Jamison  Fisher

Jamison Fisher

1642909500

Common Financial Technical indicators Implemented In Pandas

FinTA (Financial Technical Analysis)

Common financial technical indicators implemented in Pandas.

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.


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.

example

This is work in progress, bugs are expected and results of some indicators may not be accurate.

Download Details:
Author: peerchemist
Source Code: https://github.com/peerchemist/finta
License: LGPL-3.0 License

#pandas  #python #analysis #FinancialTechnical

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Common Financial Technical indicators Implemented In Pandas
Kasey  Turcotte

Kasey Turcotte

1623991620

Common Financial Technical indicators Implemented In Pandas with Python

FinTA (Financial Technical Analysis)

Common financial technical indicators implemented in Pandas.

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'

#algorithms #data analysis #pandas #python #common financial technical indicators implemented #financial technica

Jamison  Fisher

Jamison Fisher

1642909500

Common Financial Technical indicators Implemented In Pandas

FinTA (Financial Technical Analysis)

Common financial technical indicators implemented in Pandas.

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.


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.

example

This is work in progress, bugs are expected and results of some indicators may not be accurate.

Download Details:
Author: peerchemist
Source Code: https://github.com/peerchemist/finta
License: LGPL-3.0 License

#pandas  #python #analysis #FinancialTechnical

Udit Vashisht

1586702221

Python Pandas Objects - Pandas Series and Pandas Dataframe

In this post, we will learn about pandas’ data structures/objects. Pandas provide two type of data structures:-

Pandas Series

Pandas Series is a one dimensional indexed data, which can hold datatypes like integer, string, boolean, float, python object etc. A Pandas Series can hold only one data type at a time. The axis label of the data is called the index of the series. The labels need not to be unique but must be a hashable type. The index of the series can be integer, string and even time-series data. In general, Pandas Series is nothing but a column of an excel sheet with row index being the index of the series.

Pandas Dataframe

Pandas dataframe is a primary data structure of pandas. Pandas dataframe is a two-dimensional size mutable array with both flexible row indices and flexible column names. In general, it is just like an excel sheet or SQL table. It can also be seen as a python’s dict-like container for series objects.

#python #python-pandas #pandas-dataframe #pandas-series #pandas-tutorial

Oleta  Becker

Oleta Becker

1602550800

Pandas in Python

Pandas is used for data manipulation, analysis and cleaning.

What are Data Frames and Series?

Dataframe is a two dimensional, size mutable, potentially heterogeneous tabular data.

It contains rows and columns, arithmetic operations can be applied on both rows and columns.

Series is a one dimensional label array capable of holding data of any type. It can be integer, float, string, python objects etc. Panda series is nothing but a column in an excel sheet.

How to create dataframe and series?

s = pd.Series([1,2,3,4,56,np.nan,7,8,90])

print(s)

Image for post

How to create a dataframe by passing a numpy array?

  1. d= pd.date_range(‘20200809’,periods=15)
  2. print(d)
  3. df = pd.DataFrame(np.random.randn(15,4), index= d, columns = [‘A’,’B’,’C’,’D’])
  4. print(df)

#pandas-series #pandas #pandas-in-python #pandas-dataframe #python

WORKING WITH GROUPBY IN PANDAS

In my last post, I mentioned the groupby technique  in Pandas library. After creating a groupby object, it is limited to make calculations on grouped data using groupby’s own functions. For example, in the last lesson, we were able to use a few functions such as mean or sum on the object we created with groupby. But with the aggregate () method, we can use both the functions we have written and the methods used with groupby. I will show how to work with groupby in this post.

#pandas-groupby #python-pandas #pandas #data-preprocessing #pandas-tutorial