Python for Trading: Basic

Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.

No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members.

Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community

Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.

There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn most from the course.

We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.

Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.

We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.

Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.

No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.

Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.

To understand how to import the data from Python, you need to know where this data lies first. To tell you in brief, there are packages in Python and each package is a collection of Modules. Each package consists of __init.py__ file.
The main concept is, hence, a module which is a “.py” file from where the data is imported. Now, a module implies a file that consists of Python code. For example, there can be a module known as welcome.py, of which the module name is welcome. This can be imported by using:
import welcome
Modules can be functions, variables and classes and each module can be reached by using the “import” statement. This statement works by executing the code of the module to make use of the code in the input, which will bring an Output.
Conversely, if you require only a few objects from the module, then:
from scipy import mean
mean ([1,,3,)]
The Output is 3.0
Here, scipy is the package and the mean object is taken out.

Python data visualization charts are used for plotting the financial markets data so as to achieve better visualization, and thus, better understanding. In Python data visualization, you can make use of:

1. Simple dimensional graphs such as Histogram, Scattered plot, and Line chart using matplotlib.

2. 3-Dimensional plotting using the ‘pyplot’ module of Matplotlib package. This gives a 3-dimensional figure using 3 datasets.

The Python code for such representation goes as follows:

import matplotlib.pyplot as plt
%matplotlib inline

plt.plot(infy_close)
plt.show ()

3. Candlesticks - They are arguably the most beautiful and widely used representation of the data since they provide all the aspects of stock prices. A typical candlestick gives us information about 4 things - the opening & closing prices of a day and also the day's High and Low. For example, in plotting, you plot only the closing price against time as the intraday volatility given by the high-low parameters (of the stock prices) are lost.

The Python code for Candlesticks is as follows:

# Import pandas
import pandas as pd

# Read data
df = pd.read_csv('candlestick_data.csv', index_col=0)
df.head()

In Python, Condition is similar to the conditions we face in life. We make use of 'if' and 'else' in life in conditions like, for instance, rain where we make the statement- 'if it rains, then I will carry an umbrella, else I will not. Similarly, in Python, there are conditional statements which help us to make decisions in programming based on conditions. Hence, we can check if a particular condition is met by the variable, or not. In this, the program responds separately for the two opposing conditions. These statements are known as ‘if’ conditional statements.
An example of 'if':
if (stock_price_ABC < 300):
  print ('We will buy 500 shares of ABC')

elif (stock_price_ABC == 300):
  print ('We will buy 00 shares of ABC')

elif (stock_price_ABC > 300):
  print ('We will buy 150 shares of ABC')

An example of 'else': stock_price_ABC = 00

if (stock_price_ABC > 50):
  print ('We will sell the stock and book the profit')

else:
  print ('We will keep buying the stock')

A loop statement allows the user to execute a statement or group of statements multiple times. Here, 'for' loop is used to help us execute a statement or a group of statements multiple numbers of times. In Python code, the following coding is done:
Close_Price_ABC = [300,305,87,98,335,300,97,300,95,310] # Our sequence

for i in Close_Price_ABC:

  if i < 300:
    print ('We Buy')

  if i == 300:
    print ('No new positions')

  if i > 300:
    print ('We Sell')

print ('We are now out of the loop')

Above, the entire list of Close_Price_ABC is stored in variable i.

Now, 'if < 300:' will help the user print 'We buy' statement for those variables which are less than 300. Further, whenever variable i will be equal to 300, it will give the Output 'No new positions' and thus, the same statement will be repeated as long as the variable remains equal to 300. Going ahead, whenever the variable i will be more than 300, 'if > 300:' will give the Output 'We Sell'. This also will be repeated for every variable that is more than 300.

NumPy is the storage space (library) for the Python programming language. It supports high-level mathematical functions and thus, aids with multi-dimensional arrays and matrices (concepts of mathematics). Thus, it provides help to do computer programming with mathematical computing.

Pandas is a computer programming essential, which stores a collection of resources useful for the development of various software. This storage space is known as the software library. Pandas is, basically itself, a software that helps in machine learning.

Python is used for algorithmic trading as it is relatively easier to understand and use due to its simple and intuitive syntax. It also has a large number of libraries which help you focus on performing a task faster without worrying about writing each and every line of code. Python is also open-source, making it cost-effective. Another advantage of Python is the large and active community of quant enthusiasts which makes it easy to find solutions.