Algorithmic trading is a contemporary practice that has helped traders across the globe save time, effort and capital. This article comprises the 20 best algorithmic trading YouTube videos for anyone and everyone who is either a beginner or is already practising algorithmic trading.
This article includes the following videos:
- This video on Introduction to Algorithmic Trading covers the important aspects such as the essential components of algo trading. Also, the conversation is about how the people new to the concept, those from other fields, and others who are hesitant because of the preconceived notion that algorithmic trading is difficult, can begin their journey with algos. The conversation wraps up with an easy example of how to use Python code for systematic trading as well as easily accessible courses and blogs for an in-depth learning experience.
Key takeaways:
- With the learning of essential skills/components, one can become a successful algorithmic trader.
- Algorithmic trading can be learnt at any time with any educational or professional background.
- There are easily accessible learning resources such as courses, blogs and communities.
2. Get started with algorithmic trading is a wholesome webinar video by Vivek Krishnamoorthy (Head, Content & Research, QuantInsti) to help you learn some of the core concepts of algorithmic trading. This video covers everything, right from Python installation to working with market data and strategy development. Even a detailed backtesting and information about the different types of data used are covered in this comprehensive video.
This webinar is an excellent resource for anyone interested in algorithmic trading for beginners, providing step-by-step guidance to build a strong foundation and start their journey in this field.
Key takeaways:
Algorithmic trading is a step by step process that requires thorough knowledge, dedication, perseverance and optimism.
3. This video on Algorithmic trading strategies is placed on the third number in the sequence for a purpose. This video takes you to the most important step in algorithmic trading and that is “the strategy creation”. There are five different trading strategies explained in this video in less than 4 minutes and that is what makes this video worth watching. In simple words “Less time spent, a lot learnt!”
Key takeaways:
- This video explains 5 strategies to utilise and learn from.
- The trading strategies explained in this video help create an idea about strategy creation for your trades in financial markets.
- You can get in-depth learning with courses on Quantra.
4. This video titled Algorithmic Trading Strategy Using Python aims at helping you take a glimpse of how to create trading strategies with the Python language. Considered as one of the most preferred computer languages, Python holds the utmost relevance in the algorithmic trading domain.
Key takeaways:
- An algorithmic trading strategy can be created by plotting moving averages and assigning variables the entry/exit positions.
- Python language is much easier to use for strategy creation and execution of trades.
5. With this video on This quants’ approach to algorithmic trading—Michael Halls-Moore, you will cover a whole range of topics such as:
- The need for quality data
- Thinking about risk from a portfolio level
- Trading multiple automated strategies
- The role of common sense in parameter optimization
- Learning to program
Key takeaways:
- Data quality and risk management are essential.
- Trading strategies help and so does optimisation of parameters during algorithmic trading.
- Learning to program for algorithmic trading is important and not difficult.
6. The Sneak Peek inside a trading firm | Why Python? offers you a fun learning experience by listening to what the trading firm specialists have to say. This video also focuses on the relevance of the Python programming language for algorithmic trading.
Key takeaways:
- Algorithmic trading involves certain skills such as quantitative analysis, tuning the parameters on the basis of the analysis and coding the trading strategy.
Developing the skills of a quantitative trader is essential for effectively implementing and refining algorithmic trading techniques. - Algorithmic trading is easier with the Python language.
7. In an investment bank, the role of a quant developer is divided into three main categories and they are- front office, middle office and back office. This video on front office vs middle office back office takes you through a wonderful discussion to help differentiate between the three.
Key takeaways:
- As a quantitative developer, you get the opportunity to be hired in an investment bank.
- The investment bank offers you the three roles and depending on your interest, educational qualification and background, you can opt for the one that suits you best.
- With the apt knowledge, you can choose wisely.
8. How Do Stock Trading Algorithms Work? is a simple video that explains the important concept which is the “working of algorithms”. The video is all about how the algorithms help program the trading system to trade on the behalf of the trader. With algorithmic trading examples and insights from an algorithmic trading class, the viewer can get a clear picture of algorithmic trading.
Key takeaways:
The trading algorithms work by coding the instructions/conditions in the programming language such as Python about timing, price and execution of the trade. With the instructions/conditions, the algorithms fulfill the condition to trade on the behalf of the trader.
9. How To Create A Trading Algorithm From Scratch is the second last video but not in relevance since it holds a lot of important information regarding creating your own algorithm for trading. This is a webinar video that has everything you need to know for creating algorithms for trading and testing them in real market data.
Key takeaways:
The most important point here is that you learn to create the best algorithms right from the start.
10. How to choose the best stocks and live trade by Dr. Hui Liu is a webinar video in which the seasoned trader Hui Liu, who is a founder of Running River Investments LLC (private hedge fund), speaks about choosing the best stocks for live trading.
Key takeaways:
Trading live in the financial markets by implementing the right trading strategies on the best stocks is the forte of this webinar video.
11. Another video in this list is Live Algorithmic Trading in Zerodha Using Python. This video shows SMA crossover strategy using Python and Zerodha’s Kite API. With everything explained right from extracting historical data to trading in the live market, this video serves the purpose of learning for a beginner.
