Algorithmic trading is an automated trading strategy that utilizes computer programs to execute trades based on predefined rules and algorithms. As the financial markets continue to evolve, algorithmic trading has become an increasingly popular approach used by traders to execute trades faster and more efficiently. In recent years, various online courses have emerged, claiming to offer the best training in Algorithmic Trading. This review aims to provide an impartial evaluation of some of the best Algorithmic Trading courses available online. It will analyze the content, structure, and level of difficulty of various courses to help potential learners make informed decisions when choosing a course that aligns with their goals and objectives.
Here’s a look at the Best Algorithmic Trading Courses and Certifications Online and what they have to offer for you!
Algorithmic Trading & Quantitative Analysis Using Python Online Course
- Algorithmic Trading & Quantitative Analysis Using Python Online Course
- 1. Algorithmic Trading & Quantitative Analysis Using Python by Mayank Rasu (Udemy) (Our Best Pick)
- 2. Algorithmic Trading In Forex: Create Your First Forex Robot! by Kirill Eremenko, ForexBoat Team (Udemy)
- 3. Algorithmic Trading A-Z with Python, Machine Learning & AWS by Alexander Hagmann (Udemy)
- 4. Algorithmic Trading: Backtest, Optimize & Automate in Python by Mohsen Hassan, Ilyass Tabiai, bloom team (Udemy)
- 5. Forex Algorithmic Trading Course: Code a Forex Robot! by Mohsen Hassan, bloom team (Udemy)
- 6. Quantitative Finance & Algorithmic Trading in Python by Holczer Balazs (Udemy)
- 7. Python for Finance and Algorithmic Trading with QuantConnect by Jose Portilla (Udemy)
- 8. Algorithmic Trading on KiteConnect Platform by Mayank Rasu (Udemy)
- 9. Algorithmic Cryptocurrency Trading + Top 5 Robots in 2022 by Petko Zhivkov Aleksandrov (Udemy)
- 10. Algorithmic Trading using Interactive Broker’s Python API by Mayank Rasu, RASUQUANT LTD (Udemy)
The Algorithmic Trading & Quantitative Analysis Using Python course, instructed by Mayank Rasu, aims to teach students how to build a fully automated trading system and implement quantitative trading strategies using Python. The course covers steps such as extracting data, performing technical and fundamental analysis, generating signals, backtesting, and API integration. Students will learn how to code and backtest trading strategies using Python and gain an introduction to relevant Python libraries required for quantitative analysis. The course’s unique selling point is delving into API trading and familiarizing students with how to fully automate their trading strategies.
In this course, students can expect to acquire skills in extracting daily and intraday data for free using APIs and web-scraping, working with JSON data, incorporating technical indicators using Python, performing thorough quantitative analysis of fundamental data, value investing using quantitative methods, visualization of time series data, measuring the performance of trading strategies, incorporating and backtesting strategies using Python, API integration of trading scripts, and using FXCM and OANDA API. The course is divided into sections such as Introduction, Getting Data, Web Scraping to Extract Financial Data, Basic Data Handling and Operations, Technical Indicators, Performance Measurement – KPIs, Backtest Your Strategies, Value Investing, Building Automated Trading System on a Shoestring Budget, Bonus Section: Running Your Algorithms in Cloud, Bonus Section: Sentiment Analysis, and Archived Lectures.
2. Algorithmic Trading In Forex: Create Your First Forex Robot! by Kirill Eremenko, ForexBoat Team (Udemy)
The Algorithmic Trading in Forex: Create Your First Forex Robot! course, taught by Kirill Eremenko and the ForexBoat Team, is designed to teach individuals how to program in MQL4 and develop, test, and optimize their own algorithmic trading systems. No prior programming or Forex knowledge is required to enroll in this course.
