Industry This Blog

This is Why You Face Obstacles In Learning Algorithmic Trading

7 min read

By Chainika Thakar

Algorithmic trading has become quite popular in the FinTech domain. With the trading so advanced, quick and accurate, many wish to learn algorithmic trading. Be it manual traders or some who wish to shift to algorithmic trading from some other profession, everyone aims to learn algorithmic trading. Now, during the learning process, there are some obstacles that are commonly seen and that is exactly what we are discussing here in this article. Along with the obstacles, we will also see some solutions for the same.

There will be difficulty in learning algorithmic trading if:

There is resistance to learn programming


Resistance to learning programming is the main and foremost obstacle to learning algorithmic trading. Algorithmic trading and programming go hand in hand since all the trading strategies are coded in one of the programming languages. If you really want to learn algorithmic trading then do not shy away from learning to program. It is not as difficult after all.

Let us see what solution we can provide for you if you think programming is not your cup of tea!


Although there are several programming languages such as C, C++, etc., Python is considered the most favoured in the present. It is known to be a programming language that is as convenient as typing in the English language. Let us find out some interesting facts about Python:

Source: Python for Trading-An Introduction
  • Python has certain APIs and libraries that make the analysis smoother when compared to other languages.
  • Python helps with quick and easy coding for importing data and for graphical data visualization.
  • 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.

Recommended resources

You can explore all with Python for trading: Basic for learning Python as a beginner or Python for trading! As a quant or finance-technology enthusiast, Python is extremely helpful.

You are not clear with core math concepts


Not having the knowledge of core mathematical concepts is a big disadvantage here. It is with mathematics that the algorithmic traders or quants predict or forecast the performance of financial markets. Along with predicting, core mathematical concepts help with a whole lot of important things during the process of algorithmic trading. For instance, the identification and evaluation of risk are done with the help of concepts such as standard deviation.


If you are not clear with the basics of mathematical concepts, it is highly recommended that you learn the same with clarity before starting to learn algorithmic trading.

Some of the core mathematical concepts that are helpful in algorithmic trading are a measure of central tendency, a measure of dispersion, probability theory, etc. Clarification of these concepts is a must for learning algorithmic trading successfully.

Recommended resources

Find out the core mathematical concepts with Essential Mathematical Concepts for Algorithmic Trading. Going forward, other helpful reads include Beginner's Guide to Statistics and Probability Distribution as well as Central Limit Theorem explained in Python.

There is confusion between the technical approach and the quantitative approach


Confusion with regard to quantitative and technical analysis is another thing that stops you from learning algorithmic trading. The two terms are derived from the same parent, that is Quantitative analysis but hold different meanings as well as approaches which are both essential during algorithmic trading.


Let us briefly clear the concepts for a basic understanding of both the terms:

Technical analysis is a study of forecasting the price of financial securities by analysing the historical market data. Technical analysis employs models and trading rules based on price and volume, such as the relative strength index, moving averages, oscillators, or through recognition of chart patterns & waves. A technical analyst examines the price action trading of the financial markets instead of the fundamental factors that affect market price. Quantitative traders who base their strategy on technical analysis are able to scan thousands of charts per minute equipped with a lot of indicators, ratios and data points finding the right instrument to suit their algorithmic trading strategy.

Quantitative analysis is the application of mathematical and statistical methods to understand and predict the behaviour of the financial markets. Numerical values are usually used for explaining the market situation in this type of analysis. Hence, mathematics of multivariate calculus, linear algebra, differential equations etc. play a key role. Also, quantitative analysis requires programming languages such as C, Python, etc.

Technical Analysis

Quantitative Analysis

Technical analysis helps analyse historical market data.

Quantitative analysis helps develop and quantify the trading strategy.

Technical analysis makes use of technical indicators such as trend lines, support & resistance levels, oscillators, relative strength index etc.

Technical analysis makes use of the same technical indicators as in technical analysis but also uses statistical tools such as machine learning, neural networks, genetic optimisation, in sample-out of sample tests etc.

Technical analysis gives you the expected behaviour and direction of market trend 

Technical analysis gives you expected risk, expected return etc.

Recommended resources

In case you want to learn about quantitative approach for futures and options, enrol into our learning track on Quantitative Approach in Futures & Options Trading. Some other recommended resources are Algorithmic Trading And Technical Analysis and Technical analysis and quantitative analysis.

