Algorithmic Trading Tutorial for Beginners: A Complete Guide

Algorithmic trading dominates global financial markets, with more than 80% of daily trading volume in the stock market now attributed to it. The algorithmic trading market is projected to grow at a CAGR of 10.36% between 2021 and 2026, reaching a staggering $28.78 billion in value.

Given these projections, every dedicated trader should understand how algorithmic trading works and grasp its most popular strategies. 

This complete guide will explore algorithmic trading: how it works, benefits, strategies, and tax implications. We’ll also discuss how Composer’s no-code editor can help you start your algo trading on the right foot.

What is algorithmic trading?

Algorithmic trading, also known as automated trading, uses computer programs to execute trades automatically based on a predefined set of rules or conditions. A subset of quantitative trading, algorithmic trading uses predetermined if/then statements that tell the trading software when and how to place trades. 

Algorithmic trading’s roots date back to the early 1970s when the first algo traders began facilitating trading with computerized systems. This effort accelerated after the NASDAQ electronic stock exchange emerged in 1971, as it paved the way for more advanced trading algorithms to evolve. The 1980s and 1990s witnessed rapid technological advancements, such as high-frequency trading and electronic communications networks (ECNs), further enhancing trading software capabilities. 

Experts group trading algorithms into two basic categories: rule-based and machine learning-based. Rule-based algorithms rely on specific criteria or technical indicators to make trading decisions. In contrast, machine learning-based algorithms use historical data to adapt their trading strategy over time.

Machine learning-based algorithms require a substantial understanding of deep learning fundamentals, statistics, and programming. For this reason, we’ll focus on rules-based algorithms, as rules-based stock trading bots are more accessible for beginner algo traders. 

Benefits of algorithmic trading

Algorithmic trading isn’t just a flashy trend. It offers traders effective tools for limiting loss, managing risk, and reducing time to market. Its primary advantages include the following:

• Lower transaction costs: Transaction costs accumulate with each trade, eating away at returns. Algorithmic trading aims for efficiency, making the fewest trades for the most profit. 

• Reduced human error: Algorithmic trading limits errors by eliminating the need to manually place trades. This reduces the influence of trader’s emotions, facilitating precise and strategic thinking. 

• Better execution: By concurrently checking numerous market conditions, algorithmic trading strategies ensure quick and accurate order placement. Low latency limits the impact of stock price spikes, meaning orders fill at or near target levels. 

• Backtesting: It’s easy to backtest algo trading strategies using historical or real-time price data. Backtesting provides valuable information that helps determine whether you have a viable stock trading strategy. 

The most popular indicators used in algorithmic trading

Algo traders rely on various indicators when evaluating trading patterns and market conditions. Trend analysis, chart patterns, and technical indicators help traders identify opportunities and provide a framework for executing trades.

Here are some popular indicators in algorithmic trading:

Moving averages (MAs)

A moving average calculates the average price of a security over a specified period, smoothing out price fluctuations and revealing the underlying trend. There are two common types: simple moving average (SMA) and exponential moving average (EMA).

SMAs calculate a stock’s average price over a specific period, whereas EMAs emphasize the most recent data points in a series. Traders often use MAs in crossover strategies, where a short-term MA crossing a long-term MA generates buy or sell signals.

Relative strength index (RSI)

The RSI is a momentum oscillator that measures the speed and change of price movements. RSI ranges from 0 to 100, with values above 70 considered overbought and below 30 considered oversold. 

Traders use RSI to identify potential reversals and confirm trends, making it popular in momentum trading strategies, determining underlying demand or supply, and evaluating market sentiment.  

Bollinger Bands

Bollinger Bands contain a centerline (usually an SMA) and two outer bands that indicate volatility. When the bands expand, it signifies high volatility; when they contract, it implies low volatility.

Traders often use Bollinger Bands in mean reversion strategies or to identify breakouts, buying long when the price breaks through the upper band and selling short when the price falls below the lower band. 

Stochastic oscillator

The stochastic oscillator is a momentum indicator that compares a security’s closing price to its price range over a specified period. It ranges from 0 to 100, with measurements above 80 suggesting an asset is overbought and below 20 indicating it is oversold.

Traders use the stochastic oscillator to detect potential trend reversals or to confirm trend direction.

