Skip to content
Common financial technical indicators implemented in Pandas.
Python
Branch: master
Clone or download
Latest commit 21a4242 Aug 5, 2019
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
.github Create FUNDING.yml May 30, 2019
finta fix EFI indicator Aug 5, 2019
tests fix EFI indicator Aug 5, 2019
.gitignore add mypy_cache to gitignore Mar 7, 2018
.travis.yml fixing travis Feb 15, 2019
LICENSE change licence to LGPL-3 Jun 12, 2018
README.md Implemented SSMA (Simple Smoothed Moving Average) May 5, 2019
requirements-dev.txt optional requirment for tests is pytest Jun 18, 2018
requirements.txt numpy to dependencies May 5, 2019
setup.py version bump. Jun 6, 2019

README.md

FinTA (Financial Technical Analysis)

License: LGPL v3 PyPI Code style: black Build Status

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 75 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'

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 prepare the DataFrame into ohlc format you can do something as following:

standardize column names of your source

df.columns = ["date", 'close', 'volume']

set index on the date column, which is requirement to sort it by time periods

df.set_index('date', inplace=True)

select only price column, resample by time period and return daily ohlc (you can choose different time period)

ohlc = df["close"].resample("24h").ohlc()

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:

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))


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 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: 39PdX8jhXvUpzpkDibwMAVHVs6ZtoHCjnm

Peercoin: PRn448Km1ZJ2BhdPQfiSS3q4Af2vkjwwvH

You can’t perform that action at this time.