Trading using robots, also referred to as Algorithmic trading is a method of trading that helps traders automate the trading process, states Konstantin Rabin of LearnFX.
Trading robots have certain advantages over humans, for instance, algorithms don’t get tired/sick or take vacations. Algorithms are cold-blooded and are never tempted by greed, fear, or other human emotions. In addition, trading algorithms are incredibly precise. However, while human traders lack the ability to analyze technicals as well as robots, algorithms are not yet surpassing humans in analyzing overall economic conditions, and political and fundamental events.
Investment firms and technology companies invest heavily in AI (Artificial Intelligence) to create better trading robots. With the advancements in technology, Algo Trading has increasingly harnessed big data to enhance its effectiveness and efficiency.
Trading algorithms use two sources of data to predict market moves: historical data, and live market information. When analyzing live market data, trading robots take into account real-time prices, trading volumes, order book information, and news feeds. This data is collected from various financial exchanges and sources.
Historic data analysis involves collecting information about past price movements, past news, and fundamental events, and creating predictive models.
Big market data helps companies train their algorithms using machine learning and create trading bots that predict future price movements. In addition, big data analysis is used for risk management and helps measure potential losses and set risk limits.
Big data analysis plays a huge role in evaluating the potential influence of a substantial trade on the market. Through a comprehensive analysis of historical data and the order book dynamics, the system can precisely gauge how the execution of the order will ripple through and impact the prevailing market price.
Machine learning is also involved in sentiment analysis. Sentiment analysis involves analyzing news and social media events, investor sentiment, etc. Algorithmic trading is highly involved in High-Frequency Trading (HFT). HFT depends on big data and ultra-fast data processing. HFT algorithms analyze information and execute orders in milliseconds.
Algorithmic traders typically trade intraday. As trades are managed by robots, many algo traders conduct multiple trades simultaneously. Algo traders are usually using the latest hardware and fast internet connection. Algo traders play a vital role in today’s financial markets, increasing liquidity, optimizing price efficiency, and reshaping the broader trading landscape. Their proficiency in swiftly processing and analyzing extensive data has brought about a transformative shift in the trading of financial assets, rendering markets more streamlined and approachable.
Overall, algorithmic trading harnesses huge data to boost decision-making, and risk management, and execute trades efficiently. By leveraging vast amounts of data, automated trading systems are focused on gaining a competitive edge in the financial markets.
By Konstantin Rabin of LearnFX
