Metadata-Version: 2.1
Name: finta
Version: 0.3.1
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 indicators
        might not be correct.**
        
        > Supported indicators:
        
        ```
        ['SMA', 'SMM', 'EMA', 'DEMA', 'TEMA', 'TRIMA', 'TRIX', 'AMA', 'LWMA', 'VAMA', 'VIDYA', 'ER', 'KAMA', 'ZLEMA', 'WMA', 'HMA', 'VWAP', 'SMMA', 'ALMA', 'MAMA', 'FRAMA', 'MACD', 'PPO', 'VW_MACD', 'MOM', 'ROC', 'RSI', 'IFT_RSI', 'SWI', 'TR', 'ATR', 'SAR', 'BBANDS', 'BBWIDTH', 'PERCENT_B', 'KC', 'DO', 'DMI', 'ADX', 'PIVOTS', 'STOCH', 'STOCHD', 'STOCHRSI', 'WILLIAMS', 'UO', 'AO', 'MI', 'VORTEX', 'KST', 'TSI', 'TP', 'ADL', 'CHAIKIN', 'MFI', 'OBV', 'WOBV', 'VZO', 'EFI', 'CFI', 'EBBP', 'EMV', 'CCI', 'COPP', 'BASP', 'BASPN', 'CMO', 'CHANDELIER', 'QSTICK', 'TMF', 'WTO', 'FISH', 'ICHIMOKU', 'APZ', 'VR', 'SQZMI', 'VPT', 'FVE']
        ```
        
        > 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` data 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 prepare the DataFrame into `ohlc` format you can do something as following:
        
        `df.columns = ["date", 'close', 'volume']`  # standardize column names of your source
        
        `df.set_index('date', inplace=True)`  # set index on the date column, which is requirement to sort it by time periods
        
        `ohlc = df["close"].resample("24h").ohlc()`  # select only price column, resample by time period and return daily ohlc (you can choose different time period)
        
        `ohlc()` method applied on the Series above will automatically format the dataframe in format expected by the library so resulting `ohlc` Series is ready to use.
        
        ____________________________________________________________________________
        
        > Examples:
        
        `TA.SMA(ohlc, 42)` ## will return Pandas Series object with Simple
        moving average for 42 periods
        
        `TA.AO(ohlc)` ## will return Pandas Series object with "Awesome oscillator" values
        
        `TA.OBV(ohlc)` ## expects ["volume"] column as input
        
        `TA.BBANDS(ohlc)` ## will return Series with Bollinger Bands columns [BB_UPPER, BB_LOWER, BB_MIDDLE]
        
        `TA.BBANDS(ohlc, TA.KAMA(ohlc, 20))` ## 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.
        
        ------------------------------------------------------------------------
        
        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. Commit your changes (`git commit -am 'Add some feature'`)
        5. Push to the branch (`git push origin my-new-feature`)
        6. Create a new Pull Request
        
        ------------------------------------------------------------------------
        
        ## Donate
        
        Support the development by donating in cryptocurrency:
        
        XBT: 3PTyUNfn4uoSZGQ48tGMnqorca1DW9Xs4M
        
        XPC: PFdR14r9JM2EQSDh9nRZQ6EW5yzHjNJz3E
        
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