**Technical Analysis Library in Python 3.7**

**Pandas Technical Analysis** (Pandas TA) is an easy to use library that is built upon Python's Pandas library with more than 100 Indicators. These indicators are commonly used for financial time series datasets with columns or labels similar to: datetime, open, high, low, close, volume, et al. 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*) and more.

This version contains both the orignal code branch as well as a newly refactored branch with the option to use Pandas DataFrame Extension mode. All the indicators return a named Series or a DataFrame in uppercase underscore parameter format. For example, MACD(fast=12, slow=26, signal=9) will return a DataFrame with columns: ['MACD_12_26_9', 'MACDh_12_26_9', 'MACDs_12_26_9'].

**Features**

- Has 100+ indicators and utility functions.
- Option to use
**multiprocessing**when using df.ta.strategy(). See below. - Example Jupyter Notebook under the examples directory.
- A new 'ta' method called 'strategy' that be default, runs
**all**the indicators. - Abbreviated Indicator names as listed below.
**Extended Pandas DataFrame**as 'ta'.- Easily add prefixes or suffixes or both to columns names.
- Categories similar to TA-lib.

**Recent Changes**

**New DataFrame Method:**

```
strategy (strategy)
```

**Added indicators:**

```
Bias (bias)
Choppiness Index (chop)
Chande Kroll Stop (cksp)
Doji (cdl_doji)
Entropy (entropy)
Heikin-Ashi Candles (ha)
Inertia (inertia)
KDJ (kdj)
Parabolic Stop and Reverse (psar)
Price Distance (pdist)
Psycholigical Line (psl)
Percentage Volume Oscillator (pvo)
Relative Volatility Index (rvi)
Supertrend (supertrend)
Weighted Closing Price (wcp)
```

**Added utilities:**

```
Above (above)
Above Value (above_value)
Below (below)
Below Value (below_value)
Cross Value (cross_value)
```

**User Added Indicators:**

```
Aberration (aberration)
BRAR (brar)
```

**Corrected Indicators:**

```
Absolute Price Oscillator (apo)
Aroon & Aroon Oscillator (aroon)
* Fixed indicator and included oscillator in returned dataframe
Bollinger Bands (bbands)
Commodity Channel Index (cci)
Chande Momentum Oscillator (cmo)
Relative Vigor Index (rvgi)
Symmetric Weighted Moving Average (swma)
```

## What is a Pandas DataFrame Extension?

A Pandas DataFrame Extension, extends a DataFrame allowing one to add more functionality and features to Pandas to suit your needs. As such, it is now easier to run Technical Analysis on existing Financial Time Series without leaving the current DataFrame. This extension by default returns the Indicator result or it can append the result to the existing DataFrame by including the parameter 'append=True' in the method call. Examples below.

**Getting Started and Examples**

**Installation** (python 3)

`$ pip install pandas_ta`

**Latest Version**

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

**Quick Start** using the DataFrame Extension

```
import pandas as pd
import pandas_ta as ta
# Load data
df = pd.read_csv('symbol.csv', sep=',')
# Calculate Returns and append to the df DataFrame
df.ta.log_return(cumulative=True, append=True)
df.ta.percent_return(cumulative=True, append=True)
# New Columns with results
df.columns
# Take a peek
df.tail()
# vv Continue Post Processing vv
```

**Module and Indicator Help**

```
import pandas as pd
import pandas_ta as ta
# Help about this, 'ta', extension
help(pd.DataFrame().ta)
# List of all indicators
pd.DataFrame().ta.indicators()
# Help about the log_return indicator
help(ta.log_return)
```

**New DataFrame Method**: *strategy* with Multiprocessing

Strategy is a new **Pandas (TA)** method to facilitate bulk indicator processing. By default, running `df.ta.strategy()`

will append **all applicable** indicators to DataFrame `df`

. Utility methods like `above`

, `below`

et al are not included.

- The
`ta.strategy()`

method is still**under development**. Future iterations will allow you to load a`ta.json`

config file with your specific strategy name and parameters to automatically run you bulk indicators.

```
# This property only effects df.ta.strategy(). When set to True,
# it enables multiprocessing when processing "ALL" the indicators.
# Default is False
df.ta.mp = True
# Runs and appends all indicators to the current DataFrame by default
# The resultant DataFrame will be large.
df.ta.strategy()
# Or equivalently use name='all'
df.ta.strategy(name='all')
# 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)
# You can change the number of cores to use. Though the
# default will usually be best
df.ta.strategy(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
```

**New DataFrame kwargs**: *prefix* and *suffix*

```
prehl2 = df.ta.hl2(prefix="pre")
print(prehl2.name) # "pre_HL2"
endhl2 = df.ta.hl2(suffix="post")
print(endhl2.name) # "HL2_post"
bothhl2 = df.ta.hl2(prefix="pre", suffix="post")
print(bothhl2.name) # "pre_HL2_post"
```

