Best Algorithmic Trading Courses & Classes for Beginners 2023

Here are the best algorithmic trading courses and classes with low costs and fees, offering excellent education to improve your skills and knowledge.

Alexander Voigt

By Alexander Voigt | Updated September 09, 2023

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Algorithmic trading has similarities to automated trading, wherein traders open and close positions based on computerized rules and variables. Algorithmic trading systems are integrated into algorithmic trading software solutions and execute trades automatically in real time.

How exactly does algorithmic trading work, what strategies algorithmic traders use, what are the advantages and disadvantages, and what are the best algorithmic trading courses? Let’s have a closer look at it.

best algorithmic trading courses

Best Algorithmic Trading Course – Top 5

1. Algorithmic Trading & Quantitative Analysis Using Python

In this algorithmic trading course, Mayank Rasu teaches students how to build fully automated trading systems and implement quantitative trading strategies using Python.

The course is hosted on Udemy and has 3,700 ratings with an average of 4.7/5, with 32,000 students enrolled. The course is in English, was lastly updated in July 2023 and comes with auto-subtitles in English, French and simplified Chinese.

Besides, Mayank covers API trading and how to use fundamental and technical analysis programmatically during the 19.5-hour course for $69.99.

2. Complete Algorithmic Forex Trading and Back Testing System

This course by Richard Allbert goes into detail about developing trading systems by utilizing web scraping, backtesting methodologies and app development.

So far, 4,000 students have enrolled on Udemy in this 31-hour course with 310 lectures and provided a rating of 4.8/5 on average, with 500 ratings given.

The algo course covers topics such as building trading robots with Python, leveraging multi-threading, work queues and events to make trading decisions, building back testing systems and archiving historical data analysis for $84.99.

3. Algorithmic Stock Trading and Equity Investing with Python

Alexander Hagmann’s course on Udemy is relatively young, with 3,700 students enrolled and rated 4.6/5 based on 207 ratings. The course is mainly used by investors using Interactive Brokers since the main intent of the course is to teach you how to use Interactive Brokers for algorithmic trading, covering stock market analysis, portfolio investing, and ETF theory and trading.

The course costs $89.99, includes 37 hours of on-demand videos and covers algo-trading using IBKR’s API, various investment styles, equity portfolio optimization logics, technical analysis and trading indicators, plus fundamental analysis.

4. Advanced Learning Algorithms

The Coursera Advanced Learning Algorithms course teaches students how to build and train neural networks, perform multi-class classifications, and apply best practices. Due to the nature of the course, the content is primarily useful for coders and traders looking to go into details about neural networks to conclude potential implications on trading.

The four included modules are neural networks, neural network training, advice for applying machine learning and decision trees.

Your educator is Andrew Ng, who has a total of 6.6 million students on 38 courses.

The course lasts 31 hours, and so far, 3,300 ratings have been given for an average of 4.9/5.

Coursera has monthly fees for course attendees of $49, but other pricing options exist.

5. Machine Learning for Trading

The Machine Learning for Trading course is brought to you by Jack Farmer from the New York Institute of Finance. The course helps understand the techniques and structure of machine learning, reinforcement learning strategies and deep learning. It describes step-by-step how to develop a machine-learning-driven trading strategy.

For building the ML models, Tensorflow and Keras are used.

So far, 26,000 students have enrolled for the course, and 980 ratings are given for an average score of 3.9/5.

Coursera has monthly fees for course attendees of $49, but other pricing options exist.

How Does Algorithmic Trading Work?

Algorithmic trading (aka algo trading) is an advanced trading technique where traders utilize the power of computers, servers and algorithms to evaluate the best possible day trading strategies and automatically transmit buy and sell orders. High-frequency trading is likely one of the most common use cases for algorithmic trading.

As humans, we can’t handle 10, 50 or 100 trades within seconds simultaneously, while algorithmic trading systems can. Algo trading is based on various mathematical models and ranges from simple algorithmic strategies to complex ones.

But algo trading is not limited to trade execution. It also involves automated analysis of the underlying assets, where the algo identifies trading patterns and setups that work well in the current market environment.

The introduction of commission-free trading across most U.S. online brokers attracted even more algorithmic traders than ever before. What could be better than trading commission-free on autopilot?

Algorithmic Trading Strategies

Common algorithmic trading strategies include trend analysis and recognition, arbitrage and mean reversion. But algos are also used for portfolio rebalancing.

