Sep 5, 2024
Written by:
John McDowell
Algorithmic trading involves programming a computer or device to perform automated trades based on a set of instructions. For example, a trader can program the system to automatically put a buy order for a certain number of shares if the 5 moving average crosses above the 20 moving average and a sell order if the reverse occurs. The trading system monitors the market constantly, and when the condition is met, it places the order as programmed.
In other words, the trading strategy is converted into a machine-readable form called an algorithm and fed into the system to automate the trading activity. The system then uses the algorithm to execute the trading strategy, hence the name "algorithmic trading." Complex strategies involving many different parameters can be programmed into the system for automated trading.
High-frequency trading uses complex algorithms to carry out large numbers of transactions in a fraction of a second. This type of trading is ideal for scalping, a strategy that requires the buying and selling of securities quickly to benefit from small price movements.
High-frequency trading contributes to market liquidity because of the system’s quick trade execution and huge turnover of orders. Large institutional investors such as banks and hedge funds use high-frequency trading extensively and are often criticized as the system gives them an advantage over retail traders in terms of speed of trade execution.
In addition, large corporations have access to large amounts of capital, which high-frequency trading requires, to buy and sell large volumes of securities in a short time. Due to the rapid execution speeds and bulk order processing, high-frequency trading can sometimes result in unpredictable price behavior, such as rapid price hikes or drops.
Here's an example of a recent penny stock with an indication of high-frequency algorithmic trading:
As you can see from the TradingSim recording, stock symbol WISA is not a normally high-volume trading vehicle. However, periodically, it will gain attention from high-frequency traders as a type of "pump and dump" play for the day.
Algorithmic trading is done using a software or platform that is installed on a computer, laptop, tablet, smartphone, or any other device. There are plenty of algorithmic trading platforms available on the market, each providing different features.
Although the success of your trading will primarily depend on your trading strategy, the efficiency of your trading performance is also extremely important. That is the reason why you should do your research to choose the right platform. Choosing the best algorithmic trading platform can be tricky, but here are some attributes that you should look for in an algorithmic trading platform:
Algorithms in trading software are designed to work on real-time data, so the software should be have just that -- a real-time market data feed. If your trading strategy also includes working with company fundamentals data, you should choose software that has historical financials and metrics data as well. Further, the software you choose should be able to pull a data feed from multiple markets and exchanges in case you want to trade in different markets.
Algorithmic and high-frequency trading aims to make large trades in milliseconds to capitalize on small price movements. To execute the strategy efficiently, the data feed from the exchange should be quick and in real-time with no lag at all. In today’s fast-moving markets where prices change in milliseconds, the flow of market data from the exchange to your system and the sending of orders from your system to the fulfilling of those orders must have minimum latency. Even the smallest of delays in the process can render algorithmic trading systems ineffective.
Algorithmic trading software should have built-in functionality for creating and configuring trading algorithms. Using the functionality, you can tinker with the settings of different strategies to customize it to your needs. For example, if your trading software comes with a moving average crossover algorithm strategy, you can change the values and parameters of the moving averages according to your own trading strategy. Some software also allows you to write your own algorithms using popular programming languages, such as C, C++, or Python. Such customizations can give you plenty of freedom to create and modify your strategy to different markets, asset classes, and market conditions.
The algorithmic trading software should also provide you with the ability to backtest different strategies on historical data. While various third-party providers offer you historical market data for backtesting your trading strategies, it is essential that you backtest the algorithm on the historical data provided by the same software that you will be using for trading. So, having a backtesting feature in your algorithmic trading software can be handy if you want to both test and apply your strategies using one software platform.
Algorithmic trading software can be programmed to execute different types of strategies. The two most popular ones are mean reversion and trend-following strategies.
Mean reversion strategies are based on the idea that prices tend to revert to their long-term average. In fact, many technical analysis tools use price averages as the central concept to aid traders in understanding charts and finding profitable trading opportunities. A combination of Stochastic Oscillators, Moving Average Convergence Divergence (MACD), Simple Moving Average (SMA), Exponential Moving Average (EMA), Bollinger Bands, Fibonacci Retracements, Relative Strength Index (RSI) can be used to create trading algorithms based on mean reversion strategies.
Mean reversion strategies work best in ranging markets, while they might not be as useful when prices are trending. Let's take an example of how Bollinger Bands can be used to execute a mean reversion strategy.
The lower band in Bollinger bands serve as a dynamic support point whereas the upper band acts as a dynamic resistance level. The two bands are set two standard deviations apart from the mean (the middle line) and cover around 95% of the price points. This is the reason why you will see most of the candlesticks within the bands, with price usually bouncing off the bands.
