Metadata-Version: 1.1
Name: ta
Version: 0.1
Summary: Technical Analysis Library in Python
Home-page: https://github.com/bukosabino/ta
Author: Dario Lopez Padial (Bukosabino)
Author-email: bukosabino@gmail.com
License: The MIT License (MIT)
Download-URL: https://github.com/bukosabino/ta/tarball/0.1
Description: # Technical Analysis Library in Python
        
        You can use this library to add features to your finacial time series dataset.
        
        ### Volume
        
        * 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)
        
        ### Volatility
        
        * Average True Range (ATR)
        * Bollinger Bands (BB)
        * Keltner Channel (KC)
        * Donchian Channel (DC)
        
        ### Trend
        
        * 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)
        
        ### Momentum
        
        * Money Flow Index (MFI)
        * Relative Strength Index (RSI)
        
        ### Fundamental
        
        * Daily Return (DR)
        * Cumulative Return (CR)
        
        
        # How to use
        
        > pip3 install ta
        
        ### Example adding all features
        
        ```python
        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
        df = add_all_ta_features(df, "Open", "High", "Low", "Close", "Volume_BTC")
        
        # fill nan values
        df = df.fillna(method='backfill')
        ```
        
        
        ### Example adding one feature
        
        ```python
        import pandas as pd
        from ta.volume import *
        
        # load datas
        df = pd.read_csv('your-file.csv', sep=',')
        
        # clean nan values
        df = utils.dropna(df)
        
        # add ta feature
        df['cmf'] = chaikin_money_flow(df.High, df.Low, df.Close, df.Volume_BTC)
        
        # fill nan values
        df['cmf'] = df['cmf'].fillna(method='backfill')
        ```
        
        
        If you don't know any feature you can visualize them in "visualize_features.ipynb".
        
        
        # Deploy to developers
        
        > pip3 install -r requirements.txt
        
        
        # Based on:
        
        * https://en.wikipedia.org/wiki/Technical_analysis
        * https://github.com/FreddieWitherden/ta
        * https://github.com/femtotrader/pandas_talib
        
        
        # Credits:
        
        Developed by Bukosabino at Lecrin Technologies - http://lecrintech.com
        
        We are glad to receive any contribution, idea or feedback.
Keywords: technical analysis,python3,pandas,numpy,fundamental analysis
Platform: UNKNOWN