The course is divided into four sections. The first section includes installing MetaTrader 4, opening a free demo account, and learning essential theory behind algorithmic trading. The second section focuses on programming fundamentals, with the course quickly bringing students up to speed even if they’ve never programmed before. Additionally, everything learned in this section is applicable in languages like C/C++/C#/Java/etc.
Section three introduces trading system design and blends it with the programming knowledge from section two. Students learn how to open their first order through a program they’ve created and how to modify or close it at their discretion. In the fourth and final section, students come up with a unique trading strategy idea and turn it into a holistic Algorithmic Trading system. The course also briefly covers how to test and optimize a Forex Robot in the MetaTrader 4 strategy tester.
The course disclaimer states that any information or advice contained in the course is general in nature and does not constitute personal or investment advice. The course will not accept liability for any loss or damage arising from the use of or reliance on such information. All securities and financial products transactions involve risks, and past performance results are not necessarily indicative of future results.
The course consists of five sections: Introduction, Programming Core MQL4, System design and trading functions, Putting it all together, and Course summary. Students are encouraged to enroll in the course to kick-start their Algorithmic Trading journey.
The Algorithmic Trading A-Z with Python, Machine Learning & AWS course is designed for individuals interested in building their own data-driven day trading bot. The course covers creating, testing, implementing, and automating unique strategies. The course provides comprehensive coverage of the essential knowledge and skills required for success in day trading.
The course begins with an introduction to the day trading business, including mechanics, terms, and rules of day trading across different markets. Participants will learn about the five fundamental rules of day trading to avoid common pitfalls.
The course provides participants with the skills to develop powerful and unique trading strategies with Python. Participants will learn to combine simple and complex technical indicators and create machine learning- and deep learning-powered strategies. The course covers all the required coding skills (Python, Numpy, Pandas, Matplotlib, scikit-learn, Keras, Tensorflow) from scratch in a practical manner.
Participants will learn to rigorously test their strategies before investing real money using backtesting and forward testing techniques. The course covers vectorized backtesting techniques, iterative backtesting techniques, and live testing with play money.
The course emphasizes the importance of taking into account trading costs. Participants will learn to include trading costs into their strategy and backtesting/forward testing. The course demonstrates that it is challenging to find profitable strategies after trading costs and provides strategies for controlling and reducing trading costs.
Participants will learn to automate their trades using Python, powerful broker APIs, and Amazon Web Services (AWS). The course provides in-depth coverage of creating software with Python and running it in real-time on a virtual server (AWS).
The course is not just about automated day trading. The techniques and frameworks covered can be applied to long-term investing as well. Participants will learn to feed machine learning and deep learning algorithms with real-time data and take ML/DL-based actions in real-time.
The course is designed for individuals who do not have a broker account in some countries (Japan, Russian Federation, Turkey, Hong Kong).
4. Algorithmic Trading: Backtest, Optimize & Automate in Python by Mohsen Hassan, Ilyass Tabiai, bloom team (Udemy)
The Algorithmic Trading: Backtest, Optimize & Automate in Python course, taught by Mohsen Hassan, Ilyass Tabiai, and the bloom team, aims to teach students how to fully automate their cryptocurrency trading. The course teaches students how to use freqtrade, an open source code, and a virtual machine that contains all the necessary code.
The course’s modules include a Python primer for those who are new to the language, the installation and configuration of freqtrade, implementation of a strategy, in-sample and out-of-sample testing, and a bonus lecture.
Students will learn how to code any strategy in freqtrade and backtest it to determine how it would have performed in the past, optimize it to find the best parameters for the best reward/risk ratio, and perform a walk-forward analysis to minimize overfitting. They will also learn to run the strategy with paper money and eventually with real money.
Additionally, students will be able to connect their code to Telegram, enabling them to start or stop their trading algorithm from anywhere with their phone. The course concludes with an assignment, updates, and a thank you from the instructors.
