The Future of Trading Unveiled: AXE Pioneers the AI Paradigm
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The inception of algorithmic trading can be traced back to the 1980s, specifically within the bustling environment of the New York Stock Exchange (NYSE). Traditionally, executing trades required investors to communicate with stockbrokers who would then work to carry out these orders on the trading floor. Today, approximately 60-73%(3) of equities trading within US markets heavily relies on algorithmic systems.
While smaller orders can be executed without impacting the market price, the complexity escalates when dealing with larger orders. Achieving the desired market position while minimizing costs becomes a complex problem, demanding strategies to curtail trading expenses. Traditionally, financial institutions have relied on rule-based order execution systems. However, these approaches, while prevalent, often faltered in practice. Their struggle lay in optimizing orders within the constantly shifting market dynamics and torrents of stock tick data. As markets surge with volatility and a deluge of data to process, the limitations of these conventional strategies becomes unavoidable. Seeking to bring a solution to this problem, QRAFT Technologies developed its AI AXE Engine. This machine learning model employs a cutting-edge reinforcement algorithm, with the goal of bringing better trading efficiency institutions with large orders.
What is AXE?
Unlike its conventional counterparts, AXE is more than just lines of conditional rule-based code; it uses machine learning and deep reinforcement learning to learn the non-linear nature of the market, seeking to optimally place orders at specific intervals to minimize trading costs.
Trading algorithms that are rule-bound, make decisions based on predetermined guidelines. These programs are designed to buy a preset number of shares if a particular condition was reached. However, the deluge of data growth has increasingly rendered these methods ineffective. Their inherent rigidity makes it difficult to adapt swiftly to market shifts, leading to missed opportunities and suboptimal outcomes.
AXE's distinguishing characteristic lies in its capacity to learn from data and dynamically adapt to the market conditions. While traditional algorithms remained limited, AXE breaks free by continually evolving its strategies, drawing insights from petabyte of real-time data points. With this information, AXE optimally sends order signal to purchase or sell bulk orders at minimal trading costs.
Introducing AXE's Strengths: Reinforcement Learning as the Core(1)(2)
Imagine a child embarking on the journey of learning to ride a bicycle, learning through trial and error. With every attempt, the child intuitively adjusts muscles, joints, and strength to achieve equilibrium, direction, and momentum. Just as the child learns which movements lead to balance or tumble, and tries tweaking these variables on the next ride, AXE hones its decision-making acumen through similar simulations.
Unlike the child's gradual learning process, AXE's AI Reinforcement Model has simulated millions of trial-and-error cycles, thanks to the rapid improvement in computing power. It absorbs each outcome, building an intricate map to the lowest trading costs. It's akin to a pianist practicing tirelessly to master a complex piece, but over several lifetimes.
Once the Agent (Child) performs an Action, the result is tested in the Environment (Bike), which returns a State (falling vs. riding) and a Reward (Self-esteem vs self-deprecation). Based off this repeated cycle, the Agent (Child) gradually learns which Actions result in a positive Reward and acts accordingly.
Furthermore, AXE not only streamlines trading volume but also effectively reduces trading costs. In volatile markets, sudden price fluctuations or a large order can create risk on trades. AXE shows its capability utilizing AI, optimally slicing large orders into smaller orders to minimize trading costs and market impact.
The AXE Challenge: AXE put to the Test
QRAFT initiated the AXE Challenge sponsored by PwC, NVIDIA and several big firms, inviting established traders to pit their strategies against AXE. The results were nothing short of astonishing: AXE outperformed human competitors to win a total grand prize of $100,000 USD. AXE demonstrated its ability to learn stock volatility trends and execute timely trades based on its acquired artificial intelligence.
AXE places first in a Securities Dealer Order Execution Contest sponsored by NVIDIA
Conclusion
Qraft utilized reinforcement learning to enhance AI trading execution, resulting in earning first place in the Securities Dealer Order Execution Contest sponsored by NVIDIA. Embodying a paradigm shift towards AI-powered investment solutions, AXE shines as a beacon of innovation. QRAFT’s cutting-edge technology and understanding of AI trading execution gain an edge in the complex landscape of today's financial world.
(1) MDPI, Multi-Agent Reinforcement Learning Framework in SDN-IoT for Transient Load Detection and Prevention, 2021
(2) MDPI, Practical Application of Deep Reinforcement Learning to Optimal Trade Execution, 2023
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