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Technical Analysis Indicators - Pandas TA is an easy to use Python 3 Pandas Extension with 130+ Indicators

Pandas TA

Pandas TA - A Technical Analysis Library in Python 3

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Example Chart

Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas package with more than 130 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. Many commonly used indicators are included, such as: Candle Pattern(cdl_pattern), 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.

Note: TA Lib must be installed to use all the Candlestick Patterns. pip install TA-Lib. If TA Lib is not installed, then only the builtin Candlestick Patterns will be available.

Table of contents


Under Development

Pandas TA checks if the user has some common trading packages installed including but not limited to: TA Lib, Vector BT, YFinance … Much of which is experimental and likely to break until it stabilizes more.



The pip version is the last stable release. Version: 0.3.14b

$ pip install pandas_ta

Latest Version

Best choice! Version: 0.3.14b

Cutting Edge

This is the Development Version which could have bugs and other undesireable side effects. Use at own risk!

$ pip install -U git+

# Quick Start

import pandas as pd
import pandas_ta as ta

df = pd.DataFrame() # Empty DataFrame

# Load data
df = pd.read_csv("path/to/symbol.csv", sep=",")
# OR if you have yfinance installed
df = df.ta.ticker("aapl")

# 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

# Take a peek

# vv Continue Post Processing vv


Some indicator arguments have been reordered for consistency. Use help(ta.indicator_name) for more information or make a Pull Request to improve documentation.

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

# List of all indicators

# Help about an indicator such as bbands

Issues and Contributions

Thanks for using Pandas TA!


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: 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.


You explicitly define the input columns and take care of the output.

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.

Same as the last three examples, but appending the results directly to the DataFrame df.

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(
        {"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.

See the Pandas TA Strategy Examples Notebook for examples including Indicator Composition/Chaining.

Strategy Requirements

Optional Parameters

Types of Strategies


# Running the Builtin CommonStrategy as mentioned above

# The Default Strategy is the ta.AllStrategy. The following are equivalent:


# List of indicator 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


# Create your own Custom Strategy
CustomStrategy = ta.Strategy(
    name="Momo and Volatility",
    description="SMA 50,200, BBANDS, RSI, MACD and Volume SMA 20",
        {"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"


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.

# 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.
# Or the string "all"
# Or the ta.AllStrategy

# Use verbose if you want to make sure it is running.

# Use timed if you want to see how long it takes to run.

# 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

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",
        {"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

DataFrame Properties


# 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


# List of Pandas TA categories.


# 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.


# 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.


# Sets the Exchange to use when calculating the last_run property. Default: "NYSE"

# Set the Exchange to use.
# Available Exchanges: "ASX", "BMF", "DIFX", "FWB", "HKE", "JSE", "LSE", "NSE", "NYSE", "NZSX", "RTS", "SGX", "SSE", "TSE", "TSX" = "LSE"


# Returns the time Pandas TA was last run as a string.


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

prefix & suffix

# Applying a prefix to the name of an indicator.
prehl2 = df.ta.hl2(prefix="pre")
print(  # "pre_HL2"

# Applying a suffix to the name of an indicator.
endhl2 = df.ta.hl2(suffix="post")
print(  # "HL2_post"

# Applying a prefix and suffix to the name of an indicator.
bothhl2 = df.ta.hl2(prefix="pre", suffix="post")
print(  # "pre_HL2_post"


# Returns the time range of the DataFrame as a float.
# By default, it returns the time in "years"

# Available time_ranges include: "years", "months", "weeks", "days", "hours", "minutes". "seconds"
df.ta.time_range = "days"
df.ta.time_range # prints DataFrame time in "days" as float


# Sets the DataFrame index to UTC format.

DataFrame Methods


import numpy as np

# Add constant '1' to the DataFrame
df.ta.constants(True, [1])
# Remove constant '1' to the DataFrame
df.ta.constants(False, [1])

# Adding constants for charting
import numpy as np
chart_lines = np.append(np.arange(-4, 5, 1), np.arange(-100, 110, 10))
df.ta.constants(True, chart_lines)
# Removing some constants from the DataFrame
df.ta.constants(False, np.array([-60, -40, 40, 60]))


# Prints the indicators and utility functions

# Returns a list of indicators and utility functions
ind_list = df.ta.indicators(as_list=True)

# Prints the indicators and utility functions that are not in the excluded list
df.ta.indicators(exclude=["cg", "pgo", "ui"])
# Returns a list of the indicators and utility functions that are not in the excluded list
smaller_list = df.ta.indicators(exclude=["cg", "pgo", "ui"], as_list=True)


# Download Chart history using yfinance. (pip install yfinance)
# It uses the same keyword arguments as yfinance (excluding start and end)
df = df.ta.ticker("aapl") # Default ticker is "SPY"

# Period is used instead of start/end
# Valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
# Default: "max"
df = df.ta.ticker("aapl", period="1y") # Gets this past year

# History by Interval by interval (including intraday if period < 60 days)
# Valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
# Default: "1d"
df = df.ta.ticker("aapl", period="1y", interval="1wk") # Gets this past year in weeks
df = df.ta.ticker("aapl", period="1mo", interval="1h") # Gets this past month in hours


Indicators (by Category)

Candles (64)

