It is a Technical Analysis library to financial time series datasets (open, close, high, low, volume). You can use it to do feature engineering from financial datasets. It is builded on Python Pandas library.
The library has implemented 26 indicators and 49 features:
- Accumulation/Distribution Index (ADI)
- On-Balance Volume (OBV)
- On-Balance Volume mean (OBV mean)
- Chaikin Money Flow (CMF)
- Force Index (FI)
- Ease of Movement (EoM, EMV)
- Volume-price Trend (VPT)
- Average True Range (ATR)
- Bollinger Bands (BB)
- Keltner Channel (KC)
- Donchian Channel (DC)
- Moving Average Convergence Divergence (MACD)
- Average Directional Movement Index (ADX)
- Vortex Indicator (VI)
- Trix (TRIX)
- Mass Index (MI)
- Commodity Channel Index (CCI)
- Detrended Price Oscillator (DPO)
- KST Oscillator (KST)
- Ichimoku Kinkō Hyō (Ichimoku)
- Money Flow Index (MFI)
- Relative Strength Index (RSI)
- True strength index (TSI)
- Ultimate Oscillator (UO)
- Daily Return (DR)
- Cumulative Return (CR)
https://technical-analysis-library-in-python.readthedocs.io/en/latest/
- English: https://towardsdatascience.com/technical-analysis-library-to-financial-datasets-with-pandas-python-4b2b390d3543
- Spanish: https://medium.com/datos-y-ciencia/biblioteca-de-an%C3%A1lisis-t%C3%A9cnico-sobre-series-temporales-financieras-para-machine-learning-con-cb28f9427d0
> virtualenv -p python3 virtualenvironment
> source virtualenvironment/bin/activate
> pip install 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.
import pandas as pd
from ta import *
# Load datas
df = pd.read_csv('your-file.csv', sep=',')
# Clean NaN values
df = utils.dropna(df)
# Add ta features filling NaN values
df = add_all_ta_features(df, "Open", "High", "Low", "Close", "Volume_BTC", fillna=True)
import pandas as pd
from ta import *
# Load datas
df = pd.read_csv('your-file.csv', sep=',')
# Clean NaN values
df = utils.dropna(df)
# Add bollinger band high indicator filling NaN values
df['bb_high_indicator'] = bollinger_hband_indicator(df["Close"], n=20, ndev=2, fillna=True)
# Add bollinger band low indicator filling NaN values
df['bb_low_indicator'] = bollinger_lband_indicator(df["Close"], n=20, ndev=2, fillna=True)
> pip install -r requirements.txt
- https://en.wikipedia.org/wiki/Technical_analysis
- https://pandas.pydata.org
- https://github.com/FreddieWitherden/ta
- https://github.com/femtotrader/pandas_talib
- add more technical analysis features
- use dask library to parallelize
Developed by Bukosabino at Lecrin Technologies - https://lecrintech.com
Please, let us know about any comment or feedback.