Trading has evolved greatly in the past decade. What used to be buying and selling stocks based on the positive or negative news around the stocks has now transformed into technology-based strategies that help market players to trade. An idea of having a system that quickly analyses and decides on the actions to take, revolutionized trading into an automated/algorithmic trading.
Automated or algorithmic trading is the process of converting a trading logic or strategy into an algorithms or computer code. The way this strategy works is it takes an input data, processes it and generates trading signals. Based on these signals, an order is placed in an automated manner. The system also involves checking the strategy performance by backtesting of the data history. This trading logic can be based on the price change, sentiment, volatility levels and other data.
Basically speaking, algorithmic trading is a way to automate trading processes by programming a computer to execute actions in response to what happens on the market on behalf of a trader.
The speed, at which these actions can be carried out, allows traders hitting the best prices and at the same time avoiding serious value fluctuations. It reduces the risk of human error and gives the benefit of paying reduced transaction fees. The benefits of algorithmic trading are quite significant and yet, automated trading continuously seeks ways to optimise trading strategies and algos.
Let us have a look at the most useful algorithmic strategies used in automated trading to date.
1. Use of AI/ML in dynamic parameters
Algorithmic trading needs consistent changes of its infrastructure, which imposes certain difficulties on staying updated for the traders. This used to be the responsibility of algorithms providers, but it proved to be inefficient as one negative result could cancel out traders work worth of several months.
The use of AI/ML (Artificial Intelligence/Machine Learning) allows the system applying intelligent benchmarking as to which algorithm to use within OMS/EMS (Order Management System/Execution Management System). Benchmark-based algorithms help to generate better decisions due to specific parameters built on historical data. Algorithms providers are keen to invest into technology that gives a more precise and relevant to market conditions decisions, as traders will be willing to buy it.
2. TCA and algorithms integration in real-time
TCA (Transaction Cost Analysis) is another thing on traders’ agenda since TCA is used in buying and selling processes. The trouble is that TCA measures the outcome rather than helps traders to prevent poor results. The more complex the technologies and algorithms become, the harder it is to predict the settings that will benefit the trading outcome based on previous TCA results. Therefore, the real-time solutions that help to interpret TCA results to improve system configurations and get the desired outcomes become increasingly used. The real-time solutions is what makes TCA usable and allows pre-trade market impact models and algorithms be based on pre-set commands. Thus, traders can choose their course of actions for every transaction based on intelligent decisions.
3. Pre-trade recommendations for better performance
With the increasing number of algorithms to choose from traders struggle with information overload and as a rule cut the number of tools they use to 4 or 5. Similarly, asset managers tend to use Implementation Shortfall and Auctions to see the difference between the decision price and the final price. It is true to say that market players prefer to split large orders into smaller, easier to execute segments across various liquidity pools. On the one hand, it increases the quality of execution for them, and on the other hand, it helps traders to avoid extra trading costs. Thereof, there will be more intelligent algorithms reflecting the market conditions in the near future.
One of them is Quod – a data-driven intelligent trading solution. Quod represents the four core applications of ML. The first is predictive failure. It uses ML to predict loads on the system and can be used for active load balancing. The second is parameter optimisation, where ML provides direct feedback to the traders to help them understand how they can change the nuances of their trades and the settings they use to execute in the market. The third is pre-trade predictive tools that can predict, which trader may be able to handle a specific trade better, which broker, which routes and which markets to trade on. The fourth is price reversion that allows predicting market behaviour and being able to take different actions during the execution.
ML is a really fascinating domain at the moment. It is equivalent in significance to the original electronification of trading.
4. Algorithms for multiple asset classes
Algorithms used across multiple asset classes, including cross-asset automation, increasingly gain prominence. This trend moves away from basis point charges and Tier-1 brokers and benefits from making the trading strategies available to all desks at no extra charge. A good example of this is simple automation like SOR (Smart Order Routing).
5. Algorithms for better efficiency and automation
Traders are under pressure of processing client orders in more time-efficient manner. The increased volumes of trades prompted for process automation. It started with a simple routing guidance and progressed into rulebooks that instruct how to act within particular context and respond to the market conditions. These rulebooks enable complete automation of workflow and suggest recommendations of particular strategies and routes. Decision-making became much faster for traders at the moment of trade.
It is believed that algorithmic trading is a more suitable tool for big players, however there are plenty of individuals that use this technology. Trading with ready-made algo-trading software became affordable to everyone. Stay tuned and benefit from the technologies that make a real breakthrough in the world of trading today.