Pandas TA - A Technical Analysis Library in Python 3

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.

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

Features

Has 130+ indicators and utility functions.
- BETA Also Pandas TA will run TA Lib's version, this includes TA Lib's 63 Chart Patterns.
Indicators are tightly correlated with the de facto TA Lib if they share common indicators.
Example Jupyter Notebook with vectorbt Portfolio Backtesting with Pandas TA's ta.tsignals method.
Have the need for speed? By using the DataFrame strategy method, you get multiprocessing for free! Conditions permitting.
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
Potential Data Leaks: ichimoku and dpo. See indicator list below for details.

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.

If TA Lib installed, existing indicators will eventually get a TA Lib version.
Easy Downloading of ohlcv data using yfinance. See help(ta.ticker) and help(ta.yf) and examples below.
Hopefully soon a Pandas TA YAML configuration file contained in ~/pandas_ta/ can be implemented. To see the proposed specification and leave comments and suggestions on it's implementation, see Issue #258.
Some Common Performance Metrics

Installation

Stable

The pip version is the last stable release. Version: 0.3.02b

$ pip install pandas_ta

Latest Version

Best choice! Version: 0.3.02b

Includes all fixes and updates between pypi and what is covered in this README.

$ 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!

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

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
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 an indicator such as bbands
help(ta.bbands)

Issues and Contributions

Thanks for using 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?
Bugs, Indicators or Feature Requests
- 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 personally.

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: 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). 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.

# 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

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

# 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

# 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

exchange

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

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

last_run

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

reverse

# 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"

time_range

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

# 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

to_utc

# Sets the DataFrame index to UTC format.
df.ta.to_utc

DataFrame Methods

constants

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]))

indicators

# Prints the indicators and utility functions
df.ta.indicators()

# 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)

ticker

# Download Chart history using yfinance. (pip install yfinance) https://github.com/ranaroussi/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

# BUT WAIT!! THERE'S MORE!!
help(ta.yf)

Indicators (by Category)

Candles (64)

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

2crows
3blackcrows
3inside
3linestrike
3outside
3starsinsouth
3whitesoldiers
abandonedbaby
advanceblock
belthold
breakaway
closingmarubozu
concealbabyswall
counterattack
darkcloudcover
doji
dojistar
dragonflydoji
engulfing
eveningdojistar
eveningstar
gapsidesidewhite
gravestonedoji
hammer
hangingman
harami
haramicross
highwave
hikkake
hikkakemod
homingpigeon
identical3crows
inneck
inside
invertedhammer
kicking
kickingbylength
ladderbottom
longleggeddoji
longline
marubozu
matchinglow
mathold
morningdojistar
morningstar
onneck
piercing
rickshawman
risefall3methods
separatinglines
shootingstar
shortline
spinningtop
stalledpattern
sticksandwich
takuri
tasukigap
thrusting
tristar
unique3river
upsidegap2crows
xsidegap3methods
Heikin-Ashi: ha
Z Score: cdl_z

# Get all candle patterns (This is the default behaviour)
df = df.ta.cdl_pattern(name="all")

# 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)

Overlap (32)

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).
- 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 (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)

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

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 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
Trend Signals: tsignals
TTM Trend: ttm_trend
Vertical Horizontal Filter: vhf
Vortex: vortex
Cross Signals: xsignals

Average Directional Movement Index (ADX)

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)

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)

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:

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

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

See the vectorbt website more options and examples.

import pandas as pd
import pandas_ta as ta
import vectorbt as vbt

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)
print(df)

# 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())

Changes

General

A Strategy Class to help name and group your favorite indicators.
If a TA Lib is already installed, Pandas TA will run TA Lib's version. (BETA)
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 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) Now as a Pandas TA DataFrame Property to easily convert the DataFrame index to UTC.

