Algorithmic trading is the use of computer algorithms to make trading decisions in financial markets. Based on trading software programs, it is gaining more and more attention among traders nowadays. The reason is that, while minimizing the time, risk, and stress of manual trading, it enables traders to set predetermined parameters and algorithms to execute their trades. Known also as automated trading or algo-trading, Algorithmic trading is growing its scope between traders, since it is a fast and accurate program. Financial experts state that it has already won over conventional trading methods.
According to research conducted by ’’The Economic Times’’, algorithmic trading is the future of the financial markets. Providentially, algorithmic trading, by means of using computerized programs to execute trades in financial markets, is now accessible for all types of traders worldwide with major technical improvements. The traders can develop their own trading strategies.
What Are Algorithmic Trading Strategies?
Successful algorithmic trading strategies require a combination of technical knowledge, market understanding, and programming skills. These strategies are designed to process large amounts of data and execute trades at lightning-fast speeds, even taking advantage of any small price movements in the market to generate profits. Their automatic trade executions are based on pre-defined pricing, quantity, and timing rules as well as mathematical models at such speed and frequency that is not possible for a human trader to execute.
An algo trading technique needs to identify profitable trading opportunities both for increased profits and cost savings.
There are several algorithmic trading strategies that traders can use to make automated trading decisions. Here are some of the most common algorithmic trading strategies:
Here are some common algorithmic trading strategies:
Mean Reversion Strategy
This strategy checks for the assets which have moved beyond one direction and are likely to return to their average price. It operates based on the assumption that the markets range 80% of the time. By estimating an average price based on historical data, it positions the trades, expecting the current price will be returning to the calculated market price. In other words, the mean reversion strategy identifies overbought or oversold assets and makes trades based on the assumption that they will eventually revert to their mean.
Arbitrage Opportunities
Using arbitrage strategy in algo-trading means identifying price imbalances in different markets or on different exchanges to take advantage of these price discrepancies. As an example, triangular arbitrage is one of the most commonly used arbitrage strategies for higher profits, which involves two currency pairs and a currency cross between two of them. Yet, since the currency differences in FX are usually in micropipes, the trader needs to trade with large sizes to make considerable profits. The idea is to make a profit by buying at a lower price on one exchange and selling at a higher price on another.
Trend Following Strategies
Trend following is one of the simplest algorithmic trading strategies, which only requires analyzing the market trends to identify opportunities to buy or sell orders, based on technical indicators and momentum. Also, in this strategy, historical data can be considered in predicting the future movements of the trend. The idea is simply to buy when the asset is trending upwards and sell when it’s trending downwards.
News Based Strategies
The news-based strategy involves analyzing news articles, social media posts, and other sources of information to identify trading opportunities. This is profitable, but also a highly risky algo-trading strategy, where the trading system, which needs to be hooked to newswires, automatically generates trade signals, depending on the results of the comparison of the market consensus with the previous data. For example, if a company releases positive earnings news, an algorithmic trader might use this information to buy the company’s stock.
Market Sentiment Strategies
A market sentiment strategy is a trading approach, where the system observes a specific asset and detects its extremely short and long positions to identify shifts in market sentiment and sentiment extremes. It basically takes into consideration investors’ current attitudes and assumptions toward a particular asset, market, or economy. It monitors and analyzes market sentiment through indicators, such as news articles, social media sentiment, market surveys, and technical analysis as well as COT (The Commitments of Traders).
These are just a few examples of the algorithmic trading strategies that traders can use.
Steps for Creating a Successful Algorithmic Trading Strategy
A successful algorithmic trading strategy requires a combination of technical knowledge, market understanding, and surely programming skills.
Here are steps to create a successful algorithmic trading strategy:
Step 1: Define your goals: Start by defining your trading objectives, including your risk limits, return expectations, and investment horizon. These objectives will help you determine the appropriate trading strategy for your investments.
Step 2: Choose a market: Select the finance market and asset category you want to trade, based on your objectives and expertise. For instance, you can choose to trade equities, commodities, or currencies.
Step 3: Develop a trading strategy: Develop a trading strategy that takes into account market trends, historical data, and technical analysis. Use tools like backtesting and simulations to test your strategy before implementing it.
Step 4: Choose a programming language: Choose a programming language that suits your needs and expertise. Popular choices include Python, MATLAB, and R.
Step 5: Implement your strategy: Implement your strategy by writing code that can execute trades based on your rules. You may need to use APIs to interact with trading platforms.
Step 6: Monitor and adjust: Monitor your strategy’s performance and adjust it as necessary. This includes tweaking your algorithms, adding new data sources, and adjusting risk management parameters.
Step 7: Stay informed: Stay informed about market news and events that could affect your strategy. This includes monitoring economic indicators, earnings reports, and regulatory changes.
Step 8: Stay disciplined: Stick to your trading plan and remain disciplined. Avoid emotional trading decisions and stick to your risk management rules.
In summary, a successful algorithmic trading technique requires a combination of technical knowledge, market understanding, and programming skills. Define your objectives, choose a market and asset class, develop a trading strategy, implement it, monitor and adjust as necessary, stay informed, and stay disciplined.
Technical requirements for Algorithmic Trading Strategy:
Implementing an algorithmic trading strategy requires a sophisticated and integrated technology infrastructure that combines hardware, software, and data analysis tools. Traders should also have a strong understanding of the markets and the factors that affect asset prices to develop successful trading strategies. To implement an algorithmic trading strategy, investors need to have the following technical requirements:
Programming skills: Algorithmic trading requires programming skills to develop and implement trading algorithms. Traders need to be proficient in programming languages in order to run the algorithms such as Python, C++, or Java and have experience with trading APIs and libraries. They can also purchase ready software or hire programmers to develop the algorithms for them.
High-performance computing and internet connection: Algorithmic trading requires a fast and reliable computing infrastructure to process large amounts of data and execute trades quickly. This includes a powerful processor, high-speed internet connection, and low-latency network infrastructure.
Backtesting and optimization tools: The ability to backtest the system once it is developed to ensure that the program runs smoothly. Traders need backtesting and optimization tools to test and refine their trading strategies. This includes historical data sets, simulation tools, and optimization algorithms to test the algorithms.
Access to live data in different markets: Traders need monitoring tools to track the performance of their trading algorithms and identify any issues. This includes real-time data feeds, performance metrics, and alert systems.
Trading platform: A reliable, flexible, and customizable trading platform is needed to execute trades automatically with instant access for placing orders. The trading platform should support trading across multiple markets and asset classes.
Trading can be compelling at times, but we hope this article has clarified how algorithmic trading works for you. Algorithms have gained popularity among traders by identifying fruitful opportunities and turning the obtained information into trading decisions. Yet, the most exciting part is that you are choosing your own algorithmic trading strategy to execute your trades.
If you feel curious to start trading and try automated trading strategies, feel free to reach us to open an account in the latest version of MetaTrader trading platform MT5 and dive into the trading world.
Wishing our readers, a great and profitable week!