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
- Quick Start
- Help
- Issues and Contributions
- Programming Conventions
- Pandas TA Strategies
- DataFrame Properties
- Changes
- Indicators by Category
- 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
The pip version is the last most stable release.
$ pip install pandas_taBest choice!
$ pip install -U git+https://github.com/twopirllc/pandas-taimport 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 vvimport 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)Thanks for trying Pandas TA!
-
- 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?
-
- 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?
- Please be as detailed as possible. Links, screenshots, and sometimes data samples are welcome.
Thank you for your contributions!
alexonab | allahyarzadeh | codesutras | DrPaprikaa | FGU1 | lluissalord | maxdignan | pbrumblay | SoftDevDanial | YuvalWein
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.
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
- Returns a series with name:
donchiandf = ta.donchian(df["HIGH"], df["low"], lower_length=10, upper_length=15)- Returns a DataFrame named
DC_10_15and column names:DCL_10_15, DCM_10_15, DCU_10_15
- Returns a DataFrame named
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.
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
- Returns a series with name:
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().
- Returns a series with name:
donchiandf = df.ta.donchian(lower_length=10, upper_length=15)- Returns a DataFrame named
DC_10_15and column names:DCL_10_15, DCM_10_15, DCU_10_15
- Returns a DataFrame named
Same as the last three examples, but appending the results directly to the DataFrame df.
df.ta.sma(length=10, append=True)- Appends to
dfcolumn name:SMA_10.
- Appends to
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().
- Chaining Indicators require specifying the input like:
df.ta.donchian(lower_length=10, upper_length=15, append=True)- Appends to
dfwith column names:DCL_10_15, DCM_10_15, DCU_10_15.
- Appends to
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.
# (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)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.
- 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.
- 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.
# 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)# 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# 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)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.columnsRemember 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)# 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
df.ta.categories# 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# 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 '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# 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"- Doji: cdl_doji
- Inside Bar: cdl_inside
- Heikin-Ashi: ha
- 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
- Default is John Carter's. Enable Lazybear's with
- Stochastic Oscillator: stoch
- Stochastic RSI: stochrsi
- Trix: trix
- True strength index: tsi
- Ultimate Oscillator: uo
- Williams %R: willr
| Moving Average Convergence Divergence (MACD) |
|---|
 |
- 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) |
|---|
|
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) |
|---|
 |
- Entropy: entropy
- Kurtosis: kurtosis
- Mean Absolute Deviation: mad
- Median: median
- Quantile: quantile
- Skew: skew
- Standard Deviation: stdev
- Variance: variance
- Z Score: zscore
| Z Score |
|---|
 |
- 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) |
|---|
 |
- Above: above
- Above Value: above_value
- Below: below
- Below Value: below_value
- Cross: cross
- 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) |
|---|
 |
- 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) |
|---|
 |
- 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.
- 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)
- 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. Setlazybear=Trueto 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.
- 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.