Algorithmic Trading | It's Difficult but Doable

6 min read

By Chainika Thakar

Algorithmic trading is a virtue in today’s world ever since artificial intelligence has become a regular and an important part of our daily lives. With the help of algorithms, you can have access to more reliable, accurate and quick trading practices.

Also, learning algorithmic trading is not at all as difficult as you think. The key points to successful algorithmic trading are - appropriate skills, the right trading strategy, the courses which help to build the practice from scratch as well as from the point you require.

But, the point of relevance here is to understand that dedication and perseverance to learn the relevant skills are equally important in order to become an algorithmic trader. Without these traits of commitment, algorithmic trading may seem difficult.

This article covers:


What makes algorithmic trading seem difficult?

In simple words, algorithmic trading implies using a defined set of instructions in the form of algorithms to generate trading signals and placing orders.

Each algorithm can be assumed to have access to current and historical prices of instruments that can be bought and sold after performing computations based on the prices. The algorithm may even split the order into small pieces and execute them at different times to get the best possible prices.

Algorithmic trading is not difficult. On the contrary, it makes things easier for the traders. For instance, you want to calculate the returns of a few stocks in 2020 which you had bought in 2009.

Instead of manually putting down the prices in excel and calculating, you can use Python (one of the skills needed for algorithmic trading). Here is an example of a Buy and Hold strategy where you calculate as well as plot the cumulative returns:

The closing price of the stocks are stored in the csv file.

Here, read_csv method of pandas can be used to read csv files.

Output:

Date

Amazon

Apple

Walmart

Micron

Bank of America

Coca-Cola

Boeing

American Express

2009-12-31

6.492372

134.520004

34.291534

41.856789

13.179974

18.804321

10.56

40.802689

2010-01-04

6.593426

133.899994

34.630035

43.441975

13.731325

18.817513

10.85

41.398109

2010-01-05

6.604825

134.690002

34.553879

44.864773

14.177655

18.589882

11.17

40.985886

2010-01-06

6.499768

132.250000

35.112431

46.225727

14.343938

18.583282

11.22

40.894283

2010-01-07

6.487752

130.000000

35.681915

48.097031

14.816527

18.537094

10.84

40.917183

Now, you need to convert daily data to monthly data for calculating portfolio returns.

Output:

Date

Amazon

Apple

Walmart

Micron

Bank of America

Coca-Cola

Boeing

American Express

Date

2009-12-31

6.492372

134.520004

34.291534

41.856789

13.179974

18.804321

10.56

40.802689

2010-03-31

7.240106

135.770004

35.222775

56.530022

15.631181

18.443974

10.37

42.683178

2010-04-30

8.043912

137.100006

39.371647

56.389893

15.613656

17.924196

9.35

41.178520

2010-06-30

7.749377

109.260002

34.043896

49.137108

12.591907

17.094294

8.49

37.116917

2010-08-31

7.489662

124.830002

34.189678

48.159416

10.918247

19.058840

6.46

38.941193

Now, for calculating portfolio returns we will take mean of monthly returns since it is assumed that the investment will take place in each stock equally.

Now, we plot cumulative portfolio returns and for plotting we will use matplotlib.

Output:

Cumulative returns portfolio of ten years
Cumulative returns portfolio of ten years

This was just an example of a strategy. You can create your trading strategy according to the market situation and trade live in the market.


Myths around algorithmic trading

We just discussed in the beginning that algorithmic trading is a simple concept and can be learnt with ease if you have perseverance, dedication and the knowledge.

Yet, there are some myths surrounding algorithmic trading that also contribute in making the process seem difficult:

  • It is impossible to learn
  • I can skip learning an important concept and still be able to do algorithmic trading
  • Belonging to another profession for years may be a hindrance to the learning ability
  • Algorithmic trading is only for individuals from a particular educational background

It is impossible to learn

Algorithmic trading is not impossible to learn even if your educational background or professional background is an unrelated one.

