name of an indicator. endhl2 = df.ta.hl2(suffix="post") print(endhl2.name) # "HL2_post" # Applying a prefix and suffix to the name of an indicator. bothhl2 = df.ta.hl2(prefix="pre", suffix="post") print(bothhl2.name) # "pre_HL2_post" ```

# **Indicators** (_by Category_) ### **Candles** (3) * _Doji_: **cdl_doji** * _Inside Bar_: **cdl_inside** * _Heikin-Ashi_: **ha** ### **Cycles** (1) * _Even Better Sinewave_: **ebsw** ### **Momentum** (36) * _Awesome Oscillator_: **ao** * _Absolute Price Oscillator_: **apo** * _Bias_: **bias** * _Balance of Power_: **bop** * _BRAR_: **brar** * _Commodity Channel Index_: **cci** * _Chande Forecast Oscillator_: **cfo** * _Center of Gravity_: **cg** * _Chande Momentum Oscillator_: **cmo** * _Coppock Curve_: **coppock** * _Efficiency Ratio_: **er** * _Elder Ray Index_: **eri** * _Fisher Transform_: **fisher** * _Inertia_: **inertia** * _KDJ_: **kdj** * _KST Oscillator_: **kst** * _Moving Average Convergence Divergence_: **macd** * _Momentum_: **mom** * _Pretty Good Oscillator_: **pgo** * _Percentage Price Oscillator_: **ppo** * _Psychological Line_: **psl** * _Percentage Volume Oscillator_: **pvo** * _Quantitative Qualitative Estimation_: **qqe** * _Rate of Change_: **roc** * _Relative Strength Index_: **rsi** * _Relative Strength Xtra_: **rsx** * _Relative Vigor Index_: **rvgi** * _Slope_: **slope** * _SMI Ergodic_ **smi** * _Squeeze_: **squeeze** * Default is John Carter's. Enable Lazybear's with ```lazybear=True``` * _Stochastic Oscillator_: **stoch** * _Stochastic RSI_: **stochrsi** * _TD Sequential_: **td** * _Trix_: **trix** * _True strength index_: **tsi** * _Ultimate Oscillator_: **uo** * _Williams %R_: **willr** | _Moving Average Convergence Divergence_ (MACD) | |:--------:| |  | ### **Overlap** (31) * _Arnaud Legoux Moving Average_: **alma** * _Double Exponential Moving Average_: **dema** * _Exponential Moving Average_: **ema** * _Fibonacci's Weighted Moving Average_: **fwma** * _Gann High-Low Activator_: **hilo** * _High-Low Average_: **hl2** * _High-Low-Close Average_: **hlc3** * Commonly known as 'Typical Price' in Technical Analysis literature * _Hull Exponential Moving Average_: **hma** * _Holt-Winter Moving Average_: **hwma** * _Ichimoku Kinkō Hyō_: **ichimoku** * Use: help(ta.ichimoku). Returns two DataFrames. * Drop the Chikou Span Column, the final column of the first resultant DataFrame, remove potential data leak. * _Kaufman's Adaptive Moving Average_: **kama** * _Linear Regression_: **linreg** * _McGinley Dynamic_: **mcgd** * _Midpoint_: **midpoint** * _Midprice_: **midprice** * _Open-High-Low-Close Average_: **ohlc4** * _Pascal's Weighted Moving Average_: **pwma** * _WildeR's Moving Average_: **rma** * _Sine Weighted Moving Average_: **sinwma** * _Simple Moving Average_: **sma** * _Ehler's Super Smoother Filter_: **ssf** * _Supertrend_: **supertrend** * _Symmetric Weighted Moving Average_: **swma** * _T3 Moving Average_: **t3** * _Triple Exponential Moving Average_: **tema** * _Triangular Moving Average_: **trima** * _Variable Index Dynamic Average_: **vidya** * _Volume Weighted Average Price_: **vwap** * **Requires** the DataFrame index to be a DatetimeIndex * _Volume Weighted Moving Average_: **vwma** * _Weighted Closing Price_: **wcp** * _Weighted Moving Average_: **wma** * _Zero Lag Moving Average_: **zlma** | _Simple Moving Averages_ (SMA) and _Bollinger Bands_ (BBANDS) | |:--------:| |  | ### **Performance** (4) Use parameter: cumulative=**True** for cumulative results. * _Draw Down_: **drawdown** * _Log Return_: **log_return** * _Percent Return_: **percent_return** * _Trend Return_: **trend_return** | _Percent Return_ (Cumulative) with _Simple Moving Average_ (SMA) | |:--------:| |  | ### **Statistics** (9) * _Entropy_: **entropy** * _Kurtosis_: **kurtosis** * _Mean