Cryptocurrencies are known for their high volatility, with prices swinging within microseconds. While these fluctuations present numerous trading opportunities, there are times when you may need to exit a losing trade, hoping for the price to turn positive.
Recently, such a situation occurred for a fraction of a second, which you may not have noticed, resulting in a loss. Algorithmic trading offers a solution to avoid being tethered to your laptop, constantly monitoring chart movements.
What is Algorithmic Trading?
Crypto Algorithmic Trading or Algo Trading is an automated system that relies on computer programs and mathematical algorithms to execute transactions. They execute predefined rules, or algorithms, designed to analyze market conditions and open or close positions based on specific indicators.
A key component of crypto algo trading is machine learning (ML) techniques. It trains the algorithm to evolve with the market and new data inputs. Some standard ML used here are decision trees, artificial neural networks, and support vector machines.
Artificial Neural Networks mimic human decision-making processes. In crypto trading, an ANN acts like a trader who watches asset prices around the clock. It adjusts trading strategies using past and current data and other relevant metrics.
The ANN’s interconnected nodes are divided into three layers: the input layer (independent variables), the hidden layer (coefficients or weights), and the output layer (dependent variable).
Inputs are fed into the model to be processed. If the result of this summation is a value that exceeds the threshold, the node adjusts the weight metrics.
How does Algorithmic Trading Work?
Although algorithmic trading can be complex, we have broken down the process into simple steps:
Data Analysis
The process begins with analyzing historical data of a specific cryptocurrency, including order book, volume, price, and sentiment from social media. For instance, examining an asset’s closing prices over the past 30 days helps gauge its volatility and market trends.
Strategy Implementation
Based on your analysis, you decide on the optimal trade and set instructions on the platform. For example, you might buy a cryptocurrency if its price drops by 5% in a single day, anticipating a rebound.
Opportunity Identification
Your algorithm monitors real-time market data to spot opportunities aligned with your strategy. For example, it identifies a potential buying opportunity upon detecting a 5% price drop.
Automated Execution
Once an opportunity is established, the algorithm performs the trade automatically. In our example, it would place a buy order when the price drops by 5%.
Risk Management
The algorithm might set a stop-loss order 10% below the purchase price to mitigate losses.
Monitoring
Lastly, monitor the algorithm’s performance and adjust strategies as needed. For instance, you may need to adjust if the cryptocurrency price continues declining after purchase.
Trading Strategies for Crypto Algorithmic Trading
When using algorithmic trading, you can use the following strategies.
1. Trend Following
In this strategy, you identify and follow a market trend, which could be upward (bullish) or downward (bearish). You can profit from this approach when there is a large price movement. For example, if Bitcoin is in an upward trend, a trend-following algorithm would continue buying until the trend reverses.
2. Mean Reversion
This approach assumes that the cryptocurrency price will return to its average over time. This strategy allows you to take advantage of price fluctuations. For example, a mean reversion algorithm would buy Ethereum when its price falls below the average or sell when it rises above the average.
3. Momentum Trading
In this approach, you buy cryptocurrencies that have been increasing in price and sell those that have been falling. This approach lets you capitalise on market trends. For instance, if Ripple’s price rises quickly, a momentum trading algorithm would buy and hold until the upward trend weakens.
4. Statistical Arbitrage
This method involves profiting from pricing discrepancies between correlated cryptocurrencies. It allows you to capitalise on temporary price divergences. For example, if Litecoin and Bitcoin Cash normally move together but diverge, a statistical arbitrage algorithm would buy the underpriced asset and sell the overpriced one.
5. High-Frequency Trading (HFT)
This strategy involves making many trades in a very short time to profit from small price changes. It allows you to benefit from minute price changes that others can’t exploit. For example, an HFT algorithm could make hundreds of trades per second, buying low and selling high.
6. Sentiment Analysis
This strategy relies on analysing market sentiment, often through social media or news sources, to anticipate price changes. For example, if sentiment towards Ethereum is positive across social platforms, a sentiment analysis algorithm might predict a price increase and execute a buy trade.
Advantages of Crypto Algorithmic Trading
Algo trading has many perks. Let’s discuss the key ones here.
1. Speed and Efficiency
Algorithms can manage vast amounts of data and execute trades at lightning speeds. For example, you can program the algorithm to monitor the price of a specific cryptocurrency and execute a trade when certain conditions are met, all within milliseconds. This speed and efficiency can give you an edge in volatile markets.
2. Reduced Costs
Algorithmic trading often results in reduced costs related to trading. Since trades are executed automatically, there is less need for human traders, which can reduce labour costs. Moreover, because algorithms can execute trades quickly and efficiently, they can help minimize the impact of market slippage (the distinction between the predicted price of a trade and the price at which you have placed the trade).