You do not have the knowledge of data extraction and data management


When it comes to data extraction and management, there is a lot of confusion as to why is data so complicated? Confusion with regard to data is a common problem.


Data can be extracted from the sources such as Quandl, Bloomberg, etc, but what is data management?

The importance of big data is growing rapidly and is extremely crucial in algorithmic trading. Managing data implies making data a reliable one. For instance, non-reliable data has duplicates, missed out or incorrect values in the data etc. that can lead to incorrect outcomes after backtesting and hence, the wrong strategy creations.

For instance, below is an image to help you understand how the errors may show up. Here is a screenshot of data retrieved from the internet for the top 10 nifty 50 companies:

In red - Incorrect values, Blanks under column “weightage” - Missing values

Recommended resources

Our two courses namely Introduction to data science and Data and feature engineering for trading offer a comprehensive insight into data science and data management.

You confuse algorithmic trading with high-frequency trading


This is yet another important point while learning algorithmic trading since algorithmic trading is usually confused with high-frequency trading. Because of certain similarities, it gets all the way more confusing. They both are:

  • Fast (Intensity of HFT is higher)
  • Executed with algorithms
  • A part of the FinTech domain
  • Related to trading


The only, yet major difference between them both is the way each operates. Only the operations have created two separate terminologies, that is, Algorithmic trading and High-frequency trading. Let us see individual definitions of each:

Algorithmic trading implies using a defined set of instructions in the form of an algorithm 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.

Whereas, High-frequency trading (HFT) is a special category of algorithmic trading characterized by holding periods of securities ranging from microseconds to a few minutes. HFT requires powerful computers and excellent network architecture to transact with data at very high speeds. Also, there is a need to have low-latency response times and high trading volumes for it to work successfully. HFT strategies are mainly divided into market-making, statistical arbitrage, and quantitative low latency strategies.

Recommended resources

Explore all our courses on quantra’s website for learning algorithmic trading and related important concepts.

You lack the knowledge of building a strategy


When you lack the knowledge of building a trading strategy, you lack one of the most important parts of algorithmic trading. Not having the knowledge of the advanced trading strategies such as market microstructure, market making, turtle trading approach, and high-frequency trading strategies is a big hurdle.


You need to learn the basic technical trading strategies with the help of blogs, courses and videos. By learning about the strategies and how to build the same, your accuracy level with regard to applying the strategy in the particular market condition will improve.

Helpful reads:

If you wish to read about trading strategies (types, implementation and much more) here’s a perfect guide for more trading strategies.

Recommended resources

The recommended learning tracks to get a deeper insight into strategy building are Algorithmic Trading for Everyone and Learning Track: Advanced Algorithmic Trading Strategies.

You do not have a thorough knowledge of backtesting


Not having the knowledge of backtesting is one of the biggest disadvantages when you plan your trading strategy. It is the backtesting that helps you analyse the historical data in various financial markets and then build a strategy on the basis of that. Not backtesting or not having the resources to learn backtesting can make your strategy go wrong.


Backtesting is the process of testing a trading strategy using historical data to determine the effectiveness of that strategy. Backtest results usually show the strategy’s performance in terms of some popular performance statistics like Sharpe Ratio, Sortino ratio, which help to quantify the strategy’s return on risk. Having a backtesting platform such as Blueshift, you get the space for investment research, backtesting and algorithmic trading all free of cost!

Blueshift has recently introduced the alpha version which is a fast backtesting platform with minute-level data (received from multiple asset classes and markets).

Recommended resources

If you wish to read more about backtesting, dig into How to Backtest a Trading Strategy and Backtesting Long Short Moving Average Crossover Strategy in Excel for a deeper learning.


If you are a beginner and learning algorithmic trading, there can be some obstacles you are facing yet not coming to the realization that you might be missing out on something important. There are ways to cover up the missed out essentials which we aimed to cover in this article. You can explore a range of courses and can learn everything you need for successful algorithmic trading with Getting Started with Algorithmic Trading!

Disclaimer: All data and information provided in this article are for informational purposes only. QuantInsti® makes no representations as to accuracy, completeness, currentness, suitability, or validity of any information in this article and will not be liable for any errors, omissions, or delays in this information or any losses, injuries, or damages arising from its display or use. All information is provided on an as-is basis.

EOV webinar