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https://github.com/peerchemist/finta

Common financial technical indicators implemented in Pandas.
https://github.com/peerchemist/finta

algorithmic-trading algotrading fintech pandas python technical-analysis trading trading-algorithms trading-strategy

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Common financial technical indicators implemented in Pandas.

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# FinTA (Financial Technical Analysis)



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Common financial technical indicators implemented in Pandas.



![example](examples/plot.png)



*This is work in progress, bugs are expected and results of some indicators

may not be accurate.*



## Supported indicators:



Finta supports over 80 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'

* Fractal Adaptive Moving Average 'FRAMA'

* 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'

* Momentum Breakout Bands 'MOBO'

* 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'

* Squeeze Momentum Indicator 'SQZMI'

* Volume Price Trend 'VPT'

* Finite Volume Element 'FVE'

* Volume Flow Indicator 'VFI'

* Moving Standard deviation 'MSD'

* Schaff Trend Cycle 'STC'

* Mark Whistler's WAVE PM 'WAVEPM'

```



## Dependencies:



-   python (3.6+)

-   pandas (1.0.0+)



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 .csv 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, MA=TA.KAMA(ohlc, 20))`



For more examples see examples directory.



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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.



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