Revolutionize your financial analysis with the dynamic capabilities of the Ta-Lib Python library! The world of algorithmic trading is dynamic, and in this blog, we unfold the layers of Ta-Lib, one of the most revered Python libraries in the algorithmic trading community.
Technical indicators leverage historical price and volume data to predict market directions, aiding in the formulation of entry and exit signals on charts. Imagine being able to compute these indicators in Python with just a few lines of code, enabling seamless backtesting on various financial securities.
Originating from the passion project of Mario Fortier, Ta-Lib swiftly gained fame as a leading library for technical analysis. Boasting over 150 indicators, including ADX, MACD, RSI, Bollinger Bands, and candlestick pattern recognition, Ta-Lib simplifies complex analyses.
While Ta-Lib proves invaluable, its installation can be challenging. Fear not; this article demystifies the process, allowing you to focus on strategy creation and backtesting. It's your gateway to mastering the installation of Ta-Lib on every major platform — Windows, MacOS, and Linux.
We don't just show you how to install Ta-Lib; we guide you through each step with clarity and precision, ensuring you understand the intricacies. We take you beyond installation, exploring practical applications of Ta-Lib through hands-on examples of technical indicators. Whether you're a seasoned algorithmic trader or just starting, our guide ensures a seamless installation experience, allowing you to focus on what truly matters—your trading strategies.
Prepare to witness the fusion of knowledge and action—the ultimate guide to Ta-Lib installation in Python awaits.
- Install Ta-Lib on Windows PC
- Install Ta-Lib on MacOS
- Install Ta-Lib on Linux
- Technical Indicators using Python Ta-Lib
What is Ta-Lib?
Before we begin, why was Ta-Lib python library created in the first place?
Let us think about the reason functions were made. It was realised that instead of writing the same code, we could create a function and reduce the code length by a huge margin.
It is the same logic we apply to Ta-Lib. Instead of writing a few lines of code to first find the upper and lower bands and then the Bollinger band, we use Ta-Lib and input just one line of code to get the result.
That is all! Keeping that in mind, let us begin with the tutorial on Ta-Lib python library.
The EASIEST way to install Ta-Lib
The following is the easiest and the best way to install Ta-Lib across platforms using Anaconda Prompt.
Anaconda is an open-source Python distribution platform that helps individuals, as well as commercial enterprises, implement their Python codes easily. It also makes life easier when it comes to installing various Python libraries.
To install Ta-Lib easily, you can follow these steps:
- Install Anaconda
- Open the Anaconda prompt
- Write the code: conda install -c conda-forge ta-lib
- Press the “Enter” key
et voilà! Ta-Lib is now installed!
After a few moments, the Ta-lib package will be installed.
That’s all there is to it. Most of the time this method would help you install the package, but if you still face any problems, read on to find the installation instructions as per the operating system.
Note: The packages are first downloaded from the respective web location. You will then be prompted to confirm the installation process by pressing “y” on the keyboard. This acts as a confirmation for the installer to install the necessary files.
Install Ta-Lib on Windows PC
Step 0: We suggest the Anaconda installer for installing Python as it is easier to work with when it comes to programming in Python.
Step 1: Install the files
Once you have installed Anaconda, you will use the Anaconda prompt window to install the relevant files.
Before we download the Python Ta-Lib files, we have to verify the Python version as well as the Windows System architecture (32-bit or 64-bit) on our computer.
You can find the version installed in your system by simply writing the following code in the Anaconda prompt window: python --version
You will get the output as shown below:
For the windows architecture, you can simply right-click on your "computer" (usually named as "This-PC" or "My Computer") icon and click properties and your system details will be displayed.
Step 2: Installing the whl file
We cannot directly use the "pip install" command when it comes to installing Ta-Lib. Thus, we will first install the "whl" file which can be found here.
The website is a useful resource for a lot of Python extensions, but we are concerned about Ta-Lib installation for now.
