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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.

Bollinger Bands graph example

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

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

Logo OpenSistemas

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

In Progress

  • Automated tests for all the indicators.

TODO

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.

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  • Python 16.2%