Moving average convergence/divergence (MACD)

MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. You can calculate the MACD by subtracting the 26-day EMA from the 12-day EMA, which results in the MACD line.

Algo traders often use it with a signal line (9-day EMA of the MACD), which helps them generate buy and sell signals.

Average true range (ATR)

ATR is a volatility indicator that measures the degree of price movement or price range within a given time frame. It is calculated by taking the average of the true range over a specified period.

Traders often use ATR to set stop-loss orders and determine position size based on market volatility.

On-balance volume (OBV)

OBV is a momentum indicator that relates volume to price change. It accumulates volume on up days and subtracts volume on down days, providing a running total that helps traders identify buying and selling pressure in the market.

By comparing price action and OBV, traders can evaluate market sentiment and identify bullish or bearish signals. 

Fibonacci retracements

Fibonacci retracements are horizontal lines drawn on a price chart, indicating potential support and resistance levels based on the Fibonacci sequence. Each level in the sequence equates to a percentage, with levels at 23.6%, 38.2%, 61.8%, and 78.6%.

Traders use these levels to identify potential reversals and target entries or exits.

Ichimoku Cloud

The Ichimoku Cloud is a comprehensive technical indicator that provides an overview of a security’s trend, momentum, and support and resistance levels. Developed by Goichi Hosoda over 30 years and released in the late 1960s, it consists of five lines: Tenkan-sen, Kijun-sen, Senkou Span A, Senkou Span B, and Chikou Span. These lines include a 9-period average, 26-period average, 52-period average, and an average of the 9- and 26-period averages.

Traders use the Ichimoku Cloud for trend identification, potential reversals, and breakout signals.

Parabolic SAR

The parabolic SAR is a trend-following indicator that shows potential reversal points in the market. Depending on the trend direction, it appears as a series of dots placed above or below the price.

Traders use the parabolic SAR to set trailing stop-loss orders and to determine trend reversals.

Types of algorithmic trading strategies

Algorithmic traders use various strategies depending on their preferences, skills, and favored indicators. Some common algorithmic trading strategies include the following:

Momentum strategies

Momentum strategies capitalize on the continuance of an existing market trend. These strategies identify securities with strong price movements and trade in the direction of the trend. Momentum traders typically use technical indicators, such as moving averages, RSI, and MACD, to confirm the strength and direction of the trend.

Mean reversion strategies

Mean reversion strategies assume that prices will eventually revert to their historical averages. Traders identify overbought or oversold securities and take positions in anticipation of a price reversal. Bollinger Bands, RSI, and stochastic oscillators are common indicators used in mean reversion strategies.

Market making strategies

Market making strategies involve providing liquidity to the market by placing both buy and sell orders simultaneously, profiting from the bid-ask spread. Algorithmic market makers use high-frequency trading techniques and sophisticated order management systems to manage inventory risk and minimize exposure to adverse price movements. This strategy relies heavily on low latency to exploit minor price differences while managing volatility. 

Arbitrage strategies

Arbitrage strategies exploit price discrepancies between related financial instruments or markets. These strategies can involve trading across different exchanges, currencies, or securities. Algorithmic arbitrage traders use real-time data feeds and advanced algorithms to identify and capitalize on temporary price inefficiencies.

News-based strategies

News-based strategies use algorithms to analyze news releases, social media sentiment, and other data sources to generate trading signals. These strategies can react quickly to market-moving events, such as economic data releases or corporate earnings announcements, and execute trades in response to the detected sentiment or anticipated market impact. However, the best news-based strategies focus on plotting moves in advance rather than solely reacting. 

Sentiment analysis strategies

Sentiment analysis strategies use natural language processing and machine learning techniques to gauge investor sentiment from various data sources, such as news articles, social media posts, and analyst reports. Traders then use this sentiment data to make informed trading decisions, anticipating potential price movements based on the prevailing market sentiment.

Swing trading strategies

Swing trading strategies aim to capture gains in a security over a short to medium time horizon. These strategies rely on technical analysis to identify trend reversals, support and resistance levels, and other chart patterns that suggest potential price movements.

Swing traders typically hold positions for several days to weeks, often relying on indicators such as moving averages, volume, and RSI for trade signals. 