Trend Analysis and Recognition

Tools like TrendSpider focus on trend analysis and automated pattern recognition. Using stock analysis software helps identify well-known candlestick patterns and trading indicator constellations.

Algorithmic trading software identifies moving average crossovers (e.g., 20EMA crosses the 50EMA to the upside) and candlestick patterns (piercing pattern, head and shoulders pattern, etc.), enabling investors to backtest the identified trends and patterns.

Mean Reversion

The reversion to the mean is a trading theory used in finance. The theory is that the price of an asset always reverts back to an average value (mean) in the long run. The mean is calculated based on a historical data set.

So, when the market moves on high volatility and the distance from the mean gets bigger, the system speculates on the reversion of the prices to the mean value (regression to the mean).

You can also transfer the idea of mean reversion by taking the average win rate of a trade. Statistically, you have a chance of 50:50 to make a profit with each trade. If you execute 100 trades and have a win-rate of 60:40, and after 500 trades 75:25, the likeliness is high that with a more complete dataset, the win-rate will see a regression to the mean of 50:50.


The times of cross-border arbitrage are long over, at least the super-profitable times. 20 years ago, you could buy 100 shares of company XYZ on Nasdaq and then cross-sell it for a profit on XETRA. Depending on the opening gap, you made 20% or more per trade.

But algorithmic trading systems quickly found their way to this profitable trading approach, and now the algo systems fight for a cent in profits. While trading those strategies with huge share sizes is still possible for profit, retail investors can’t make money here anymore.

Portfolio Rebalancing

Imagine managing a hedge fund portfolio with 4 billion assets under management and doing all portfolio rebalancing updates manually. I think you agree that this is not feasible anymore.

Instead, the portfolio manager defines algorithmic-based variables, and the algorithmic trading system handles the rebalancing. This is efficient and a way to reduce costs and offer investors finance products for a lower price (e.g., management fees).

Algorithmic Trading Components

What does it need to make algorithmic trading work? It’s basically the same for institutional investors and retail traders. While the budgets of both are different, the components remain the same.

Coding Knowledge

As the term algorithmic suggests, trading based on algorithms is all about 0 and 1. It is pure math. Therefore coding knowledge is the most important component of algo trading. Even if you have all the other components in a perfect environment, without a great programmer, you have a problem.

You programmers should understand trading and be able to transition the requested and described trading strategies to the correct combination of 0 and 1.

Network Infrastructure

Trade execution time is crucial for successful trading based on algorithms. The shorter the way from the server where the strategy is hosted to the stock exchange, the better. If you trade stocks on the New York Stock Exchange, having your server and mainframes near the stock exchange is better.

Otherwise, other algo traders get the faster fills while you either don’t get any trade execution or do not for the intended price.

As retail traders, you must also ensure a proper finance network infrastructure with fast fiberglass or DSL connection and hosting of algo strategies on servers near the stock exchange. An internet outage should not influence the algorithms since you typically trade on high volume.

Analysis Features

How do you evaluate algo strategies that work well under current market conditions? You use platforms that enable you to identify the most profitable trading strategies. These can be technical analysis-based platforms for fundamental research platforms.

Backtesting Features

Once a trader visually confirms a trade idea and manually backtests the efficiency of the strategy, it’s time to put it on a test on extended historical data. Did the strategy work for the first time in years, or does the trading strategy consistently make money?

While backtesting only informs about the efficiency of a trading strategy in the past, it gives you a good understanding of the identified strategies’ profitability.

Once the backtest within the backtesting software shows promising results, it is possible to do some forward testing and put the strategy on the test within a simulated environment. Trading strategies can also be optimized, while investors need to be careful to avoid overoptimization.

Automated Trading Platforms

An algorithmic trading system only has value when an automated trading platform is available for routing orders to the exchange in real time. Placing 100 trades in seconds is not a viable option.

You can choose an automated trading platform that connects to your direct-access broker or a platform that fully integrates with coding capabilities, strategy testing, automated order routing, and trade monitoring.