You can scroll down to the section where we will find the relevant links, as shown below:
One should note that you should download the file keeping your Python version and Windows architecture (32 bit or 64 bit) in mind. E.g. Since we have the python version 3.7 installed and 64 bit Windows 7 system, we will download the file, “TA_Lib‑0.4.17‑cp37‑cp37m‑win_amd64.whl”.
As you might have guessed “cp37” implies Python version 3.7 and “win_amd64” implies Windows 64 bit operating system.
By default, the "whl" file gets downloaded in the "Downloads" folder. Since we have used Anaconda installer for Python, we will place the file in the same folder location which is shown on the Anaconda prompt.
For eg: C:\Users\rekhit.p
And now comes the easy part.
In the Anaconda prompt, use the pip install command with the file name of the downloaded file.
Since we have downloaded the file, “TA_Lib‑0.4.17‑cp37‑cp37m‑win_amd64.whl”, the command would be,
pip install TA_Lib-0.4.17-cp37-cp37m-win_amd64.whl
If there is no error, then it implies that we have successfully installed Ta-Lib. You should get a screen with the following messages.
Great! We have just finished installing the Ta-Lib Python library.
But how do we check if it is correctly installed?
- On the Anaconda command prompt, you will first type “python” to interpret the program.
- We will then type the following command, “import talib” and then press Enter.
- If there are no errors, then it means that the python Ta-Lib library has been successfully installed on your computer.
Alternatively, you can also open Jupyter notebook (or Spyder) and try to run the same code to check if the Python Ta-Lib is installed correctly.
Install Ta-Lib on MacOS
In comparison to the windows installation, Python Ta-Lib installation is relatively straightforward and easy on the MacOS. But before we talk about the installation of Ta-Lib, we have to make sure the python environment is installed as well.
In our opinion, it is generally a good idea to install Anaconda as it helps build an environment for your python code to be executed seamlessly.
Note: During the Anaconda installation, you will be asked to install either Python 2.7 or 3.7. Since the support for Python 2.7 ended on January 1, 2020, it would be advisable to select the 3.7 version. For a seamless installation, choose the graphical installer.
You will then be greeted with the following dialog box.
Select “Continue” tab and choose the appropriate install destination and you will finish installation in a short time.
All right, now we move on to the main event, i.e. Ta-lib python library installation. Ok, there is one small step you need to do before the main install.
Check if you can use the ‘brew’ command. For this tutorial on how to install Ta-Lib, let us assume that the ‘brew’ command throws an error. To rectify this, we install the necessary package by using the following line of code in the MacOS shell:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
If it is successful, you will see the following messages:
You might be asked to enter your password, which is part of the process.
Of course, you will get a prompt which asks you to press “Return” to continue the installation or any other key to cancel the installation.
If you are curious about the ‘brew’ command, you can always visit this website to know more.
Great! Now that the brew command is installed, use the following code to install the Ta-Lib operating system dependencies:
brew install ta-lib
You will see the following screen:
Now, we use the familiar command i.e. “pip” to install Ta-Lib on our MacOS. The command is as follows:
pip install ta-lib
You see the following files being installed on your system.
Great! We have been successful in our quest to install Ta-Lib on MacOS.
But how do we check if it is correctly installed on the system?
Simple, we use the following commands.
First, we call the python language by simply writing “python” in the terminal. We then use the following command to import the Ta-Lib library.
If there is no error, then we should be able to see the following screen:
Install Ta-Lib on Linux
The Ta-lib installation in Linux can be a bit tricky. The libraries necessary for the Ta-lib installation should be installed first before you install the Python wrapper.
But don’t worry! We have provided comprehensive steps on how to install the Ta-lib library in your Linux system.
Prerequisite: Install Anaconda in your Linux system
You can follow the documentation or execute the commands as shared below.
- Install the prerequisites
- Install Anaconda on your system
Step 1: Install Ta-Lib dependencies
Install the Ta-lib dependencies and Python wrapper by executing the following command.
This will take a couple of minutes and install all the libraries necessary for using Ta-lib.
Step 2: Test the installation
Test the Ta-lib installation by executing a sample script. We take some data and try to find the moving average using the Ta-lib libraries.
