Pandas TA - A Technical Analysis Library in Python 3

Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas library with more than 120 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.

Features
Installation
- Stable
- Latest Version
Quick Start
Help
Issues and Contributions
Programming Conventions
Pandas TA Strategies
- Types of Strategies
DataFrame Properties
Changes
Indicators by Category
- Candles
- Momentum
- Overlap
- Performance
- Statistics
- Trend
- Utility
- Volatility
- Volume

Features

Has 120+ indicators and utility functions.
Indicators are tightly correlated with the de facto TA Lib if they share common indicators.
Have the need for speed? By using the 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 directory, including how to create Custom Strategies using the new Strategy Class

Installation

Stable

The pip version is the last most stable release.

$ pip install pandas_ta

Latest Version

Best choice!

$ pip install -U git+https://github.com/twopirllc/pandas-ta

Quick Start

import pandas as pd
import pandas_ta as ta

# Load data
df = pd.read_csv("path/to/symbol.csv", sep=",")

# 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

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 trying Pandas TA!

Comments and Feedback
- 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?
  - Did they help?
  - What is missing?
  - Could you help improve them?
- Did you know you can easily build Custom Strategies with the Strategy Class?
- Documentation could always be improved. Can you help contribute?
Indicator or Feature Requests & Contributions
- Please be as detailed as possible. Links, screenshots, and sometimes data samples are welcome.
  - 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?

Contributors

Thank you for your contributions!

Programming Conventions

Pandas TA has three primary "styles" of processing Technical Indicators for your use case and/or requirements. They are: Conventional, 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.

Conventional

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)
- Conventional Chaining is possible but more 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). There are different types of Strategies listed in the following section.

Here are the previous Styles implemented using a Strategy Class:

# (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 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

# 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

# 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

# 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.

# 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.
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

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

# 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

cores

# Set the number of cores to use for strategy multiprocessing
# Defaults to the number of cpus you have
df.ta.cores = 4

# Returns the number of cores you set or your default number of cpus.
df.ta.cores

datetime_ordered

# 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

# 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

# The 'reverse' is a helper property that returns the DataFrame
# in reverse order
df.ta.reverse

prefix & suffix

# 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

Momentum (34)

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
Rate of Change: roc
Relative Strength Index: rsi
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
Trix: trix
True strength index: tsi
Ultimate Oscillator: uo
Williams %R: willr

Moving Average Convergence Divergence (MACD)

Overlap (27)

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
Ichimoku Kinkō Hyō: ichimoku
- Use: help(ta.ichimoku). Returns two DataFrames.
Kaufman's Adaptive Moving Average: kama
Linear Regression: linreg
Midpoint: midpoint
Midprice: midprice
Open-High-Low-Close Average: ohlc4
Pascal's Weighted Moving Average: pwma
William's Moving Average: rma
Sine Weighted Moving Average: sinwma
Simple Moving Average: sma
Supertrend: supertrend
Symmetric Weighted Moving Average: swma
T3 Moving Average: t3
Triple Exponential Moving Average: tema
Triangular Moving Average: trima
Volume Weighted Average Price: vwap
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 (3)

Use parameter: cumulative=True for cumulative results.

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
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
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 (12)

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
True Range: true_range
Ulcer Index: ui

Average True Range (ATR)

Volume (13)

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 Trend: pvt
Volume Profile: vp

On-Balance Volume (OBV)

Changes

Recent

A Strategy Class to help name and group your favorite indicators.
An experimental and independent Watchlist Class located in the Examples 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.

Breaking

Stochastic Oscillator (stoch): Now in line with Trading View's calculation. See: help(ta.stoch)
Linear Decay (linear_decay): Renamed to Decay (decay) and with the option for Exponential decay using mode="exp". See: help(ta.decay)

New

Chande Forecast Oscillator (cfo) It calculates the percentage difference between the actual price and the Time Series Forecast (the endpoint of a linear regression line).
Gann High-Low Activator (hilo) The Gann High Low Activator Indicator was created by Robert Krausz in a 1998.
Inside Bar (cdl_inside) An Inside Bar is a bar contained within it's previous bar's high and low See: help(ta.cdl_inside)
SMI Ergodic (smi) Developed by William Blau, the SMI Ergodic Indicator is the same as the True Strength Index (TSI) except the SMI includes a signal line and oscillator.
Squeeze (squeeze). A Momentum indicator. Both John Carter's TTM and Lazybear's TradingView versions are implemented. The default is John Carter's, or lazybear=False. Set lazybear=True to enable Lazybear's.
Stochastic RSI (stochrsi) "Stochastic RSI and Dynamic Momentum Index" was created by Tushar Chande and Stanley Kroll. In line with Trading View's calculation. See: help(ta.stochrsi)
TTM Trend (ttm_trend). 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.

Updated

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. The notebook is still a work in progress and open to colloboration.