Fast TA Documentation¶

Fast TA is an optimized, high-level technical analysis library used to compute technical indicators on financial datasets. It is written entirely in C, and uses AVX vectorization as well. Fast TA is built with the NumPy C API.

Additional Support¶

Ask or search for questions in StackOverflow using the fast-ta tag.
Report bugs in our issue tracker.

Introduction¶

Fast TA Installation Guide¶

Installing Fast TA¶

Fast TA runs on Python 3.x under CPython. NOTE: Fast TA requires NumPy.

To install Fast TA, run:

pip install fast-ta

PYPI UPSTREAM NOT SET UP YET

Report any installation issues in our issue tracker.

Fast TA Installation Guide: Get Fast TA installed on your computer.

Indicators¶

Momentum Indicators¶

Awesome Oscillator¶

fast_ta.momentum.AO(high : np.array, low : np.array, s : int = 5, l : int = 34) → np.array¶

Compute the Awesome Oscillator Indicator.

Parameters:	high (np.array) – High Time Series low (np.array) – Low Time Series s (int) – Short Period l (int) – Long Period
Returns:	Awesome Oscillator Indicator
Return type:	np.array

KAMA¶

fast_ta.momentum.KAMA(close : np.array, n : int = 10, f : int = 2, s : int = 30) → np.array¶

Compute the KAMA Indicator.

Parameters:	close (np.array) – Close Time Series n (int) – Period f (int) – Fast EMA Periods s (int) – Slow EMA Periods
Returns:	KAMA Indicator
Return type:	np.array

ROC¶

fast_ta.momentum.ROC(close : np.array, n : int = 12) → np.array¶

Compute the ROC Indicator.

Parameters:	close (np.array) – Close Time Series n (int) – Period
Returns:	ROC Indicator
Return type:	np.array

RSI¶

fast_ta.momentum.RSI(close : np.array, n : int = 14, threads : int = 1) → np.array¶

Compute the Relative Strength Indicator.

Parameters:	close (np.array) – Close Time Series n (int) – Period threads (int) – Number of Threads to Use During Computation (Experimental)
Returns:	Relative Strength Indicator
Return type:	np.array

Stochastic Oscillator¶

fast_ta.momentum.StochasticOscillator(high : np.array, low : np.array, close : np.array, n : int = 14, d_n : int = 3) → np.array¶

Compute the Stochastic Oscillator Indicator.

Parameters:	high (np.array) – High Time Series low (np.array) – Low Time Series close (np.array) – Close Time Series n (int) – Period d_n (int) – SMA Period
Returns:	Stochastic Oscillator Indicator And Signal Line
Return type:	(np.array, np.array)

TSI¶

fast_ta.momentum.TSI(close : np.array, r : int = 25, s : int = 13) → np.array¶

Compute the True Strength Indicator.

Parameters:	close (np.array) – Close Time Series r (int) – First EMA Period s (int) – Second EMA Period
Returns:	True Strength Indicator
Return type:	np.array

Ultimate Oscillator¶

fast_ta.momentum.UltimateOscillator(high : np.array, low : np.array, close : np.array, s : int = 7, m : int = 14, l : int = 28, ws : float = 4, wm : float = 2, wl : float = 1) → np.array¶

Compute the Ultimate Oscillator Indicator.

Parameters:	high (np.array) – High Time Series low (np.array) – Low Time Series close (np.array) – Close Time Series s (int) – Short Period m (int) – Medium Period l (int) – Long Period ws (float) – Short Period Weight wm (float) – Medium Period Weight wl (float) – Long Period Weight
Returns:	Ultimate Oscillator Indicator
Return type:	np.array

Williams %R¶

fast_ta.momentum.WilliamsR(high : np.array, low : np.array, close : np.array, n : int = 14) → np.array¶

Compute the Williams %R Indicator.

Parameters:	high (np.array) – High Time Series low (np.array) – Low Time Series close (np.array) – Close Time Series n (int) – Period
Returns:	Williams %R Indicator
Return type:	np.array

Momentum Indicators: View the documentation for the momentum indicators.

Contributing¶

Fast TA Testing And Benchmarking¶

Testing¶

The tests.py script located in the tests folder is the script we use to test the Fast TA library.

To view help, simply run:

python3.x tests/tests.py

NOTE: The TA library is required for testing.

Benchmarking¶

Withing the benchmarks folder, there are scripts written to benchmark the relative performance between the Fast TA and TA libraries.

To execute all benchmarks, simply run:

cd benchmarks && ./run_all.sh

NOTE: The TA library is required for benchmarking.

Fast TA Testing And Benchmarking: Learn how to test and benchmark the Fast TA library.

License¶

The MIT License (MIT)¶

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

The MIT License (MIT): The Fast TA Library is licensed under the MIT License.

Credits¶

Developed with passion by the Fast TA Team.