Key takeaways:
- Live algorithmic trading requires a step by step process
- There are many strategies and SMA crossover strategy is one of them to trade successfully.
- With an API and Python language, it is simpler to trade in financial markets.
12. With this video on How to Build a Profitable Python Trading Algorithm in 5 Minutes, you will learn how to build a model with Python for algorithmic trading. Using Anaconda, and Jupyter notebook, you can code an algorithm in Python. The video shows the Python coding which ends with plotting a model and also calculating the maximum drawdown to ascertain the time periods where portfolio’s value went down with the past data. The maximum drawdown is one of the important concepts in backtesting and live trading as it shows the maximum a portfolio/model can reduce in value. Based on the learning about maximum drawdowns, a trader can build successful strategies.
Key takeaways:
- With the step by step process, algorithmic trading models can be built with Python.
- Python is really convenient to not only build the trading model but also for graphical representation of the model as well as for identifying the risks of the portfolio.
13. This video on Using Indicators to Build Algorithmic Trading Systems takes you through an in-depth learning on why and how to use indicators for beginning algorithmic trading. In this video, specifically, the two indicators namely stochastic and ATR (Average True Range) are compared.
Key takeaways:
- For analysing the performance of the financial markets, technical indicators can be used.
- One can predict future price movements of the financial markets with a simple approach.
14. This interesting video titled A day in the life of an algorithmic trader navigates through the everyday life of the trader in the algorithmic trading domain. The video explains how it is not a fancy video showing anything extravagant but surely shows an occupied and productive day in the life of a trader. From learning through videos, being on calls, gymming to strategy development, an algorithmic trader does it all dedicatedly and passionately day after day.
Key takeaways:
- The everyday life of an algorithmic trader is an interestingly productive one which revolves around his/her profession as well as other usual activities that involve learning.
15. Trading Journey of an ALGORITHMIC Trader is yet another video that takes the viewer through the entire journey of the interviewee, from educational background to beginning professionally as an algorithmic trader.
Key takeaways:
- The journey of this algorithmic trader can help one learn the important aspects while becoming a professional algorithmic trader.
16. This video titled 5 Steps For Creating An Algorithmic Trading Bot covers the important steps by an analyst, Milandeep Bassi. He explains the 5 steps such as:
- Creating the environment for the algo
- Researching the strategy
- Converting the strategy in code
- Combining strategy and platform
- Paper/Live trading
- Amplifying algorithmic trading simulation
These above-mentioned five steps are explained in detail by Mandeep.
Key takeaways:
- Once you know what you want from your algorithmic trading bot, you can automate the trading strategy by including conditions.
- Each step is essential for executing the trade order in the live market.
17. Vivek Krishnamoorthy, a quant, explains in the video how retail traders can compete in algorithmic trading. Also, the video covers the benefits that retail traders have as well as the difference between retail traders and institutional traders in large firms with regard to the business model.
Key takeaway:
Retail traders can compete with institutional traders in algorithmic trading domain and get certain benefits.
18. The video titled Algo Trading for Retail Traders | Q&A session on Trading Systems will serve the purpose of learning answers to some of the most common questions asked by many. After learning about retail traders in the previous video, this video will take you to the in depth details on
- Failure of traders
- Cost of algorithmic trading
- Common mistakes of traders
- Different Trading Strategy details
- How to backtest a strategy?
- How to handle drawdown?
- How square off fully automated trading bot can help retail traders?
- Dos & don'ts in a Trader's life
Key takeaway:
All the questions regarding retail traders’ journey are answered with the conclusion that retail traders can do algorithmic trading.
19. Here is an interesting video on Resources to start coding trading algorithms that will guide you with regard to beginning with algorithmic trading. The resources, which are available online, are handpicked by the host of the video after he himself learnt to code. Along with his stock market knowledge, the host could get his hands on coding and that is what he speaks about in the video. These resources are his personal best and do not mean that you must try them. Use your own logic and then move forward
For those seeking a structured path, a guide to algo for trading can provide valuable insights. This type of guide helps beginners understand the essential concepts and tools needed to build and optimize their own trading algorithms. By following a comprehensive guide, aspiring traders can navigate the complexities of coding and market strategies with greater confidence.
Key takeaway:
There are interesting and helpful resources available online to start coding trading algorithms.
20. Stock data analysis (Algo Trading Webinar) is a webinar video by Jay Parmar, Associate, Content and Research, QuantInsti. With this video, learn to analyse and backtest financial market data using Excel and Python. Moreover, you will learn about the difference between both tools and their respective pros and cons and get to know which one is best suited for you.
Key takeaways:
- Backtesting requires a step by step process.
- Excel can be used for backtesting.
- Backtesting can be done on multiple stocks and the demo of the same is explained in the video.
- The shortfalls of Excel can be overcome.
Conclusion
To make full use of the algorithmic technology that has got combined with finance, you can gather the apt knowledge from online resources such as videos, webinars and tutorials. The acquired knowledge and experience will undoubtedly aid in becoming a top algorithmic trader who effectively employs the logical reasoning inherent in algorithms.
If you wish to gain expertise in algorithmic trading, begin your endeavour with our course 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.