The Forex Algorithmic Trading Course, taught by Mohsen Hassan and the bloom team, guides students through the creation of a fully automated Forex Trading Robot from scratch using the MQL4 programming language. The course begins by teaching basic programming concepts and then delves into the specifics of the MQL4 language, covering topics such as live price updates, technical indicators, order execution functions, and risk management. The course is highly engaging and interactive, with many coding assignments along the way. By the end of the course, students will have created their own fully automated trading robot, backtested it for profitability, and learned how to run it on a demo or live account.
The course is divided into 12 sections, starting with an introduction to the topic and then moving on to cover MetaQuotes and Expert Advisors, programming basics, control flow and conditional operators, functions, preprocessor, and storage classes, arrays and for loops, MQL4 technical indicators, order execution functions, risk management, and finally, the creation of the Expert Advisor. The course also includes a section on backtesting and a bonus lecture.
The course is designed with beginners in mind, so no prior programming knowledge is necessary. The course content is presented in a way that is easy to understand and relevant to trading. All of the code created during the course is available to students, making it easy to follow along and practice coding. Overall, the Forex Algorithmic Trading Course is an excellent resource for anyone looking to learn how to create their own automated trading robot using the MQL4 programming language.
The Quantitative Finance & Algorithmic Trading in Python course, taught by Holczer Balazs, covers topics such as stock market basics, bond theory and implementation, modern portfolio theory, capital asset pricing model, derivatives, random behavior in finance, Black-Scholes model, value at risk, collateralized debt obligations, interest rate modeling, and long-term investing. The course is designed for individuals interested in statistics and mathematics in the context of financial engineering.
The first section of the course covers installation of Python, why it is used in financial modeling, and the limitations of historical data. The second section covers topics such as present and future value of money, stocks and shares, commodities, and short and long positions. The third section delves into bond theory and implementation, including bond pricing theory and Macaulay duration.
The fourth section covers modern portfolio theory, discussing concepts such as diversification, mean and variance, and efficient frontier. The fifth section covers the capital asset pricing model, including systematic and unsystematic risks, beta and alpha parameters, linear regression, and market risk.
The sixth section covers derivatives basics, including put and call options, forward and future contracts, credit default swaps, and interest rate swaps. The seventh section discusses random behavior in finance, wiener processes, stochastic calculus, Ito’s lemma, and brownian motion. The eighth section covers the Black-Scholes model, Monte-Carlo simulations for option pricing, and the Greeks.
The ninth section covers value at risk and Monte-Carlo simulation to calculate risks. The tenth section covers collateralized debt obligations and the financial crisis of 2008. The eleventh section covers interest rate models, including mean reverting stochastic processes, the Ornstein-Uhlenbeck process, and the Vasicek model. The twelfth and final section covers value investing, long-term investing, and the efficient market hypothesis.
The Python for Finance and Algorithmic Trading with QuantConnect course is designed to teach learners how to perform financial analysis and trading using Python, Pandas, Matplotlib, and the QuantConnect Lean Engine. The course covers a wide range of topics, including Python fundamentals, stock market analysis, volatility and securities risk, algorithmic trading, and much more.
The course starts by introducing learners to important Python libraries for data analysis and visualization, such as NumPy, Pandas, and Matplotlib. Each lecture includes a high-quality HD video with clear instructions and theory slides, as well as a full Jupyter Notebook with explanatory code and text.
One unique feature of this course is its complete coverage, which allows learners to implement their ideas as algorithms and trade with their new knowledge. Additionally, the course offers an online community with QA forums and thousands of students, as well as a 30-day money-back guarantee.
The course is specifically designed to connect core financial concepts to clear Python code, and to teach in-demand real-world skills that are highly sought after in the fintech ecosystem. The course also covers important topics such as portfolio allocation optimization, efficient frontier and Markowitz optimization, and the capital asset pricing model.
Overall, this course is a comprehensive and valuable resource for anyone interested in learning how to apply Python, Pandas, Matplotlib, and the QuantConnect Lean Engine to finance and algorithmic trading.