Breaking / Depreciated Indicators

Trend Return (trend_return) has been removed and replaced with tsignals. When given a trend Series like close > sma(close, 50) it returns the Trend, Trade Entries and Trade Exits of that trend to make it compatible with vectorbt by setting asbool=True to get boolean Trade Entries and Exits. See help(ta.tsignals)

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) trading account, or fund. See help(ta.drawdown)
Candle Patterns (cdl_pattern) If TA Lib is installed, then all those Candle Patterns are available. See the list and examples above on how to call the patterns. See help(ta.cdl_pattern)
Candle Z Score (cdl_z) normalizes OHLC Candles with a rolling Z Score. See help(ta.cdl_z)
Correlation Trend Indicator (cti) is an oscillator created by John Ehler in 2020. See help(ta.cti)
Cross Signals (xsignals) was created by Kevin Johnson. It is a wrapper of Trade Signals that returns Trends, Trades, Entries and Exits. Cross Signals are commonly used for bbands, rsi, zscore crossing some value either above or below two values at different times. See help(ta.xsignals)
Directional Movement (dm) developed by J. Welles Wilder in 1978 attempts to determine which direction the price of an asset is moving. See help(ta.dm)
Even Better Sinewave (ebsw) measures market cycles and uses a low pass filter to remove noise. See: help(ta.ebsw)
Klinger Volume Oscillator (kvo) was developed by Stephen J. Klinger. It is designed to predict price reversals in a market by comparing volume to price.. See help(ta.kvo)
Schaff Trend Cycle (stc) is an evolution of the popular MACD incorportating two cascaded stochastic calculations with additional smoothing. See help(ta.stc)
Squeeze Pro (squeeze_pro) is an extended version of "TTM Squeeze" from John Carter. See help(ta.squeeze_pro)
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)
Think or Swim Standard Deviation All (tos_stdevall) indicator which returns the standard deviation of data for the entire plot or for the interval of the last bars defined by the length parameter. See help(ta.tos_stdevall)
Vertical Horizontal Filter (vhf) was created by Adam White to identify trending and ranging markets.. See help(ta.vhf)

Updated Indicators

ADX (adx): Added mamode with default "RMA" and with the same mamode options as TradingView. New argument lensig so it behaves like TradingView's builtin ADX indicator. See help(ta.adx).
Archer Moving Averages Trends (amat): Added drift argument and more descriptive column names.
Average True Range (atr): The default mamode is now "RMA" and with the same mamode options as TradingView. See help(ta.atr).
Bollinger Bands (bbands): New argument ddoff to control the Degrees of Freedom. Also included BB Percent (BBP) as the final column. Default is 0. See help(ta.bbands).
Choppiness Index (chop): New argument ln to use Natural Logarithm (True) instead of the Standard Logarithm (False). Default is False. See help(ta.chop).
Chande Kroll Stop (cksp): Added tvmode with default True. When tvmode=False, cksp implements “The New Technical Trader” with default values. See help(ta.cksp).
Decreasing (decreasing): New argument strict checks if the series is continuously decreasing over period length with a faster calculation. Default: False. The percent argument has also been added with default None. See help(ta.decreasing).
Increasing (increasing): New argument strict checks if the series is continuously increasing over period length with a faster calculation. Default: False. The percent argument has also been added with default None. See help(ta.increasing).
Parabolic Stop and Reverse (psar): Bug fix and adjustment to match TradingView's sar. New argument af0 to initialize the Acceleration Factor. See help(ta.psar).
Percentage Price Oscillator (ppo): Included new argument mamode as an option. Default is sma to match TA Lib. See help(ta.ppo).
Volume Profile (vp): Calculation improvements. See Pull Request #320 See help(ta.vp).
Volume Weighted Moving Average (vwma): Fixed bug in DataFrame Extension call. See help(ta.vwma).
Volume Weighted Average Price (vwap): Added a new parameter called anchor. Default: "D" for "Daily". See Timeseries Offset Aliases for additional options. Requires the DataFrame index to be a DatetimeIndex. See help(ta.vwap).
Z Score (zscore): Changed return column name from Z_length to ZS_length. See help(ta.zscore).

Sources

Name		Name	Last commit message	Last commit date
Latest commit History 529 Commits
.github/ISSUE_TEMPLATE		.github/ISSUE_TEMPLATE
data		data
docs		docs
examples		examples
images		images
pandas_ta		pandas_ta
tests		tests
.gitignore		.gitignore
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
LICENSE		LICENSE
Makefile		Makefile
README.md		README.md
__init__.py		__init__.py
_config.yml		_config.yml
requirements.txt		requirements.txt
setup.py		setup.py

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schwaa/pandas-ta

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