Metadata-Version: 2.1
Name: finta
Version: 0.4.3
Summary:  Common financial technical indicators implemented in Pandas.
Home-page: https://github.com/peerchemist/finta
Author: Peerchemist
Author-email: peerchemist@protonmail.ch
License: LGPLv3+
Description: # FinTA (Financial Technical Analysis)
        
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        Common financial technical indicators implemented in Pandas.
        
        *This is work in progress, bugs are expected and results of some indicators
        may not be accurate.*
        
        ## Supported indicators:
        
        Finta supports 76 trading indicators:
        
        ```
        * Simple Moving Average 'SMA'
        * Simple Moving Median 'SMM'
        * Smoothed Simple Moving Average 'SSMA'
        * Exponential Moving Average 'EMA'
        * Double Exponential Moving Average 'DEMA'
        * Triple Exponential Moving Average 'TEMA'
        * Triangular Moving Average 'TRIMA'
        * Triple Exponential Moving Average Oscillator 'TRIX'
        * Volume Adjusted Moving Average 'VAMA'
        * Kaufman Efficiency Indicator 'ER'
        * Kaufman's Adaptive Moving Average 'KAMA'
        * Zero Lag Exponential Moving Average 'ZLEMA'
        * Weighted Moving Average 'WMA'
        * Hull Moving Average 'HMA'
        * Elastic Volume Moving Average 'EVWMA'
        * Volume Weighted Average Price 'VWAP'
        * Smoothed Moving Average 'SMMA'
        * Moving Average Convergence Divergence 'MACD'
        * Percentage Price Oscillator 'PPO'
        * Volume-Weighted MACD 'VW_MACD'
        * Elastic-Volume weighted MACD 'EV_MACD'
        * Market Momentum 'MOM'
        * Rate-of-Change 'ROC'
        * Relative Strenght Index 'RSI'
        * Inverse Fisher Transform RSI 'IFT_RSI'
        * True Range 'TR'
        * Average True Range 'ATR'
        * Stop-and-Reverse 'SAR'
        * Bollinger Bands 'BBANDS'
        * Bollinger Bands Width 'BBWIDTH'
        * Percent B 'PERCENT_B'
        * Keltner Channels 'KC'
        * Donchian Channel 'DO'
        * Directional Movement Indicator 'DMI'
        * Average Directional Index 'ADX'
        * Pivot Points 'PIVOT'
        * Fibonacci Pivot Points 'PIVOT_FIB'
        * Stochastic Oscillator %K 'STOCH'
        * Stochastic oscillator %D 'STOCHD'
        * Stochastic RSI 'STOCHRSI'
        * Williams %R 'WILLIAMS'
        * Ultimate Oscillator 'UO'
        * Awesome Oscillator 'AO'
        * Mass Index 'MI'
        * Vortex Indicator 'VORTEX'
        * Know Sure Thing 'KST'
        * True Strength Index 'TSI'
        * Typical Price 'TP'
        * Accumulation-Distribution Line 'ADL'
        * Chaikin Oscillator 'CHAIKIN'
        * Money Flow Index 'MFI'
        * On Balance Volume 'OBV'
        * Weighter OBV 'WOBV'
        * Volume Zone Oscillator 'VZO'
        * Price Zone Oscillator 'PZO'
        * Elder's Force Index 'EFI'
        * Cummulative Force Index 'CFI'
        * Bull power and Bear Power 'EBBP'
        * Ease of Movement 'EMV'
        * Commodity Channel Index 'CCI'
        * Coppock Curve 'COPP'
        * Buy and Sell Pressure 'BASP'
        * Normalized BASP 'BASPN'
        * Chande Momentum Oscillator 'CMO'
        * Chandelier Exit 'CHANDELIER'
        * Qstick 'QSTICK'
        * Twiggs Money Index 'TMF'
        * Wave Trend Oscillator 'WTO'
        * Fisher Transform 'FISH'
        * Ichimoku Cloud 'ICHIMOKU'
        * Adaptive Price Zone 'APZ'
        * Vector Size Indicator 'VR'
        * Squeeze Momentum Indicator 'SQZMI'
        * Volume Price Trend 'VPT'
        * Finite Volume Element 'FVE'
        * Volume Flow Indicator 'VFI'
        * Moving Standard deviation 'MSD'
        * Schaff Trend Cycle 'STC'
        ```
        
        ## Dependencies:
        
        -   python (3.4+)
        -   pandas (0.21.1+)
        
        TA class is very well documented and there should be no trouble
        exploring it and using with your data. Each class method expects proper `ohlc` DataFrame as input.
        
        ## Install:
        
        `pip install finta`
        
        or latest development version:
        
        `pip install git+git://github.com/peerchemist/finta.git`
        
        ## Import
        
        `from finta import TA`
        
        Prepare data to use with finta:
        
        finta expects properly formated `ohlc` DataFrame, with column names in `lowercase`:
        ["open", "high", "low", "close"] and ["volume"] for indicators that expect `ohlcv` input.
        
        ### to resample by time period (you can choose different time period)
        `ohlc = resample(df, "24h")`
        
        ### You can also load a ohlc DataFrame from .cvs file
        
        `data_file = ("data/bittrex:btc-usdt.csv")`
        
        `ohlc = pd.read_csv(data_file, index_col="date", parse_dates=True)`
        
        ____________________________________________________________________________
        
        ## Examples:
        
        ### will return Pandas Series object with the Simple moving average for 42 periods
        `TA.SMA(ohlc, 42)`
        
        ### will return Pandas Series object with "Awesome oscillator" values
        `TA.AO(ohlc)`
        
        ### expects ["volume"] column as input
        `TA.OBV(ohlc)`
        
        ### will return Series with Bollinger Bands columns [BB_UPPER, BB_LOWER]
        `TA.BBANDS(ohlc)`
        
        ### will return Series with calculated BBANDS values but will use KAMA instead of MA for calculation, other types of Moving Averages are allowed as well.
        `TA.BBANDS(ohlc, TA.KAMA(ohlc, 20))`
        
        
        For more examples see examples directory.
        
        ------------------------------------------------------------------------
        
        I welcome pull requests with new indicators or fixes for existing ones.
        Please submit only indicators that belong in public domain and are
        royalty free.
        
        ## Contributing
        
        1. Fork it (https://github.com/peerchemist/finta/fork)
        2. Study how it's implemented.
        3. Create your feature branch (`git checkout -b my-new-feature`).
        4. Run [black](https://github.com/ambv/black) code formatter on the finta.py to ensure uniform code style.
        5. Commit your changes (`git commit -am 'Add some feature'`).
        6. Push to the branch (`git push origin my-new-feature`).
        7. Create a new Pull Request.
        
        ------------------------------------------------------------------------
        
        ## Donate
        
        Buy me a beer 🍺:
        
        Bitcoin: 3NibjuvQPzcfuLaefhUEEFBcmHpXgKgs4m
        
        Peercoin: P9dAfWoxT7kksKAStubDQR6RhdXk5z12rV
        
Keywords: technical analysis,ta,pandas,finance,numpy,analysis
Platform: UNKNOWN
Description-Content-Type: text/markdown
