As governments and businesses exponentially increase^[4] their spending on AI research and adoption, AI-based solutions rise in popularity worldwide. However, the optimal practices and approaches to using AI-based technologies are yet to be decided, as AI adoption shows^[5] varying scale and efficiency in different sectors.

Applications of AI in trading In trading, a field where AI-based technologies are becoming^[6] increasingly popular, prediction and analysis go hand in hand. For many retail traders, assessing their previous orders is one of the main prerequisites for improving their decision-making skills.

According to Kar Yong Ang, a financial market analyst for an international broker Octa, AI has already gained a solid foothold in trading: deep learning frameworks generate market signals, dedicated bots allow traders to automate order execution, and AI-based solutions support them in developing and backtesting their trading strategies. ‘Even though various AI- and ML-based tools are already used for trading on financial markets, the broader adoption of these technologies in trading is yet to come as many traders prefer to fall back on less advanced approaches, missing out on this opportunity’, Kar Yong Ang said.

Assessing one's trades: a way to improve trading results Octa has conducted research to gauge traders' attitudes towards reviewing their trades. Out of 821 respondents, 85% marked this self-assessment as necessary, while 70% already practise it regularly.

One out of three survey participants said they analysed their past trades using third-party services instead of within the trading platform. More than half don't use any dedicated analytical tools at all, making do with manual research. Only a few traders are lucky enough to have a trusted mentor who can break down their sessions and provide constructive, impartial feedback, allowing them to improve by not repeating the same mistakes.

To address this pressing issue, Octa recently launched a new feature built into its proprietary trading platform, OctaTrader. From now on, OctaTrader clients can analyse their order history with OctaVision. This embedded AI-based toolkit offers personalised recommendations and allows traders to level up their decision-making skills with each session.

OctaVision: an AI-based recommendation engine as a mentor Created as a balanced combination of human expertise and AI's data-processing prowess, OctaVision allows to analyse an individual closed order or a whole trading session in bulk. While OctaVision uses a built-in AI engine to create data-driven unbiased recommendations, it is also anchored in extensive market knowledge of Octa's expert traders who oversaw the development and content creation process, double-checking each outcome against their hands-on experience.

As a detailed and personalised source of trading feedback, OctaVision is indispensable as an objective, impersonal mentor who helps hone trading skills step by step. OctaVision uses plain language to point out strengths and weaknesses in decisions, substantiating the assessment with statistics while providing personalised, actionable advice.

OctaVision has already been deployed for all of OctaTrader's clients in all regions. The toolkit generates recommendations in English and is currently available for the trading platform's desktop version.

OctaTrader currently offers two proprietary embedded toolkits that empower traders and help them achieve consistent results. While an analytical hub called Space provides timely market insights and serves as a decision-making support mechanism, the recently introduced OctaVision plays the role of an AI-powered digital mentor, boosting traders' ability to analyse their past trades and delivering the lessons learned in a concise, result-oriented manner.

Hashtag: #Octa

The issuer is solely responsible for the content of this announcement.

References

1. ^{^} Media OutReach Newswire (www.media-outreach.com)
2. ^{^} leading adopters (highpeaksw.com)
3. ^{^} recent research (www.forbes.com)
4. ^{^} increase (blogs.idc.com)
5. ^{^} shows (www.pwc.com)
6. ^{^} are becoming (www.sciencedirect.com)