- Install yfinance library if you do not have it already
- Run Python in the terminal
- Paste the following code lines to check if Ta-lib is working properly or not.
- You should see a Simple MA and EMA column similar to the screenshot below.
The Ta-lib library is now installed in the 'quantra' environment! To know how to activate and switch environments, please refer to the Conda resource.
Awesome! We have now been successful in installing Python Ta-Lib on Windows, MacOS and Linux. But can we really stop here?
In the next section, we try a simple code using the Ta-Lib library.
Technical Indicators using Python Ta-Lib
We will first import the Python Ta-Lib library since we are using it to work out different indicators. Along with that, we use the python matplotlib to draw their graphs for analysis. Since we are going to be working on the stock prices, we will import the data from Yahoo Finance.
Thus, the code will be as follows:
import talib as ta import matplotlib.pyplot as plt plt.style.use('bmh') import yfinance as yf aapl = yf.download('AAPL', '2019-1-1','2019-12-27')
In the above example, we have imported the stock price data of Apple from 1 January 2019 to 27 December 2019.
Let us start off with something simple. We will find the Simple Moving Average and the Exponential Moving Average of Apple stock data. We will use the following code:
aapl['Simple MA'] = ta.SMA(aapl['Close'],14) aapl['EMA'] = ta.EMA(aapl['Close'], timeperiod = 14) # Plot aapl [['Close','Simple MA','EMA']].plot(figsize=(15,15)) plt.show()
The output is as follows:
That was simple, wasn’t it?
Recall that at the start of this tutorial on Installing Python Ta-Lib, we talked about Bollinger Bands. Well, let’s try that one now.
# Bollinger Bands aapl['upper_band'], aapl['middle_band'], aapl['lower_band'] = ta.BBANDS(aapl['Close'], timeperiod =20) # Plot aapl[['Close','upper_band','middle_band','lower_band']].plot(figsize=(15,15)) plt.show()
Wasn’t it easy? We are sure you are getting it but let us break down the line and see how Python Ta-Lib worked out the Bollinger Bands.
To create the Bollinger bands, we simply passed the Closing Price ie. the “Close” column and defined a time period of 20 for the moving average. This is done using the “ta.BBANDS()” function.
As the Bollinger bands consist of three bands, we store the data in the following three columns, “upper_band”, “middle_band” and “lower_band”.
aapl['upper_band'], aapl['middle_band'], aapl['lower_band'] = ta.BBANDS(aapl['Close'], timeperiod =20)
Now, can you figure out from the code below, what are the parameters passed?
aapl['RSI'] = ta.RSI(aapl['Close'],14) aapl['RSI'].plot(figsize=(15,15)) plt.show()
As a bonus, we plotted the Stochastic oscillators as well, the code is given below:
aapl['slowk'], aapl['slowd'] = ta.STOCH(aapl['High'], aapl['Low'], aapl['Close'], fastk_period=14, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) aapl[['slowk','slowd']].plot(figsize=(15,15)) plt.show()
aapl['fastk'], aapl['fastd'] = ta.STOCHF(aapl['High'], aapl['Low'], aapl['Close'], fastk_period=14, fastd_period=3, fastd_matype=0) aapl[['fastk','fastd']].plot(figsize=(15,15)) plt.show()
Phew! You can check out the other indicators here as well.
You will find it very useful and knowledgeable to read through this curated compilation of some of our top blogs on:
- Python for Trading
- Machine Learning
- Sentiment Trading
- Algorithmic Trading
- Options Trading
- Technical Analysis
From the inception of Mario Fortier's brainchild to the installation processes, we've unraveled the complexities surrounding Ta-Lib, empowering you to seamlessly integrate its prowess into your financial analysis toolkit.
We have gone through the Tutorial on how to install Ta-Lib python library on Windows, MacOS as well as Linux. We have also found how to create a few technical indicators using Ta-Lib and plot them. Empower your trading strategies by leveraging Ta-Lib's capabilities in technical analysis.
In the dynamic landscape of algorithmic trading, your commitment to mastering tools like Ta-Lib positions you as a navigator of change, ready to capitalize on opportunities that others may overlook.
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