Basics of Python Programming – Part I


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Python is undoubtedly one of the most popular programming languages in today’s world. With its quickly updated libraries and the ease to code, Python has managed to make its place in the rapidly growing technology era.

You can refer to the Python handbook that has everything from basic learning to gaining knowledge about Pandas along with a lot of direct examples. With this book, you can learn the most relevant information before starting to practically use the Python language. This book is also meant for those programmers who want to quickly refresh their knowledge on Python for data analysis. One of the best parts is that it is available for FREE.

Let us find out some commonly asked questions on Python programming:

  • What is Python programming?
  • Benefits of Python in trading
  • Applications of Python in trading
  • Running of Python scripts
  • What is syntax?
  • What is indentation?
  • What are variables and operators?
  • Conditional statements and loops
  • What are functions?
  • Modules, packages and libraries

What is Python programming?

Python is a programming language that places weight on coding productivity and code readability. Python makes use of coding which looks like written English. Moreover, the coding is done in words and sentences, rather than characters.

An example of a simple Python code goes as follows:



In trading, Python provides several benefits. Let us find out what are those points that make Python popular.

Benefits of Python in trading

Python is known to be a preferred language for developing trading strategies by programmers/developers since it provides benefits such as:

  • Python has certain APIs and libraries for machine learning as well as data science that make the analysis smoother as compared to other languages.
  • It helps the trader with quick and easy coding for importing data and for data visualization in the form of graphs.
  • Most quant traders prefer Python as it helps them build their own data connectors, execution mechanisms, backtesting, risk and order management, walk forward analysis and optimization testing modules.
  • First updates to Python trading libraries are a regular occurrence in the developer community.

Next, let us find out about applications of Python in trading.

Applications of Python in trading

Python, like any other programming language, can help an algorithmic trader to trade in the stock market with minimum human intervention. Python helps you program the trading system with the Python codes (set of instructions) for executing the trade orders. With the help of different codes, you can program the system to do the data analysis, find out the best entry and exit positions and then execute the trade.

Hence, apart from Python’s extensive use in other fields like medicine, marketing etc. it is used for algorithmic trading across the globe.

Technically speaking, Python has a huge significance in the overall trading process as python codes are used to develop quantitative models for trading strategies.

Python trading has gained traction in the quant finance community as it makes it easy to build intricate statistical models due to the availability of sufficient scientific libraries like Pandas, NumPy, scikit-learn and more.

Take a look at the areas where Python is applicable during algorithmic trading:

Quantitative data analysis

For executing the trade order, the first step consists of acquiring the data for the particular stock and then conducting a quantitative data analysis. Quantitative analysis is the process that helps to find out the performance of the strategy in the market by using statistical tools such as machine learning, neural networks, etc.

Developing trading algorithms

Developing trading algorithms implies converting trading hypotheses into trading algorithms based on specific rules of entry/exit. These algorithms are developed with the help of coding in Python.


It has never been easier to backtest your trading strategies as much as it is with Python. With just a few lines of code, Python backtests your trading strategies on historical data. A platform like Blueshift can help you take your trading strategies live after converting the strategy in event-driven type.

Evaluating trading strategies

Next step is to evaluate the trading strategies after the backtest. Now Python coding aids by helping to find out how beneficial the strategy will be after being deployed. For evaluating the trading strategy we can calculate metrics such as annualised return, annualised volatility, sharpe ratio etc. On the basis of these figures, you can measure the possible return and risks of deploying the trading strategy. We have covered the explanation of each in detail in our blog article on Python for Trading: An Introduction.

Further, Python also helps with integration of the strategy code with broker API for taking the strategy live.

Going forward, let us take a look at how the Python scripts are run.

Running of Python scripts

For running the scripts in Python, a Python environment is needed. An environment consists of an interpreter for Python standard libraries and pre-installed packages. Anaconda is this environment where Jupyter is the framework using which you can code in Python and run your scripts for the desirable output. Also, Spyder is an IDE or Integrated development environment which is used for running python codes as well.

For installing Anaconda and setting up a Python environment, you can refer to the blog on setting up Python.

Let us take a look at the example below:

How would you make Python print “Hello World” for you? Well, it’s never been this easy, just use the print command:


Hello World!


I am new to programming!

Python is cool!

Running the Python scripts is simple and fun! Since you do not need an extremely strong computer science background for Python programming, it is a favoured language for almost all beginners.

Going forward, let us find out about syntax in the next section.

Stay tuned for the next installment, in which Chainika Thakar will go over the syntax, indentation, variables and operators.

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