Absolute Deviation_: **mad** * _Median_: **median** * _Quantile_: **quantile** * _Skew_: **skew** * _Standard Deviation_: **stdev** * _Variance_: **variance** * _Z Score_: **zscore** | _Z Score_ | |:--------:| |  | ### **Trend** (15) * _Average Directional Movement Index_: **adx** * Also includes **dmp** and **dmn** in the resultant DataFrame. * _Archer Moving Averages Trends_: **amat** * _Aroon & Aroon Oscillator_: **aroon** * _Choppiness Index_: **chop** * _Chande Kroll Stop_: **cksp** * _Decay_: **decay** * Formally: **linear_decay** * _Decreasing_: **decreasing** * _Detrended Price Oscillator_: **dpo** * Set ```centered=False``` to remove potential data leak. * _Increasing_: **increasing** * _Long Run_: **long_run** * _Parabolic Stop and Reverse_: **psar** * _Q Stick_: **qstick** * _Short Run_: **short_run** * _TTM Trend_: **ttm_trend** * _Vortex_: **vortex** | _Average Directional Movement Index_ (ADX) | |:--------:| |  | ### **Utility** (5) * _Above_: **above** * _Above Value_: **above_value** * _Below_: **below** * _Below Value_: **below_value** * _Cross_: **cross** ### **Volatility** (13) * _Aberration_: **aberration** * _Acceleration Bands_: **accbands** * _Average True Range_: **atr** * _Bollinger Bands_: **bbands** * _Donchian Channel_: **donchian** * _Keltner Channel_: **kc** * _Mass Index_: **massi** * _Normalized Average True Range_: **natr** * _Price Distance_: **pdist** * _Relative Volatility Index_: **rvi** * _Elder's Thermometer_: **thermo** * _True Range_: **true_range** * _Ulcer Index_: **ui** | _Average True Range_ (ATR) | |:--------:| |  | ### **Volume** (14) * _Accumulation/Distribution Index_: **ad** * _Accumulation/Distribution Oscillator_: **adosc** * _Archer On-Balance Volume_: **aobv** * _Chaikin Money Flow_: **cmf** * _Elder's Force Index_: **efi** * _Ease of Movement_: **eom** * _Money Flow Index_: **mfi** * _Negative Volume Index_: **nvi** * _On-Balance Volume_: **obv** * _Positive Volume Index_: **pvi** * _Price-Volume_: **pvol** * _Price Volume Rank_: **pvr** * _Price Volume Trend_: **pvt** * _Volume Profile_: **vp** | _On-Balance Volume_ (OBV) | |:--------:| |  |

# **Performance Metrics**   _BETA_ _Performance Metrics_ are a **new** addition to the package and consequentially are likely unreliable. **Use at your own risk.** These metrics return a _float_ and are _not_ part of the _DataFrame_ Extension. They are called the Standard way. For Example: ```python import pandas_ta as ta result = ta.cagr(df.close) ``` ### Available Metrics * _Compounded Annual Growth Rate_: **cagr** * _Calmar Ratio_: **calmar_ratio** * _Downside Deviation_: **downside_deviation** * _Jensen's Alpha_: **jensens_alpha** * _Log Max Drawdown_: **log_max_drawdown** * _Max Drawdown_: **max_drawdown** * _Pure Profit Score_: **pure_profit_score** * _Sharpe Ratio_: **sharpe_ratio** * _Sortino Ratio_: **sortino_ratio** * _Volatility_: **volatility**

# **Changes** ## **General** * A __Strategy__ Class to help name and group your favorite indicators. * Some indicators have had their ```mamode``` _kwarg_ updated with more _moving average_ choices with the **Moving Average Utility** function ```ta.ma()```. For simplicity, all _choices_ are single source _moving averages_. This is primarily an internal utility used by indicators that have a ```mamode``` _kwarg_. This includes indicators: _accbands_, _amat_, _aobv_, _atr_, _bbands_, _bias_, _efi_, _hilo_, _kc_, _natr_, _qqe_, _rvi_, and _thermo_; the default ```mamode``` parameters have not changed. However, ```ta.ma()``` can be used by the user as well if needed. For more information: ```help(ta.ma)``` * **Moving Average Choices**: dema, ema, fwma, hma, linreg, midpoint, pwma, rma, sinwma, sma, swma, t3, tema, trima, vidya, wma, zlma. * An _experimental_ and independent __Watchlist__ Class located in the [Examples](https://github.com/twopirllc/pandas-ta/tree/master/examples/watchlist.py) Directory that can be used in conjunction with the new __Strategy__ Class. * _Linear Regression_ (**linear_regression**) is a new utility method for Simple Linear Regression using _Numpy_ or _Scikit Learn_'s implementation. * Added utility/convience function, ```to_utc```, to convert the DataFrame index to UTC. See: ```help(ta.to_utc)```
## **Breaking Indicators** * _Bollinger Bands_ (**bbands**): New column 'bandwidth' appended to the returning DataFrame. See: ```help(ta.bbands)``` * _Volume Weighted Average Price_ (**vwap**): **Requires** the DataFrame index to be a DatetimeIndex. ## **New Indicators** * _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)``` * _Drawdown_ (**drawdown**) shows the peak-to-trough decline during a specific period for an investment, trading account, or fund. See: ```help(ta.drawdown)``` * _Even Better Sinewave_ (**ebsw**) measures market cycles and uses a low pass filter to remove noise. See: ```help(ta.ebsw)``` * _Gann High-Low Activator_ (**hilo**) was created by Robert Krausz in a 1998. See: ```help(ta.hilo)``` * _Holt-Winter Moving Average_ (**hwma**) is a three-parameter moving average by the Holt-Winter method. * _McGinley Dynamic_ (**mcgd**) is an overlap indicator developed by John R. McGinley, a Certified Market Technician. See: ```help(ta.mcgd)``` * _Price Volume Rank_ (**pvr**) was created by Anthony J. Macek. See: ```help(ta.pvr)``` * _Quantitative Qualitative Estimation_ (**qqe**) is like SuperTrend for a Smoothed RSI. See: ```help(ta.qqe)``` article in the June, 1994 issue of Technical Analysis of Stocks & Commodities Magazine. See: ```help(ta.pvr)``` * _Relative Strength Xtra_ (**rsx**) is based on the popular RSI indicator and inspired by the work Jurik Research. See: ```help(ta.rsx)``` * _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)``` * _Elder's Thermometer_ (**thermo**) measures price volatility. See: ```help(ta.thermo)``` * _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)``` * _Variable Index Dynamic Average_ (**vidya**) is a popular Dynamic Moving Average created by Tushar Chande. See: ```help(ta.vidya)``` ## **Updated Indicators** * _Average True Range_ (**atr**): The default ```mamode``` is now "**RMA**" and with the same ```mamode``` options as TradingView. See ```help(ta.atr)```. * _Decreasing_ (**decreasing**): New argument ```strict``` checks if the series is continuously decreasing over period ```length```. Default: ```False```. See ```help(ta.decreasing)```. * _Increasing_ (**increasing**): New argument ```strict``` checks if the series is continuously increasing over period ```length```. Default: ```False```. See ```help(ta.increasing)```. * _Trend Return_ (**trend_return**): Returns a DataFrame now instead of Series with pertinenet trade info for a _trend_. An example can be found in the [AI Example Notebook](https://github.com/twopirllc/pandas-ta/tree/master/examples/AIExample.ipynb). The notebook is still a work in progress and open to colloboration. * _Volume Weighted Average Price_ (**vwap**) Added a new parameter called ```anchor```. Default: "D" for "Daily". See [Timeseries Offset Aliases](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#timeseries-offset-aliases) for additional options. **Requires** the DataFrame index to be a DatetimeIndex
# **Sources** [Original TA-LIB](http://ta-lib.org/) | [TradingView](http://www.tradingview.com) | [Sierra Chart](https://search.sierrachart.com/?Query=indicators&submitted=true) | [MQL5](https://www.mql5.com) | [FM Labs](https://www.fmlabs.com/reference/default.htm) | [Pro Real Code](https://www.prorealcode.com/prorealtime-indicators) | [User 42](https://user42.tuxfamily.org/chart/manual/index.html)