What Is Algorithmic Trading
Algorithmic trading, often called algo trading, is the practice of trading financial assets (e.g., stocks, commodities, and cryptocurrencies) using computer-based algorithms.
Algorithmic trading is a highly automated investment process in equities, currencies, futures, and exchange-traded bonds and derivatives, where an algorithm selects the investments and implements the trades to achieve the desired portfolios.
Barbosa, R. P., & Belo, O. (2008)
Algorithmic trading accounts for roughly 70% of trading volume in U.S. capital markets.
It has existed since the late 1980s, when the Internet first became publicly available. The transition to decimalization (i.e., decimal trading) in the U.S. stock market after 2001 tightened bid-ask spreads, increased liquidity, and significantly contributed to the proliferation of algorithmic High-Frequency Trading (HFT).
How Does Algo-Trading Work
Algo trading works by feeding market price, volume, and other data into a computer program designed to follow a specific trading strategy.
The program interprets the market data and potentially runs it through additional scripts designed to generate technical trading signals, thus gaining a picture of the current state of the market.
Depending on the algorithm’s sophistication, it may further analyze and interpret the signals or simply trigger a predefined condition to trade once a signal is generated.
Algorithmic trading is a form of automated trading that consists in [sic] the use of computer programs for placing trading orders, with the computer algorithm deciding on the different characteristics of the order, such as the size and the price.
Vynckier et al., (n.d.)
What Are the Advantages and Disadvantages of Algo-Trading
The major advantages of algorithmic trading over manual trading revolve around trading volume, accuracy, and diversification. The disadvantages concern over-fitting trading strategies, overreliance on automation, and compounding transaction fees:
Advantages of Algo Trading
- Trading frequency: Algorithms can process market data much faster than humans, enabling them to create and track hundreds, even thousands, of orders every minute.
- Diversification: Manual traders can only effectively trade a handful of financial assets at any given time before they become overwhelmed by all the information processing required. In contrast, algo trading affords the trader the ability to tap into dozens of markets simultaneously. This exposes them to more opportunities for profit and protects the trader’s portfolio from unexpected market changes.
- Accuracy and discipline: Manual traders can become distracted, make mistakes, and react emotionally to changes in the market. Algorithms, on the other hand, will follow their directives to the letter; they will never get emotional or distracted. They also analyze and store all their trading data for later analysis, something manual traders may struggle with.
- Backtesting: Algorithmic trading strategies can be backtested using a backtester — software designed to deploy automated trading strategies on real-world historical market data. Backtesting allows traders to optimize the performance of their algorithmic trading strategies before they’re deployed to live markets.
Disadvantages of Algo Trading
- High trading fees: Trading on exchanges incurs processing fees. The high-frequency trading (HFT) often performed by algorithmic bots can significantly impact profitability if those aren’t taken into account.
- Overreliance on automation: For some traders, exclusive use of algorithms to trade may lead to an inability to spot specific market opportunities. Sometimes, market signals that are obvious to humans like extremely high volume, may be irrelevant to improperly or inadequately configured algorithmic bots.
- Over-fitting: Algo trading goes hand in hand with backtesting and too much strategy optimization may lead to overfitting — aligning a strategy so precisely to past market data that it no longer works in currency market conditions.
Strategies for Algorithmic Trading
Practically every manual trading strategy can be deployed algorithmically. However, some benefit from automation more than others. Here are some popular cases for algo trading:
Index Fund Rebalancing
Index funds periodically rebalance their holdings. This creates a trading opportunity for algorithmic traders who anticipate the price changes brought on by the buying and selling done by funds.
Algos and Arbitrage
Arbitrage is an algorithmic trading staple. Arbitrage refers to trading the same asset across different markets (e.g., on two different exchanges).
Because these markets are separate, the price of the traded asset is almost always slightly different between them. Algorithms can be deployed to track this difference, such as buying and selling the same asset on either exchange and pocketing the difference.
Mean Reversion
Mean reversion is another popular algorithmic trading strategy.
The theory behind mean reversion is that prices always tend to revert to the mean (i.e., average) price over time. This creates a simple overbought or oversold signal for algorithmic bots to use when trading — if prices are above the mean, they sell, and if they fall below it, they sell.
Market Timing
If configured correctly, Algos can be incredibly precise, especially in the short term. This makes them ideal tools for timing market events such as channel breakouts, trend changes, etc. Because of their speed, they are well suited to this type of trading.
Algorithmic Crypto Trading
Algorithmic crypto trading works just like any other algorithmic trading, only for cryptocurrencies.
There are numerous popular centralized cryptocurrency exchanges (CEXs) providing algorithmic trading services like:
- Binance
- Kraken
- Bybit
- Gate.io
Third-party bot providers also exist, with some of the most notable being:
- Cryptohopper
- 3Commas
- Dash 2 Trade
Trading crypto algorithmically can be profitable if the market’s particular quirks, specifically its volatility and security issues, are taken into account.
When trading crypto algorithms, traders should avoid unpopular products and stick to well-established, centralized exchanges. Decentralized algorithm trading in decentralized finance (DeFi) is also possible, but the security risks are notable.
Conclusion
Algo trading is everywhere, from crypto to commodities, stocks to all sorts of financial instruments. In the ever-growing race for profit and with the continuing evolution of the world’s financial markets, bots have become the de-facto trading tool driving a big part of global finance. Their benefits are obvious, and their downsides are few and far between.
Barbosa, R. P., & Belo, O. (2008). Algorithmic trading using intelligent agents. In Proceedings of the 2008 International Conference on Artificial Intelligence (ICAI 2008). University of Minho.
https://www.researchgate.net/publication/220835450_Algorithmic_Trading_Using_Intelligent_AgentsVynckier, E., Pirastru, G., Ashley, J., Manthey, K., & Miller, D. (n.d.). Algorithmic trading HPC & AI reference guide. Dell Technologies and NVIDIA.
https://www.delltechnologies.com/asset/en-us/products/ready-solutions/industry-market/hpc-ai-algorithmic-trading-guide.pdf
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