Technical Analysis Library in Python

It is a Technical Analysis library useful to do feature engineering from financial time series datasets (Open, Close, High, Low, Volume). It is built on Pandas and Numpy.

The library has implemented 43 indicators:

Volume

ID	Name	Class	defs
1	Money Flow Index (MFI)	MFIIndicator	money_flow_index
2	Accumulation/Distribution Index (ADI)	AccDistIndexIndicator	acc_dist_index
3	On-Balance Volume (OBV)	OnBalanceVolumeIndicator	on_balance_volume
4	Chaikin Money Flow (CMF)	ChaikinMoneyFlowIndicator	chaikin_money_flow
5	Force Index (FI)	ForceIndexIndicator	force_index
6	Ease of Movement (EoM, EMV)	EaseOfMovementIndicator	ease_of_movement sma_ease_of_movement
7	Volume-price Trend (VPT)	VolumePriceTrendIndicator	volume_price_trend
8	Negative Volume Index (NVI)	NegativeVolumeIndexIndicator	negative_volume_index
9	Volume Weighted Average Price (VWAP)	VolumeWeightedAveragePrice	volume_weighted_average_price

Volatility

ID	Name	Class	defs
10	Average True Range (ATR)	AverageTrueRange	average_true_range
11	Bollinger Bands (BB)	BollingerBands	bollinger_hband bollinger_hband_indicator bollinger_lband bollinger_lband_indicator bollinger_mavg bollinger_pband bollinger_wband
12	Keltner Channel (KC)	KeltnerChannel	keltner_channel_hband keltner_channel_hband_indicator keltner_channel_lband keltner_channel_lband_indicator keltner_channel_mband keltner_channel_pband keltner_channel_wband
13	Donchian Channel (DC)	DonchianChannel	donchian_channel_hband donchian_channel_lband donchian_channel_mban donchian_channel_pband donchian_channel_wband
14	Ulcer Index (UI)	UlcerIndex	ulcer_index

Trend

ID	Name	Class	defs
15	Simple Moving Average (SMA)	SMAIndicator	sma_indicator
16	Exponential Moving Average (EMA)	EMAIndicator	ema_indicator
17	Weighted Moving Average (WMA)	WMAIndicator	wma_indicator
18	Moving Average Convergence Divergence (MACD)	MACD	macd macd_diff macd_signal
19	Average Directional Movement Index (ADX)	ADXIndicator	adx adx_neg adx_pos
20	Vortex Indicator (VI)	VortexIndicator	vortex_indicator_neg vortex_indicator_pos
21	Trix (TRIX)	TRIXIndicator	trix
22	Mass Index (MI)	MassIndex	mass_index
23	Commodity Channel Index (CCI)	CCIIndicator	cci
24	Detrended Price Oscillator (DPO)	DPOIndicator	dpo
25	KST Oscillator (KST)	KSTIndicator	kst kst_sig
26	Ichimoku Kinkō Hyō (Ichimoku)	IchimokuIndicator	ichimoku_a ichimoku_b ichimoku_base_line ichimoku_conversion_line
27	Parabolic Stop And Reverse (Parabolic SAR)	PSARIndicator	psar_down psar_down_indicator psar_up psar_up_indicator
28	Schaff Trend Cycle (STC)	STCIndicator	stc
29	Aroon Indicator	AroonIndicator	aroon_down aroon_up

Momentum

ID	Name	Class	defs
30	Relative Strength Index (RSI)	RSIIndicator	rsi
31	Stochastic RSI (SRSI)	StochRSIIndicator	stochrsi stochrsi_d stochrsi_k
32	True strength index (TSI)	TSIIndicator	tsi
33	Ultimate Oscillator (UO)	UltimateOscillator	ultimate_oscillator
34	Stochastic Oscillator (SR)	StochasticOscillator	stoch stoch_signal
35	Williams %R (WR)	WilliamsRIndicator	williams_r
36	Awesome Oscillator (AO)	AwesomeOscillatorIndicator	awesome_oscillator
37	Kaufman's Adaptive Moving Average (KAMA)	KAMAIndicator	kama
38	Rate of Change (ROC)	ROCIndicator	roc
39	Percentage Price Oscillator (PPO)	PercentagePriceOscillator	ppo ppo_hist ppo_signal
40	Percentage Volume Oscillator (PVO)	PercentageVolumeOscillator	pvo pvo_hist pvo_signal

