The Difference Between HFT and Algorithmic Trading
Algorithmic, high-frequency, algo-, or automated trading are terms that often show up in trading-related articles. It’s easy to get confused and find yourself lost in all the definitions. This happens because both — the technological and financial landscapes have many nuances, and slight differences in operations create new terms.
Moreover, in some cases, certain phrases end up being used interchangeably and adding to the disorientation. So what is algorithmic trading and does it differ from HFT? To help you better understand this area of finance, we’re going to discuss each trading method in more detail.
Algorithmic Trading
Algorithm trading is also known as algo-trading or black-box trading. It’s a trading solution that uses coded sets of algorithms and execution strategies to submit orders to a market or exchange automatically after a technical analysis. So how do trading algorithms work?
Algorithm trading involves the use of predefined sets of variables such as price, time, and volume by pre-programmed trading instructions. These instructions, known as an execution algorithm, send child orders (small slices) to make up for larger orders that are too big to send at once.
Slicing into small orders helps attain good pricing within a specified time. Reduction in the size of orders is good for an aggressive market. Thus, making algorithmic trading widely applicable to trading with high market volumes such as mutual funds, investment banks, hedge funds, etc.
The main objective of algo-trading is not just to profit by trading but to save costs, minimize market impact, and the execution risk of a trading order. With algorithmic trading software, traders don’t need to watch stocks or send slices manually.
Benefits of Algorithmic Trading
Now that you know what algorithmic stock trading entails, you’re likely already guessing the advantages it can bring. Just to be sure, let’s go over some of the main ones.
Better prices. With algo-trading, trades are executed at the best possible price thanks to being instantly timed to avoid large price fluctuations.
Improved accuracy. If a computer executes a trade instead of a person, there’s a lower likelihood of a mistake being made. The human factor often affects the levels of accuracy. Hence, when software that uses trading algorithms is involved, precision is increased.
Increased speed. Since algorithms are pre-written and executed automatically, the speed at which trades are carried out is significantly boosted.
Lower transaction costs. Thanks to traders not needing to monitor the markets as closely and execution being carried out without their involvement, their time is freed up. Thus, transaction costs are reduced and traders can engage in other activities.
Algorithmic Trading Strategies
Algorithmic trading can be approached in a few ways. Everything depends on the end goal and preferences. These are the nine most common types of algorithmic trading strategies:
- Trend-following. It capitalizes on persistent price trends in financial markets. With it, traders analyze historical data and technical indicators to identify trends and generate buy or sell signals. The trend-following strategy helps capture profits by riding trends until signs of reversal. It has a potential to boost consistent returns in markets with strong and prolonged trends.
- Arbitrage leveraging. This approach exploits price discrepancies across platforms or exchanges. It helps traders monitor multiple markets for differences in asset prices and swiftly execute trades to capture the price differential. With its precise and rapid execution, it minimizes market exposure and risk.
- Index fund rebalancing. It adjusts a portfolio’s holdings periodically to maintain a target allocation. This strategy is based on the principle of maintaining a consistent exposure to different asset classes or sectors represented by the underlying index. It ensures diversification, mitigates risk, and aligns with the index’s objectives.
- Mathematical model-based. It uses quantitative models and algorithms to make trading decisions. Traders develop mathematical models that incorporate various market data, indicators, and statistical techniques to generate signals for buying or selling assets. This kind of strategies offer systematic approach, potential for automation, and ability to exploit patterns and signals in the market for consistent profitability.
- Volume-weighted average pricing. This is a method used to calculate the average price of an asset based on its trading volume throughout a given period. This strategy assigns greater weight to trades executed with higher volumes, reflecting the market’s actual buying and selling pressure. Traders utilize VWAP to assess whether their trades were executed at prices favorable to the average price. It is particularly helpful for large institutional investors who aim to minimize market impact and achieve cost-efficient executions.
- Time-weighted average pricing. TWAP focuses on time-based execution, aiming to minimize market impact by spreading trades evenly throughout the chosen timeframe, regardless of volume fluctuations. This strategy provides traders with an average price that closely aligns with the market’s average price during the chosen timeframe.
- Mean reversion. This strategy is a trading approach that comes in handy when asset prices tend to revert to their mean or average levels over time. It’s based on the belief that when an asset’s price deviates significantly from its historical average, it is likely to eventually return to that average. With it, traders identify these price divergences using statistical techniques and technical indicators and they take positions opposite to the recent price movement.
