Date of publication: 
Time to read: 
Views: 
Technologies are increasingly being integrated into the financial sector and one of them is algorithmic trading. This type of trading is interesting because it provides many options for customization and trading strategies, as well as helps to completely eliminate the human factor in trading on the market.
Algorithmic trading (algotrading) - what it is
Algorithmic trading (algorithmic trading) is a process of automated trading of financial assets based on predetermined algorithms. These algorithms use mathematical models and statistical analysis to make decisions on buying or selling assets without human participation.
Principle of algotrading
Algotrading goes through several stages, starting from strategy selection and ending with analyzing the efficiency of the algorithm.
Choosing a trading strategy
The first step is to determine the strategy on the basis of which the algorithm will work. There are different approaches such as arbitrage, market making, trend following and mean reversion.
Writing an algorithm based on the strategy
Once a strategy is selected, an algorithm is developed which includes parameters for entering and exiting trades, risk levels and trading restrictions.
Testing the algorithm
Before launching the algorithm, it is tested on historical data (backtesting) to check its efficiency and identify possible problems.
Use in real trading
After successful testing, the algorithm is run in the real market. This can happen either with real money or in test mode.
Performance evaluation
The algorithm is regularly analyzed, its results are evaluated and, if necessary, adjustments are made to improve its efficiency.
Indicators (strategies) of algotrading
Many indicators are used in algotrading to work efficiently and minimize the number of any errors.
Volume Weighted Average Price (VWAP)
VWAP (Volume Weighted Average Price) is an indicator that shows the average price of an asset taking into account the trading volume, helping traders to minimize the market impact of trades.
Time Weighted Average Price (TWAP)
TWAP (Time Weighted Average Price) is used to distribute trades evenly over time, reducing the likelihood of sudden price changes.
Percentage of Volume (POV)
POV (Percentage of Volume) executes trades based on trading volume to minimize market impact.
Features of algo-trading on crypto exchanges
1. Speed and automation
Algotrading executes transactions instantly based on predetermined parameters. Human intervention is minimal, with algorithms able to analyze large amounts of data and react instantly to market events faster than any manual trader.
Trade execution time can be in milliseconds
Ability to work 24/7 without breaks
No emotional factor influencing decision making.
2. types of algorithms in crypto trading
1. HFT (High-Frequency Trading)
High-frequency trading utilizes powerful computing systems to execute thousands or even millions of trades per day. The main strategies are:
Market making - providing liquidity by placing buy and sell orders at the same time
Arbitrage - capitalizing on price differences on different exchanges.
Scalping - repeated transactions with small profits in a short period of time.
2. Medium-term and long-term algorithms
Some algorithms do not work on speed, but on analytics:
Trend strategies - buy when trending up and sell when trending down
Market-neutral strategies - minimizing risk by balancing long and short positions
Neural network models - using machine learning to predict market movements
3. API and infrastructure
For algo-trading crypto exchanges provide an API (Application Programming Interface) that allows software to interact with the platform:
REST API - suitable for medium-term trading
WebSocket API - provides real-time streaming data retrieval
FIX API - used by professional traders to work with large volumes.
Algotraders use low-latency servers and dedicated communication channels to minimize latency in data transfer.
4. Risk management and capital protection
Although algotrading eliminates the emotional factor, it does not guarantee 100% profitability. Therefore, it is important to consider:
Stop Losses and Take Profits - automatic closing of positions when the set levels are reached
Diversification - trading different assets to minimize risks
Monitoring of algorithms - regular checking of their efficiency and correction of strategies.
5. Crypto market volatility
The cryptocurrency market is characterized by high volatility, which creates both opportunities and additional risks. Unlike the stock market, it operates around the clock, which requires algorithms to adapt to sudden changes in prices and events.
6. Regulation and legal aspects
Algotrading on crypto exchanges may be subject to the laws of different countries. It is important to take into account:
Possible restrictions on automated trading
Taxation of crypto-income
Exchange policies regarding bots and APIs
Trading bots for algorithmic cryptocurrency trading
Trading bots are automated programs that execute trades on cryptocurrency exchanges. They can follow various strategies such as arbitrage, market making and trend analysis. Since the cryptocurrency market operates around the clock, crypto trading bots can also trade non-stop and without direct human involvement.
What is the difference between algorithmic trading and automated trading
Objective:
Algorithmic trading focuses on analyzing market data and applying mathematical models to optimize strategies.
Automated trading simplifies the trade execution process and reduces the need for manual intervention.
Principle of operation:
Algorithmic trading utilizes complex algorithms, machine learning and statistical models.
Autotrading simply executes trades according to predetermined conditions (e.g., when a certain price is reached).
Flexibility:
Algorithms can adapt to changing market conditions and change strategy.
Auto trading works on fixed parameters that need to be changed manually.
Level of autonomy:
Algotrading can make trading decisions on its own by analyzing complex data.
Autotrading executes predetermined commands but does not make decisions on its own.
Usage:
Algorithmic trading is used by professional traders, hedge funds and large investors.