When the price touches the lower band, it could be a signal of oversold conditions, indicating a buying opportunity. In contrast, if the price touches the upper band, it could be a signal of overbought conditions, indicating a short opportunity. However, the chances of success can increase if the reversal or change in price direction is confirmed by a bullish or bearish candlestick reversal pattern.
Arbitrage trading is also called Pairs Trading. It involves choosing two correlated stocks or assets whose prices move in similar fashion to the other and trading when discrepancies in the pattern occur. Suppose you observe that prices of two stocks have historically been moving similarly and you conclude that the stocks are correlated. Now, you need to monitor the two closely for divergences in the correlation, which could provide you with profitable trading opportunities.
An example of this could be the Bitcoin US Dollar pair, as seen below:
If divergence occurs, you buy the underperforming security or asset while shorting the outperforming security or asset. When the price of the underperforming stock rises and the outperformer drops, you generate a net profit as the correlation between the two assets is restored.
The strategy can be encoded into an algorithmic trading platform to automate the process. Depending on the algorithm, the software can be programmed to identify highly correlated stocks by calculating correlations between different stocks over a certain period using historical data. Once correlated stocks are found, the system can also be programmed to automatically execute trades if the correlation drops to a certain level and exit the trade when the correlation restores to a certain level.
Quantitative trading uses various parameters like price, volume, and fundamentals to create mathematical and statistical models. These models are first back-tested on historical data, and after delivering promising results, they are then used to carry out computations and identify profitable trading opportunities.
Quantitative trading, due to its data and computations-centric nature, is done using algorithmic trading software. This type of trading was once exclusive to large financial institutions, but ever since algorithmic trading became accessible to retail traders, they can also perform quantitative trading using algorithmic trading software.
The arbitrage trading strategy that we discussed above is an example of quantitative trading that uses statistical or mathematical models coded into an algorithm to execute trades. Similarly, algorithmic trading software could also execute mean reversion strategies, trend-following strategies, and momentum trading strategies using some sort of mathematical or statistical models.
Automated trading systems make use of programmed algorithms and quantitative models to execute trades. Depending on the type of trading software, you can encode your trading strategy into an algorithm using a programming language like Python, C, MQL4 (MT4) or C++. If your trading platform comes with built-in trading strategies, you can customize the parameters according to your strategy. With the advancements in AI, traders can now further improve their algorithmic trading capabilities by integrating it into their process to leverage its strengths.
There are plenty of algorithmic trading courses available that teach how to design trading algorithms and automate execution of different trading strategies. You can take these courses on Udemy and Coursera and on various other platforms. The courses can vary in their content and the type of programming language and platform they use for teaching.
If you are already into trading and want to take your trading to the next level, investing your time and money into taking an algorithmic trading course could be fruitful. Even if you don’t have any exposure to a programming language, you can still take many of the courses that are specifically designed for beginners with little to no prior programming knowledge.
Python is one of the most popular programming languages and is beginner-friendly due to its simpler syntax. It is versatile and is widely used in data science and machine learning fields. It has plenty of libraries and frameworks for algorithmic trading, such as FinTA, Zipline, Freqtrade, CCXT, YFinance, TensorTrade, Backtrader, and Trump2Cash. Further, you can use Numpy, Keras, Gym, and Pandas to analyze large amounts of data to make informed decisions.
You can use libraries and frameworks for a variety of tasks, including data collection, data manipulation, backtesting, technical analysis, plotting graphs, and machine learning. Learning Python can be extremely beneficial if you want to develop algorithms for executing complex trading strategies.
Python can also be used for machine learning and deep learning in trading. This means that you can train your system to interpret different kinds of data using algorithms. Machine learning in trading involves working with Numpy and Pandas for data importing and manipulation, creating Deep Neural Networks (DNN) and Recurrent Neural Networks (RNN) for deep learning, vectorized backtesting for backtesting the strategy based on DNN. You can also use platforms like MetaTrader to execute and run the program created after the machine learning process.
Algorithmic trading has been in use in the financial markets for decades but with limited retail trader participation. However, with the advancements in AI, machine learning, and deep learning, retail traders can now access the advanced features required for automating their trading strategies. No matter how complex or simple your trading strategy is, you can program the software to execute it automatically with little to no human supervision.
One of the biggest advantages of algorithmic trading is that your trading strategy will be backed by data and logic, not by your gut feeling or emotions. From identifying a trading opportunity to exiting the position, every step would be performed automatically by the system, allowing you to focus on refining your strategy rather than manually monitoring the outcome of the trade.
Python is well-known for deep learning and machine learning, which are essential for creating a robust algorithm-based trading strategy. If you are interested in learning algorithmic trading, you can consider learning Python programming and the associated libraries and frameworks. Fortunately, there are plenty of paid and free online learning resources available that you can consider.
Considering back-testing your algo strategies? Try our 7-day free trial at Tradingsim to work on your historical chart strategies and replay the markets in real-time.
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