Others

ID	Name	Class	defs
41	Daily Return (DR)	DailyReturnIndicator	daily_return
42	Daily Log Return (DLR)	DailyLogReturnIndicator	daily_log_return
43	Cumulative Return (CR)	CumulativeReturnIndicator	cumulative_return

Documentation

https://technical-analysis-library-in-python.readthedocs.io/en/latest/

Motivation to use

• English
• Spanish

How to use (Python 3)

$ pip install --upgrade ta

To use this library you should have a financial time series dataset including Timestamp, Open, High, Low, Close and Volume columns.

You should clean or fill NaN values in your dataset before add technical analysis features.

You can get code examples in examples_to_use folder.

You can visualize the features in this notebook.

Example adding all features

import pandas as pd
from ta import add_all_ta_features
from ta.utils import dropna


# Load datas
df = pd.read_csv('ta/tests/data/datas.csv', sep=',')

# Clean NaN values
df = dropna(df)

# Add all ta features
df = add_all_ta_features(
    df, open="Open", high="High", low="Low", close="Close", volume="Volume_BTC")

Example adding particular feature

import pandas as pd
from ta.utils import dropna
from ta.volatility import BollingerBands


# Load datas
df = pd.read_csv('ta/tests/data/datas.csv', sep=',')

# Clean NaN values
df = dropna(df)

# Initialize Bollinger Bands Indicator
indicator_bb = BollingerBands(close=df["Close"], window=20, window_dev=2)

# Add Bollinger Bands features
df['bb_bbm'] = indicator_bb.bollinger_mavg()
df['bb_bbh'] = indicator_bb.bollinger_hband()
df['bb_bbl'] = indicator_bb.bollinger_lband()

# Add Bollinger Band high indicator
df['bb_bbhi'] = indicator_bb.bollinger_hband_indicator()

# Add Bollinger Band low indicator
df['bb_bbli'] = indicator_bb.bollinger_lband_indicator()

# Add Width Size Bollinger Bands
df['bb_bbw'] = indicator_bb.bollinger_wband()

# Add Percentage Bollinger Bands
df['bb_bbp'] = indicator_bb.bollinger_pband()

Deploy and develop (for developers)

$ git clone https://github.com/bukosabino/ta.git
$ cd ta
$ pip install -r requirements-play.txt
$ make test

Sponsor

Thank you to OpenSistemas! It is because of your contribution that I am able to continue the development of this open source library.

Based on

• https://en.wikipedia.org/wiki/Technical_analysis
• https://pandas.pydata.org
• https://github.com/FreddieWitherden/ta
• https://github.com/femtotrader/pandas_talib

In Progress

• Automated tests for all the indicators.

TODO

• Use NumExpr to speed up the NumPy/Pandas operations? Article Motivation
• Add more technical analysis features.
• Wrapper to get financial data.
• Use of the Pandas multi-indexing techniques to calculate several indicators at the same time.
• Use Plotly/Streamlit to visualize features

Changelog

Check the changelog of project.

Donation

If you think ta library help you, please consider buying me a coffee.

Credits

Developed by Darío López Padial (aka Bukosabino) and other contributors.

Please, let me know about any comment or feedback.

Also, I am a software engineer freelance focused on Data Science using Python tools such as Pandas, Scikit-Learn, Backtrader, Zipline or Catalyst. Don't hesitate to contact me if you need to develop something related with this library, Python, Technical Analysis, AlgoTrading, Machine Learning, etc.