finta

Common financial technical indicators implemented in Pandas.

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Quick Overview

Finta is a Python library for calculating technical indicators in financial markets. It provides a comprehensive set of functions to compute various technical analysis indicators for stocks, cryptocurrencies, and other financial instruments. The library is designed to be easy to use and integrate with popular data analysis tools like pandas.

Pros

Extensive collection of technical indicators (over 80)
Compatible with pandas DataFrames for easy integration
Well-documented functions with clear parameter descriptions
Actively maintained and regularly updated

Cons

Limited to technical analysis indicators only
Requires basic knowledge of financial markets and technical analysis
May have performance limitations for large datasets
Some less common indicators might be missing

Code Examples

Calculating Simple Moving Average (SMA):

from finta import TA
import pandas as pd

# Assuming 'df' is a pandas DataFrame with 'close' price column
sma = TA.SMA(df, period=14)

Computing Relative Strength Index (RSI):

rsi = TA.RSI(df)

Calculating Bollinger Bands:

bb = TA.BBANDS(df)

Multiple indicators in one call:

indicators = TA.TA(df, ["SMA", "EMA", "RSI"], periods=[14, 20, 14])

Getting Started

To get started with Finta, follow these steps:

Install the library using pip:
```
pip install finta
```

Import the library in your Python script:

from finta import TA
import pandas as pd

Prepare your data in a pandas DataFrame with at least 'open', 'high', 'low', and 'close' columns.

Calculate indicators using the TA class methods:

df = pd.read_csv('your_data.csv')
sma = TA.SMA(df, period=14)
rsi = TA.RSI(df)
macd = TA.MACD(df)

Use the calculated indicators for further analysis or visualization.

Competitor Comparisons

ta-lib-python

9,473

Python wrapper for TA-Lib (http://ta-lib.org/).

Pros of ta-lib-python

Extensive library with over 150 technical indicators and chart patterns
Highly optimized C implementation for fast calculations
Well-established and widely used in the financial industry

Cons of ta-lib-python

Requires compilation of C extensions, which can be challenging on some systems
More complex installation process compared to pure Python libraries
Steeper learning curve due to its comprehensive nature

Code Comparison

ta-lib-python:

import talib
import numpy as np

close_prices = np.random.random(100)
sma = talib.SMA(close_prices, timeperiod=14)
rsi = talib.RSI(close_prices, timeperiod=14)

finta:

from finta import TA
import pandas as pd

ohlc = pd.DataFrame(...)  # OHLC data
sma = TA.SMA(ohlc, period=14)
rsi = TA.RSI(ohlc, period=14)

Key Differences

ta-lib-python works with NumPy arrays, while finta uses Pandas DataFrames
finta is a pure Python implementation, making it easier to install and modify
ta-lib-python offers more indicators and is generally faster for large datasets
finta has a simpler API and is more beginner-friendly

Both libraries serve similar purposes, but ta-lib-python is more comprehensive and performant, while finta is easier to use and integrate into existing Python projects.

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More comprehensive set of technical indicators and overlays
Better documentation and examples
Actively maintained with regular updates

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Slower performance for large datasets
More complex API, steeper learning curve
Heavier dependencies

Code Comparison

ta:

import pandas as pd
from ta import add_all_ta_features

df = pd.read_csv('your_data.csv')
df = add_all_ta_features(df, open="Open", high="High", low="Low", close="Close", volume="Volume")

finta:

from finta import TA
import pandas as pd

df = pd.read_csv('your_data.csv')
rsi = TA.RSI(df)
macd = TA.MACD(df)

Summary

ta offers a more extensive set of indicators and better documentation, making it suitable for complex analysis. However, it may be slower and more complex to use. finta is simpler and faster but has fewer features and less active maintenance. Choose based on your specific needs and performance requirements.

pandas-ta

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More comprehensive set of technical indicators (130+)
Better integration with pandas DataFrame structure
More active development and community support

Cons of pandas-ta

Steeper learning curve due to more complex API
Potentially slower performance for simpler calculations

Code Comparison

finta:

from finta import TA

TA.SMA(df, 14)
TA.RSI(df, 14)

pandas-ta:

import pandas_ta as ta

df.ta.sma(length=14)
df.ta.rsi(length=14)

Key Differences

pandas-ta offers a more pandas-like syntax, allowing method chaining
finta uses a class-based approach, while pandas-ta extends pandas functionality
pandas-ta provides more customization options for indicators

Use Cases

finta: Simpler projects with basic technical analysis needs
pandas-ta: Advanced trading systems requiring a wide range of indicators and customization

Community and Support

pandas-ta has more frequent updates and a larger user base
finta is more lightweight and easier to get started with for beginners

zipline

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Pros of Zipline

Comprehensive backtesting engine for trading strategies
Integrates with Quantopian's research platform
Supports multiple asset classes and data sources

Cons of Zipline

Steeper learning curve due to complexity
Requires more setup and configuration
Less frequently updated (last commit over 2 years ago)

Code Comparison

Zipline example:

from zipline.api import order, record, symbol

def initialize(context):
    context.asset = symbol('AAPL')

def handle_data(context, data):
    order(context.asset, 10)
    record(AAPL=data.current(context.asset, 'price'))

Finta example:

from finta import TA
import pandas as pd

df = pd.read_csv('AAPL.csv')
sma = TA.SMA(df)
rsi = TA.RSI(df)

Summary

Zipline is a more comprehensive backtesting engine suitable for complex trading strategies, while Finta focuses on providing technical analysis indicators. Zipline offers a complete ecosystem for strategy development but requires more setup. Finta is simpler to use and integrate into existing projects, making it ideal for quick technical analysis tasks.

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README

FinTA (Financial Technical Analysis)

Common financial technical indicators implemented in Pandas.

example

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.

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

Fork it (https://github.com/peerchemist/finta/fork)
Study how it's implemented.
Create your feature branch (git checkout -b my-new-feature).
Run black code formatter on the finta.py to ensure uniform code style.
Commit your changes (git commit -am 'Add some feature').
Push to the branch (git push origin my-new-feature).
Create a new Pull Request.

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