Machine Learning For Algorithmic Trading
Machine Learning for Algorithmic Trading
Algorithmic Trading comprises of two words – Algorithmic and Trading. An algorithm is a set of pre-programmed instructions which run without human intervention, along with high accuracy and speed. The term trading refers to the activity of buying or selling goods. So, Algorithmic Trading is a complex process of automated programming to buy or sell financial securities on an exchange with high speed data processing (sometimes known as – High Frequency Trading) and accuracy without human intervention.
Algorithmic trading is also known as Automated Trading, Black-box Trading or Algo-Trading. Machine learning (ML) algorithms can process and analyse billions of records simultaneously within fraction of a second and produce the predicted results.
What does Algorithmic Trading control?
Algorithmic Trading controls numerous situations (apart from the control items on the right side) such as Order execution, arbitrage and trend trading strategies.
Top Algorithmic Trading Fields
- Ones mentioned on the left are the top areas and on the right are examples of major companies/firms which use Algorithmic Trading.
- In 2016, a report showed that over 80% of trading in the FOREX market was done by trading algorithms rather than humans.
- Algorithms are playing a vital role in controlling key decisions with a few constraints set by the client.
Conceptual process of Algorithmic Trading:
In the process of Algo-trading, system data plays very crucial role. In order to build a Model and obtain better results, availability of good, relevant and dynamic data is necessary. The data can be either structured or unstructured or both. Modelling is the complex process which contains ML algorithms and other methodologies to produce the constructive and decision-making result.
The role of execution step is to perform trading with input from Model step. This is the last and final step where some human intervention is required to decide, stop and change the objective function.
Data-Driven Approach Machine Learning Algorithm Decision
Design Thinking - Speed & Precision
Labour Cost Reduction
Diversify Trades - Simultaneously
Along with the advantages, there are a few bottlenecks as well with Algo-trading. For example, the liquidity which happens due to quick trading, results in instant loss. Researchers believe that Algorithmic trading played a major role in Flash crash of 2010 and Euro peg in 2015.
Top ML algorithms Used for Algo-Trading
There are a few well-known algorithms used in trading market, such as Reinforcement Learning, Percentage of Volume, Pegged, VWAP, TWAP, Implementation shortfall, Target close and so on.
Is Reinforcement Learning the best option?
Reinforcement Learning (RL) is a machine learning technique which enables an agent to learn in an interactive environment by trial and error method using feedback from its own actions/experiences.
Q-learning and State-Action-Reward-State-Action (SARSA) are two widely used RL algorithms for trading. RL works on the concept of dynamic programming and updating where rewards are given for every decision. In case of a wrong decision the algorithm is punished by reducing the weightage of that specific component and increases the weight for positive results (right decisions). There are a few open source learning frameworks to start on – OpenAIbaselines, dopamine, deepmind/trfl and Ray RLlib.
Future of Algorithmic Trading
It is evident from the above pictures, that Algorithmic trading is the second highest investment going forward in the upcoming years. Apart from Algorithmic trading, there are new emerging platforms which enable individuals to do their own trading in finance, making trading an easy activity at global level. Hence, Artificial Intelligence is an excellent way to execute Algorithmic trading in an efficient way in the real world.
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Technology Specialist – Analytics (Data Science & AI)