Simple Algo Trading: A 101 for Beginners

Diving deep into algorithmic trading is a thrilling step toward mastering a dynamic short-term trading strategy. With the basics under your belt, it's the perfect time to put that algo trading knowledge into action. 

In this guide, we'll walk you through the top and simple trading algorithms that work for both beginners and seasoned quants. From understanding each algorithm to applying them in real-time trading scenarios, we have you covered.

What is algo trading?

Algorithmic trading (or algo trading) is an investment strategy that uses automated, computer-controlled programs to speed up the trading process. It revolves around using custom-designed algorithms, which are sets of instructions programmed to make buy or sell decisions in financial markets based on the data they're fed.

Well-designed algos can speed up intraday trading by analyzing vast quantities of market data faster than humans and then comparing that data to the instructions it's been programmed with. The trick is to employ a smart, logic-based, and data-driven investment strategy in the trading algorithm to generate impressive results.

How does algorithmic trading work?

Trading algorithms are meticulously designed to follow specific trading strategies based on predefined rules and criteria. They include a few components and basic infrastructure to function. Here are a few:

Market data feed

Algorithms rely on real-time market data, which is often provided through market data feeds. These feeds offer information on stock prices, volumes, and other relevant market indicators.

Trading platform

A robust trading platform (or form of trading software or trading bot) is essential for executing algorithmic strategies. It provides the necessary infrastructure for order placement and execution.

Algorithmic models

These models contain the trading logic and rules that guide decision-making. They can range from simple strategies like moving averages to complex machine learning (ML) algorithms.

Risk management

Effective risk management tools and mechanisms are vital to controlling potential losses in algo trading. Algorithms often include risk parameters to limit exposure.

Connectivity

Low-latency connectivity to exchanges or trading venues is critical. The faster an algorithm can access market data and execute orders, the more competitive it can be.

Backtesting and simulation

Algorithms are often tested and optimized through backtesting and simulation to assess their historical performance before they’re deployed in live markets.

Monitoring and oversight

Ongoing monitoring of algorithms is essential to ensure they operate as intended. Human oversight is often necessary to intervene in unexpected market conditions.

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How do programmers create trading algorithms?

Programmers (aka algo developers) code algo trading strategies by hand. They often write in Python, an object-oriented programming language. If users want to create their own algorithms, they’ll need to learn Python or have someone else write the algorithms for them. They’ll also need to program their algorithms with a specific strategy or trading system before letting it loose on the markets. 

For beginners just getting used to algorithmic trading, keeping the initial strategies relatively simple and basic is recommended. This is even more important if they’re simultaneously learning to program in Python.

Advantages of algo trading

Algorithmic trading, leveraging advanced technology and sophisticated strategies, offers several compelling benefits to significantly enhance trading performance. From unparalleled speed in execution to the elimination of emotional biases, algo trading is reshaping financial trading. 

Here are its few key advantages:

Increased speed and efficiency

Algorithms can execute trades with lightning speed, far faster than human traders. This high-speed execution is critical in markets where price changes happen in milliseconds. It helps traders seize opportunities and minimize slippage. 

Fast speed also helps algos find practical applications in high-frequency trading (HFT).

Reduced emotional decision-making

Human emotions like fear and greed can lead to impulsive and irrational trading decisions. Algorithmic trading eliminates these emotional factors, ensuring trades are executed based on predefined rules and criteria, leading to more disciplined and consistent trading.

Better access to many data and market indicators

Algorithms can analyze a vast amount of market data and indicators simultaneously. This data-driven approach facilitates informed trading decisions and enables opportunities or patterns that may not be evident to human traders.

Amplified backtesting and optimization for improved performance

Backtesting and optimizing algorithms beforehand using historical data helps fine-tune strategies, identify weaknesses, and enhance overall performance, ultimately increasing the likelihood of success. 

In fact, backtested data can help traders feel more secure in their decisions. 

Potential for automation and 24/7 trading

Algorithms can operate around the clock, acting upon opportunities in different time zones and ensuring continuous monitoring of positions. This 24/7 nature of algo trading can be particularly advantageous in global markets, although investors should bear transaction costs and similar factors in mind.