**New DataFrame Properties**: *reverse* & *datetime_ordered*

```
# The 'reverse' is a helper property that returns the DataFrame
# in reverse order
df = df.ta.reverse
# The 'datetime_ordered' property returns True if the DataFrame
# index is of Pandas datetime64 and df.index[0] < df.index[-1]
# Otherwise it return False
time_series_in_order = df.ta.datetime_ordered
```

**DataFrame Property**: *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
```

**Technical Analysis Indicators** (*by Category*)

*Candles* (2)

*Doji*:**cdl_doji***Heikin-Ashi*:**ha**

*Momentum* (27)

*Awesome Oscillator*:**ao***Absolute Price Oscillator*:**apo***Bias*:**bias***Balance of Power*:**bop***BRAR*:**brar***Commodity Channel Index*:**cci***Center of Gravity*:**cg***Chande Momentum Oscillator*:**cmo***Coppock Curve*:**coppock***Fisher Transform*:**fisher***Inertia*:**inertia***KDJ*:**kdj***KST Oscillator*:**kst***Moving Average Convergence Divergence*:**macd***Momentum*:**mom***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**Stochastic Oscillator*:**stoch***Trix*:**trix***True strength index*:**tsi***Ultimate Oscillator*:**uo***Williams %R*:**willr**

Moving Average Convergence Divergence (MACD) |
---|

*Overlap* (26)

*Double Exponential Moving Average*:**dema***Exponential Moving Average*:**ema***Fibonacci's Weighted Moving Average*:**fwma***High-Low Average*:**hl2***High-Low-Close Average*:**hlc3**- Commonly known as 'Typical Price' in Technical Analysis literature

*Hull Exponential Moving Average*:**hma***Ichimoku Kinkō Hyō*:**ichimoku**- Use: help(ta.ichimoku). Returns two DataFrames.

*Kaufman's Adaptive Moving Average*:**kama***Linear Regression*:**linreg***Midpoint*:**midpoint***Midprice*:**midprice***Open-High-Low-Close Average*:**ohlc4***Pascal's Weighted Moving Average*:**pwma***William's Moving Average*:**rma***Sine Weighted Moving Average*:**sinwma***Simple Moving Average*:**sma***Supertrend*:**supertrend***Symmetric Weighted Moving Average*:**swma***T3 Moving Average*:**t3***Triple Exponential Moving Average*:**tema***Triangular Moving Average*:**trima***Volume Weighted Average Price*:**vwap***Volume Weighted Moving Average*:**vwma***Weighted Closing Price*:**wcp***Weighted Moving Average*:**wma***Zero Lag Moving Average*:**zlma**

Simple Moving Averages (SMA) and Bollinger Bands (BBANDS) |
---|

*Performance* (3)

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

*Log Return*:**log_return***Percent Return*:**percent_return***Trend Return*:**trend_return**

Percent Return (Cumulative) with Simple Moving Average (SMA) |
---|

*Statistics* (9)

*Entropy*:**entropy***Kurtosis*:**kurtosis***Mean Absolute Deviation*:**mad***Median*:**median***Quantile*:**quantile***Skew*:**skew***Standard Deviation*:**stdev***Variance*:**variance***Z Score*:**zscore**

Z Score |
---|

*Trend* (14)

*Average Directional Movement Index*:**adx***Archer Moving Averages Trends*:**amat***Aroon & Aroon Oscillator*:**aroon***Choppiness Index*:**chop***Chande Kroll Stop*:**cksp***Decreasing*:**decreasing***Detrended Price Oscillator*:**dpo***Increasing*:**increasing***Linear Decay*:**linear_decay***Long Run*:**long_run***Parabolic Stop and Reverse*:**psar***Q Stick*:**qstick***Short Run*:**short_run***Vortex*:**vortex**

Average Directional Movement Index (ADX) |
---|

*Utility* (5)

*Above*:**above***Above Value*:**above_value***Below*:**below***Below Value*:**below_value***Cross*:**cross**

*Volatility* (11)

*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**

Average True Range (ATR) |
---|

*Volume* (13)

*Accumulation/Distribution Index*:**ad***Accumulation/Distribution Oscillator*:**adosc***Archer On-Balance Volume*:**aobv***Chaikin Money Flow*:**cmf***Elder's Force Index*:**efi***Ease of Movement*:**eom***Money Flow Index*:**mfi***Negative Volume Index*:**nvi***On-Balance Volume*:**obv***Positive Volume Index*:**pvi***Price-Volume*:**pvol***Price Volume Trend*:**pvt***Volume Profile*:**vp**

On-Balance Volume (OBV) |
---|

## Contributors

## Inspiration

- TradingView: http://www.tradingview.com
- Original TA-LIB: http://ta-lib.org/

Please leave any comments, feedback, suggestions, or indicator requests.