Patterns that are not bold, require TA-Lib to be installed: pip install TA-Lib

Get only one pattern

df = df.ta.cdl_pattern(name=”doji”)

Get some patterns

df = df.ta.cdl_pattern(name=[“doji”, “inside”])


### **Cycles** (1)
* _Even Better Sinewave_: **ebsw**


### **Momentum** (41)
* _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**
* _Correlation Trend Indicator_: **cti**
    * A wrapper for ```ta.linreg(series, r=True)```
* _Directional Movement_: **dm**
* _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**
* _Schaff Trend Cycle_: **stc**
* _Slope_: **slope**
* _SMI Ergodic_ **smi**
* _Squeeze_: **squeeze**
    * Default is John Carter's. Enable Lazybear's with ```lazybear=True```
* _Squeeze Pro_: **squeeze_pro**
* _Stochastic Oscillator_: **stoch**
* _Stochastic RSI_: **stochrsi**
* _TD Sequential_: **td_seq**
    * Excluded from ```df.ta.strategy()```.
* _Trix_: **trix**
* _True strength index_: **tsi**
* _Ultimate Oscillator_: **uo**
* _Williams %R_: **willr**

| _Moving Average Convergence Divergence_ (MACD) |
| ![Example MACD](/pandas-ta/images/SPY_MACD.png) |


### **Overlap** (33)

* _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**
    * Returns two DataFrames. For more information: ```help(ta.ichimoku)```.
    * ```lookahead=False``` drops the Chikou Span Column to prevent potential data leak.
* _Jurik Moving Average_: **jma**
* _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) |
| ![Example Chart](/pandas-ta/images/TA_Chart.png) |


### **Performance** (3)

Use parameter: cumulative=**True** for cumulative results.

* _Draw Down_: **drawdown**
* _Log Return_: **log_return**
* _Percent Return_: **percent_return**

| _Percent Return_ (Cumulative) with _Simple Moving Average_ (SMA) |
| ![Example Cumulative Percent Return](/pandas-ta/images/SPY_CumulativePercentReturn.png) |

### **Statistics** (11)

* _Entropy_: **entropy**
* _Kurtosis_: **kurtosis**
* _Mean Absolute Deviation_: **mad**
* _Median_: **median**
* _Quantile_: **quantile**
* _Skew_: **skew**
* _Standard Deviation_: **stdev**
* _Think or Swim Standard Deviation All_: **tos_stdevall**
* _Variance_: **variance**
* _Z Score_: **zscore**

| _Z Score_ |
| ![Example Z Score](/pandas-ta/images/SPY_ZScore.png) |

### **Trend** (18)

* _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 ```lookahead=False``` to disable centering and remove potential data leak.
* _Increasing_: **increasing**
* _Long Run_: **long_run**
* _Parabolic Stop and Reverse_: **psar**
* _Q Stick_: **qstick**
* _Short Run_: **short_run**
* _Trend Signals_: **tsignals**
* _TTM Trend_: **ttm_trend**
* _Vertical Horizontal Filter_: **vhf**
* _Vortex_: **vortex**
* _Cross Signals_: **xsignals**

| _Average Directional Movement Index_ (ADX) |
| ![Example ADX](/pandas-ta/images/SPY_ADX.png) |


### **Utility** (5)

* _Above_: **above**
* _Above Value_: **above_value**
* _Below_: **below**
* _Below Value_: **below_value**
* _Cross_: **cross**


### **Volatility** (14)

* _Aberration_: **aberration**
* _Acceleration Bands_: **accbands**
* _Average True Range_: **atr**
* _Bollinger Bands_: **bbands**
* _Donchian Channel_: **donchian**
* _Holt-Winter Channel_: **hwc**
* _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) |
| ![Example ATR](/pandas-ta/images/SPY_ATR.png) |


### **Volume** (15)

* _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**
* _Klinger Volume Oscillator_: **kvo**
* _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) |
| ![Example OBV](/pandas-ta/images/SPY_OBV.png) |


# **Performance Metrics** &nbsp; _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:

import pandas_ta as ta
result = ta.cagr(df.close)

Available Metrics

Backtesting with vectorbt

For easier integration with vectorbt’s Portfolio from_signals method, the ta.trend_return method has been replaced with ta.tsignals method to simplify the generation of trading signals. For a comprehensive example, see the example Jupyter Notebook VectorBT Backtest with Pandas TA in the examples directory.

Brief example

df = pd.DataFrame().ta.ticker(“AAPL”) # requires ‘yfinance’ installed

Create the “Golden Cross”

df[“GC”] = df.ta.sma(50, append=True) > df.ta.sma(200, append=True)

Create boolean Signals(TS_Entries, TS_Exits) for vectorbt

golden = df.ta.tsignals(df.GC, asbool=True, append=True)

Sanity Check (Ensure data exists)


Create the Signals Portfolio

pf = vbt.Portfolio.from_signals(df.close, entries=golden.TS_Entries, exits=golden.TS_Exits, freq=”D”, init_cash=100_000, fees=0.0025, slippage=0.0025)

Print Portfolio Stats and Return Stats

print(pf.stats()) print(pf.returns_stats()) ```



Breaking / Depreciated Indicators

New Indicators

Updated Indicators


Original TA-LIB | TradingView | Sierra Chart | MQL5 | FM Labs | Pro Real Code | User 42


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