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        "author": "Peerchemist",
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        "description": "# FinTA (Financial Technical Analysis)\n\n[![License: LGPL v3](https://img.shields.io/badge/License-LGPL%20v3-blue.svg)](https://www.gnu.org/licenses/lgpl-3.0)\n[![PyPI](https://img.shields.io/pypi/v/finta.svg?style=flat-square)](https://pypi.python.org/pypi/finta/)\n[![](https://img.shields.io/badge/python-3.4+-blue.svg)](https://www.python.org/download/releases/3.4.0/)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n[![Build Status](https://travis-ci.org/peerchemist/finta.svg?branch=master)](https://travis-ci.org/peerchemist/finta)\n\nCommon financial technical indicators implemented in Pandas.\n\n*This is work in progress, bugs are expected and results of some indicators\nmay not be accurate.*\n\n## Supported indicators:\n\nFinta supports 75 trading indicators:\n\n```\n* Simple Moving Average 'SMA'\n* Simple Moving Median 'SMM'\n* Smoothed Simple Moving Average 'SSMA'\n* Exponential Moving Average 'EMA'\n* Double Exponential Moving Average 'DEMA'\n* Triple Exponential Moving Average 'TEMA'\n* Triangular Moving Average 'TRIMA'\n* Triple Exponential Moving Average Oscillator 'TRIX'\n* Volume Adjusted Moving Average 'VAMA'\n* Kaufman Efficiency Indicator 'ER'\n* Kaufman's Adaptive Moving Average 'KAMA'\n* Zero Lag Exponential Moving Average 'ZLEMA'\n* Weighted Moving Average 'WMA'\n* Hull Moving Average 'HMA'\n* Elastic Volume Moving Average 'EVWMA'\n* Volume Weighted Average Price 'VWAP'\n* Smoothed Moving Average 'SMMA'\n* Moving Average Convergence Divergence 'MACD'\n* Percentage Price Oscillator 'PPO'\n* Volume-Weighted MACD 'VW_MACD'\n* Elastic-Volume weighted MACD 'EV_MACD'\n* Market Momentum 'MOM'\n* Rate-of-Change 'ROC'\n* Relative Strenght Index 'RSI'\n* Inverse Fisher Transform RSI 'IFT_RSI'\n* True Range 'TR'\n* Average True Range 'ATR'\n* Stop-and-Reverse 'SAR'\n* Bollinger Bands 'BBANDS'\n* Bollinger Bands Width 'BBWIDTH'\n* Percent B 'PERCENT_B'\n* Keltner Channels 'KC'\n* Donchian Channel 'DO'\n* Directional Movement Indicator 'DMI'\n* Average Directional Index 'ADX'\n* Pivot Points 'PIVOT'\n* Fibonacci Pivot Points 'PIVOT_FIB'\n* Stochastic Oscillator %K 'STOCH'\n* Stochastic oscillator %D 'STOCHD'\n* Stochastic RSI 'STOCHRSI'\n* Williams %R 'WILLIAMS'\n* Ultimate Oscillator 'UO'\n* Awesome Oscillator 'AO'\n* Mass Index 'MI'\n* Vortex Indicator 'VORTEX'\n* Know Sure Thing 'KST'\n* True Strength Index 'TSI'\n* Typical Price 'TP'\n* Accumulation-Distribution Line 'ADL'\n* Chaikin Oscillator 'CHAIKIN'\n* Money Flow Index 'MFI'\n* On Balance Volume 'OBV'\n* Weighter OBV 'WOBV'\n* Volume Zone Oscillator 'VZO'\n* Price Zone Oscillator 'PZO'\n* Elder's Force Index 'EFI'\n* Cummulative Force Index 'CFI'\n* Bull power and Bear Power 'EBBP'\n* Ease of Movement 'EMV'\n* Commodity Channel Index 'CCI'\n* Coppock Curve 'COPP'\n* Buy and Sell Pressure 'BASP'\n* Normalized BASP 'BASPN'\n* Chande Momentum Oscillator 'CMO'\n* Chandelier Exit 'CHANDELIER'\n* Qstick 'QSTICK'\n* Twiggs Money Index 'TMF'\n* Wave Trend Oscillator 'WTO'\n* Fisher Transform 'FISH'\n* Ichimoku Cloud 'ICHIMOKU'\n* Adaptive Price Zone 'APZ'\n* Vector Size Indicator 'VR'\n* Squeeze Momentum Indicator 'SQZMI'\n* Volume Price Trend 'VPT'\n* Finite Volume Element 'FVE'\n* Volume Flow Indicator 'VFI'\n* Moving Standard deviation 'MSD'\n```\n\n## Dependencies:\n\n-   python (3.4+)\n-   pandas (0.21.1+)\n\nTA class is very well documented and there should be no trouble\nexploring it and using with your data. Each class method expects proper `ohlc` DataFrame as input.\n\n## Install:\n\n`pip install finta`\n\nor latest development version:\n\n`pip install git+git://github.com/peerchemist/finta.git`\n\n## Import\n\n`from finta import TA`\n\nPrepare data to use with finta:\n\nfinta expects properly formated `ohlc` DataFrame, with column names in `lowercase`:\n[\"open\", \"high\", \"low\", close\"] and [\"volume\"] for indicators that expect `ohlcv` input.\n\nTo prepare the DataFrame into `ohlc` format you can do something as following:\n\n### standardize column names of your source\n`df.columns = [\"date\", 'close', 'volume']`\n\n### set index on the date column, which is requirement to sort it by time periods\n`df.set_index('date', inplace=True)`\n\n### select only price column, resample by time period and return daily ohlc (you can choose different time period)\n`ohlc = df[\"close\"].resample(\"24h\").ohlc()`\n\n\n`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.\n____________________________________________________________________________\n\n## Examples:\n\n### will return Pandas Series object with the Simple moving average for 42 periods\n`TA.SMA(ohlc, 42)`\n\n### will return Pandas Series object with \"Awesome oscillator\" values\n`TA.AO(ohlc)`\n\n### expects [\"volume\"] column as input\n`TA.OBV(ohlc)`\n\n### will return Series with Bollinger Bands columns [BB_UPPER, BB_LOWER]\n`TA.BBANDS(ohlc)`\n\n### 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.\n`TA.BBANDS(ohlc, TA.KAMA(ohlc, 20))`\n\n------------------------------------------------------------------------\n\nI welcome pull requests with new indicators or fixes for existing ones.\nPlease submit only indicators that belong in public domain and are\nroyalty free.\n\n## Contributing\n\n1. Fork it (https://github.com/peerchemist/finta/fork)\n2. Study how it's implemented.\n3. Create your feature branch (`git checkout -b my-new-feature`).\n4. Run [black](https://github.com/ambv/black) code formatter on the finta.py to ensure uniform code style.\n5. Commit your changes (`git commit -am 'Add some feature'`).\n6. Push to the branch (`git push origin my-new-feature`).\n7. Create a new Pull Request.\n\n------------------------------------------------------------------------\n\n## Donate\n\nBuy me a beer \ud83c\udf7a:\n\nBitcoin: 39PdX8jhXvUpzpkDibwMAVHVs6ZtoHCjnm\n\nPeercoin: PRn448Km1ZJ2BhdPQfiSS3q4Af2vkjwwvH",
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