Technical Analysis (TA) is an easy to use library that is built upon Python's Pandas library with more than 85 Indicators. These indicators are comminly 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: Moving Average Convergence Divergence (MACD), Hull Exponential Moving Average (HMA), Bollinger Bands (BBANDS), On-Balance Volume (OBV), 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'].
- Has 89+ indicators.
- Example Jupyter Notebook under the examples directory.
- Abbreviated Indicator names as listed below.
- Extended Pandas DataFrame as 'ta'. See examples below.
- Categories similar to TA-lib.
- Updated the example notebook.
- Trend Return,
ta.trend_return(), has a proper index. More consistent with the module. - Added an 'adjusted' property to the 'ta' extension so you can override the default 'close' column. By default,
df.ta.adjusted = None. If the adjusted close column is 'adj_close' for example, then setdf.ta.adjusted = 'adj_close'.
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, inclusively, it can append the result to the existing DataFrame by including the parameter 'append=True' in the method call. See examples below.
$ pip install pandas_ta$ pip install -U git+https://github.com/twopirllc/pandas-taimport 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 vvimport 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)
# Help about the log_return indicator as a DataFrame Extension
help(pd.DataFrame().ta.log_return)# 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# 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- Awesome Oscillator: ao
- Absolute Price Oscillator: apo
- Balance of Power: bop
- Commodity Channel Index: cci
- Center of Gravity: cg
- Chande Momentum Oscillator: cmo
- Coppock Curve: coppock
- Fisher Transform: fisher
- KST Oscillator: kst
- Moving Average Convergence Divergence: macd
- Momentum: mom
- Percentage Price Oscillator: ppo
- Rate of Change: roc
- Relative Strength Index: rsi
- Relative Vigor Index: rvi
- Slope: slope
- Stochastic Oscillator: stoch
- 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
- High-Low Average: hl2
- High-Low-Close Average: hlc3
- Commonly known as 'Typical Price' in Technical Analysis literature
- Hull Exponential Moving Average: hma
- Kaufman's Adaptive Moving Average: kama
- Ichimoku Kinkō Hyō: ichimoku
- Use: help(ta.ichimoku). Returns two DataFrames.
- Linear Regression: linreg
- Midpoint: midpoint
- Midprice: midprice
- Open-High-Low-Close Average: ohlc4
- Pascal's Weighted Moving Average: pwma
- William's Moving Average: rma
- Simple Moving Average: sma
- Sine Weighted Moving Average: sinwma
- 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 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) |
|---|
 |
- 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 Oscillator: aroon
- Decreasing: decreasing
- Detrended Price Oscillator: dpo
- Increasing: increasing
- Linear Decay: linear_decay
- Long Run: long_run
- Q Stick: qstick
- Short Run: short_run
- Vortex: vortex
| Average Directional Movement Index (ADX) |
|---|
 |
- Cross: cross
- Acceleration Bands: accbands
- Average True Range: atr
- Bollinger Bands: bbands
- Donchian Channel: donchian
- Keltner Channel: kc
- Mass Index: massi
- Normalized Average True Range: natr
- True Range: true_range
| 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) |
|---|
 |
- TradingView: http://www.tradingview.com
- Original TA-LIB: http://ta-lib.org/
- Bukosabino: https://github.com/bukosabino/ta
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