3. Unbiased Trading
Algorithms adhere to set criteria, avoiding emotional influences. For example, a human trader might hesitate to exit a losing trade, hoping for a turnaround. An algorithm, on the other hand, would execute the sale if the rules require it, potentially cutting losses.
4. 24/7 Market
Unlike traditional markets, crypto markets operate 24/7. A human can’t track markets continuously, but an algorithm can, making trades at any hour. For example, if Ethereum’s price hits a certain threshold at 3 AM, the algorithm can execute a trade while you are asleep.
5. Back-testing
By using historical data, you can test your algorithmic trading strategies. This process offers crucial insights and may improve strategy success. For example, testing a strategy of buying Bitcoin after a 10% weekly drop can reveal its historical performance.
6. Scalability
Algorithmic trading can quickly scale up or down based on your needs. For example, you may start trading with a small amount of crypto and gradually increase it as you become more confident in your algorithm.
Disadvantages of Crypto Algorithmic Trading
While algorithmic trading has many advantages, it also comes with various risks.
1. Complexity
You need a deep knowledge of financial markets and programming to carry out algorithm trades. This is not something that you can easily pick up as a beginner.
2. Technical Issues
Algorithms are only as good as the technology supporting them. If there is a glitch in the system or an internet outage, your algorithm won’t be able to execute trades during a critical trading period. You will not only miss out on an opportunity here but may face a loss.
3. Lack of Flexibility
Algorithms stick to pre-set rules and can’t adjust to sudden market changes. During a market crash or surge, they might continue executing trades based on outdated data, causing potential losses.
4. Over-Optimisation Risk
An algorithm might be overly tailored to past data, failing to predict future market conditions accurately. This is similar to ‘curve fitting,’ where the model works well with historical data but underperforms with new data.
5. Costs
Creating trading algorithms involves considerable expenses, such as hiring expert programmers and subscribing to high-speed trading platforms. For small traders, the costs may surpass the potential profits.
6. Emotional Detachment
While algorithms help avoid impulsive decisions driven by emotions, they may also cause traders to become detached and less critically involved in market analysis.
7. Flash Crashes
High-frequency trading algorithms can cause flash crashes. It is the scenario where prices drop dramatically in a very short time. The infamous example of this event was when, in 2010, the Dow Jones Industrial Average fell by almost 1,000 points in around 10 minutes and erased $1 trillion in market value.
The crash was associated with a single selling order involving many E-Mini S&P contracts and subsequent aggressive selling orders executed by high-frequency algorithms.
8. Regulatory Risks
Governments around the world are still formulating laws on how to regulate cryptocurrency. Future regulations could affect algorithmic crypto trading and limit the types of strategies that you can use, rendering some algorithms useless.
Conclusion
In a market where prices can swing drastically in moments, automated systems driven by complex algorithms help traders execute trades swiftly without requiring much manual intervention. Whether identifying trends, exploiting price discrepancies or capitalising on market sentiment,crypto algorithmic trading operates precisely and quickly.
Frequently Asked Questions
Q. Is Crypto Algorithmic Trading in cryptocurrency profitable?
Algo Trading leverages historical data, statistical models, and predefined rules to make rapid, precise decisions. It eliminates emotional biases and allows the systematic execution of strategies, potentially improving profitability. However, success depends on well-designed, tested strategies and an understanding of market conditions.
Q. How does algorithmic trading differ from automatic trading?
The process of algorithmic trading involves the use of computer algorithms to automatically carry out predefined trading strategies. It relies on mathematical models and historical data to decide to buy or sell financial assets. Automatic trading, on the other hand, broadly refers to any form of trading that operates with minimal human intervention, including algorithmic trading and simpler forms like automated order execution.
Q. How do crypto trading bots differ from algorithmic trading?
Crypto trading bots are automated software programs that place trades on behalf of users based on predefined strategies. They differ from algorithmic trading because bots often operate with simpler, predefined rules and may not require sophisticated mathematical models.
Q. What are the risks associated with crypto algorithmic trading ?
Algorithmic trading carries risks such as technological failures leading to execution errors, market volatility causing unexpected losses, and susceptibility to cyber threats like hacking. Additionally, algorithmic strategies reliant on historical data may not accurately predict future market conditions, which may lead to financial losses.
Q. What strategies should one follow when involved in algorithmic trading?
Strategies for algorithmic trading include thorough backtesting to validate performance. It would be best to implement risk management protocols to control exposure while continuously monitoring live trading for anomalies. Try to adapt strategies based on market conditions.
Krishnan is a Bangalore-based crypto writer dedicated to simplifying complex crypto concepts. He covers blockchain, DeFi, and NFTs, with a focus on real-world asset tokenization and digital trust. Previously he has written on Real Estate related assets for NoBroker. Krishnan holds a B.Tech degree from the College of Engineering Trivandrum.