Algo-Trading Use Cases

The majority of algo-trading systems are high-frequency trading systems (HFT) that can transmit thousands of orders in seconds across international exchanges, aiming to make small profits per trade. Still, retail investors also use algo-trading concepts to code trading systems:

  • Scalpers: Scalpers use algo-based trading systems to frequently trade their favorite assets. Scalping in trading is the fastest form of trade execution across private investors and has the highest trade frequency.
  • Day Traders: Day trading involves buying and selling assets on the same day. The holding times vary from seconds to hours. At the end of the day, all positions are closed.
  • Swing Traders: Swing traders hold their investments overnight, unlike scalpers and day traders. A swing trader holds the position for days and sometimes weeks.

Algorithmic Trading Example

Let’s assume you identified a trading strategy that goes long when the price of the stock XYZ crosses the anchored VWAP for the first time today to the upside and closes a minimum of 3 ticks above it in the used time frame.

You want to go long automatically, always risking $100 when the trade triggers where the stop loss is below the previous day low.

You now provide this information to your coder, and he first used a backtesting platform to evaluate the profit potential. He identifies a 50/50 win rate but finds that the win rate goes to 70/30 when a filter is used where the S&P500 needs to be green (current price > previous day close).

Now you decide to test the strategy on the paper trading account. To do this, you need the algorithmic trading strategy coded considering all the information. The entry signal is a simple if, then, else element.

At the same time, the trading size for the trade is a bit more complex because it’s needed to measure the trade entry price relative to the previous day low and then divide the $100 risk per trade by the distance (e.g., $0.50) to get the trade size of 200 shares.

This is a simple system, but you already see that it is impossible to do all of this in fractions of a second for a stock universe of 100 or more.

That’s why you need algorithmic trading and automated trade execution, at least if you want to go this route.

Programming Language

C++ is the most popular programming language for algorithmic trading systems. It allows to code and process millions of data sets efficiently. An alternative to C++ is Python, which can also be used for coding trading systems (automated + algorithmic).

Retail traders frequently use configuration wizards implemented in their trading platform or modified programming languages such as EasyLanguage (intuitive coding language from TradeStation) or NinjaScript (a variation of C#).

Advantages & Disadvantages

Using algorithmic trading systems has advantages and disadvantages, which must be considered before starting.


  • No human error: Trades now get executed based on the coded algorithm. If everything is coded correctly, the trade gets transmitted in the way defined. Ho hesitation, no fat finger typo during trade execution and no wrong interpretation of data.
  • Less emotional stress: Automated trading based on coded algorithms reduces the emotional stress of a trader. Instead of starring on the screen for hours waiting for a setup to form, the trader can now use the time to find even more and better trading strategies and monitor the algorithmically triggered trades parallel.
  • Fast trade executions: The trading signal comes in, and the trading system gets triggered in fractions of a second. Gone are the times when you thought, wait, is this the trading signal? Should I really transmit the order? Once the setup is there, the order gets routed to the desired exchange.


  • High bid-ask spreads: High bid-ask spreads can be your salt in the trading soup. If you backtested and forward-tested a strategy without considering the bid-ask spread, you have a problem. Imagine trading a $500 thinly traded stock with a bid-ask spread of $0.01. Yeah, that would be nice. But in reality, it is $1, a bit more or less. Once your market order hits the order book to buy 1,000 shares, well…
  • Higher costs: Excellent trading infrastructure, experienced programmers, algo trading tools, etc. All of this costs money, and you need to consider those costs when calculating the profitability of your strategy.
  • Complex algorithms: Many trading platforms have integrated trading strategy wizards, where you can click the variables and enter the desired values. Yet, finding more profitable trade ideas might need more complex strategies, and it is necessary to code those properly to ensure correct trade executions.


Algorithmic trading is gaining popularity due to significant improvements in trading platform technologies and since the introduction of commission-free trading across all major U.S. online brokers. Algo trading reduces the stress level of a trader and enables him to focus on strategy development and analysis.

Related: Best Day Trading Courses and Best Stock Trading Courses


Is Algorithmic Trading Legal?

Yes, algorithmic trading is legal. Using algorithms for trading is not permitted by law. However, stock exchanges might limit the number of instant changes in order flow that influences the order book and their network facilities.

Is Algorithmic Trading Profitable?

Yes, algorithmic trading is profitable. It is among the most popular forms of trading across financial institutions and hedge funds. Also, algo trading enables firms to manage multi-billion dollar assets under management with less human effort.

Alexander Voigt
Alexander Voigt is the founder of DAYTRADINGz, was a regular contributor to Benzinga and has been featured and quoted on leading financial websites such as Business Insider, Investors, Capital and Forbes.