With so many self-taught algorithmic traders out there serving as the live examples of algo trading being not impossible to learn, one must know that it requires perseverance, dedication and self-confidence to become an algorithmic trader.

However, there is no doubt that the information and online courses for algorithmic trading are avaiable everywhere. But, it is also a fact that the algorithmic trading related information is not available in a structured manner everywhere. Moreover, one needs to be sure about the authenticity.

Hence, you must get enrolled only in the recognised ones and algorithmic trading will not be as difficult as it seems. Right course implies the one with all the required knowledge/skills/practical information in one place.

The most important thing here is to know which skills you need to work on. The beginners need to enrol in such courses which offer them a thorough knowledge right from the scratch.

I can skip learning an important concept and still be able to do algorithmic trading

This is a big misconception because some people skip an important part of algorithmic trading which might look meagre but holds a lot of importance.

For instance, an individual with the knowledge of programming, analysis, mathematics etc. may think that in-depth knowledge of backtesting can be skipped. Backtesting is an extremely important step of algorithmic trading and helps you evaluate your trading strategy.

Belonging to another profession for years may be a hindrance to the learning ability

Contrary to the popular belief, it is not only the PhD holders, C++ programmers etc. who get into algorithmic trading. You definitely need an in-depth knowledge of the fundamentals of algorithmic trading such as programming, experience in trading and of financial markets etc.

But belonging to another profession can never be a hindrance to learning algorithmic trading. You can begin anytime, anywhere and at any age.

Algorithmic trading is only for individuals from a particular educational background

As opposed to this belief of many individuals, an algorithmic trader can be from any educational background and still be successful. Having the right approach is what helps and the right approach is to find out where you need to pick up from.

You can read our blog on Making a career in algorithmic trading to find out about the skills required for algorithmic trading. From there you can find out which skills are already covered by you and which are missed out.

That way, you can work on the missed out skills and bridge the gap to become a successful algorithmic trader. Also, to work on the missed out skills, you can explore courses on Quantra which are available in a structured manner to help you navigate easily.


How can you make practising algorithmic trading easy?

Below, I have mentioned such approaches which can make your practise easier, and they are:

  • Question answer communities
  • Machine learning approach
  • Python
  • Supportive faculty (even after completion of the course)

Question answer communities

The question-answer communities such as QuantInsti, Quora, Stack exchange etc. are extremely useful as they help with discussions amongst individuals belonging to the same field.

Every community has its own approach. Some engage in discussions, some have only a straightforward approach which is answering the questions asked on the community chat. You can opt for the one you find the most suitable according to your needs.

Machine learning approach

Machine learning helps with various algorithmic trading related work such as:

  • For analyzing historical market data
  • For determining the optimal predictors to a strategy
  • And for determining the optimal strategy parameters

With the machine learning approach for trading, you can implement different machine learning algorithms on financial markets data and successfully execute trading strategies.

Python

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:

Output:

5

With Python programming, you can learn algorithmic trading without digging into the programming languages which are a bit more complicated.

Supportive faculty (even after completion of the course)

A supportive faculty is the biggest advantage of any course. With the help of faculty in touch, you can find out answers to various questions while practising algorithmic trading.

This way you can be sure of having an expert to rely on once you sail into the world of live trading!


Relatable success stories

You can find several success stories as the examples of how so many individuals from different educational backgrounds, professional backgrounds and experiences have found success with algorithmic trading.

There are a whole bunch of individuals who have found their interest in algorithmic trading a bit later in life, a bit earlier and even in the middle of their successful careers. One thing is common - Dedication and will to learn algorithmic trading!

With our course on algorithmic trading, begin your journey and learn all the important concepts.


Conclusion

This article aimed to help you understand how algorithmic trading is not as difficult as assumed by many. There are some approaches we discussed in the article that can be learned with ease for successful algorithmic trading practice.

With our course, you too can get started with algorithmic trading and begin your journey into algorithmic trading by learning important concepts.


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