Automated trading is convenient for retail traders and investors using trading bots.
Examples:
Algorithmic trading: HFT (high frequency trading), arbitrage, neural network predictive models.
Autotrading: bots for trading on Binance, MetaTrader with automated orders.
Indicators (strategies) of cryptocurrency algotrading
Algotrading allows you to apply many strategies, some of them are used by manual traders, but algorithms trade on these strategies much more effectively.
Arbitrage Trading
Using the difference in prices of one asset on different exchanges to make a profit.
Market-Making
Placing limit orders to provide liquidity to buyers and sellers in order to capitalize on the difference between the buy and sell prices (spread).
Trend Following
Trading in the direction of a major trend using indicators such as moving averages.
Mean Reversion
Assumes that the price of an asset will revert to its average after deviations.
Advantages and disadvantages of algorithmic trading
Advantages of algorithmic trading:
1. Speed of trade execution
Algorithms are able to analyze market data and execute trades in fractions of a second, which is impossible with manual trading. This is especially important in volatile markets such as cryptocurrency.
Example: High Frequency Traders (HFT) can buy an asset at a low price and sell it at a high price while regular traders only react to the changes.
2. Absence of human (emotional) factor
The human factor often leads to impulsive decisions based on fear or greed. Algotrading operates strictly according to a set strategy, which eliminates emotional mistakes.
Example: During a sharp market decline, a trader may panic and close a position, whereas the algorithm will continue to follow the set strategy.
3. Ability to test strategies
Before launching on the real market, algorithms can be tested on historical data (backtesting), which allows you to evaluate their effectiveness and identify possible weaknesses.
Example: A trader can test a strategy on past market data to see if it would have made a profit or loss.
4. Time optimization
Algotrading works around the clock, without the need for constant monitoring by the trader. This is especially important in cryptocurrency markets as they have no breaks.
Example: A trader can set up an algorithm and do other things while the program automatically executes trades.
5. Efficient risk management
Algorithms can automatically set stop-losses, take-profits, and use sophisticated money management techniques to minimize losses.
Example: A robot can automatically close a position when a certain level of loss is reached, preventing significant losses.
6. Access to sophisticated strategies
Algotrading allows you to use strategies that are difficult to implement manually, for example:
Arbitrage - differences in the exchange rate of the same asset on different platforms.
Market making - placing counter orders to make money on the spread.
Algorithms based on machine learning - predicting market movements based on Big Data.
Example: An algorithm can simultaneously analyze hundreds of trading pairs and find the most profitable trades.
Disadvantages of algorithmic trading:
1. High entry threshold
Developing an effective trading algorithm requires knowledge in programming, finance and statistics. In addition, computing power is required for quality backtesting and strategy optimization.
Example: It is difficult for a novice trader to create an algorithm on his own if he does not know programming languages (Python, C++, etc.).
2. Technical failures and delays
Any automated system is prone to technical failures that can lead to losses. For example, a failure in the connection to the exchange or an error in the algorithm code can lead to incorrect execution of trades.
Example: In 2012, Knight Capital fund lost $440 million due to a bug in the algorithm that started trading stocks erratically.
3. The need for constant tracking
Despite automation, algorithms require regular monitoring and adjustments. Market conditions change, and a strategy that has worked in the past can become unprofitable.
Example: An algorithm designed for a stable market may not be effective during times of high volatility.
4. Risk of insufficient liquidity
Some algorithms require high liquidity to work. If the market is illiquid, the algorithm may not be able to find suitable trades or may get trapped when the price changes dramatically.
Example: If an algo trader places a large order in an illiquid cryptocurrency, it may move the market against itself.
5. Competing with big players
Large hedge funds and investment companies spend significant budgets on algorithm development and optimization. It is difficult for private traders to compete with such systems.
Example: HFT-firms use servers with minimal latency, which gives them an advantage over regular traders.
6. Regulatory risks
In many countries, algorithmic trading is subject to strict regulation. Some strategies such as flash crashing or manipulative market making can lead to legal consequences.
Example: In 2010, the U.S. stock market experienced a “Flash Crash” when algorithms crashed the Dow Jones index by 9% in a matter of minutes.
Conclusion
Algorithm trading provides powerful tools for trading but requires careful customization, testing and monitoring. Successful application of algorithms requires knowledge of programming, math, and financial markets.
Frequently Asked Questions (FAQ)
1. Can I use algotrading without programming skills?
Yes, the crypto market bot creation platform - Veles Finance provides ready-made trading bots as well as the ability to create your own bots.
2. How safe is algotrading?
Risks depend on the quality of the algorithm, the level of testing and control over its work.
3. What initial capital is needed for algotrading?
It depends on the strategy, but you may need large sums to trade the stock market effectively.
4. Can algotrading be applied to cryptocurrency exchanges?
Yes, crypto exchanges support algorithmic trading and it is one of the popular markets to use trading bots.
5. What skills are needed to create your own trading algorithm?
Programming (Python, C++), knowledge of financial markets, statistical analysis and testing algorithms on historical data.