Popular easy trading algorithms for beginners 

For beginners venturing into algorithmic trading, selecting the right strategy that aligns with their understanding and comfort level is crucial. Algo trading encompasses various techniques, each with its own set of principles and market approaches. Here are a few popular and relatively simple trading algorithms suitable for beginners:

Mean reversion

Mean reversion strategies are based on how temporary high and low asset prices are. Think of it this way: Assets will revert to their average (or mean) value over a period of time. 

The trick is to identify when the next mean reversion is about to take place and take action to make a profit. If a reversion will increase an asset’s price, you should buy; likewise, if the price is about to drop because of a mean reversion, you should sell.

Trend following

Better known as time-series momentum, trend following requires paying close attention to the historical returns on an investment and then comparing them to the asset’s current trend. The idea is that future asset price returns will be in line with historical ones. 

In other words, if an asset's price shows signs of rising, it's likely to continue rising in the short term (and vice versa).

These algorithms also depend on closely defining instances where volatility increases, volume profiles skew, or range breakouts occur. Here, traders can implement a simple moving average crossover, which compares the short-period moving average values of specific assets against their long-period moving average value and then triggers a buy order (or a sell order, if the inverse is happening).

Pairs trading/long-short equity

Pairs trading, also called long-short equity, involves matching a long position with a short position for two highly correlated assets. The strategy is best used when there are correlation discrepancies. Because, historically, the two assets tend to maintain a highly correlated relationship, a change in the value of one is often considered an indicator that the other asset is about to follow suit. 

In essence, programming a pairs-trading algorithm is all about analyzing historical data to first identify two highly correlated asset pairs.

Chart pattern analysis

Chart pattern analysis is a highly versatile trading strategy adaptable for intraday trading and longer-duration trading. It’s a form of quantitative trading and involves taking market data from a specific period and processing that data in a visual form. As patterns emerge and technical indicators provide actionable information, traders can use these to make predictions about market activity. 

Some common chart patterns include bullish flags, head and shoulders, and rounding bottom.

Arbitrage

Arbitrage strategies are a favorite of hedge funds but aren’t necessarily complicated for beginners. For example, let's say a company announces a merger or acquisition, and the target company's stock price doesn't immediately reach the acquisition offer price. An arbitrage strategy might buy the target company's stock at its current lower price, anticipating it’ll eventually rise to the offer price once the merger is completed. 

This strategy aims to profit from the price difference between the current market price and the expected future price based on the merger terms and can be accomplished through algo trading. 

Fundamental analysis

Fundamental analysis aims to determine an asset's fair market value and focuses on analyzing the intrinsic economic and financial factors that are directly related to the asset. Anything that affects the asset's value, from larger factors like the general economic outlook to smaller ones like company management, can all be considered relevant to fundamental analysis.

The goal is to generate an intrinsic value for the asset and, after comparing it with its actual value, appropriately make a buy or sell order.

Portfolio rebalancing

Investment portfolios often leverage index funds, each linked to its benchmark index. These funds have periods where they rebalance their holdings to bring them in line with the index they're tracking. 

If investors can identify the securities large index funds will need to buy and sell during rebalancing, they can potentially make profitable trades. An algorithm can make multiple buy and sell decisions before a human trader can complete even one.

Begin your algorithmic trading journey with Composer  

Learning algorithmic trading is a major endeavor, but it's certainly worth the effort. Unlocking the potential of automated trading, thanks to well-coded algorithms, is a suitable reward for spending all that time and energy developing an investment strategy. Even a basic, beginner-level algo trading strategy can bear fruit, offering you even more incentive to keep going.

That's why many algo traders just starting out turn to Composer as their algo trading platform of choice. Composer's approach to algo trading incorporates a no-coding solution that lets you get started trading with algorithms in a fraction of the time it would take you otherwise. Having the option to begin actively trading with Composer's unique tools while still working on and testing your own algorithm behind the scenes offers the best of both worlds to beginner algorithmic traders. 

Sign up for a free 14-day trial today.