Trend following strategies

Trend following strategies aim to capture gains by trading in the direction of the prevailing market trend. These strategies use technical indicators, such as moving averages, the Ichimoku Cloud, and parabolic SAR, to identify and confirm trends. Trend followers typically enter trades when a trend is established and exit when the trend weakens or reverses.

Scalping strategies

Scalping strategies involve entering and exiting trades quickly, capturing small price movements, or exploiting temporary price inefficiencies. Algorithmic scalpers use high-frequency trading techniques and low-latency infrastructure to execute trades rapidly and minimize market impact. Popular indicators used in scalping strategies include MACD and candlestick charts. 

Buying the dips

Buying the dips refers to purchasing a security when its price experiences a temporary decline, with the expectation that it will rebound and resume its upward trend. Traders employing this strategy use technical analysis to identify support levels, oversold conditions, and other signals that suggest a suitable entry point for a long position.

This strategy can lower your average cost basis and typically yields the best results in overall uptrends. 

Tax implications of algorithmic trading

The tax implications of algorithmic trading can be complex, depending on factors such as trade frequency and the financial instruments being traded. Traders should know potential tax liabilities, including short and long-term capital gains taxes and K-1 returns for certain investment vehicles. 

Important tax concepts for traders include:

• Realized versus unrealized gains: Realized gains occur when you materialize a profit by selling a stock for a gain. In contrast, unrealized gains exist only on paper, as you still hold the asset showing a gain. 

• Tax-loss harvesting: In some cases, losing can provide a benefit. You can claim a tax break from investing losses, as claiming losses can offset your tax liability by reducing your taxable income. 

• Wash sales: A wash sale occurs when an individual sells a security at a loss and buys or acquires substantially identical securities within 30 days before or after the sale. This rule is designed to prevent traders from abusing tax-loss harvesting.

Before algo trading, consult a tax professional to ensure compliance with local tax laws. Other important considerations include your optimal account type, estate planning, and how strategies such as portfolio rebalancing will affect your tax situation. 

What are the technical requirements for algorithmic trading?

Algorithmic trading requires sophisticated software and a solid understanding of market data, risk management, and technical indicators. Although you don’t need the most high-tech equipment, you need access to the following minimum requirements: 

1. Low-latency infrastructure

Algorithmic trading relies on timely order placement and execution. You need a fast network connection—at least 100 megabits per second (Mbps)—to avoid glitches or dropped connectivity. Ideally, you should use an Ethernet connection rather than Wi-Fi, as a hard-wired Ethernet cable connection offers greater stability and security.

2. Robust and scalable software platform

Choose an algo trading platform that suits your short and long-term goals. Ideally, the platform should offer various tools and sufficient scalability so you can grow your strategy as your trading skills increase. 

3. Reliable market data feeds

Along with a stable connection, you need fast and reliable market data. In volatile markets, prices can change rapidly and significantly. With real-time market feeds, you receive reliable, up-to-date data about stock price movements. Other benefits include reduced costs, accurate data analysis, improved workflow, and faster response times. 

4. Coding knowledge 

Most algorithmic traders possess some computer programming knowledge. Common languages used in algo trading include Java and C++, but most traders prefer using Python because it’s open-source, relatively simple, and excels at data analysis.

Most automated trading systems let you import Pandas, NumPy, or other high-performance language libraries for building your algorithms and backtesting strategies. Others let you leverage charting software (like TradingView) or use no-code editors (like Composer) to develop your trading strategy.  

Discover the ultimate algorithmic trading platform with Composer

As algorithmic trading continues to evolve and reshape the financial landscape, traders must stay informed about the latest trends, tools, and approaches. After exploring the history, strategies, tax implications, and top algorithmic trading platforms, you’re ready for the exciting and dynamic world of automated trading systems.

Whether you're a seasoned trader or just getting started, there's never been a better time to dive into the realm of automated trading systems and unlock their potential. 

Composer is a no-code algorithmic trading platform designed for new and advanced algorithmic traders. We provide a user-friendly interface for building and customizing strategies without coding knowledge. Our library of pre-built strategies, institutional-grade data, and free backtesting enable traders to test and optimize their strategy before entering the live market.

Try Composer for yourself and discover the best algorithmic trading has to offer.