Pandas Technical Analysis (Pandas TA) is an easy to use library that leverages the Pandas library 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
  • Stable
  • Latest Version
  • Cutting Edge
• Quick Start
• Help
• Issues and Contributions
• Programming Conventions
  • Standard
  • Pandas TA DataFrame Extension
  • Pandas TA Strategy
• Pandas TA Strategies
  • Types of Strategies
• DataFrame Properties
• DataFrame Methods
• Indicators by Category
  • Candles
  • Cycles
  • Momentum
  • Overlap
  • Performance
  • Statistics
  • Trend
  • Utility
  • Volatility
  • Volume
• Performance Metrics
• Changes
  • General
  • Breaking Indicators
  • New Indicators
  • Updated Indicators

• 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!
• 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.

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

The pip version is the last most stable release. Version: 0.2.45b

$ pip install pandas_ta

Best choice! Version: 0.2.93b

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

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

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

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)

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.

Thank you for your contributions!

AbyssAlora | alexonab | allahyarzadeh | CMobley7 | codesutras | DrPaprikaa | daikts | dorren | edwardwang1 | ffhirata | FGU1 | floatinghotpot | GSlinger | JoeSchr | lluissalord | luisbarrancos | M6stafa | maxdignan | mchant | moritzgun | nicoloridulfo NkosenhleDuma | pbrumblay | RajeshDhalange | rengel8 | rluong003 | SoftDevDanial | tg12 | twrobel | WellMaybeItIs | whubsch | witokondoria | wouldayajustlookatit | YuvalWein

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.

• 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.

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.

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.

# 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

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)

# 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

# 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

# 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

# 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"

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

# 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"

# 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

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

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
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)

# 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)

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"])

• Even Better Sinewave: ebsw

• 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
• 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)

• 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)

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)

• 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
  • 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)

• 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
• 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)

• 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 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)

• 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

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.

• 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())

• 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.

• 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)

• 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)
• 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)
• Vertical Horizontal Filter (vhf) was created by Adam White to identify trending and ranging markets.. See: help(ta.vhf)

• 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. 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. help(ta.psar).
• 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
• Z Score (zscore): Changed return column name from Z_length to ZS_length.

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