- Percentage of volume. It executes trades based on a specified percentage of the total trading volume for a given asset. Traders utilizing this strategy determine a desired percentage of the overall trading volume they wish to trade and then execute orders accordingly. By implementing the POV strategy, traders can strike a balance between market participation and minimizing their influence on price movements.
- Implementation shortfall. It seeks to balance the urgency of executing a trade with the desire to minimize transaction costs. It helps achieve the best possible execution price by considering factors such as market liquidity, price volatility, and order size. The strategy involves a series of decisions, including determining the optimal timing for trade execution and carefully managing trade-offs between price impact and the risk of not completing the trade.
Difference Between Algorithmic Trading and Automated Trading
Before we move on to high-frequency trading, we have to talk about one more term that you’ll likely see come up.
At the beginning of today’s piece, we mentioned that some trading-related terms are used interchangeably. In the case of algorithmic and automated trading this is also true. Some traders consider the two as the same, but we believe there is one key difference.
As discussed, algorithmic trading is used to buy and sell large amounts of assets while minimizing transaction costs and increasing speed. These systems only execute the provided orders. Conversely, automated trading software involves the complete automation of the trading process. This means that even the buying and selling decisions are automatic.
So, make sure you remember this detail when diving deeper into the subject of financial trading.
High-Frequency Trading
High-Frequency Trading is a subset of algo-trading. Its major characteristics are high speed, a huge turnover rate, co-location, and high order-to-order ratios. It operates by using complex algorithms and sophisticated technological tools to trade securities.
Software used for high-frequency trading manages small scale trade orders sending them to a market or exchange at great speed. It benefits from bid-ask spreads. The height of the speed involved in the transaction process makes this trading approach a market maker.
Learn more: In Pursuit of Ultra-Low Latency: FPGA in High-Frequency Trading
Real-time data feeds are needed to reduce microseconds delay and avoid profit loss. Usually, the latency should be between 300 – 800 nanoseconds. This is achieved with a high-performance software, low-latency networks, and FPGA-based hardware acceleration.
Opportunities are noted through sensing large size orders that are pending by placing small-sized multiple orders and analyzing the pending and execution time. The successfully noted opportunities in the form of pending orders are then capitalized by adjusting prices to cover them and make profits.
Benefits of High-Frequency Trading
HFT solutions use complex algorithms to spot emerging trends in milliseconds. As you can imagine, this has some specific benefits.
Large wins on small price changes. High-frequency trading systems help traders earn significant profits even from minor price fluctuations. With these tools, financial institutions can attain significant bid-ask spread returns.
Expanded opportunities. Since algorithms are able to scan multiple markets and stock exchanges, traders are able to discover more opportunities. For example, arbitraging the same asset with minor price differences on two separate exchanges.
Higher market liquidity. By increasing competition on the market due to larger volumes and speed of execution, high-frequency trading makes markets more price-efficient. Consequently, market risk declines since there’s always someone to buy what you’re selling and vice versa.
High-Frequency Trading Strategies
High-frequency trading is a very complex process which is why it’s usually only leveraged by large institutions like proprietary firms, investment banks, and hedge funds. These organizations rely on various money-earning strategies, some more controversial than others.
Read about the Development of a High-Frequency Trading System
Either way, it’s worth quickly mentioning the most popular ones:
- Market-making. It is a type of HFT strategy where traders continuously provide liquidity to the market by simultaneously placing buy and sell orders for a particular asset. By actively quoting both buy and sell prices and quickly adjusting them based on market conditions, it’s possible to facilitate trading and contribute to market liquidity while generating profits from the spread.
- Liquidity provision. It involves continuously monitoring the market and adjusting orders to accommodate changes in supply and demand. By providing liquidity, traders can facilitate trading activity, reduce bid-ask spreads, and enhance efficiency while seeking to capture profits from the spread and transaction fees.
- Statistical arbitrage. This approach seeks to profit from pricing inefficiencies identified through statistical analysis. Traders identify relationships between multiple assets based on historical data and statistical models. They then take advantage of temporary deviations from the expected relationship by simultaneously buying and selling assets to capitalize on the expected reversion to the mean.
- Price movement ignition. Helps profit from the acceleration of price movements triggered by specific events or conditions. With it, traders closely monitor market news, announcements, or technical indicators to identify potential catalysts that can trigger significant price movements. This strategy requires a proactive and agile approach, as traders aim to capture profits from the rapid price acceleration that follows the ignition event.
