Algorithmic trading is a strategy in which trading decisions are made by specially designed mathematical and statistical algorithms rather than the trader.
Such algorithms analyze various indicators - from the price history of a particular cryptocurrency, current volume, and market trends, or project-specific information. In fact, this strategy is widely used by financial institutions, including hedge funds, investment banks, and pension funds.
By automating the investment process, algorithmic trading increases the efficiency and accuracy of trading decisions made in the market for a given financial instrument. The algorithms immediately react to market changes, making it easy to adapt decisions in real-time.
This strategy utilizes a vast amount of historical and live data from the respective markets and analyzes it to determine the optimal buy/sell points. If applied correctly, an automated trading process enables higher chances of increased profits along with reduced risk but still requires appropriate risk management.
As mentioned, statistics and mathematical algorithms are at the core of this mechanism, with the whole process based on the continuous collection and analysis of data from all accessible sources. Moreover, during the research, the algorithms search for patterns that may signal specific trends and, therefore - investment opportunities.
Based on the analyzed data, algorithms decide to buy or sell a given currency with the possibility to set parameters such as stop-loss orders and profit and loss levels. Once these indicators are set, trades are executed automatically, and the process becomes fast and efficient.
As you might surmise, given the above information, the algorithmic trading strategy is rather complex and requires some programming knowledge and skills.
Starting algorithmic trading can therefore be done by acquiring the necessary knowledge of the financial markets and instruments that will be the subject of your investments. One should also become familiar with Python, C++, or Java, the most popular programming languages in algorithmic trading that allow relevant algorithmic creation.
However, if you have already obtained the necessary knowledge, you can go straight to choosing the right trading platform. Among many available, opt for one that suits your needs and fits your programming skill levels, such as Pionex, Interactive Brokers, or Quantconnect.
After selecting and familiarizing yourself with the chosen platform, testing your algorithm and optimizing on historical data is essential. Adjust algorithms to your demands and test their performance before trading. It is only then you can begin trading on the financial markets. Despite the automated trading process, remember that trading still demands constant monitoring and adjustment of the algorithms.
Like any trading, algorithmic trading is not foolproof and comes with market, technological, and management risks. It is necessary to study these issues before, as well as during trading and be aware of the risks involved.
At this point, it is important to look at examples of trading strategies used in algorithmic trading.
This strategy is essentially based on following market trends (analysis of price movements and related technical indicators). It's straightforward and can be implemented through algorithmic trading, requiring no additional predictions or price forecasts.
HFT involves performing an extensive number of trades in a short time - the algorithms here use brief periods requiring a high-speed internet connection. The topic of high-frequency trading was discussed in a separate article.
Includes a delta-neutral strategy that allows trading combinations of options and the underlying security. This strategy consists of multiple positions balancing negative and positive deltas - so that the overall delta of the asset is zero.
In this strategy, as long as the trading order is not filled, algorithms send partial orders according to the market's defined participation rate and trading volume.
The trading strategies outlined above are the most commonly used in the market with algorithmic trading. Combining an understanding of a particular cryptocurrency with expertise can help you become a successful trader.
Algorithmic trading uses mathematical and statistical algorithms to make investment decisions, analyze various indicators and react rapidly to market changes. To start trading, one must acquire the necessary knowledge, choose a trading platform, test algorithms on historical data, and optimize performance.
Common algorithmic trading strategies include trend-following, high-frequency, mathematical model-based, average return, and volume percentage. However, algorithmic trading is not foolproof and comes with risks that should be considered at any trading stage.