This course titled Algorithmic Trading on KiteConnect Platform is designed to teach individuals how to implement algorithmic trading strategies using the KiteConnect platform. The course instructors are Mayank Rasu.
The course covers various topics, including automating every step of the strategy, technical analysis, generating signals, risk management, and designing and deploying trading strategies on Kiteconnect platform. Additionally, participants will gain a thorough understanding of Restful APIs and kiteconnect python wrapper, and learn how to deploy their strategies on cloud.
Upon completion of the course, individuals can expect to gain skills in API trading, streaming tick level data analysis, technical indicator incorporation using Python, chart pattern analysis, end-to-end strategy design, and deployment, among others.
The course content is arranged into various sections, including introduction to Zerodha Kiteconnect, setting up a trading app and establishing connection, key API calls, coding technical indicators, price action concepts, building a candlestick pattern scanner, designing and deploying strategy on Zerodha platform, handling streaming tick data, streaming tick data-based strategies, and deploying strategies on the cloud.
The Algorithmic Cryptocurrency Trading + Top 5 Robots in 2022 course is designed to teach traders how to trade 5 cryptocurrency trading strategies manually and automatically. The course instructor, Petko Aleksandrov, provides access to monthly parameters and 5 robots for Bitcoin and Ethereum trading. The course is suitable for beginners and experienced traders who want to diversify their risk and achieve stable results.
The course covers 5 different strategies for cryptocurrency trading, teaching traders how to trade each strategy manually and use the best parameters for any given moment. Additionally, traders learn how to correctly place expert advisors on MetaTrader and test on a virtual account until they are confident with the strategies. The course also includes 5 EAs for algorithmic cryptocurrency trading as a bonus.
No IT skills are required to take this course, as students receive the EAs ready to use and learn how to correctly put them on the MetaTrader platform. The course emphasizes diversifying risk and avoiding greed and fear in trading. The instructor, Petko Aleksandrov, is an experienced trader with a focus on algorithmic cryptocurrency trading.
The course includes sections for beginner traders, the trading strategies, automated trading with the same strategies, results from trading, and conclusion. Traders will learn how to select and compare each strategy using the EA Studio strategy builder, perform a robustness test for each strategy, and place the 5 expert advisors on one trading account.
Students receive lifetime access to the course and updates and improvements to the trading system from Petko Aleksandrov. There is also a 30-day money-back guarantee for those who feel that the course is not suitable for them.
This course titled Algorithmic Trading using Interactive Broker’s Python API is offered by Mayank Rasu of RASUQUANT LTD. The course aims to teach participants how to design and deploy algorithmic trading strategies on Interactive Broker’s platform. This can be achieved by automating each step of the strategy, such as extracting data, performing technical/fundamental analysis, generating signals, placing trades, risk management, etc. Participants will also gain a thorough understanding of the native interactive broker’s API.
The course covers a variety of skills that participants can expect to gain. These skills include API trading, advanced python concepts such as OOP and multi-threading, extracting historical and fundamental data, harnessing streaming tick level data, incorporating technical indicators using python, end-to-end strategy design and deployment, handling asynchronous calls, and SQLite database management. Participants will also learn about relevant account settings in IB and the Interactive Broker’s TWS terminal.
It is important to note that this course has certain prerequisites. Basic python proficiency is mandatory, and participants should be comfortable with basic python data types and structures such as lists, dictionaries, and tuples. They should also know how to create python functions, implement loops in python, and install and import libraries. This is because the Interactive Broker API’s python client uses advanced OOP and asynchronous programming concepts, and students with no python knowledge may struggle to follow along.
The course is divided into several sections, including an introduction to Interactive Brokers and its API, advanced python concepts, understanding IB API Python Wrapper, historical data, order management, other important API calls, technical indicators in IB, backtesting strategies, designing and deploying strategies on IB, streaming market data, and extracting fundamental data.